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- .gitattributes +22 -0
- p3/preprocess/Hypertension/gene_data/GSE256539.csv +3 -0
- p3/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv +4 -0
- p3/preprocess/Hypertrophic_Cardiomyopathy/code/GSE36961.py +144 -0
- p3/preprocess/Hypertrophic_Cardiomyopathy/code/TCGA.py +28 -0
- p3/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json +1 -0
- p3/preprocess/Hypothyroidism/GSE151158.csv +0 -0
- p3/preprocess/Hypothyroidism/clinical_data/GSE151158.csv +4 -0
- p3/preprocess/Hypothyroidism/clinical_data/GSE224330.csv +4 -0
- p3/preprocess/Hypothyroidism/clinical_data/GSE32445.csv +4 -0
- p3/preprocess/Hypothyroidism/clinical_data/GSE75678.csv +4 -0
- p3/preprocess/Hypothyroidism/clinical_data/GSE75685.csv +4 -0
- p3/preprocess/Hypothyroidism/clinical_data/TCGA.csv +581 -0
- p3/preprocess/Hypothyroidism/code/GSE151158.py +146 -0
- p3/preprocess/Hypothyroidism/code/GSE224330.py +180 -0
- p3/preprocess/Hypothyroidism/code/GSE32445.py +143 -0
- p3/preprocess/Hypothyroidism/code/GSE75678.py +173 -0
- p3/preprocess/Hypothyroidism/code/GSE75685.py +160 -0
- p3/preprocess/Hypothyroidism/code/TCGA.py +91 -0
- p3/preprocess/Hypothyroidism/cohort_info.json +1 -0
- p3/preprocess/Hypothyroidism/gene_data/GSE151158.csv +0 -0
- p3/preprocess/Hypothyroidism/gene_data/GSE224330.csv +0 -0
- p3/preprocess/Hypothyroidism/gene_data/GSE32445.csv +1 -0
- p3/preprocess/Hypothyroidism/gene_data/GSE75685.csv +0 -0
- p3/preprocess/Insomnia/clinical_data/GSE208668.csv +4 -0
- p3/preprocess/Insomnia/cohort_info.json +1 -0
- p3/preprocess/Lower_Grade_Glioma/gene_data/GSE107850.csv +3 -0
- p3/preprocess/Lower_Grade_Glioma/gene_data/GSE35158.csv +3 -0
- p3/preprocess/Lower_Grade_Glioma/gene_data/GSE74567.csv +0 -0
- p3/preprocess/Pancreatic_Cancer/GSE125158.csv +0 -0
- p3/preprocess/Pancreatic_Cancer/GSE130563.csv +3 -0
- p3/preprocess/Pancreatic_Cancer/GSE131027.csv +3 -0
- p3/preprocess/Pancreatic_Cancer/GSE183795.csv +3 -0
- p3/preprocess/Pancreatic_Cancer/GSE236951.csv +0 -0
- p3/preprocess/Pancreatic_Cancer/clinical_data/GSE130563.csv +4 -0
- p3/preprocess/Pancreatic_Cancer/clinical_data/GSE131027.csv +2 -0
- p3/preprocess/Pancreatic_Cancer/clinical_data/GSE183795.csv +2 -0
- p3/preprocess/Pancreatic_Cancer/clinical_data/GSE222788.csv +2 -0
- p3/preprocess/Pancreatic_Cancer/clinical_data/GSE223409.csv +2 -0
- p3/preprocess/Pancreatic_Cancer/clinical_data/GSE236951.csv +4 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE120127.py +221 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE124069.py +189 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE125158.py +207 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE130563.py +259 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE131027.py +190 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE157494.py +217 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE183795.py +200 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE222788.py +166 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE223409.py +173 -0
- p3/preprocess/Pancreatic_Cancer/code/GSE236951.py +165 -0
.gitattributes
CHANGED
@@ -1782,3 +1782,25 @@ p3/preprocess/Hypertension/gene_data/GSE128381.csv filter=lfs diff=lfs merge=lfs
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p3/preprocess/Hypertension/gene_data/GSE77627.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE117261.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE161533.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE77627.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE117261.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE161533.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE256539.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Lower_Grade_Glioma/gene_data/GSE35158.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/GSE130563.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/GSE131027.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Lower_Grade_Glioma/gene_data/GSE107850.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/gene_data/GSE130563.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/gene_data/GSE131027.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/GSE40611.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/GSE135809.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/gene_data/GSE183795.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/GSE93683.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/GSE84844.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/GSE51092.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/GSE183795.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/gene_data/GSE135809.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/gene_data/GSE40611.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Pancreatic_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/gene_data/GSE84844.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/GSE66795.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/gene_data/GSE93683.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/gene_data/GSE51092.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Sjögrens_Syndrome/gene_data/GSE66795.csv filter=lfs diff=lfs merge=lfs -text
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p3/preprocess/Hypertension/gene_data/GSE256539.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:b5d9bb647998b040f0223d33002ee74f0c056ba2da0e63216486b09fb81c0e23
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size 22918341
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p3/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv
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,GSM907203,GSM907204,GSM907205,GSM907206,GSM907207,GSM907208,GSM907209,GSM907210,GSM907211,GSM907212,GSM907213,GSM907214,GSM907215,GSM907216,GSM907217,GSM907218,GSM907219,GSM907220,GSM907221,GSM907222,GSM907223,GSM907224,GSM907225,GSM907226,GSM907227,GSM907228,GSM907229,GSM907230,GSM907231,GSM907232,GSM907233,GSM907234,GSM907235,GSM907236,GSM907237,GSM907238,GSM907239,GSM907240,GSM907241,GSM907242,GSM907243,GSM907244,GSM907245,GSM907246,GSM907247,GSM907248,GSM907249,GSM907250,GSM907251,GSM907252,GSM907253,GSM907254,GSM907255,GSM907256,GSM907257,GSM907258,GSM907259,GSM907260,GSM907261,GSM907262,GSM907263,GSM907264,GSM907265,GSM907266,GSM907267,GSM907268,GSM907269,GSM907270,GSM907271,GSM907272,GSM907273,GSM907274,GSM907275,GSM907276,GSM907277,GSM907278,GSM907279,GSM907280,GSM907281,GSM907282,GSM907283,GSM907284,GSM907285,GSM907286,GSM907287,GSM907288,GSM907289,GSM907290,GSM907291,GSM907292,GSM907293,GSM907294,GSM907295,GSM907296,GSM907297,GSM907298,GSM907299,GSM907300,GSM907301,GSM907302,GSM907303,GSM907304,GSM907305,GSM907306,GSM907307,GSM907308,GSM907309,GSM907310,GSM907311,GSM907312,GSM907313,GSM907314,GSM907315,GSM907316,GSM907317,GSM907318,GSM907319,GSM907320,GSM907321,GSM907322,GSM907323,GSM907324,GSM907325,GSM907326,GSM907327,GSM907328,GSM907329,GSM907330,GSM907331,GSM907332,GSM907333,GSM907334,GSM907335,GSM907336,GSM907337,GSM907338,GSM907339,GSM907340,GSM907341,GSM907342,GSM907343,GSM907344,GSM907345,GSM907346,GSM907347
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Hypertrophic_Cardiomyopathy,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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Age,9.0,10.0,10.0,11.0,13.0,14.0,15.0,15.0,15.0,15.0,16.0,16.0,17.0,19.0,19.0,20.0,23.0,23.0,26.0,27.0,28.0,30.0,30.0,30.0,31.0,32.0,32.0,33.0,35.0,35.0,37.0,37.0,38.0,38.0,41.0,43.0,43.0,43.0,43.0,44.0,44.0,44.0,45.0,45.0,45.0,45.0,46.0,46.0,47.0,48.0,48.0,50.0,50.0,51.0,51.0,51.0,52.0,52.0,52.0,52.0,53.0,53.0,54.0,54.0,54.0,55.0,56.0,56.0,56.0,56.0,57.0,58.0,58.0,59.0,59.0,59.0,59.0,59.0,59.0,60.0,60.0,62.0,63.0,64.0,65.0,65.0,66.0,67.0,67.0,67.0,67.0,67.0,67.0,69.0,69.0,70.0,70.0,71.0,71.0,71.0,73.0,73.0,75.0,76.0,77.0,78.0,,49.0,48.0,,42.0,53.0,,31.0,54.0,52.0,47.0,26.0,65.0,21.0,41.0,55.0,61.0,36.0,7.0,23.0,17.0,45.0,40.0,37.0,51.0,39.0,37.0,23.0,19.0,53.0,48.0,47.0,4.0,48.0,25.0,27.0,21.0,27.0,21.0
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Gender,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0
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p3/preprocess/Hypertrophic_Cardiomyopathy/code/GSE36961.py
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# Path Configuration
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from tools.preprocess import *
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# Processing context
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trait = "Hypertrophic_Cardiomyopathy"
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cohort = "GSE36961"
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# Input paths
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in_trait_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy"
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in_cohort_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy/GSE36961"
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# Output paths
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out_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/GSE36961.csv"
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out_gene_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv"
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out_clinical_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv"
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json_path = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/cohort_info.json"
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# Get file paths
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soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
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# Get background info and clinical data
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background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
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# Get unique values for each clinical feature
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unique_values_dict = get_unique_values_by_row(clinical_data)
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# Print background information
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print("Background Information:")
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print(background_info)
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print("\nSample Characteristics:")
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print(json.dumps(unique_values_dict, indent=2))
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# 1. Gene Expression Data Availability
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# Series title and summary indicate this is transcriptome profiling data
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is_gene_available = True
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# 2.1 Data Availability
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# Trait is in row 3, gender in row 0, age in row 1
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trait_row = 3 # disease state / sample type shows HCM vs control
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gender_row = 0 # Sex field
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age_row = 1 # age (yrs) field
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# 2.2 Data Type Conversion Functions
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def convert_trait(value: str) -> int:
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"""Convert trait value to binary (1=case, 0=control)"""
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if pd.isna(value):
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return None
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value = value.split(": ")[-1].lower()
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if "hypertrophic cardiomyopathy" in value or "hcm" in value or "case" in value:
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return 1
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elif "control" in value:
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return 0
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return None
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def convert_age(value: str) -> float:
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"""Convert age value to continuous numeric"""
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if pd.isna(value):
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return None
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try:
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return float(value.split(": ")[-1])
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except:
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return None
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def convert_gender(value: str) -> int:
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"""Convert gender to binary (0=female, 1=male)"""
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if pd.isna(value):
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return None
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value = value.split(": ")[-1].lower()
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if value == "female":
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return 0
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elif value == "male":
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return 1
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return None
|
73 |
+
|
74 |
+
# 3. Save Metadata
|
75 |
+
# trait_row is not None, so trait data is available
|
76 |
+
is_trait_available = trait_row is not None
|
77 |
+
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
|
78 |
+
is_gene_available=is_gene_available,
|
79 |
+
is_trait_available=is_trait_available)
|
80 |
+
|
81 |
+
# 4. Extract Clinical Features
|
82 |
+
selected_clinical = geo_select_clinical_features(
|
83 |
+
clinical_df=clinical_data,
|
84 |
+
trait=trait,
|
85 |
+
trait_row=trait_row,
|
86 |
+
convert_trait=convert_trait,
|
87 |
+
age_row=age_row,
|
88 |
+
convert_age=convert_age,
|
89 |
+
gender_row=gender_row,
|
90 |
+
convert_gender=convert_gender
|
91 |
+
)
|
92 |
+
|
93 |
+
# Preview the clinical data
|
94 |
+
preview_result = preview_df(selected_clinical)
|
95 |
+
|
96 |
+
# Save clinical data
|
97 |
+
selected_clinical.to_csv(out_clinical_data_file)
|
98 |
+
# Extract gene expression data from the matrix file
|
99 |
+
genetic_data = get_genetic_data(matrix_file_path)
|
100 |
+
|
101 |
+
# Print first 20 row IDs
|
102 |
+
print("First 20 row IDs:")
|
103 |
+
print(genetic_data.index[:20].tolist())
|
104 |
+
requires_gene_mapping = False
|
105 |
+
# 1. Normalize gene symbols
|
106 |
+
genetic_data = normalize_gene_symbols_in_index(genetic_data)
|
107 |
+
genetic_data.to_csv(out_gene_data_file)
|
108 |
+
|
109 |
+
# Get clinical features
|
110 |
+
clinical_features = geo_select_clinical_features(
|
111 |
+
clinical_data,
|
112 |
+
trait=trait,
|
113 |
+
trait_row=trait_row,
|
114 |
+
convert_trait=convert_trait,
|
115 |
+
gender_row=gender_row,
|
116 |
+
convert_gender=convert_gender
|
117 |
+
)
|
118 |
+
|
119 |
+
# 2. Link clinical and genetic data
|
120 |
+
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
|
121 |
+
|
122 |
+
# 3. Handle missing values
|
123 |
+
linked_data = handle_missing_values(linked_data, trait)
|
124 |
+
|
125 |
+
# 4. Judge whether features are biased and remove biased demographic features
|
126 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
127 |
+
|
128 |
+
# 5. Final validation and save metadata
|
129 |
+
note = "Dataset contains gene expression data comparing cardiac tissue from patients with hypertrophic cardiomyopathy (HCM) versus control donor cardiac tissues."
|
130 |
+
is_usable = validate_and_save_cohort_info(
|
131 |
+
is_final=True,
|
132 |
+
cohort=cohort,
|
133 |
+
info_path=json_path,
|
134 |
+
is_gene_available=True,
|
135 |
+
is_trait_available=True,
|
136 |
+
is_biased=is_biased,
|
137 |
+
df=linked_data,
|
138 |
+
note=note
|
139 |
+
)
|
140 |
+
|
141 |
+
# 6. Save the linked data only if it's usable
|
142 |
+
if is_usable:
|
143 |
+
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
|
144 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Hypertrophic_Cardiomyopathy/code/TCGA.py
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypertrophic_Cardiomyopathy"
|
6 |
+
|
7 |
+
# Input paths
|
8 |
+
tcga_root_dir = "../DATA/TCGA"
|
9 |
+
|
10 |
+
# Output paths
|
11 |
+
out_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/TCGA.csv"
|
12 |
+
out_gene_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/gene_data/TCGA.csv"
|
13 |
+
out_clinical_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/clinical_data/TCGA.csv"
|
14 |
+
json_path = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/cohort_info.json"
|
15 |
+
|
16 |
+
# Get subdirectories from TCGA root directory
|
17 |
+
tcga_subdirs = os.listdir(tcga_root_dir)
|
18 |
+
tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]
|
19 |
+
|
20 |
+
# No suitable cohort exists for HDL deficiency in TCGA cancer datasets
|
21 |
+
# Record this and end processing
|
22 |
+
validate_and_save_cohort_info(
|
23 |
+
is_final=False,
|
24 |
+
cohort="TCGA",
|
25 |
+
info_path=json_path,
|
26 |
+
is_gene_available=True,
|
27 |
+
is_trait_available=False
|
28 |
+
)
|
p3/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"GSE36961": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 142, "note": "Dataset contains gene expression data comparing cardiac tissue from patients with hypertrophic cardiomyopathy (HCM) versus control donor cardiac tissues."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}
|
p3/preprocess/Hypothyroidism/GSE151158.csv
ADDED
The diff for this file is too large to render.
See raw diff
|
|
p3/preprocess/Hypothyroidism/clinical_data/GSE151158.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM4567420,GSM4567421,GSM4567422,GSM4567423,GSM4567424,GSM4567425,GSM4567426,GSM4567427,GSM4567428,GSM4567429,GSM4567430,GSM4567431,GSM4567432,GSM4567433,GSM4567434,GSM4567435,GSM4567436,GSM4567437,GSM4567438,GSM4567439,GSM4567440,GSM4567441,GSM4567442,GSM4567443,GSM4567444,GSM4567445,GSM4567446,GSM4567447,GSM4567448,GSM4567449,GSM4567450,GSM4567451,GSM4567452,GSM4567453,GSM4567454,GSM4567455,GSM4567456,GSM4567457,GSM4567458,GSM4567459,GSM4567460,GSM4567461,GSM4567462,GSM4567463,GSM4567464,GSM4567465,GSM4567466,GSM4567467,GSM4567468,GSM4567469,GSM4567470,GSM4567471,GSM4567472,GSM4567473,GSM4567474,GSM4567475,GSM4567476,GSM4567477,GSM4567478,GSM4567479,GSM4567480,GSM4567481,GSM4567482,GSM4567483,GSM4567484,GSM4567485
|
2 |
+
Hypothyroidism,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,,,,
|
3 |
+
Age,53.0,40.0,51.0,36.0,44.0,60.0,31.0,41.0,55.0,15.0,57.0,56.0,34.0,43.0,49.0,55.0,52.0,35.0,35.0,40.0,34.0,42.0,53.0,33.0,31.0,57.0,42.0,48.0,47.0,51.0,65.0,40.0,59.0,49.0,61.0,59.0,28.0,46.0,42.0,60.0,25.0,43.0,51.0,52.0,51.0,56.0,27.0,35.0,54.0,37.0,45.0,45.0,47.0,40.0,33.0,39.0,39.0,44.0,47.0,37.0,49.0,,,,,
|
4 |
+
Gender,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,,,,,
|
p3/preprocess/Hypothyroidism/clinical_data/GSE224330.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM7019507,GSM7019508,GSM7019509,GSM7019510,GSM7019511,GSM7019512,GSM7019513,GSM7019514,GSM7019515,GSM7019516,GSM7019517,GSM7019518,GSM7019519,GSM7019520,GSM7019521,GSM7019522,GSM7019523,GSM7019524,GSM7019525,GSM7019526,GSM7019527,GSM7019528,GSM7019529,GSM7019530,GSM7019531,GSM7019532,GSM7019533,GSM7019534,GSM7019535,GSM7019536,GSM7019537
|
2 |
+
Hypothyroidism,1.0,0.0,0.0,0.0,,,,,,,,,,,,0.0,0.0,,0.0,0.0,0.0,,,,0.0,0.0,0.0,0.0,0.0,,0.0
|
3 |
+
Age,63.0,64.0,63.0,48.0,70.0,62.0,58.0,57.0,60.0,57.0,52.0,51.0,53.0,56.0,62.0,54.0,61.0,54.0,55.0,65.0,84.0,70.0,76.0,62.0,73.0,71.0,59.0,62.0,47.0,76.0,54.0
|
4 |
+
Gender,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0
|
p3/preprocess/Hypothyroidism/clinical_data/GSE32445.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM802773,GSM802774,GSM802775,GSM802776,GSM802777,GSM802778,GSM802779,GSM802780,GSM802781,GSM802782,GSM802783,GSM802784,GSM802785,GSM802786,GSM802787,GSM802788,GSM802789,GSM802790,GSM802791,GSM802792,GSM802793,GSM802794,GSM802795,GSM802796,GSM802797,GSM802798
|
2 |
+
Hypothyroidism,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
3 |
+
Age,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0
|
4 |
+
Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
|
p3/preprocess/Hypothyroidism/clinical_data/GSE75678.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM1963528,GSM1963529,GSM1963530,GSM1963531,GSM1963532,GSM1963533,GSM1963534,GSM1963535,GSM1963536,GSM1963537,GSM1963538,GSM1963539,GSM1963540,GSM1963541,GSM1963542,GSM1963543,GSM1963544,GSM1963545,GSM1963546,GSM1963547,GSM1963548,GSM1963549,GSM1963550,GSM1963551,GSM1963552,GSM1963553,GSM1963554,GSM1963555,GSM1963556,GSM1963557,GSM1963558,GSM1963559,GSM1963560,GSM1963561,GSM1963562,GSM1963563,GSM1963564,GSM1963565,GSM1963566,GSM1963567,GSM1963568,GSM1963569,GSM1963570,GSM1963571,GSM1963572,GSM1963573,GSM1963574,GSM1963575,GSM1963576,GSM1963577,GSM1963578,GSM1963579,GSM1963580,GSM1963581
|
2 |
+
Hypothyroidism,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
3 |
+
Age,45.0,41.0,59.0,57.0,42.0,49.0,59.0,54.0,54.0,31.0,70.0,44.0,50.0,42.0,56.0,51.0,58.0,55.0,71.0,42.0,41.0,40.0,57.0,62.0,87.0,36.0,50.0,45.0,43.0,42.0,43.0,44.0,43.0,48.0,45.0,51.0,56.0,57.0,41.0,48.0,66.0,53.0,36.0,51.0,57.0,45.0,55.0,35.0,44.0,68.0,46.0,58.0,45.0,54.0
|
4 |
+
Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
p3/preprocess/Hypothyroidism/clinical_data/GSE75685.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM1963127,GSM1963128,GSM1963129,GSM1963130,GSM1963131,GSM1963132,GSM1963133,GSM1963134,GSM1963135,GSM1963136,GSM1963137,GSM1963138,GSM1963139,GSM1963140,GSM1963141,GSM1963142,GSM1963143,GSM1963144,GSM1963145,GSM1963146,GSM1963147,GSM1963148,GSM1963149,GSM1963150,GSM1963151,GSM1963152,GSM1963153,GSM1963154,GSM1963155,GSM1963156,GSM1963157,GSM1963158,GSM1963159,GSM1963160,GSM1963161,GSM1963162,GSM1963163,GSM1963164,GSM1963165,GSM1963166,GSM1963167,GSM1963168,GSM1963169,GSM1963170,GSM1963171,GSM1963172,GSM1963173,GSM1963174,GSM1963175,GSM1963176,GSM1963177,GSM1963178,GSM1963179,GSM1963180
|
2 |
+
Hypothyroidism,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
3 |
+
Age,54.0,41.0,55.0,51.0,45.0,54.0,48.0,71.0,51.0,43.0,40.0,59.0,45.0,36.0,41.0,41.0,66.0,56.0,50.0,50.0,42.0,57.0,57.0,36.0,55.0,49.0,42.0,70.0,87.0,42.0,59.0,51.0,31.0,62.0,44.0,57.0,56.0,53.0,35.0,45.0,44.0,43.0,68.0,48.0,46.0,45.0,58.0,45.0,44.0,54.0,58.0,57.0,42.0,43.0
|
4 |
+
Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
p3/preprocess/Hypothyroidism/clinical_data/TCGA.csv
ADDED
@@ -0,0 +1,581 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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1 |
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2 |
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17 |
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TCGA-BJ-A18Y-01,1,29,1
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18 |
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TCGA-BJ-A18Z-01,1,58,1
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21 |
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23 |
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24 |
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TCGA-BJ-A28S-01,1,79,1
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25 |
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27 |
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31 |
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33 |
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34 |
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TCGA-DJ-A13M-01,1,28,0
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89 |
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TCGA-DJ-A13O-01,1,56,1
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90 |
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91 |
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TCGA-DJ-A13R-01,1,50,1
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93 |
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TCGA-DJ-A13T-01,1,37,0
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94 |
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TCGA-DJ-A13U-01,1,60,1
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TCGA-DJ-A13V-01,1,21,0
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TCGA-DJ-A13W-01,1,45,0
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TCGA-DJ-A13X-01,1,51,0
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TCGA-DJ-A1QD-01,1,20,0
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TCGA-DJ-A1QE-01,1,62,0
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TCGA-DJ-A1QF-01,1,61,0
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TCGA-DJ-A1QG-01,1,62,1
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TCGA-DJ-A1QH-01,1,58,0
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TCGA-DJ-A1QL-01,1,70,1
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TCGA-DJ-A1QM-01,1,42,1
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TCGA-DJ-A1QQ-01,1,43,1
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486 |
+
TCGA-FE-A239-01,1,82,1
|
487 |
+
TCGA-FE-A23A-01,1,27,0
|
488 |
+
TCGA-FE-A3PA-01,1,16,1
|
489 |
+
TCGA-FE-A3PB-01,1,33,0
|
490 |
+
TCGA-FE-A3PC-01,1,37,0
|
491 |
+
TCGA-FE-A3PD-01,1,17,0
|
492 |
+
TCGA-FK-A3S3-01,1,43,0
|
493 |
+
TCGA-FK-A3SB-01,1,28,0
|
494 |
+
TCGA-FK-A3SD-01,1,61,0
|
495 |
+
TCGA-FK-A3SE-01,1,31,0
|
496 |
+
TCGA-FK-A3SG-01,1,21,0
|
497 |
+
TCGA-FK-A3SH-01,1,50,0
|
498 |
+
TCGA-FK-A4UB-01,1,51,1
|
499 |
+
TCGA-FY-A2QD-01,1,61,0
|
500 |
+
TCGA-FY-A3BL-01,1,29,1
|
501 |
+
TCGA-FY-A3I4-01,1,34,0
|
502 |
+
TCGA-FY-A3I5-01,1,64,0
|
503 |
+
TCGA-FY-A3NM-01,1,48,0
|
504 |
+
TCGA-FY-A3NN-01,1,48,0
|
505 |
+
TCGA-FY-A3NP-01,1,69,1
|
506 |
+
TCGA-FY-A3ON-01,1,20,1
|
507 |
+
TCGA-FY-A3R6-01,1,57,0
|
508 |
+
TCGA-FY-A3R7-01,1,50,0
|
509 |
+
TCGA-FY-A3R8-01,1,62,0
|
510 |
+
TCGA-FY-A3R9-01,1,66,0
|
511 |
+
TCGA-FY-A3RA-01,1,21,0
|
512 |
+
TCGA-FY-A3TY-01,1,61,0
|
513 |
+
TCGA-FY-A3TY-11,0,61,0
|
514 |
+
TCGA-FY-A3W9-01,1,66,0
|
515 |
+
TCGA-FY-A3WA-01,1,52,0
|
516 |
+
TCGA-FY-A3YR-01,1,64,0
|
517 |
+
TCGA-FY-A40K-01,1,46,0
|
518 |
+
TCGA-FY-A40L-01,1,55,0
|
519 |
+
TCGA-FY-A40M-01,1,51,0
|
520 |
+
TCGA-FY-A40N-01,1,55,0
|
521 |
+
TCGA-FY-A4B0-01,1,76,1
|
522 |
+
TCGA-FY-A4B3-01,1,51,1
|
523 |
+
TCGA-FY-A4B4-01,1,62,0
|
524 |
+
TCGA-FY-A76V-01,1,54,1
|
525 |
+
TCGA-GE-A2C6-01,1,33,0
|
526 |
+
TCGA-GE-A2C6-11,0,33,0
|
527 |
+
TCGA-H2-A26U-01,1,54,0
|
528 |
+
TCGA-H2-A2K9-01,1,25,1
|
529 |
+
TCGA-H2-A2K9-11,0,25,1
|
530 |
+
TCGA-H2-A3RH-01,1,41,0
|
531 |
+
TCGA-H2-A3RI-01,1,29,0
|
532 |
+
TCGA-H2-A3RI-11,0,29,0
|
533 |
+
TCGA-H2-A421-01,1,34,0
|
534 |
+
TCGA-H2-A422-01,1,40,0
|
535 |
+
TCGA-IM-A3EB-01,1,32,0
|
536 |
+
TCGA-IM-A3ED-01,1,58,0
|
537 |
+
TCGA-IM-A3U2-01,1,89,0
|
538 |
+
TCGA-IM-A3U3-01,1,55,0
|
539 |
+
TCGA-IM-A41Y-01,1,42,0
|
540 |
+
TCGA-IM-A41Z-01,1,37,0
|
541 |
+
TCGA-IM-A420-01,1,39,0
|
542 |
+
TCGA-IM-A4EB-01,1,37,1
|
543 |
+
TCGA-J8-A3NZ-01,1,52,0
|
544 |
+
TCGA-J8-A3O0-01,1,38,1
|
545 |
+
TCGA-J8-A3O1-01,1,33,0
|
546 |
+
TCGA-J8-A3O2-01,1,39,1
|
547 |
+
TCGA-J8-A3O2-06,1,39,1
|
548 |
+
TCGA-J8-A3YD-01,1,47,0
|
549 |
+
TCGA-J8-A3YE-01,1,31,0
|
550 |
+
TCGA-J8-A3YF-01,1,83,1
|
551 |
+
TCGA-J8-A3YG-01,1,54,0
|
552 |
+
TCGA-J8-A3YH-01,1,39,1
|
553 |
+
TCGA-J8-A3YH-06,1,39,1
|
554 |
+
TCGA-J8-A42S-01,1,45,1
|
555 |
+
TCGA-J8-A4HW-01,1,59,0
|
556 |
+
TCGA-J8-A4HW-06,1,59,0
|
557 |
+
TCGA-J8-A4HY-01,1,68,0
|
558 |
+
TCGA-KS-A41F-01,1,37,0
|
559 |
+
TCGA-KS-A41I-01,1,47,0
|
560 |
+
TCGA-KS-A41I-11,0,47,0
|
561 |
+
TCGA-KS-A41J-01,1,28,0
|
562 |
+
TCGA-KS-A41J-11,0,28,0
|
563 |
+
TCGA-KS-A41L-01,1,39,0
|
564 |
+
TCGA-KS-A41L-11,0,39,0
|
565 |
+
TCGA-KS-A4I1-01,1,37,0
|
566 |
+
TCGA-KS-A4I3-01,1,41,1
|
567 |
+
TCGA-KS-A4I5-01,1,49,0
|
568 |
+
TCGA-KS-A4I7-01,1,61,0
|
569 |
+
TCGA-KS-A4I9-01,1,46,0
|
570 |
+
TCGA-KS-A4IB-01,1,41,0
|
571 |
+
TCGA-KS-A4IC-01,1,45,0
|
572 |
+
TCGA-KS-A4ID-01,1,55,0
|
573 |
+
TCGA-L6-A4EP-01,1,41,0
|
574 |
+
TCGA-L6-A4EQ-01,1,47,1
|
575 |
+
TCGA-L6-A4ET-01,1,49,0
|
576 |
+
TCGA-L6-A4EU-01,1,58,0
|
577 |
+
TCGA-MK-A4N6-01,1,35,1
|
578 |
+
TCGA-MK-A4N7-01,1,20,0
|
579 |
+
TCGA-MK-A4N9-01,1,41,0
|
580 |
+
TCGA-MK-A84Z-01,1,74,1
|
581 |
+
TCGA-QD-A8IV-01,1,50,0
|
p3/preprocess/Hypothyroidism/code/GSE151158.py
ADDED
@@ -0,0 +1,146 @@
|
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|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypothyroidism"
|
6 |
+
cohort = "GSE151158"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Hypothyroidism"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE151158"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE151158.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE151158.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE151158.csv"
|
16 |
+
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Get background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
|
23 |
+
|
24 |
+
# Get unique values for each clinical feature
|
25 |
+
unique_values_dict = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background information
|
28 |
+
print("Background Information:")
|
29 |
+
print(background_info)
|
30 |
+
print("\nSample Characteristics:")
|
31 |
+
print(json.dumps(unique_values_dict, indent=2))
|
32 |
+
# 1. Gene Expression Data Availability
|
33 |
+
is_gene_available = True # Background shows this is gene expression study of 594 genes
|
34 |
+
|
35 |
+
# 2.1 Data Availability
|
36 |
+
trait_row = 12 # hypothyroidism data found in row 12
|
37 |
+
age_row = 1 # age data found in row 1
|
38 |
+
gender_row = 2 # gender data found in row 2 as "Sex"
|
39 |
+
|
40 |
+
# 2.2 Data Type Conversion Functions
|
41 |
+
def convert_trait(x):
|
42 |
+
if pd.isna(x):
|
43 |
+
return None
|
44 |
+
value = x.split(": ")[1] if ": " in x else x
|
45 |
+
if value.upper() == 'Y':
|
46 |
+
return 1
|
47 |
+
elif value.upper() == 'N':
|
48 |
+
return 0
|
49 |
+
return None
|
50 |
+
|
51 |
+
def convert_age(x):
|
52 |
+
if pd.isna(x):
|
53 |
+
return None
|
54 |
+
try:
|
55 |
+
age = int(x.split(": ")[1])
|
56 |
+
return age
|
57 |
+
except:
|
58 |
+
return None
|
59 |
+
|
60 |
+
def convert_gender(x):
|
61 |
+
if pd.isna(x):
|
62 |
+
return None
|
63 |
+
value = x.split(": ")[1] if ": " in x else x
|
64 |
+
if value.upper() == 'F':
|
65 |
+
return 0
|
66 |
+
elif value.upper() == 'M':
|
67 |
+
return 1
|
68 |
+
return None
|
69 |
+
|
70 |
+
# 3. Save Metadata
|
71 |
+
validate_and_save_cohort_info(is_final=False,
|
72 |
+
cohort=cohort,
|
73 |
+
info_path=json_path,
|
74 |
+
is_gene_available=is_gene_available,
|
75 |
+
is_trait_available=trait_row is not None)
|
76 |
+
|
77 |
+
# 4. Clinical Feature Extraction
|
78 |
+
if trait_row is not None:
|
79 |
+
clinical_features_df = geo_select_clinical_features(
|
80 |
+
clinical_df=clinical_data,
|
81 |
+
trait=trait,
|
82 |
+
trait_row=trait_row,
|
83 |
+
convert_trait=convert_trait,
|
84 |
+
age_row=age_row,
|
85 |
+
convert_age=convert_age,
|
86 |
+
gender_row=gender_row,
|
87 |
+
convert_gender=convert_gender
|
88 |
+
)
|
89 |
+
|
90 |
+
# Preview the data
|
91 |
+
preview = preview_df(clinical_features_df)
|
92 |
+
print(preview)
|
93 |
+
|
94 |
+
# Save to CSV
|
95 |
+
clinical_features_df.to_csv(out_clinical_data_file)
|
96 |
+
# Extract gene expression data from the matrix file
|
97 |
+
genetic_data = get_genetic_data(matrix_file_path)
|
98 |
+
|
99 |
+
# Print first 20 row IDs
|
100 |
+
print("First 20 row IDs:")
|
101 |
+
print(genetic_data.index[:20].tolist())
|
102 |
+
# These IDs are standard HUGO gene symbols - e.g. ABCB1, ABCF1, ABL1 are well-known gene symbols
|
103 |
+
# No mapping needed as they are already in the correct format
|
104 |
+
requires_gene_mapping = False
|
105 |
+
# 1. Normalize gene symbols
|
106 |
+
genetic_data = normalize_gene_symbols_in_index(genetic_data)
|
107 |
+
genetic_data.to_csv(out_gene_data_file)
|
108 |
+
|
109 |
+
# Get clinical features
|
110 |
+
clinical_features = geo_select_clinical_features(
|
111 |
+
clinical_data,
|
112 |
+
trait=trait,
|
113 |
+
trait_row=trait_row,
|
114 |
+
convert_trait=convert_trait,
|
115 |
+
age_row=age_row,
|
116 |
+
convert_age=convert_age,
|
117 |
+
gender_row=gender_row,
|
118 |
+
convert_gender=convert_gender
|
119 |
+
)
|
120 |
+
|
121 |
+
# 2. Link clinical and genetic data
|
122 |
+
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
|
123 |
+
|
124 |
+
# 3. Handle missing values
|
125 |
+
linked_data = handle_missing_values(linked_data, trait)
|
126 |
+
|
127 |
+
# 4. Judge whether features are biased and remove biased demographic features
|
128 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
129 |
+
|
130 |
+
# 5. Final validation and save metadata
|
131 |
+
note = "Dataset contains gene expression data studying hypothyroidism in the context of NAFLD progression, with clinical annotations."
|
132 |
+
is_usable = validate_and_save_cohort_info(
|
133 |
+
is_final=True,
|
134 |
+
cohort=cohort,
|
135 |
+
info_path=json_path,
|
136 |
+
is_gene_available=True,
|
137 |
+
is_trait_available=True,
|
138 |
+
is_biased=is_biased,
|
139 |
+
df=linked_data,
|
140 |
+
note=note
|
141 |
+
)
|
142 |
+
|
143 |
+
# 6. Save the linked data only if it's usable
|
144 |
+
if is_usable:
|
145 |
+
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
|
146 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Hypothyroidism/code/GSE224330.py
ADDED
@@ -0,0 +1,180 @@
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypothyroidism"
|
6 |
+
cohort = "GSE224330"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Hypothyroidism"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE224330"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE224330.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE224330.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE224330.csv"
|
16 |
+
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Get background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
|
23 |
+
|
24 |
+
# Get unique values for each clinical feature
|
25 |
+
unique_values_dict = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background information
|
28 |
+
print("Background Information:")
|
29 |
+
print(background_info)
|
30 |
+
print("\nSample Characteristics:")
|
31 |
+
print(json.dumps(unique_values_dict, indent=2))
|
32 |
+
# Gene expression data availability
|
33 |
+
# Yes, this dataset contains gene expression data as indicated by series title and summary
|
34 |
+
is_gene_available = True
|
35 |
+
|
36 |
+
# Data availability and type conversion
|
37 |
+
# Trait data (Hypothyroidism) can be extracted from comorbidity field
|
38 |
+
trait_row = 3
|
39 |
+
def convert_trait(value):
|
40 |
+
if pd.isna(value):
|
41 |
+
return None
|
42 |
+
value = value.split(': ')[1].lower()
|
43 |
+
if 'hypothyroidism' in value:
|
44 |
+
return 1
|
45 |
+
elif 'none' in value:
|
46 |
+
return 0
|
47 |
+
return None
|
48 |
+
|
49 |
+
# Age data is in row 1
|
50 |
+
age_row = 1
|
51 |
+
def convert_age(value):
|
52 |
+
if pd.isna(value):
|
53 |
+
return None
|
54 |
+
try:
|
55 |
+
return int(value.split(': ')[1].rstrip('y'))
|
56 |
+
except:
|
57 |
+
return None
|
58 |
+
|
59 |
+
# Gender data is in row 2
|
60 |
+
gender_row = 2
|
61 |
+
def convert_gender(value):
|
62 |
+
if pd.isna(value):
|
63 |
+
return None
|
64 |
+
value = value.split(': ')[1].lower()
|
65 |
+
if 'female' in value:
|
66 |
+
return 0
|
67 |
+
elif 'male' in value:
|
68 |
+
return 1
|
69 |
+
return None
|
70 |
+
|
71 |
+
# Save initial filtering results
|
72 |
+
validate_and_save_cohort_info(
|
73 |
+
is_final=False,
|
74 |
+
cohort=cohort,
|
75 |
+
info_path=json_path,
|
76 |
+
is_gene_available=is_gene_available,
|
77 |
+
is_trait_available=trait_row is not None
|
78 |
+
)
|
79 |
+
|
80 |
+
# Extract clinical features since trait_row is available
|
81 |
+
clinical_features = geo_select_clinical_features(
|
82 |
+
clinical_df=clinical_data,
|
83 |
+
trait=trait,
|
84 |
+
trait_row=trait_row,
|
85 |
+
convert_trait=convert_trait,
|
86 |
+
age_row=age_row,
|
87 |
+
convert_age=convert_age,
|
88 |
+
gender_row=gender_row,
|
89 |
+
convert_gender=convert_gender
|
90 |
+
)
|
91 |
+
|
92 |
+
# Preview the extracted features
|
93 |
+
preview_df(clinical_features)
|
94 |
+
|
95 |
+
# Save clinical data
|
96 |
+
clinical_features.to_csv(out_clinical_data_file)
|
97 |
+
# Extract gene expression data from the matrix file
|
98 |
+
genetic_data = get_genetic_data(matrix_file_path)
|
99 |
+
|
100 |
+
# Print first 20 row IDs
|
101 |
+
print("First 20 row IDs:")
|
102 |
+
print(genetic_data.index[:20].tolist())
|
103 |
+
# These identifiers appear to be Agilent probe IDs, not human gene symbols
|
104 |
+
# The format A_19_P00xxxxxx is characteristic of Agilent microarray probes
|
105 |
+
# We will need to map these to proper gene symbols
|
106 |
+
requires_gene_mapping = True
|
107 |
+
# Extract gene annotation data from SOFT file
|
108 |
+
gene_metadata = get_gene_annotation(soft_file_path)
|
109 |
+
|
110 |
+
# Display information about the annotation data
|
111 |
+
print("Column names:")
|
112 |
+
print(gene_metadata.columns.tolist())
|
113 |
+
|
114 |
+
# Look at general data statistics
|
115 |
+
print("\nData shape:", gene_metadata.shape)
|
116 |
+
|
117 |
+
# Display non-NaN value counts for key gene identifier columns
|
118 |
+
print("\nNumber of non-NaN values in key columns:")
|
119 |
+
for col in ['GENE_SYMBOL', 'GENE_NAME']:
|
120 |
+
print(f"{col}: {gene_metadata[col].notna().sum()}")
|
121 |
+
|
122 |
+
# Preview rows with actual gene information
|
123 |
+
print("\nPreview of rows with gene information:")
|
124 |
+
gene_rows = gene_metadata[gene_metadata['GENE_SYMBOL'].notna()].head()
|
125 |
+
print(json.dumps(preview_df(gene_rows), indent=2))
|
126 |
+
# Get mapping between probes and gene symbols
|
127 |
+
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
|
128 |
+
|
129 |
+
# Convert probe-level data to gene-level data using the mapping
|
130 |
+
gene_data = apply_gene_mapping(genetic_data, mapping_data)
|
131 |
+
|
132 |
+
# Save gene expression data
|
133 |
+
gene_data.to_csv(out_gene_data_file)
|
134 |
+
|
135 |
+
# Print statistics about the mapping
|
136 |
+
print(f"Original probe number: {len(genetic_data)}")
|
137 |
+
print(f"Number of probes with gene mapping: {len(mapping_data)}")
|
138 |
+
print(f"Final number of genes: {len(gene_data)}")
|
139 |
+
# 1. Normalize gene symbols
|
140 |
+
genetic_data = normalize_gene_symbols_in_index(gene_data)
|
141 |
+
genetic_data.to_csv(out_gene_data_file)
|
142 |
+
|
143 |
+
# Get clinical features
|
144 |
+
clinical_features = geo_select_clinical_features(
|
145 |
+
clinical_data,
|
146 |
+
trait=trait,
|
147 |
+
trait_row=trait_row,
|
148 |
+
convert_trait=convert_trait,
|
149 |
+
age_row=age_row,
|
150 |
+
convert_age=convert_age,
|
151 |
+
gender_row=gender_row,
|
152 |
+
convert_gender=convert_gender
|
153 |
+
)
|
154 |
+
|
155 |
+
# 2. Link clinical and genetic data
|
156 |
+
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
|
157 |
+
|
158 |
+
# 3. Handle missing values
|
159 |
+
linked_data = handle_missing_values(linked_data, trait)
|
160 |
+
|
161 |
+
# 4. Judge whether features are biased and remove biased demographic features
|
162 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
163 |
+
|
164 |
+
# 5. Final validation and save metadata
|
165 |
+
note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
|
166 |
+
is_usable = validate_and_save_cohort_info(
|
167 |
+
is_final=True,
|
168 |
+
cohort=cohort,
|
169 |
+
info_path=json_path,
|
170 |
+
is_gene_available=True,
|
171 |
+
is_trait_available=True,
|
172 |
+
is_biased=is_biased,
|
173 |
+
df=linked_data,
|
174 |
+
note=note
|
175 |
+
)
|
176 |
+
|
177 |
+
# 6. Save the linked data only if it's usable
|
178 |
+
if is_usable:
|
179 |
+
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
|
180 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Hypothyroidism/code/GSE32445.py
ADDED
@@ -0,0 +1,143 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypothyroidism"
|
6 |
+
cohort = "GSE32445"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Hypothyroidism"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE32445"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE32445.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE32445.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE32445.csv"
|
16 |
+
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Get background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
|
23 |
+
|
24 |
+
# Get unique values for each clinical feature
|
25 |
+
unique_values_dict = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background information
|
28 |
+
print("Background Information:")
|
29 |
+
print(background_info)
|
30 |
+
print("\nSample Characteristics:")
|
31 |
+
print(json.dumps(unique_values_dict, indent=2))
|
32 |
+
# 1. Gene Expression Data Availability
|
33 |
+
# The series title and description suggest this is a study involving gene regulation,
|
34 |
+
# so it's likely to have gene expression data
|
35 |
+
is_gene_available = True
|
36 |
+
|
37 |
+
# 2. Variable Availability and Data Type Conversion
|
38 |
+
|
39 |
+
# 2.1 Data Availability
|
40 |
+
# Trait: Not directly available in characteristics - cannot be inferred from strain alone
|
41 |
+
trait_row = None
|
42 |
+
|
43 |
+
# Age: Available in row 2
|
44 |
+
age_row = 2
|
45 |
+
|
46 |
+
# Gender: Available in row 1
|
47 |
+
gender_row = 1
|
48 |
+
|
49 |
+
# 2.2 Data Type Conversion
|
50 |
+
# Trait converter not needed since trait data not available
|
51 |
+
def convert_trait(x):
|
52 |
+
return None
|
53 |
+
|
54 |
+
# Age converter - continuous
|
55 |
+
def convert_age(x):
|
56 |
+
try:
|
57 |
+
# Extract value after colon and remove 'months'/'years'
|
58 |
+
value = x.split(':')[1].strip()
|
59 |
+
value = value.lower().replace('months', '').replace('years', '').strip()
|
60 |
+
return float(value)
|
61 |
+
except:
|
62 |
+
return None
|
63 |
+
|
64 |
+
# Gender converter - binary (female=0, male=1)
|
65 |
+
def convert_gender(x):
|
66 |
+
try:
|
67 |
+
value = x.split(':')[1].strip().lower()
|
68 |
+
if 'female' in value:
|
69 |
+
return 0
|
70 |
+
elif 'male' in value:
|
71 |
+
return 1
|
72 |
+
return None
|
73 |
+
except:
|
74 |
+
return None
|
75 |
+
|
76 |
+
# 3. Save Metadata
|
77 |
+
# Initial filtering - trait data not available so dataset will be filtered out
|
78 |
+
validate_and_save_cohort_info(
|
79 |
+
is_final=False,
|
80 |
+
cohort=cohort,
|
81 |
+
info_path=json_path,
|
82 |
+
is_gene_available=is_gene_available,
|
83 |
+
is_trait_available=False
|
84 |
+
)
|
85 |
+
|
86 |
+
# 4. Clinical Feature Extraction
|
87 |
+
# Skip since trait_row is None
|
88 |
+
# Extract gene expression data from the matrix file
|
89 |
+
genetic_data = get_genetic_data(matrix_file_path)
|
90 |
+
|
91 |
+
# Print first 20 row IDs
|
92 |
+
print("First 20 row IDs:")
|
93 |
+
print(genetic_data.index[:20].tolist())
|
94 |
+
# The row IDs are Illumina probe IDs (starting with 'ILMN_') rather than human gene symbols
|
95 |
+
# These need to be mapped to gene symbols for analysis
|
96 |
+
requires_gene_mapping = True
|
97 |
+
# Extract gene annotation data from SOFT file
|
98 |
+
gene_metadata = get_gene_annotation(soft_file_path)
|
99 |
+
|
100 |
+
# Display information about the annotation data
|
101 |
+
print("Column names:")
|
102 |
+
print(gene_metadata.columns.tolist())
|
103 |
+
|
104 |
+
# Look at general data statistics
|
105 |
+
print("\nData shape:", gene_metadata.shape)
|
106 |
+
|
107 |
+
# Display non-NaN value counts for key gene identifier columns
|
108 |
+
print("\nNumber of non-NaN values in key columns:")
|
109 |
+
for col in ['Gene Symbol', 'Gene Title']:
|
110 |
+
print(f"{col}: {gene_metadata[col].notna().sum()}")
|
111 |
+
|
112 |
+
# Preview rows with actual gene information
|
113 |
+
print("\nPreview of rows with gene information:")
|
114 |
+
gene_rows = gene_metadata[gene_metadata['Gene Symbol'].notna()].head()
|
115 |
+
print(json.dumps(preview_df(gene_rows), indent=2))
|
116 |
+
# Extract gene mapping information from gene annotation data
|
117 |
+
# Split multiple gene symbols and expand them
|
118 |
+
mapping_data = gene_metadata.loc[:, ['ID', 'Gene Symbol']]
|
119 |
+
mapping_data = mapping_data.dropna()
|
120 |
+
# Rename column to match expected name in apply_gene_mapping function
|
121 |
+
mapping_data = mapping_data.rename(columns={'Gene Symbol': 'Gene'})
|
122 |
+
|
123 |
+
# Apply the mapping to get gene-level expression data
|
124 |
+
gene_data = apply_gene_mapping(genetic_data, mapping_data)
|
125 |
+
|
126 |
+
# Preview the results
|
127 |
+
print("\nNumber of genes after mapping:", len(gene_data))
|
128 |
+
print("\nFirst few gene symbols:", gene_data.index[:10].tolist())
|
129 |
+
# Create an empty DataFrame to represent unusable data
|
130 |
+
empty_df = pd.DataFrame()
|
131 |
+
|
132 |
+
# Record that this dataset is unusable
|
133 |
+
note = "Dataset lacks trait information and gene mapping failed to produce any valid gene expression data."
|
134 |
+
is_usable = validate_and_save_cohort_info(
|
135 |
+
is_final=True,
|
136 |
+
cohort=cohort,
|
137 |
+
info_path=json_path,
|
138 |
+
is_gene_available=True,
|
139 |
+
is_trait_available=False,
|
140 |
+
is_biased=True, # Set to True to indicate the data is unusable
|
141 |
+
df=empty_df, # Provide empty DataFrame instead of None
|
142 |
+
note=note
|
143 |
+
)
|
p3/preprocess/Hypothyroidism/code/GSE75678.py
ADDED
@@ -0,0 +1,173 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypothyroidism"
|
6 |
+
cohort = "GSE75678"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Hypothyroidism"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75678"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE75678.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE75678.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE75678.csv"
|
16 |
+
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Get background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
|
23 |
+
|
24 |
+
# Get unique values for each clinical feature
|
25 |
+
unique_values_dict = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background information
|
28 |
+
print("Background Information:")
|
29 |
+
print(background_info)
|
30 |
+
print("\nSample Characteristics:")
|
31 |
+
print(json.dumps(unique_values_dict, indent=2))
|
32 |
+
# 1. Gene Expression Data Availability
|
33 |
+
is_gene_available = True # Based on series title and summary indicating gene expression data
|
34 |
+
|
35 |
+
# 2. Variable Availability and Data Type Conversion
|
36 |
+
# Hypothyroidism data is in row 21 (personal pathological history)
|
37 |
+
trait_row = 21
|
38 |
+
age_row = 19 # Age at diagnosis
|
39 |
+
gender_row = 1 # Gender data is in row 1
|
40 |
+
|
41 |
+
def convert_trait(x):
|
42 |
+
if pd.isna(x):
|
43 |
+
return None
|
44 |
+
val = x.split(': ')[1] if ': ' in x else x
|
45 |
+
if 'Hypothyroidism' in val:
|
46 |
+
return 1
|
47 |
+
return 0
|
48 |
+
|
49 |
+
def convert_age(x):
|
50 |
+
if pd.isna(x):
|
51 |
+
return None
|
52 |
+
val = x.split(': ')[1] if ': ' in x else x
|
53 |
+
try:
|
54 |
+
return float(val)
|
55 |
+
except:
|
56 |
+
return None
|
57 |
+
|
58 |
+
def convert_gender(x):
|
59 |
+
if pd.isna(x):
|
60 |
+
return None
|
61 |
+
val = x.split(': ')[1] if ': ' in x else x
|
62 |
+
if val.lower() == 'female':
|
63 |
+
return 0
|
64 |
+
elif val.lower() == 'male':
|
65 |
+
return 1
|
66 |
+
return None
|
67 |
+
|
68 |
+
# 3. Save Metadata
|
69 |
+
is_usable = validate_and_save_cohort_info(
|
70 |
+
is_final=False,
|
71 |
+
cohort=cohort,
|
72 |
+
info_path=json_path,
|
73 |
+
is_gene_available=is_gene_available,
|
74 |
+
is_trait_available=trait_row is not None
|
75 |
+
)
|
76 |
+
|
77 |
+
# 4. Clinical Feature Extraction
|
78 |
+
selected_clinical = geo_select_clinical_features(
|
79 |
+
clinical_df=clinical_data,
|
80 |
+
trait=trait,
|
81 |
+
trait_row=trait_row,
|
82 |
+
convert_trait=convert_trait,
|
83 |
+
age_row=age_row,
|
84 |
+
convert_age=convert_age,
|
85 |
+
gender_row=gender_row,
|
86 |
+
convert_gender=convert_gender
|
87 |
+
)
|
88 |
+
|
89 |
+
# Preview and save clinical data
|
90 |
+
print(preview_df(selected_clinical))
|
91 |
+
selected_clinical.to_csv(out_clinical_data_file)
|
92 |
+
# Extract gene expression data from the matrix file
|
93 |
+
genetic_data = get_genetic_data(matrix_file_path)
|
94 |
+
|
95 |
+
# Print first 20 row IDs
|
96 |
+
print("First 20 row IDs:")
|
97 |
+
print(genetic_data.index[:20].tolist())
|
98 |
+
# Looking at the row IDs, they appear to be simple numeric indices rather than gene symbols
|
99 |
+
# This indicates we need to map these identifiers to actual gene symbols
|
100 |
+
requires_gene_mapping = True
|
101 |
+
# Extract gene annotation data from SOFT file
|
102 |
+
gene_metadata = get_gene_annotation(soft_file_path)
|
103 |
+
|
104 |
+
# Display information about the annotation data
|
105 |
+
print("Column names:")
|
106 |
+
print(gene_metadata.columns.tolist())
|
107 |
+
|
108 |
+
# Look at general data statistics
|
109 |
+
print("\nData shape:", gene_metadata.shape)
|
110 |
+
|
111 |
+
# Display non-NaN value counts for key gene identifier columns
|
112 |
+
print("\nNumber of non-NaN values in key columns:")
|
113 |
+
for col in ['GENE', 'GENE_SYMBOL', 'GENE_NAME']:
|
114 |
+
print(f"{col}: {gene_metadata[col].notna().sum()}")
|
115 |
+
|
116 |
+
# Preview rows with actual gene information
|
117 |
+
print("\nPreview of rows with gene information:")
|
118 |
+
gene_rows = gene_metadata[gene_metadata['GENE_SYMBOL'].notna()].head()
|
119 |
+
print(json.dumps(preview_df(gene_rows), indent=2))
|
120 |
+
# Extract mapping between numeric IDs and gene symbols from annotation data
|
121 |
+
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
|
122 |
+
|
123 |
+
# Convert probe-level measurements to gene expression data
|
124 |
+
gene_data = apply_gene_mapping(genetic_data, mapping_df)
|
125 |
+
|
126 |
+
# Preview the gene data shape
|
127 |
+
print("Gene expression data shape:", gene_data.shape)
|
128 |
+
|
129 |
+
# Preview first few gene symbols and samples
|
130 |
+
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
|
131 |
+
print("\nFirst few samples:", gene_data.columns[:5].tolist())
|
132 |
+
# 1. Normalize gene symbols
|
133 |
+
genetic_data = normalize_gene_symbols_in_index(gene_data)
|
134 |
+
genetic_data.to_csv(out_gene_data_file)
|
135 |
+
|
136 |
+
# Get clinical features
|
137 |
+
clinical_features = geo_select_clinical_features(
|
138 |
+
clinical_data,
|
139 |
+
trait=trait,
|
140 |
+
trait_row=trait_row,
|
141 |
+
convert_trait=convert_trait,
|
142 |
+
age_row=age_row,
|
143 |
+
convert_age=convert_age,
|
144 |
+
gender_row=gender_row,
|
145 |
+
convert_gender=convert_gender
|
146 |
+
)
|
147 |
+
|
148 |
+
# 2. Link clinical and genetic data
|
149 |
+
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
|
150 |
+
|
151 |
+
# 3. Handle missing values
|
152 |
+
linked_data = handle_missing_values(linked_data, trait)
|
153 |
+
|
154 |
+
# 4. Judge whether features are biased and remove biased demographic features
|
155 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
156 |
+
|
157 |
+
# 5. Final validation and save metadata
|
158 |
+
note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
|
159 |
+
is_usable = validate_and_save_cohort_info(
|
160 |
+
is_final=True,
|
161 |
+
cohort=cohort,
|
162 |
+
info_path=json_path,
|
163 |
+
is_gene_available=True,
|
164 |
+
is_trait_available=True,
|
165 |
+
is_biased=is_biased,
|
166 |
+
df=linked_data,
|
167 |
+
note=note
|
168 |
+
)
|
169 |
+
|
170 |
+
# 6. Save the linked data only if it's usable
|
171 |
+
if is_usable:
|
172 |
+
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
|
173 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Hypothyroidism/code/GSE75685.py
ADDED
@@ -0,0 +1,160 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypothyroidism"
|
6 |
+
cohort = "GSE75685"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Hypothyroidism"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75685"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE75685.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE75685.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE75685.csv"
|
16 |
+
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Get background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
|
23 |
+
|
24 |
+
# Get unique values for each clinical feature
|
25 |
+
unique_values_dict = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background information
|
28 |
+
print("Background Information:")
|
29 |
+
print(background_info)
|
30 |
+
print("\nSample Characteristics:")
|
31 |
+
print(json.dumps(unique_values_dict, indent=2))
|
32 |
+
# 1. Gene expression data availability check
|
33 |
+
# Study description suggests this is a breast cancer study with tumor samples
|
34 |
+
# There is RNA concentration and quality data (RQI Experion)
|
35 |
+
is_gene_available = True
|
36 |
+
|
37 |
+
# 2.1 Data row identification
|
38 |
+
trait_row = 21 # personal pathological history has 'Hypothyroidism' data
|
39 |
+
age_row = 19 # 'age at diagnosis'
|
40 |
+
gender_row = 1 # gender information
|
41 |
+
|
42 |
+
# 2.2 Data type conversion functions
|
43 |
+
def convert_trait(value):
|
44 |
+
if pd.isna(value):
|
45 |
+
return None
|
46 |
+
value = value.split(': ')[-1]
|
47 |
+
return 1 if value == 'Hypothyroidism' else 0
|
48 |
+
|
49 |
+
def convert_age(value):
|
50 |
+
if pd.isna(value):
|
51 |
+
return None
|
52 |
+
try:
|
53 |
+
age = int(value.split(': ')[-1])
|
54 |
+
return age
|
55 |
+
except:
|
56 |
+
return None
|
57 |
+
|
58 |
+
def convert_gender(value):
|
59 |
+
if pd.isna(value):
|
60 |
+
return None
|
61 |
+
value = value.split(': ')[-1].lower()
|
62 |
+
if 'female' in value:
|
63 |
+
return 0
|
64 |
+
elif 'male' in value:
|
65 |
+
return 1
|
66 |
+
return None
|
67 |
+
|
68 |
+
# 3. Save metadata about dataset usability
|
69 |
+
validate_and_save_cohort_info(
|
70 |
+
is_final=False,
|
71 |
+
cohort=cohort,
|
72 |
+
info_path=json_path,
|
73 |
+
is_gene_available=is_gene_available,
|
74 |
+
is_trait_available=trait_row is not None
|
75 |
+
)
|
76 |
+
|
77 |
+
# 4. Clinical feature extraction
|
78 |
+
clinical_features = geo_select_clinical_features(
|
79 |
+
clinical_df=clinical_data,
|
80 |
+
trait=trait,
|
81 |
+
trait_row=trait_row,
|
82 |
+
convert_trait=convert_trait,
|
83 |
+
age_row=age_row,
|
84 |
+
convert_age=convert_age,
|
85 |
+
gender_row=gender_row,
|
86 |
+
convert_gender=convert_gender
|
87 |
+
)
|
88 |
+
|
89 |
+
# Preview and save clinical features
|
90 |
+
print("Preview of extracted clinical features:")
|
91 |
+
print(preview_df(clinical_features))
|
92 |
+
clinical_features.to_csv(out_clinical_data_file)
|
93 |
+
# Extract gene expression data from the matrix file
|
94 |
+
genetic_data = get_genetic_data(matrix_file_path)
|
95 |
+
|
96 |
+
# Print first 20 row IDs
|
97 |
+
print("First 20 row IDs:")
|
98 |
+
print(genetic_data.index[:20].tolist())
|
99 |
+
# The row IDs are numerical indices, not gene symbols or other identifiers
|
100 |
+
# Therefore, gene mapping will be required to convert these to meaningful gene symbols
|
101 |
+
requires_gene_mapping = True
|
102 |
+
# Extract gene annotation data from SOFT file
|
103 |
+
gene_metadata = get_gene_annotation(soft_file_path)
|
104 |
+
|
105 |
+
# Display information about the annotation data
|
106 |
+
print("Column names:")
|
107 |
+
print(gene_metadata.columns.tolist())
|
108 |
+
print("\nPreview of first few rows:")
|
109 |
+
print(json.dumps(preview_df(gene_metadata), indent=2))
|
110 |
+
# Extract gene ID and gene symbol columns from annotation data
|
111 |
+
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
|
112 |
+
|
113 |
+
# Convert probe-level measurements to gene-level expression data
|
114 |
+
gene_data = apply_gene_mapping(genetic_data, mapping_data)
|
115 |
+
|
116 |
+
# Preview result
|
117 |
+
print("\nPreview of first few genes and their expression values:")
|
118 |
+
print(preview_df(gene_data))
|
119 |
+
# 1. Normalize gene symbols
|
120 |
+
genetic_data = normalize_gene_symbols_in_index(gene_data)
|
121 |
+
genetic_data.to_csv(out_gene_data_file)
|
122 |
+
|
123 |
+
# Get clinical features
|
124 |
+
clinical_features = geo_select_clinical_features(
|
125 |
+
clinical_data,
|
126 |
+
trait=trait,
|
127 |
+
trait_row=trait_row,
|
128 |
+
convert_trait=convert_trait,
|
129 |
+
age_row=age_row,
|
130 |
+
convert_age=convert_age,
|
131 |
+
gender_row=gender_row,
|
132 |
+
convert_gender=convert_gender
|
133 |
+
)
|
134 |
+
|
135 |
+
# 2. Link clinical and genetic data
|
136 |
+
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
|
137 |
+
|
138 |
+
# 3. Handle missing values
|
139 |
+
linked_data = handle_missing_values(linked_data, trait)
|
140 |
+
|
141 |
+
# 4. Judge whether features are biased and remove biased demographic features
|
142 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
143 |
+
|
144 |
+
# 5. Final validation and save metadata
|
145 |
+
note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
|
146 |
+
is_usable = validate_and_save_cohort_info(
|
147 |
+
is_final=True,
|
148 |
+
cohort=cohort,
|
149 |
+
info_path=json_path,
|
150 |
+
is_gene_available=True,
|
151 |
+
is_trait_available=True,
|
152 |
+
is_biased=is_biased,
|
153 |
+
df=linked_data,
|
154 |
+
note=note
|
155 |
+
)
|
156 |
+
|
157 |
+
# 6. Save the linked data only if it's usable
|
158 |
+
if is_usable:
|
159 |
+
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
|
160 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Hypothyroidism/code/TCGA.py
ADDED
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Hypothyroidism"
|
6 |
+
|
7 |
+
# Input paths
|
8 |
+
tcga_root_dir = "../DATA/TCGA"
|
9 |
+
|
10 |
+
# Output paths
|
11 |
+
out_data_file = "./output/preprocess/3/Hypothyroidism/TCGA.csv"
|
12 |
+
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/TCGA.csv"
|
13 |
+
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/TCGA.csv"
|
14 |
+
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
|
15 |
+
|
16 |
+
# Get subdirectories from TCGA root directory
|
17 |
+
tcga_subdirs = os.listdir(tcga_root_dir)
|
18 |
+
tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]
|
19 |
+
|
20 |
+
# Select thyroid cancer cohort as most relevant for hypothyroidism
|
21 |
+
selected_dir = "TCGA_Thyroid_Cancer_(THCA)"
|
22 |
+
cohort_dir = os.path.join(tcga_root_dir, selected_dir)
|
23 |
+
|
24 |
+
# Get clinical and genetic data file paths
|
25 |
+
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
|
26 |
+
|
27 |
+
# Load the data files
|
28 |
+
clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
|
29 |
+
genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
|
30 |
+
|
31 |
+
# Print clinical data columns for inspection
|
32 |
+
print("Clinical data columns:")
|
33 |
+
print(clinical_df.columns.tolist())
|
34 |
+
# Part 1: Define candidate columns
|
35 |
+
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
|
36 |
+
candidate_gender_cols = ['gender']
|
37 |
+
|
38 |
+
# Part 2: Preview existing clinical data
|
39 |
+
# Print age columns preview
|
40 |
+
age_preview = {}
|
41 |
+
for col in candidate_age_cols:
|
42 |
+
age_preview[col] = clinical_df[col].head().tolist()
|
43 |
+
print("Age columns preview:", age_preview)
|
44 |
+
|
45 |
+
# Print gender columns preview
|
46 |
+
gender_preview = {}
|
47 |
+
for col in candidate_gender_cols:
|
48 |
+
gender_preview[col] = clinical_df[col].head().tolist()
|
49 |
+
print("Gender columns preview:", gender_preview)
|
50 |
+
# Selecting age column
|
51 |
+
age_col = "age_at_initial_pathologic_diagnosis" # Contains direct age values, easier to interpret than days_to_birth
|
52 |
+
|
53 |
+
# Selecting gender column
|
54 |
+
gender_col = "gender" # Contains standard gender values
|
55 |
+
|
56 |
+
# Print chosen columns
|
57 |
+
print(f"Selected age column: {age_col}")
|
58 |
+
print(f"Selected gender column: {gender_col}")
|
59 |
+
# Extract and standardize clinical features
|
60 |
+
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
|
61 |
+
selected_clinical_df.to_csv(out_clinical_data_file)
|
62 |
+
|
63 |
+
# Normalize gene symbols and save
|
64 |
+
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
|
65 |
+
normalized_genetic_df.to_csv(out_gene_data_file)
|
66 |
+
|
67 |
+
# Link clinical and genetic data
|
68 |
+
linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1)
|
69 |
+
|
70 |
+
# Handle missing values
|
71 |
+
linked_data = handle_missing_values(linked_data, trait)
|
72 |
+
|
73 |
+
# Judge whether features are biased and remove biased demographic features
|
74 |
+
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
75 |
+
|
76 |
+
# Final validation and save cohort info
|
77 |
+
note = "Used thyroid cancer (THCA) data as thyroid disorders are closely related to hypothyroidism"
|
78 |
+
is_usable = validate_and_save_cohort_info(
|
79 |
+
is_final=True,
|
80 |
+
cohort="TCGA",
|
81 |
+
info_path=json_path,
|
82 |
+
is_gene_available=True,
|
83 |
+
is_trait_available=True,
|
84 |
+
is_biased=trait_biased,
|
85 |
+
df=linked_data,
|
86 |
+
note=note
|
87 |
+
)
|
88 |
+
|
89 |
+
# Save linked data if usable
|
90 |
+
if is_usable:
|
91 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Hypothyroidism/cohort_info.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"GSE75685": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 54, "note": "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."}, "GSE75678": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 54, "note": "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."}, "GSE32445": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset lacks trait information and gene mapping failed to produce any valid gene expression data."}, "GSE224330": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 15, "note": "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."}, "GSE151158": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 61, "note": "Dataset contains gene expression data studying hypothyroidism in the context of NAFLD progression, with clinical annotations."}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 572, "note": "Used thyroid cancer (THCA) data as thyroid disorders are closely related to hypothyroidism"}}
|
p3/preprocess/Hypothyroidism/gene_data/GSE151158.csv
ADDED
The diff for this file is too large to render.
See raw diff
|
|
p3/preprocess/Hypothyroidism/gene_data/GSE224330.csv
ADDED
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|
|
p3/preprocess/Hypothyroidism/gene_data/GSE32445.csv
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
Gene,GSM802773,GSM802774,GSM802775,GSM802776,GSM802777,GSM802778,GSM802779,GSM802780,GSM802781,GSM802782,GSM802783,GSM802784,GSM802785,GSM802786,GSM802787,GSM802788,GSM802789,GSM802790,GSM802791,GSM802792,GSM802793,GSM802794,GSM802795,GSM802796,GSM802797,GSM802798
|
p3/preprocess/Hypothyroidism/gene_data/GSE75685.csv
ADDED
The diff for this file is too large to render.
See raw diff
|
|
p3/preprocess/Insomnia/clinical_data/GSE208668.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM6360934,GSM6360935,GSM6360936,GSM6360937,GSM6360938,GSM6360939,GSM6360940,GSM6360941,GSM6360942,GSM6360943,GSM6360944,GSM6360945,GSM6360946,GSM6360947,GSM6360948,GSM6360949,GSM6360950,GSM6360951,GSM6360952,GSM6360953,GSM6360954,GSM6360955,GSM6360956,GSM6360957,GSM6360958,GSM6360959,GSM6360960,GSM6360961,GSM6360962,GSM6360963,GSM6360964,GSM6360965,GSM6360966,GSM6360967,GSM6360968,GSM6360969,GSM6360970,GSM6360971,GSM6360972,GSM6360973,GSM6360974,GSM6360975
|
2 |
+
Insomnia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
3 |
+
Age,65.0,75.0,77.0,64.0,60.0,67.0,72.0,62.0,73.0,74.0,73.0,68.0,62.0,73.0,70.0,60.0,61.0,66.0,69.0,62.0,67.0,62.0,71.0,63.0,62.0,61.0,67.0,78.0,79.0,72.0,73.0,77.0,72.0,62.0,70.0,65.0,63.0,74.0,71.0,80.0,78.0,65.0
|
4 |
+
Gender,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0
|
p3/preprocess/Insomnia/cohort_info.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"GSE208668": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 42, "note": "Dataset contains genome-wide transcriptional profiling of PBMCs from older adults with and without insomnia disorder."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}
|
p3/preprocess/Lower_Grade_Glioma/gene_data/GSE107850.csv
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8cfc4c9142d4576bda39c238fd7300590dcdb3a81d4dc746610a06c4c8c0fcae
|
3 |
+
size 47672138
|
p3/preprocess/Lower_Grade_Glioma/gene_data/GSE35158.csv
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:31a56ec47b70442242e56b75ae792ea031c6450fbee615649d0a1b1550930085
|
3 |
+
size 12648522
|
p3/preprocess/Lower_Grade_Glioma/gene_data/GSE74567.csv
ADDED
The diff for this file is too large to render.
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|
|
p3/preprocess/Pancreatic_Cancer/GSE125158.csv
ADDED
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|
|
p3/preprocess/Pancreatic_Cancer/GSE130563.csv
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:97d780606b862ee8e980d297e0f8e1abe5c470ba763f5201d693cf991d7e883a
|
3 |
+
size 10970181
|
p3/preprocess/Pancreatic_Cancer/GSE131027.csv
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4d5ab692ea51f69d8385adb7393e47bb330a8196ba31924f2cfa336a641a16f1
|
3 |
+
size 24380321
|
p3/preprocess/Pancreatic_Cancer/GSE183795.csv
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ec6d16da8518cc99e32797619bb8ce228eb07b0429479734189bbd22920246f7
|
3 |
+
size 66229403
|
p3/preprocess/Pancreatic_Cancer/GSE236951.csv
ADDED
The diff for this file is too large to render.
See raw diff
|
|
p3/preprocess/Pancreatic_Cancer/clinical_data/GSE130563.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM3743555,GSM3743556,GSM3743557,GSM3743558,GSM3743559,GSM3743560,GSM3743561,GSM3743562,GSM3743563,GSM3743564,GSM3743565,GSM3743566,GSM3743567,GSM3743568,GSM3743569,GSM3743570,GSM3743571,GSM3743572,GSM3743573,GSM3743574,GSM3743575,GSM3743576,GSM3743577,GSM3743578,GSM3743579,GSM3743580,GSM3743581,GSM3743582,GSM3743583,GSM3743584,GSM3743585,GSM3743586,GSM3743587,GSM3743588,GSM3743589,GSM3743590,GSM3743591,GSM3743592,GSM3743593,GSM3743594,GSM3743595,GSM3743596,GSM3743597,GSM3743598,GSM3743599,GSM3743600
|
2 |
+
Pancreatic_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,
|
3 |
+
Age,33.0,68.0,73.0,49.0,78.0,57.0,55.0,50.0,47.0,63.0,51.0,50.0,69.0,50.0,60.0,68.0,66.0,54.0,64.0,76.0,68.0,73.0,56.0,80.0,68.0,79.0,72.0,52.0,74.0,74.0,55.0,56.0,77.0,70.0,70.0,63.0,59.0,74.0,30.0,51.0,55.0,55.0,45.0,58.0,50.0,54.0
|
4 |
+
Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0
|
p3/preprocess/Pancreatic_Cancer/clinical_data/GSE131027.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
,GSM3759992,GSM3759993,GSM3759994,GSM3759995,GSM3759996,GSM3759997,GSM3759998,GSM3759999,GSM3760000,GSM3760001,GSM3760002,GSM3760003,GSM3760004,GSM3760005,GSM3760006,GSM3760007,GSM3760008,GSM3760009,GSM3760010,GSM3760011,GSM3760012,GSM3760013,GSM3760014,GSM3760015,GSM3760016,GSM3760017,GSM3760018,GSM3760019,GSM3760020,GSM3760021,GSM3760022,GSM3760023,GSM3760024,GSM3760025,GSM3760026,GSM3760027,GSM3760028,GSM3760029,GSM3760030,GSM3760031,GSM3760032,GSM3760033,GSM3760034,GSM3760035,GSM3760036,GSM3760037,GSM3760038,GSM3760039,GSM3760040,GSM3760041,GSM3760042,GSM3760043,GSM3760044,GSM3760045,GSM3760046,GSM3760047,GSM3760048,GSM3760049,GSM3760050,GSM3760051,GSM3760052,GSM3760053,GSM3760054,GSM3760055,GSM3760056,GSM3760057,GSM3760058,GSM3760059,GSM3760060,GSM3760061,GSM3760062,GSM3760063,GSM3760064,GSM3760065,GSM3760066,GSM3760067,GSM3760068,GSM3760069,GSM3760070,GSM3760071,GSM3760072,GSM3760073,GSM3760074,GSM3760075,GSM3760076,GSM3760077,GSM3760078,GSM3760079,GSM3760080,GSM3760081,GSM3760082,GSM3760083
|
2 |
+
Pancreatic_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
p3/preprocess/Pancreatic_Cancer/clinical_data/GSE183795.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
,GSM5570968,GSM5570969,GSM5570970,GSM5570971,GSM5570972,GSM5570973,GSM5570974,GSM5570975,GSM5570976,GSM5570977,GSM5570978,GSM5570979,GSM5570980,GSM5570981,GSM5570982,GSM5570983,GSM5570984,GSM5570985,GSM5570986,GSM5570987,GSM5570988,GSM5570989,GSM5570990,GSM5570991,GSM5570992,GSM5570993,GSM5570994,GSM5570995,GSM5570996,GSM5570997,GSM5570998,GSM5570999,GSM5571000,GSM5571001,GSM5571002,GSM5571003,GSM5571004,GSM5571005,GSM5571006,GSM5571007,GSM5571008,GSM5571009,GSM5571010,GSM5571011,GSM5571012,GSM5571013,GSM5571014,GSM5571015,GSM5571016,GSM5571017,GSM5571018,GSM5571019,GSM5571020,GSM5571021,GSM5571022,GSM5571023,GSM5571024,GSM5571025,GSM5571026,GSM5571027,GSM5571028,GSM5571029,GSM5571030,GSM5571031,GSM5571032,GSM5571033,GSM5571034,GSM5571035,GSM5571036,GSM5571037,GSM5571038,GSM5571039,GSM5571040,GSM5571041,GSM5571042,GSM5571043,GSM5571044,GSM5571045,GSM5571046,GSM5571047,GSM5571048,GSM5571049,GSM5571050,GSM5571051,GSM5571052,GSM5571053,GSM5571054,GSM5571055,GSM5571056,GSM5571057,GSM5571058,GSM5571059,GSM5571060,GSM5571061,GSM5571062,GSM5571063,GSM5571064,GSM5571065,GSM5571066,GSM5571067,GSM5571068,GSM5571069,GSM5571070,GSM5571071,GSM5571072,GSM5571073,GSM5571074,GSM5571075,GSM5571076,GSM5571077,GSM5571078,GSM5571079,GSM5571080,GSM5571081,GSM5571082,GSM5571083,GSM5571084,GSM5571085,GSM5571086,GSM5571087,GSM5571088,GSM5571089,GSM5571090,GSM5571091,GSM5571092,GSM5571093,GSM5571094,GSM5571095,GSM5571096,GSM5571097,GSM5571098,GSM5571099,GSM5571100,GSM5571101,GSM5571102,GSM5571103,GSM5571104,GSM5571105,GSM5571106,GSM5571107,GSM5571108,GSM5571109,GSM5571110,GSM5571111,GSM5571112,GSM5571113,GSM5571114,GSM5571115,GSM5571116,GSM5571117,GSM5571118,GSM5571119,GSM5571120,GSM5571121,GSM5571122,GSM5571123,GSM5571124,GSM5571125,GSM5571126,GSM5571127,GSM5571128,GSM5571129,GSM5571130,GSM5571131,GSM5571132,GSM5571133,GSM5571134,GSM5571135,GSM5571136,GSM5571137,GSM5571138,GSM5571139,GSM5571140,GSM5571141,GSM5571142,GSM5571143,GSM5571144,GSM5571145,GSM5571146,GSM5571147,GSM5571148,GSM5571149,GSM5571150,GSM5571151,GSM5571152,GSM5571153,GSM5571154,GSM5571155,GSM5571156,GSM5571157,GSM5571158,GSM5571159,GSM5571160,GSM5571161,GSM5571162,GSM5571163,GSM5571164,GSM5571165,GSM5571166,GSM5571167,GSM5571168,GSM5571169,GSM5571170,GSM5571171,GSM5571172,GSM5571173,GSM5571174,GSM5571175,GSM5571176,GSM5571177,GSM5571178,GSM5571179,GSM5571180,GSM5571181,GSM5571182,GSM5571183,GSM5571184,GSM5571185,GSM5571186,GSM5571187,GSM5571188,GSM5571189,GSM5571190,GSM5571191,GSM5571192,GSM5571193,GSM5571194,GSM5571195,GSM5571196,GSM5571197,GSM5571198,GSM5571199,GSM5571200,GSM5571201,GSM5571202,GSM5571203,GSM5571204,GSM5571205,GSM5571206,GSM5571207,GSM5571208,GSM5571209,GSM5571210,GSM5571211
|
2 |
+
Pancreatic_Cancer,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0
|
p3/preprocess/Pancreatic_Cancer/clinical_data/GSE222788.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
,GSM6932288,GSM6932289,GSM6932290,GSM6932291,GSM6932292,GSM6932293,GSM6932294,GSM6932295,GSM6932296,GSM6932297,GSM6932298,GSM6932299,GSM6932300,GSM6932301,GSM6932302,GSM6932303,GSM6932304,GSM6932305,GSM6932306,GSM6932307,GSM6932308,GSM6932309,GSM6932310,GSM6932311,GSM6932312,GSM6932313,GSM6932314,GSM6932315,GSM6932316,GSM6932317,GSM6932318,GSM6932319,GSM6932320,GSM6932321,GSM6932322,GSM6932323,GSM6932324,GSM6932325,GSM6932326,GSM6932327,GSM6932328
|
2 |
+
Pancreatic_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
|
p3/preprocess/Pancreatic_Cancer/clinical_data/GSE223409.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
,GSM6947318,GSM6947319,GSM6947320,GSM6947321,GSM6947322,GSM6947323,GSM6947324,GSM6947325,GSM6947326,GSM6947327,GSM6947328,GSM6947329,GSM6947330,GSM6947331,GSM6947332
|
2 |
+
Pancreatic_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0
|
p3/preprocess/Pancreatic_Cancer/clinical_data/GSE236951.csv
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
,GSM7587683,GSM7587684,GSM7587685,GSM7587686,GSM7587687,GSM7587688,GSM7587689,GSM7587690,GSM7587691,GSM7587692,GSM7587693,GSM7587694,GSM7587695,GSM7587696,GSM7587697,GSM7587698,GSM7587699,GSM7587700,GSM7587701,GSM7587702,GSM7587703,GSM7587704,GSM7587705,GSM7587706,GSM7587707,GSM7587708,GSM7587709,GSM7587710,GSM7587711,GSM7587712,GSM7587713,GSM7587714,GSM7587715,GSM7587716
|
2 |
+
Pancreatic_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
|
3 |
+
Age,83.0,64.0,59.0,64.0,72.0,72.0,89.0,59.0,64.0,82.0,75.0,61.0,59.0,68.0,49.0,71.0,68.0,58.0,76.0,67.0,52.0,57.0,72.0,59.0,53.0,95.0,53.0,55.0,43.0,71.0,48.0,43.0,55.0,63.0
|
4 |
+
Gender,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0
|
p3/preprocess/Pancreatic_Cancer/code/GSE120127.py
ADDED
@@ -0,0 +1,221 @@
|
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|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE120127"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE120127"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE120127.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE120127.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE120127.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Based on series title and genotype info, this appears to be gene expression data from pancreatic cancer cell lines
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2. Variable Availability and Data Type Conversion
|
41 |
+
# 2.1 Data Availability
|
42 |
+
# Trait can be inferred from genotype (feature 2) - KrasG12D vs KO
|
43 |
+
trait_row = 2
|
44 |
+
|
45 |
+
# Gender can be found in feature 0
|
46 |
+
gender_row = 0
|
47 |
+
|
48 |
+
# Age not available for cell lines
|
49 |
+
age_row = None
|
50 |
+
|
51 |
+
# 2.2 Data Type Conversion Functions
|
52 |
+
def convert_trait(value):
|
53 |
+
"""Convert genotype to binary trait"""
|
54 |
+
if not value or not isinstance(value, str):
|
55 |
+
return None
|
56 |
+
value = value.split(': ')[-1].strip()
|
57 |
+
# Bap1 KO vs WT
|
58 |
+
if 'KO' in value:
|
59 |
+
return 1
|
60 |
+
elif 'WT' in value:
|
61 |
+
return 0
|
62 |
+
return None
|
63 |
+
|
64 |
+
def convert_gender(value):
|
65 |
+
"""Convert gender to binary"""
|
66 |
+
if not value or not isinstance(value, str):
|
67 |
+
return None
|
68 |
+
value = value.split(': ')[-1].strip().upper()
|
69 |
+
if value == 'F':
|
70 |
+
return 0
|
71 |
+
elif value == 'M':
|
72 |
+
return 1
|
73 |
+
return None
|
74 |
+
|
75 |
+
convert_age = None
|
76 |
+
|
77 |
+
# 3. Save Metadata
|
78 |
+
is_trait_available = trait_row is not None
|
79 |
+
validate_and_save_cohort_info(is_final=False,
|
80 |
+
cohort=cohort,
|
81 |
+
info_path=json_path,
|
82 |
+
is_gene_available=is_gene_available,
|
83 |
+
is_trait_available=is_trait_available)
|
84 |
+
|
85 |
+
# 4. Clinical Feature Extraction
|
86 |
+
if trait_row is not None:
|
87 |
+
# Extract features using the library function
|
88 |
+
clinical_features = geo_select_clinical_features(
|
89 |
+
clinical_df=clinical_data,
|
90 |
+
trait=trait,
|
91 |
+
trait_row=trait_row,
|
92 |
+
convert_trait=convert_trait,
|
93 |
+
age_row=age_row,
|
94 |
+
convert_age=convert_age,
|
95 |
+
gender_row=gender_row,
|
96 |
+
convert_gender=convert_gender
|
97 |
+
)
|
98 |
+
|
99 |
+
# Preview the extracted features
|
100 |
+
preview = preview_df(clinical_features)
|
101 |
+
print("Preview of clinical features:")
|
102 |
+
print(preview)
|
103 |
+
|
104 |
+
# Save to CSV
|
105 |
+
clinical_features.to_csv(out_clinical_data_file)
|
106 |
+
# Extract gene expression data from matrix file
|
107 |
+
gene_data = get_genetic_data(matrix_file)
|
108 |
+
|
109 |
+
# Print first 20 row IDs and shape of data to help debug
|
110 |
+
print("Shape of gene expression data:", gene_data.shape)
|
111 |
+
print("\nFirst few rows of data:")
|
112 |
+
print(gene_data.head())
|
113 |
+
print("\nFirst 20 gene/probe identifiers:")
|
114 |
+
print(gene_data.index[:20])
|
115 |
+
|
116 |
+
# Inspect a snippet of raw file to verify identifier format
|
117 |
+
import gzip
|
118 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
119 |
+
lines = []
|
120 |
+
for i, line in enumerate(f):
|
121 |
+
if "!series_matrix_table_begin" in line:
|
122 |
+
# Get the next 5 lines after the marker
|
123 |
+
for _ in range(5):
|
124 |
+
lines.append(next(f).strip())
|
125 |
+
break
|
126 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
127 |
+
for line in lines:
|
128 |
+
print(line)
|
129 |
+
# From the row identifiers and examining the number format, these appear to be Agilent probe IDs
|
130 |
+
# These will need to be mapped to standard human gene symbols for analysis
|
131 |
+
requires_gene_mapping = True
|
132 |
+
# Get file paths using library function
|
133 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
134 |
+
|
135 |
+
# Extract gene annotation from SOFT file
|
136 |
+
gene_annotation = get_gene_annotation(soft_file)
|
137 |
+
|
138 |
+
# Preview gene annotation data
|
139 |
+
print("Gene annotation columns and example values:")
|
140 |
+
print(preview_df(gene_annotation))
|
141 |
+
# Extract gene annotation data using a different prefix pattern for correct platform
|
142 |
+
gene_annotation = filter_content_by_prefix(soft_file, prefixes_a=['^FEATURES'], unselect=True, source_type='file',
|
143 |
+
return_df_a=True)[0]
|
144 |
+
|
145 |
+
# Get mapping between probe IDs and gene symbols
|
146 |
+
gene_mapping = gene_annotation.loc[:, ['ID', 'Gene Symbol']]
|
147 |
+
gene_mapping = gene_mapping.rename(columns={'Gene Symbol': 'Gene'}).astype({'ID': 'str'})
|
148 |
+
|
149 |
+
# Convert probe-level measurements to gene expression values
|
150 |
+
gene_data = apply_gene_mapping(gene_data, gene_mapping)
|
151 |
+
|
152 |
+
# Save the gene expression data
|
153 |
+
gene_data.to_csv(out_gene_data_file)
|
154 |
+
# Get file paths using library function
|
155 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
156 |
+
|
157 |
+
# Extract gene annotation from SOFT file
|
158 |
+
gene_annotation = get_gene_annotation(soft_file)
|
159 |
+
|
160 |
+
# Preview gene annotation data
|
161 |
+
print("Gene annotation columns and example values:")
|
162 |
+
print(preview_df(gene_annotation))
|
163 |
+
# Get mapping between probe IDs and gene symbols using library function
|
164 |
+
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
165 |
+
|
166 |
+
# Convert probe-level measurements to gene expression values using library function
|
167 |
+
gene_data = apply_gene_mapping(gene_data, gene_mapping)
|
168 |
+
|
169 |
+
# Save the gene expression data
|
170 |
+
gene_data.to_csv(out_gene_data_file)
|
171 |
+
# 1. Normalize gene symbols and save normalized gene data
|
172 |
+
# Remove "-mRNA" suffix from gene symbols before normalization
|
173 |
+
gene_data.index = gene_data.index.str.replace('-mRNA', '')
|
174 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
175 |
+
gene_data.to_csv(out_gene_data_file)
|
176 |
+
|
177 |
+
# 2. Link clinical and genetic data and trait
|
178 |
+
# First get selected clinical features using the extraction function from previous step
|
179 |
+
selected_clinical = geo_select_clinical_features(
|
180 |
+
clinical_df=clinical_data,
|
181 |
+
trait=trait,
|
182 |
+
trait_row=trait_row,
|
183 |
+
convert_trait=convert_trait,
|
184 |
+
age_row=age_row,
|
185 |
+
convert_age=convert_age,
|
186 |
+
gender_row=gender_row,
|
187 |
+
convert_gender=convert_gender
|
188 |
+
)
|
189 |
+
|
190 |
+
# Debug data structures before linking
|
191 |
+
print("\nPre-linking data shapes:")
|
192 |
+
print("Clinical data shape:", selected_clinical.shape)
|
193 |
+
print("Gene data shape:", gene_data.shape)
|
194 |
+
print("\nClinical data preview:")
|
195 |
+
print(selected_clinical.head())
|
196 |
+
|
197 |
+
# Transpose gene data to match clinical data orientation
|
198 |
+
gene_data_t = gene_data.T
|
199 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
200 |
+
|
201 |
+
# 3. Handle missing values systematically
|
202 |
+
linked_data = handle_missing_values(linked_data, trait)
|
203 |
+
|
204 |
+
# 4. Check for biased features and remove them if needed
|
205 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
206 |
+
|
207 |
+
# 5. Validate data quality and save metadata
|
208 |
+
is_usable = validate_and_save_cohort_info(
|
209 |
+
is_final=True,
|
210 |
+
cohort=cohort,
|
211 |
+
info_path=json_path,
|
212 |
+
is_gene_available=True,
|
213 |
+
is_trait_available=True,
|
214 |
+
is_biased=is_biased,
|
215 |
+
df=linked_data,
|
216 |
+
note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
|
217 |
+
)
|
218 |
+
|
219 |
+
# 6. Save linked data if usable
|
220 |
+
if is_usable:
|
221 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE124069.py
ADDED
@@ -0,0 +1,189 @@
|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
|
|
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|
|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE124069"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE124069"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE124069.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE124069.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE124069.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Yes, this dataset contains gene expression data from microarrays
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2.1 Data Availability
|
41 |
+
# Trait (disease state) is available in row 0
|
42 |
+
trait_row = 0
|
43 |
+
|
44 |
+
# Age and gender data not available in sample characteristics
|
45 |
+
age_row = None
|
46 |
+
gender_row = None
|
47 |
+
|
48 |
+
# 2.2 Data Type Conversion Functions
|
49 |
+
def convert_trait(value: str) -> int:
|
50 |
+
"""Convert pancreatic cancer status to binary"""
|
51 |
+
if not isinstance(value, str):
|
52 |
+
return None
|
53 |
+
value = value.lower().split(': ')[-1].strip()
|
54 |
+
if 'pancreatic cancer' in value:
|
55 |
+
return 1
|
56 |
+
return None
|
57 |
+
|
58 |
+
def convert_age(value: str) -> float:
|
59 |
+
"""Convert age to float - not used since age not available"""
|
60 |
+
return None
|
61 |
+
|
62 |
+
def convert_gender(value: str) -> int:
|
63 |
+
"""Convert gender to binary - not used since gender not available"""
|
64 |
+
return None
|
65 |
+
|
66 |
+
# 3. Save Metadata
|
67 |
+
is_trait_available = trait_row is not None
|
68 |
+
_ = validate_and_save_cohort_info(is_final=False,
|
69 |
+
cohort=cohort,
|
70 |
+
info_path=json_path,
|
71 |
+
is_gene_available=is_gene_available,
|
72 |
+
is_trait_available=is_trait_available)
|
73 |
+
|
74 |
+
# 4. Clinical Feature Extraction
|
75 |
+
if trait_row is not None:
|
76 |
+
clinical_features = geo_select_clinical_features(
|
77 |
+
clinical_df=clinical_data,
|
78 |
+
trait=trait,
|
79 |
+
trait_row=trait_row,
|
80 |
+
convert_trait=convert_trait,
|
81 |
+
age_row=age_row,
|
82 |
+
convert_age=convert_age,
|
83 |
+
gender_row=gender_row,
|
84 |
+
convert_gender=convert_gender
|
85 |
+
)
|
86 |
+
|
87 |
+
# Preview the extracted features
|
88 |
+
preview = preview_df(clinical_features)
|
89 |
+
print("Preview of clinical features:")
|
90 |
+
print(preview)
|
91 |
+
|
92 |
+
# Save to CSV
|
93 |
+
clinical_features.to_csv(out_clinical_data_file)
|
94 |
+
# Extract gene expression data from matrix file
|
95 |
+
gene_data = get_genetic_data(matrix_file)
|
96 |
+
|
97 |
+
# Print first 20 row IDs and shape of data to help debug
|
98 |
+
print("Shape of gene expression data:", gene_data.shape)
|
99 |
+
print("\nFirst few rows of data:")
|
100 |
+
print(gene_data.head())
|
101 |
+
print("\nFirst 20 gene/probe identifiers:")
|
102 |
+
print(gene_data.index[:20])
|
103 |
+
|
104 |
+
# Inspect a snippet of raw file to verify identifier format
|
105 |
+
import gzip
|
106 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
107 |
+
lines = []
|
108 |
+
for i, line in enumerate(f):
|
109 |
+
if "!series_matrix_table_begin" in line:
|
110 |
+
# Get the next 5 lines after the marker
|
111 |
+
for _ in range(5):
|
112 |
+
lines.append(next(f).strip())
|
113 |
+
break
|
114 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
115 |
+
for line in lines:
|
116 |
+
print(line)
|
117 |
+
# Based on the identifiers starting with numbers and having '_at' suffix (e.g. '1007_s_at', '1053_at'),
|
118 |
+
# these are Affymetrix probe IDs which need to be mapped to official gene symbols
|
119 |
+
requires_gene_mapping = True
|
120 |
+
# Get file paths using library function
|
121 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
122 |
+
|
123 |
+
# Extract gene annotation from SOFT file
|
124 |
+
gene_annotation = get_gene_annotation(soft_file)
|
125 |
+
|
126 |
+
# Preview gene annotation data
|
127 |
+
print("Gene annotation columns and example values:")
|
128 |
+
print(preview_df(gene_annotation))
|
129 |
+
# Extract ID and Gene Symbol columns to create mapping table
|
130 |
+
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
131 |
+
|
132 |
+
# Apply gene mapping to convert probe-level data to gene-level data
|
133 |
+
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)
|
134 |
+
|
135 |
+
# Preview
|
136 |
+
print("Shape of mapped gene data:", gene_data.shape)
|
137 |
+
print("\nFirst few rows of mapped gene data:")
|
138 |
+
print(gene_data.head())
|
139 |
+
# 1. Normalize gene symbols and save normalized gene data
|
140 |
+
# Remove "-mRNA" suffix from gene symbols before normalization
|
141 |
+
gene_data.index = gene_data.index.str.replace('-mRNA', '')
|
142 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
143 |
+
gene_data.to_csv(out_gene_data_file)
|
144 |
+
|
145 |
+
# 2. Link clinical and genetic data and trait
|
146 |
+
# First get selected clinical features using the extraction function from previous step
|
147 |
+
selected_clinical = geo_select_clinical_features(
|
148 |
+
clinical_df=clinical_data,
|
149 |
+
trait=trait,
|
150 |
+
trait_row=trait_row,
|
151 |
+
convert_trait=convert_trait,
|
152 |
+
age_row=age_row,
|
153 |
+
convert_age=convert_age,
|
154 |
+
gender_row=gender_row,
|
155 |
+
convert_gender=convert_gender
|
156 |
+
)
|
157 |
+
|
158 |
+
# Debug data structures before linking
|
159 |
+
print("\nPre-linking data shapes:")
|
160 |
+
print("Clinical data shape:", selected_clinical.shape)
|
161 |
+
print("Gene data shape:", gene_data.shape)
|
162 |
+
print("\nClinical data preview:")
|
163 |
+
print(selected_clinical.head())
|
164 |
+
|
165 |
+
# Transpose gene data to match clinical data orientation
|
166 |
+
gene_data_t = gene_data.T
|
167 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
168 |
+
|
169 |
+
# 3. Handle missing values systematically
|
170 |
+
linked_data = handle_missing_values(linked_data, trait)
|
171 |
+
|
172 |
+
# 4. Check for biased features and remove them if needed
|
173 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
174 |
+
|
175 |
+
# 5. Validate data quality and save metadata
|
176 |
+
is_usable = validate_and_save_cohort_info(
|
177 |
+
is_final=True,
|
178 |
+
cohort=cohort,
|
179 |
+
info_path=json_path,
|
180 |
+
is_gene_available=True,
|
181 |
+
is_trait_available=True,
|
182 |
+
is_biased=is_biased,
|
183 |
+
df=linked_data,
|
184 |
+
note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
|
185 |
+
)
|
186 |
+
|
187 |
+
# 6. Save linked data if usable
|
188 |
+
if is_usable:
|
189 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE125158.py
ADDED
@@ -0,0 +1,207 @@
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE125158"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE125158"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE125158.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE125158.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE125158.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Yes, this dataset contains mRNA data from whole blood cells, as indicated in the series title
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2. Variable Identification and Conversion Functions
|
41 |
+
# 2.1 Data Availability
|
42 |
+
trait_row = 0 # diagnosis
|
43 |
+
age_row = 3 # age
|
44 |
+
gender_row = 2 # gender
|
45 |
+
|
46 |
+
# 2.2 Data Type Conversion Functions
|
47 |
+
def convert_trait(x):
|
48 |
+
if not isinstance(x, str):
|
49 |
+
return None
|
50 |
+
x = x.lower()
|
51 |
+
if 'diagnosis:' not in x:
|
52 |
+
return None
|
53 |
+
val = x.split('diagnosis:')[1].strip()
|
54 |
+
if 'pancreatic cancer' in val:
|
55 |
+
return 1
|
56 |
+
elif 'healthy' in val:
|
57 |
+
return 0
|
58 |
+
return None
|
59 |
+
|
60 |
+
def convert_age(x):
|
61 |
+
if not isinstance(x, str):
|
62 |
+
return None
|
63 |
+
if 'age:' not in x:
|
64 |
+
return None
|
65 |
+
try:
|
66 |
+
return float(x.split('age:')[1].strip())
|
67 |
+
except:
|
68 |
+
return None
|
69 |
+
|
70 |
+
def convert_gender(x):
|
71 |
+
if not isinstance(x, str):
|
72 |
+
return None
|
73 |
+
x = x.lower()
|
74 |
+
if 'gender:' not in x:
|
75 |
+
return None
|
76 |
+
val = x.split('gender:')[1].strip()
|
77 |
+
if val == 'female':
|
78 |
+
return 0
|
79 |
+
elif val == 'male':
|
80 |
+
return 1
|
81 |
+
return None
|
82 |
+
|
83 |
+
# 3. Save Metadata
|
84 |
+
is_trait_available = trait_row is not None
|
85 |
+
validate_and_save_cohort_info(is_final=False,
|
86 |
+
cohort=cohort,
|
87 |
+
info_path=json_path,
|
88 |
+
is_gene_available=is_gene_available,
|
89 |
+
is_trait_available=is_trait_available)
|
90 |
+
|
91 |
+
# 4. Clinical Feature Extraction
|
92 |
+
if trait_row is not None:
|
93 |
+
clinical_features = geo_select_clinical_features(
|
94 |
+
clinical_df=clinical_data,
|
95 |
+
trait=trait,
|
96 |
+
trait_row=trait_row,
|
97 |
+
convert_trait=convert_trait,
|
98 |
+
age_row=age_row,
|
99 |
+
convert_age=convert_age,
|
100 |
+
gender_row=gender_row,
|
101 |
+
convert_gender=convert_gender
|
102 |
+
)
|
103 |
+
|
104 |
+
# Preview the processed clinical data
|
105 |
+
preview = preview_df(clinical_features)
|
106 |
+
print("Preview of processed clinical data:")
|
107 |
+
print(preview)
|
108 |
+
|
109 |
+
# Save clinical features
|
110 |
+
clinical_features.to_csv(out_clinical_data_file)
|
111 |
+
# Extract gene expression data from matrix file
|
112 |
+
gene_data = get_genetic_data(matrix_file)
|
113 |
+
|
114 |
+
# Print first 20 row IDs and shape of data to help debug
|
115 |
+
print("Shape of gene expression data:", gene_data.shape)
|
116 |
+
print("\nFirst few rows of data:")
|
117 |
+
print(gene_data.head())
|
118 |
+
print("\nFirst 20 gene/probe identifiers:")
|
119 |
+
print(gene_data.index[:20])
|
120 |
+
|
121 |
+
# Inspect a snippet of raw file to verify identifier format
|
122 |
+
import gzip
|
123 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
124 |
+
lines = []
|
125 |
+
for i, line in enumerate(f):
|
126 |
+
if "!series_matrix_table_begin" in line:
|
127 |
+
# Get the next 5 lines after the marker
|
128 |
+
for _ in range(5):
|
129 |
+
lines.append(next(f).strip())
|
130 |
+
break
|
131 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
132 |
+
for line in lines:
|
133 |
+
print(line)
|
134 |
+
# Looking at the ID patterns (prefixes like A_23_P, eQC, etc.),
|
135 |
+
# these appear to be Agilent probe IDs rather than human gene symbols
|
136 |
+
requires_gene_mapping = True
|
137 |
+
# Get file paths using library function
|
138 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
139 |
+
|
140 |
+
# Extract gene annotation from SOFT file
|
141 |
+
gene_annotation = get_gene_annotation(soft_file)
|
142 |
+
|
143 |
+
# Preview gene annotation data
|
144 |
+
print("Gene annotation columns and example values:")
|
145 |
+
print(preview_df(gene_annotation))
|
146 |
+
# 'ID' in annotation matches the probe IDs in expression data
|
147 |
+
# 'GENE_SYMBOL' contains the corresponding gene symbols
|
148 |
+
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
|
149 |
+
|
150 |
+
# Apply gene mapping to convert probe-level data to gene-level data
|
151 |
+
gene_data = apply_gene_mapping(gene_data, mapping_data)
|
152 |
+
|
153 |
+
# Print shape and preview results
|
154 |
+
print("Shape of mapped gene expression data:", gene_data.shape)
|
155 |
+
print("\nFirst few rows of mapped gene expression data:")
|
156 |
+
print(gene_data.head())
|
157 |
+
# 1. Normalize gene symbols and save normalized gene data
|
158 |
+
# Remove "-mRNA" suffix from gene symbols before normalization
|
159 |
+
gene_data.index = gene_data.index.str.replace('-mRNA', '')
|
160 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
161 |
+
gene_data.to_csv(out_gene_data_file)
|
162 |
+
|
163 |
+
# 2. Link clinical and genetic data and trait
|
164 |
+
# First get selected clinical features using the extraction function from previous step
|
165 |
+
selected_clinical = geo_select_clinical_features(
|
166 |
+
clinical_df=clinical_data,
|
167 |
+
trait=trait,
|
168 |
+
trait_row=trait_row,
|
169 |
+
convert_trait=convert_trait,
|
170 |
+
age_row=age_row,
|
171 |
+
convert_age=convert_age,
|
172 |
+
gender_row=gender_row,
|
173 |
+
convert_gender=convert_gender
|
174 |
+
)
|
175 |
+
|
176 |
+
# Debug data structures before linking
|
177 |
+
print("\nPre-linking data shapes:")
|
178 |
+
print("Clinical data shape:", selected_clinical.shape)
|
179 |
+
print("Gene data shape:", gene_data.shape)
|
180 |
+
print("\nClinical data preview:")
|
181 |
+
print(selected_clinical.head())
|
182 |
+
|
183 |
+
# Transpose gene data to match clinical data orientation
|
184 |
+
gene_data_t = gene_data.T
|
185 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
186 |
+
|
187 |
+
# 3. Handle missing values systematically
|
188 |
+
linked_data = handle_missing_values(linked_data, trait)
|
189 |
+
|
190 |
+
# 4. Check for biased features and remove them if needed
|
191 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
192 |
+
|
193 |
+
# 5. Validate data quality and save metadata
|
194 |
+
is_usable = validate_and_save_cohort_info(
|
195 |
+
is_final=True,
|
196 |
+
cohort=cohort,
|
197 |
+
info_path=json_path,
|
198 |
+
is_gene_available=True,
|
199 |
+
is_trait_available=True,
|
200 |
+
is_biased=is_biased,
|
201 |
+
df=linked_data,
|
202 |
+
note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
|
203 |
+
)
|
204 |
+
|
205 |
+
# 6. Save linked data if usable
|
206 |
+
if is_usable:
|
207 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE130563.py
ADDED
@@ -0,0 +1,259 @@
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE130563"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE130563"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE130563.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE130563.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE130563.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Yes - this is a microarray study analyzing transcriptional profiling data
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2. Variable Availability and Data Types
|
41 |
+
|
42 |
+
# 2.1 Data Availability
|
43 |
+
trait_row = 0 # Diagnosis info in row 0
|
44 |
+
age_row = 4 # Age info in row 4
|
45 |
+
gender_row = 1 # Sex info in row 1
|
46 |
+
|
47 |
+
# 2.2 Data Type Conversion Functions
|
48 |
+
def convert_trait(value: str) -> int:
|
49 |
+
"""Convert diagnosis info to binary: 1 for PDAC, 0 for non-cancer controls"""
|
50 |
+
if value is None or 'diagnosis:' not in value:
|
51 |
+
return None
|
52 |
+
diagnosis = value.split('diagnosis:')[1].strip().lower()
|
53 |
+
if 'pancreatic ductal adenocarcinoma' in diagnosis:
|
54 |
+
return 1
|
55 |
+
elif 'chronic pancreatitis' in diagnosis: # Excluded from analysis per background info
|
56 |
+
return None
|
57 |
+
else: # All other diagnoses are non-cancer controls
|
58 |
+
return 0
|
59 |
+
|
60 |
+
def convert_age(value: str) -> float:
|
61 |
+
"""Convert age to continuous value"""
|
62 |
+
if value is None or 'age:' not in value:
|
63 |
+
return None
|
64 |
+
try:
|
65 |
+
return float(value.split('age:')[1].strip())
|
66 |
+
except:
|
67 |
+
return None
|
68 |
+
|
69 |
+
def convert_gender(value: str) -> int:
|
70 |
+
"""Convert sex to binary: 0 for female, 1 for male"""
|
71 |
+
if value is None or 'Sex:' not in value:
|
72 |
+
return None
|
73 |
+
sex = value.split('Sex:')[1].strip().upper()
|
74 |
+
if sex == 'F':
|
75 |
+
return 0
|
76 |
+
elif sex == 'M':
|
77 |
+
return 1
|
78 |
+
return None
|
79 |
+
|
80 |
+
# 3. Save Metadata
|
81 |
+
is_trait_available = trait_row is not None
|
82 |
+
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
|
83 |
+
is_gene_available=is_gene_available,
|
84 |
+
is_trait_available=is_trait_available)
|
85 |
+
|
86 |
+
# 4. Clinical Feature Extraction
|
87 |
+
if trait_row is not None:
|
88 |
+
selected_clinical_df = geo_select_clinical_features(
|
89 |
+
clinical_df=clinical_data,
|
90 |
+
trait=trait,
|
91 |
+
trait_row=trait_row,
|
92 |
+
convert_trait=convert_trait,
|
93 |
+
age_row=age_row,
|
94 |
+
convert_age=convert_age,
|
95 |
+
gender_row=gender_row,
|
96 |
+
convert_gender=convert_gender
|
97 |
+
)
|
98 |
+
|
99 |
+
# Preview the extracted features
|
100 |
+
print("Preview of extracted clinical features:")
|
101 |
+
print(preview_df(selected_clinical_df))
|
102 |
+
|
103 |
+
# Save to CSV
|
104 |
+
selected_clinical_df.to_csv(out_clinical_data_file)
|
105 |
+
# Extract gene expression data from matrix file
|
106 |
+
gene_data = get_genetic_data(matrix_file)
|
107 |
+
|
108 |
+
# Print first 20 row IDs and shape of data to help debug
|
109 |
+
print("Shape of gene expression data:", gene_data.shape)
|
110 |
+
print("\nFirst few rows of data:")
|
111 |
+
print(gene_data.head())
|
112 |
+
print("\nFirst 20 gene/probe identifiers:")
|
113 |
+
print(gene_data.index[:20])
|
114 |
+
|
115 |
+
# Inspect a snippet of raw file to verify identifier format
|
116 |
+
import gzip
|
117 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
118 |
+
lines = []
|
119 |
+
for i, line in enumerate(f):
|
120 |
+
if "!series_matrix_table_begin" in line:
|
121 |
+
# Get the next 5 lines after the marker
|
122 |
+
for _ in range(5):
|
123 |
+
lines.append(next(f).strip())
|
124 |
+
break
|
125 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
126 |
+
for line in lines:
|
127 |
+
print(line)
|
128 |
+
# The gene identifiers end with '_at', which is a characteristic format of Affymetrix
|
129 |
+
# microarray probe IDs rather than standard human gene symbols
|
130 |
+
requires_gene_mapping = True
|
131 |
+
# Get file paths using library function
|
132 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
133 |
+
|
134 |
+
# Let's inspect more of the raw SOFT file to find the annotation data
|
135 |
+
import gzip
|
136 |
+
start_line = "!platform_table_begin"
|
137 |
+
end_line = "!platform_table_end"
|
138 |
+
found_data = False
|
139 |
+
print("Sample of annotation data from SOFT file:")
|
140 |
+
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
|
141 |
+
for line in f:
|
142 |
+
if start_line in line:
|
143 |
+
found_data = True
|
144 |
+
# Skip the header line
|
145 |
+
next(f)
|
146 |
+
# Print first few lines of actual data
|
147 |
+
for _ in range(5):
|
148 |
+
print(next(f).strip())
|
149 |
+
break
|
150 |
+
|
151 |
+
# Extract gene annotation data - exclude metadata prefixes and keep data between platform table markers
|
152 |
+
gene_annotation = get_gene_annotation(soft_file)
|
153 |
+
|
154 |
+
# Preview annotation data
|
155 |
+
print("\nGene annotation columns and example values:")
|
156 |
+
print(preview_df(gene_annotation))
|
157 |
+
|
158 |
+
# Display column names to help identify relevant fields
|
159 |
+
print("\nAvailable columns:")
|
160 |
+
print(gene_annotation.columns.tolist())
|
161 |
+
# Since we can't access the proper gene symbol mapping file,
|
162 |
+
# let's look for gene annotation information in the SOFT file
|
163 |
+
import gzip
|
164 |
+
|
165 |
+
# Search for gene symbols in the SOFT file
|
166 |
+
found_symbols = False
|
167 |
+
gene_symbols = []
|
168 |
+
|
169 |
+
with gzip.open(soft_file, 'rt') as f:
|
170 |
+
for line in f:
|
171 |
+
# Look for platform table begin marker
|
172 |
+
if "!Platform_table_begin" in line:
|
173 |
+
headers = next(f).strip().split('\t')
|
174 |
+
# Find columns that might contain gene symbol information
|
175 |
+
symbol_cols = [i for i, h in enumerate(headers)
|
176 |
+
if 'symbol' in h.lower() or 'gene' in h.lower()]
|
177 |
+
if symbol_cols:
|
178 |
+
found_symbols = True
|
179 |
+
# Extract gene symbols from identified columns
|
180 |
+
for line in f:
|
181 |
+
if "!Platform_table_end" in line:
|
182 |
+
break
|
183 |
+
values = line.strip().split('\t')
|
184 |
+
for col in symbol_cols:
|
185 |
+
if col < len(values):
|
186 |
+
gene_symbols.append(values[col])
|
187 |
+
break
|
188 |
+
|
189 |
+
if found_symbols and len(gene_symbols) > 0:
|
190 |
+
# Create mapping using found gene symbols
|
191 |
+
unique_probes = gene_annotation['ID'].unique()
|
192 |
+
mapping_df = pd.DataFrame({
|
193 |
+
'ID': unique_probes,
|
194 |
+
'Gene': gene_symbols[:len(unique_probes)]
|
195 |
+
})
|
196 |
+
else:
|
197 |
+
# If no gene symbols found, create temporary mapping using probe IDs
|
198 |
+
# This allows pipeline to continue but indicates mapping needs to be updated
|
199 |
+
mapping_df = pd.DataFrame({
|
200 |
+
'ID': gene_annotation['ID'],
|
201 |
+
'Gene': gene_annotation['ID']
|
202 |
+
})
|
203 |
+
print("WARNING: No gene symbols found. Using probe IDs as temporary mapping.")
|
204 |
+
|
205 |
+
# Convert probe-level measurements to gene-level measurements
|
206 |
+
gene_data = apply_gene_mapping(gene_data, mapping_df)
|
207 |
+
|
208 |
+
print("Shape of gene expression data after mapping:", gene_data.shape)
|
209 |
+
print("\nPreview of mapped gene expression data:")
|
210 |
+
print(gene_data.head())
|
211 |
+
# 1. Skip normalization and use probe-level data since gene mapping failed
|
212 |
+
gene_data = get_genetic_data(matrix_file)
|
213 |
+
print("WARNING: Using probe IDs instead of gene symbols due to failed mapping")
|
214 |
+
gene_data.to_csv(out_gene_data_file)
|
215 |
+
|
216 |
+
# 2. Link clinical and genetic data and trait
|
217 |
+
selected_clinical = geo_select_clinical_features(
|
218 |
+
clinical_df=clinical_data,
|
219 |
+
trait=trait,
|
220 |
+
trait_row=trait_row,
|
221 |
+
convert_trait=convert_trait,
|
222 |
+
age_row=age_row,
|
223 |
+
convert_age=convert_age,
|
224 |
+
gender_row=gender_row,
|
225 |
+
convert_gender=convert_gender
|
226 |
+
)
|
227 |
+
|
228 |
+
# Debug pre-linking
|
229 |
+
print("\nPre-linking data shapes:")
|
230 |
+
print("Clinical data shape:", selected_clinical.shape)
|
231 |
+
print("Gene data shape:", gene_data.shape)
|
232 |
+
print("\nClinical data preview:")
|
233 |
+
print(selected_clinical.head())
|
234 |
+
|
235 |
+
# Link the data
|
236 |
+
gene_data_t = gene_data.T
|
237 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
238 |
+
|
239 |
+
# 3. Handle missing values
|
240 |
+
linked_data = handle_missing_values(linked_data, trait)
|
241 |
+
|
242 |
+
# 4. Check for biased features
|
243 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
244 |
+
|
245 |
+
# 5. Validate data quality and save metadata
|
246 |
+
is_usable = validate_and_save_cohort_info(
|
247 |
+
is_final=True,
|
248 |
+
cohort=cohort,
|
249 |
+
info_path=json_path,
|
250 |
+
is_gene_available=True,
|
251 |
+
is_trait_available=True,
|
252 |
+
is_biased=is_biased,
|
253 |
+
df=linked_data,
|
254 |
+
note="Gene expression data from pancreatic cancer study. Using probe IDs instead of gene symbols."
|
255 |
+
)
|
256 |
+
|
257 |
+
# 6. Save if usable
|
258 |
+
if is_usable:
|
259 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE131027.py
ADDED
@@ -0,0 +1,190 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE131027"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE131027"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE131027.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE131027.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE131027.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Based on the series title and design, this appears to be focused on germline mutations and variants
|
38 |
+
# rather than gene expression data
|
39 |
+
is_gene_available = False
|
40 |
+
|
41 |
+
# 2.1 Data Row Identifiers
|
42 |
+
# For trait, we can use cancer types from Feature 1
|
43 |
+
trait_row = 1
|
44 |
+
|
45 |
+
# Age and gender are not recorded in the characteristics
|
46 |
+
age_row = None
|
47 |
+
gender_row = None
|
48 |
+
|
49 |
+
# 2.2 Conversion Functions
|
50 |
+
def convert_trait(value: str) -> int:
|
51 |
+
"""Convert cancer type to binary indicating if it's pancreatic cancer"""
|
52 |
+
if not value or ':' not in value:
|
53 |
+
return None
|
54 |
+
cancer_type = value.split(':')[1].strip().lower()
|
55 |
+
return 1 if 'pancreatic cancer' in cancer_type else 0
|
56 |
+
|
57 |
+
def convert_age(value: str) -> float:
|
58 |
+
"""Placeholder function since age data is not available"""
|
59 |
+
return None
|
60 |
+
|
61 |
+
def convert_gender(value: str) -> int:
|
62 |
+
"""Placeholder function since gender data is not available"""
|
63 |
+
return None
|
64 |
+
|
65 |
+
# 3. Save Initial Metadata
|
66 |
+
is_trait_available = trait_row is not None
|
67 |
+
validate_and_save_cohort_info(is_final=False,
|
68 |
+
cohort=cohort,
|
69 |
+
info_path=json_path,
|
70 |
+
is_gene_available=is_gene_available,
|
71 |
+
is_trait_available=is_trait_available)
|
72 |
+
|
73 |
+
# 4. Extract Clinical Features
|
74 |
+
if trait_row is not None:
|
75 |
+
selected_clinical_df = geo_select_clinical_features(
|
76 |
+
clinical_df=clinical_data,
|
77 |
+
trait=trait,
|
78 |
+
trait_row=trait_row,
|
79 |
+
convert_trait=convert_trait,
|
80 |
+
age_row=age_row,
|
81 |
+
convert_age=convert_age,
|
82 |
+
gender_row=gender_row,
|
83 |
+
convert_gender=convert_gender
|
84 |
+
)
|
85 |
+
|
86 |
+
# Preview the data
|
87 |
+
print("Preview of selected clinical features:")
|
88 |
+
print(preview_df(selected_clinical_df))
|
89 |
+
|
90 |
+
# Save to CSV
|
91 |
+
selected_clinical_df.to_csv(out_clinical_data_file)
|
92 |
+
# Extract gene expression data from matrix file
|
93 |
+
gene_data = get_genetic_data(matrix_file)
|
94 |
+
|
95 |
+
# Print first 20 row IDs and shape of data to help debug
|
96 |
+
print("Shape of gene expression data:", gene_data.shape)
|
97 |
+
print("\nFirst few rows of data:")
|
98 |
+
print(gene_data.head())
|
99 |
+
print("\nFirst 20 gene/probe identifiers:")
|
100 |
+
print(gene_data.index[:20])
|
101 |
+
|
102 |
+
# Inspect a snippet of raw file to verify identifier format
|
103 |
+
import gzip
|
104 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
105 |
+
lines = []
|
106 |
+
for i, line in enumerate(f):
|
107 |
+
if "!series_matrix_table_begin" in line:
|
108 |
+
# Get the next 5 lines after the marker
|
109 |
+
for _ in range(5):
|
110 |
+
lines.append(next(f).strip())
|
111 |
+
break
|
112 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
113 |
+
for line in lines:
|
114 |
+
print(line)
|
115 |
+
# Based on the identifiers like "1007_s_at", "1053_at", these are Affymetrix probe IDs
|
116 |
+
# rather than standard human gene symbols. They need to be mapped to gene symbols.
|
117 |
+
requires_gene_mapping = True
|
118 |
+
# Get file paths using library function
|
119 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
120 |
+
|
121 |
+
# Extract gene annotation from SOFT file
|
122 |
+
gene_annotation = get_gene_annotation(soft_file)
|
123 |
+
|
124 |
+
# Preview gene annotation data
|
125 |
+
print("Gene annotation columns and example values:")
|
126 |
+
print(preview_df(gene_annotation))
|
127 |
+
# Get mapping data between probe IDs and gene symbols
|
128 |
+
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
129 |
+
|
130 |
+
# Apply gene mapping to convert probe data to gene expression
|
131 |
+
mapped_gene_data = apply_gene_mapping(gene_data, mapping_data)
|
132 |
+
gene_data = mapped_gene_data
|
133 |
+
|
134 |
+
# Preview results
|
135 |
+
print("Shape of mapped gene expression data:", gene_data.shape)
|
136 |
+
print("\nFirst few rows of mapped data:")
|
137 |
+
print(gene_data.head())
|
138 |
+
print("\nFirst 20 gene symbols:")
|
139 |
+
print(gene_data.index[:20])
|
140 |
+
# 1. Normalize gene symbols and save normalized gene data
|
141 |
+
# Remove "-mRNA" suffix from gene symbols before normalization
|
142 |
+
gene_data.index = gene_data.index.str.replace('-mRNA', '')
|
143 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
144 |
+
gene_data.to_csv(out_gene_data_file)
|
145 |
+
|
146 |
+
# 2. Link clinical and genetic data and trait
|
147 |
+
# First get selected clinical features using the extraction function from previous step
|
148 |
+
selected_clinical = geo_select_clinical_features(
|
149 |
+
clinical_df=clinical_data,
|
150 |
+
trait=trait,
|
151 |
+
trait_row=trait_row,
|
152 |
+
convert_trait=convert_trait,
|
153 |
+
age_row=age_row,
|
154 |
+
convert_age=convert_age,
|
155 |
+
gender_row=gender_row,
|
156 |
+
convert_gender=convert_gender
|
157 |
+
)
|
158 |
+
|
159 |
+
# Debug data structures before linking
|
160 |
+
print("\nPre-linking data shapes:")
|
161 |
+
print("Clinical data shape:", selected_clinical.shape)
|
162 |
+
print("Gene data shape:", gene_data.shape)
|
163 |
+
print("\nClinical data preview:")
|
164 |
+
print(selected_clinical.head())
|
165 |
+
|
166 |
+
# Transpose gene data to match clinical data orientation
|
167 |
+
gene_data_t = gene_data.T
|
168 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
169 |
+
|
170 |
+
# 3. Handle missing values systematically
|
171 |
+
linked_data = handle_missing_values(linked_data, trait)
|
172 |
+
|
173 |
+
# 4. Check for biased features and remove them if needed
|
174 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
175 |
+
|
176 |
+
# 5. Validate data quality and save metadata
|
177 |
+
is_usable = validate_and_save_cohort_info(
|
178 |
+
is_final=True,
|
179 |
+
cohort=cohort,
|
180 |
+
info_path=json_path,
|
181 |
+
is_gene_available=True,
|
182 |
+
is_trait_available=True,
|
183 |
+
is_biased=is_biased,
|
184 |
+
df=linked_data,
|
185 |
+
note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
|
186 |
+
)
|
187 |
+
|
188 |
+
# 6. Save linked data if usable
|
189 |
+
if is_usable:
|
190 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE157494.py
ADDED
@@ -0,0 +1,217 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE157494"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE157494"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE157494.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE157494.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE157494.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = filter_content_by_prefix(matrix_file,
|
23 |
+
prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
|
24 |
+
prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'],
|
25 |
+
unselect=False,
|
26 |
+
source_type='file',
|
27 |
+
return_df_a=False,
|
28 |
+
return_df_b=True,
|
29 |
+
transpose=True)
|
30 |
+
|
31 |
+
# Get unique values per clinical feature
|
32 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
33 |
+
|
34 |
+
# Print background info
|
35 |
+
print("Dataset Background Information:")
|
36 |
+
print(f"{background_info}\n")
|
37 |
+
|
38 |
+
# Print sample characteristics
|
39 |
+
print("Sample Characteristics:")
|
40 |
+
for feature, values in sample_characteristics.items():
|
41 |
+
print(f"Feature: {feature}")
|
42 |
+
print(f"Values: {values}\n")
|
43 |
+
# Get file paths
|
44 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
45 |
+
|
46 |
+
# Extract background info and clinical data
|
47 |
+
background_info, clinical_data = filter_content_by_prefix(
|
48 |
+
matrix_file,
|
49 |
+
prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
|
50 |
+
prefixes_b=['!Sample_characteristics_ch'],
|
51 |
+
unselect=False,
|
52 |
+
source_type='file',
|
53 |
+
return_df_a=False,
|
54 |
+
return_df_b=True
|
55 |
+
)
|
56 |
+
|
57 |
+
# Get unique values per clinical feature
|
58 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
59 |
+
|
60 |
+
# Print background info
|
61 |
+
print("Dataset Background Information:")
|
62 |
+
print(f"{background_info}\n")
|
63 |
+
|
64 |
+
# Print sample characteristics
|
65 |
+
print("Sample Characteristics:")
|
66 |
+
for feature, values in sample_characteristics.items():
|
67 |
+
print(f"Feature: {feature}")
|
68 |
+
print(f"Values: {values}\n")
|
69 |
+
# 1. Gene Expression Data Availability
|
70 |
+
# Yes - the series summary mentions gene expression profiling with Affymetrix Gene Chip
|
71 |
+
is_gene_available = True
|
72 |
+
|
73 |
+
# 2. Variable Availability and Data Type Conversion
|
74 |
+
# Sample Characteristics output is empty, indicating no clinical data available
|
75 |
+
trait_row = None
|
76 |
+
age_row = None
|
77 |
+
gender_row = None
|
78 |
+
|
79 |
+
def convert_trait(x):
|
80 |
+
return None
|
81 |
+
|
82 |
+
def convert_age(x):
|
83 |
+
return None
|
84 |
+
|
85 |
+
def convert_gender(x):
|
86 |
+
return None
|
87 |
+
|
88 |
+
# 3. Save metadata
|
89 |
+
# Initial filtering - save info that this dataset has gene data but no clinical data
|
90 |
+
validate_and_save_cohort_info(is_final=False,
|
91 |
+
cohort=cohort,
|
92 |
+
info_path=json_path,
|
93 |
+
is_gene_available=is_gene_available,
|
94 |
+
is_trait_available=False)
|
95 |
+
|
96 |
+
# 4. Clinical Feature Extraction
|
97 |
+
# Skip since trait_row is None (no clinical data available)
|
98 |
+
# Extract gene expression data from matrix file
|
99 |
+
gene_data = get_genetic_data(matrix_file)
|
100 |
+
|
101 |
+
# Print first 20 row IDs and shape of data to help debug
|
102 |
+
print("Shape of gene expression data:", gene_data.shape)
|
103 |
+
print("\nFirst few rows of data:")
|
104 |
+
print(gene_data.head())
|
105 |
+
print("\nFirst 20 gene/probe identifiers:")
|
106 |
+
print(gene_data.index[:20])
|
107 |
+
|
108 |
+
# Inspect a snippet of raw file to verify identifier format
|
109 |
+
import gzip
|
110 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
111 |
+
lines = []
|
112 |
+
for i, line in enumerate(f):
|
113 |
+
if "!series_matrix_table_begin" in line:
|
114 |
+
# Get the next 5 lines after the marker
|
115 |
+
for _ in range(5):
|
116 |
+
lines.append(next(f).strip())
|
117 |
+
break
|
118 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
119 |
+
for line in lines:
|
120 |
+
print(line)
|
121 |
+
# Looking at the IDs (e.g. 1007_s_at, 1053_at), these are Affymetrix probe IDs
|
122 |
+
# from HG-U133_Plus_2 array platform, not gene symbols.
|
123 |
+
# They need to be mapped to human gene symbols for standardized analysis
|
124 |
+
requires_gene_mapping = True
|
125 |
+
# Get file paths using library function
|
126 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
127 |
+
|
128 |
+
# Extract gene annotation from SOFT file
|
129 |
+
gene_annotation = get_gene_annotation(soft_file)
|
130 |
+
|
131 |
+
# Preview gene annotation data
|
132 |
+
print("Gene annotation columns and example values:")
|
133 |
+
print(preview_df(gene_annotation))
|
134 |
+
# Looking at gene_data index ['1007_s_at', '1053_at', '117_at'...] and
|
135 |
+
# gene_annotation dictionary preview, 'ID' column contains probe IDs matching gene_data index,
|
136 |
+
# and 'Gene Symbol' column contains the gene symbols we need
|
137 |
+
|
138 |
+
# Create mapping between probe IDs and gene symbols
|
139 |
+
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
140 |
+
|
141 |
+
# Convert probe-level measurements to gene expression data
|
142 |
+
gene_data = apply_gene_mapping(gene_data, gene_mapping)
|
143 |
+
|
144 |
+
# Normalize gene symbols to standard format using synonym dictionary
|
145 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
146 |
+
# First get the gene expression data again
|
147 |
+
gene_data = get_genetic_data(matrix_file)
|
148 |
+
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
149 |
+
gene_data = apply_gene_mapping(gene_data, gene_mapping)
|
150 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
151 |
+
|
152 |
+
# Save normalized gene data
|
153 |
+
gene_data.to_csv(out_gene_data_file)
|
154 |
+
|
155 |
+
# Update cohort info - dataset unusable due to lack of clinical data
|
156 |
+
is_usable = validate_and_save_cohort_info(
|
157 |
+
is_final=True,
|
158 |
+
cohort=cohort,
|
159 |
+
info_path=json_path,
|
160 |
+
is_gene_available=True,
|
161 |
+
is_trait_available=False,
|
162 |
+
is_biased=None,
|
163 |
+
df=None,
|
164 |
+
note="Gene expression data available but no clinical annotations/controls present."
|
165 |
+
)
|
166 |
+
# Get file paths using library function
|
167 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
168 |
+
|
169 |
+
# Extract gene annotation from SOFT file
|
170 |
+
gene_annotation = get_gene_annotation(soft_file)
|
171 |
+
|
172 |
+
# Preview gene annotation data
|
173 |
+
print("Gene annotation columns and example values:")
|
174 |
+
print(preview_df(gene_annotation))
|
175 |
+
# Get file paths
|
176 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
177 |
+
|
178 |
+
# Get gene annotation first
|
179 |
+
gene_annotation = get_gene_annotation(soft_file)
|
180 |
+
|
181 |
+
# Get gene expression data
|
182 |
+
gene_data = get_genetic_data(matrix_file)
|
183 |
+
|
184 |
+
# Create mapping between probe IDs and gene symbols
|
185 |
+
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
186 |
+
|
187 |
+
# Convert probe-level measurements to gene expression data
|
188 |
+
gene_data = apply_gene_mapping(gene_data, gene_mapping)
|
189 |
+
|
190 |
+
# Print dimensions of result for verification
|
191 |
+
print(f"\nShape of gene expression data after mapping: {gene_data.shape}")
|
192 |
+
# Get file paths
|
193 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
194 |
+
|
195 |
+
# Get gene annotation and expression data
|
196 |
+
gene_annotation = get_gene_annotation(soft_file)
|
197 |
+
gene_data = get_genetic_data(matrix_file)
|
198 |
+
|
199 |
+
# Create mapping and apply it
|
200 |
+
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
|
201 |
+
gene_data = apply_gene_mapping(gene_data, gene_mapping)
|
202 |
+
|
203 |
+
# Normalize gene symbols and save gene data
|
204 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
205 |
+
gene_data.to_csv(out_gene_data_file)
|
206 |
+
|
207 |
+
# Update cohort info - dataset unusable due to lack of clinical data
|
208 |
+
is_usable = validate_and_save_cohort_info(
|
209 |
+
is_final=True,
|
210 |
+
cohort=cohort,
|
211 |
+
info_path=json_path,
|
212 |
+
is_gene_available=True,
|
213 |
+
is_trait_available=False,
|
214 |
+
is_biased=None,
|
215 |
+
df=None,
|
216 |
+
note="Gene expression data available but no clinical annotations/controls present."
|
217 |
+
)
|
p3/preprocess/Pancreatic_Cancer/code/GSE183795.py
ADDED
@@ -0,0 +1,200 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE183795"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE183795"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE183795.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE183795.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE183795.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Based on the background info, this is a microarray gene expression dataset
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2.1 Data Availability
|
41 |
+
# For trait: Feature 0 contains tissue type info (tumor vs non-tumor)
|
42 |
+
trait_row = 0
|
43 |
+
|
44 |
+
# Age is not available in the data
|
45 |
+
age_row = None
|
46 |
+
|
47 |
+
# Gender is not available in the data
|
48 |
+
gender_row = None
|
49 |
+
|
50 |
+
# 2.2 Data Type Conversion Functions
|
51 |
+
def convert_trait(value: str) -> int:
|
52 |
+
"""Convert tissue type to binary: 1 for tumor, 0 for non-tumor/normal"""
|
53 |
+
if not value or 'tissue:' not in value:
|
54 |
+
return None
|
55 |
+
value = value.split('tissue:')[1].strip().lower()
|
56 |
+
if 'tumor' in value and 'non' not in value:
|
57 |
+
return 1
|
58 |
+
elif 'non-tumor' in value or 'normal' in value:
|
59 |
+
return 0
|
60 |
+
return None
|
61 |
+
|
62 |
+
def convert_age(value: str) -> float:
|
63 |
+
"""Convert age to float"""
|
64 |
+
return None
|
65 |
+
|
66 |
+
def convert_gender(value: str) -> int:
|
67 |
+
"""Convert gender to binary"""
|
68 |
+
return None
|
69 |
+
|
70 |
+
# 3. Save metadata
|
71 |
+
is_trait_available = trait_row is not None
|
72 |
+
validate_and_save_cohort_info(is_final=False,
|
73 |
+
cohort=cohort,
|
74 |
+
info_path=json_path,
|
75 |
+
is_gene_available=is_gene_available,
|
76 |
+
is_trait_available=is_trait_available)
|
77 |
+
|
78 |
+
# 4. Extract clinical features
|
79 |
+
if trait_row is not None:
|
80 |
+
clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
|
81 |
+
trait=trait,
|
82 |
+
trait_row=trait_row,
|
83 |
+
convert_trait=convert_trait,
|
84 |
+
age_row=age_row,
|
85 |
+
convert_age=convert_age,
|
86 |
+
gender_row=gender_row,
|
87 |
+
convert_gender=convert_gender)
|
88 |
+
|
89 |
+
print("Preview of extracted clinical features:")
|
90 |
+
print(preview_df(clinical_features))
|
91 |
+
|
92 |
+
# Save clinical data
|
93 |
+
clinical_features.to_csv(out_clinical_data_file)
|
94 |
+
# Extract gene expression data from matrix file
|
95 |
+
gene_data = get_genetic_data(matrix_file)
|
96 |
+
|
97 |
+
# Print first 20 row IDs and shape of data to help debug
|
98 |
+
print("Shape of gene expression data:", gene_data.shape)
|
99 |
+
print("\nFirst few rows of data:")
|
100 |
+
print(gene_data.head())
|
101 |
+
print("\nFirst 20 gene/probe identifiers:")
|
102 |
+
print(gene_data.index[:20])
|
103 |
+
|
104 |
+
# Inspect a snippet of raw file to verify identifier format
|
105 |
+
import gzip
|
106 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
107 |
+
lines = []
|
108 |
+
for i, line in enumerate(f):
|
109 |
+
if "!series_matrix_table_begin" in line:
|
110 |
+
# Get the next 5 lines after the marker
|
111 |
+
for _ in range(5):
|
112 |
+
lines.append(next(f).strip())
|
113 |
+
break
|
114 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
115 |
+
for line in lines:
|
116 |
+
print(line)
|
117 |
+
# The identifiers appear to be probe IDs, not gene symbols
|
118 |
+
# The format is numerical IDs (e.g. 7896748, 7896754) which are probe identifiers
|
119 |
+
# from the microarray platform
|
120 |
+
# These need to be mapped to gene symbols
|
121 |
+
|
122 |
+
requires_gene_mapping = True
|
123 |
+
# Get file paths using library function
|
124 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
125 |
+
|
126 |
+
# Extract gene annotation from SOFT file
|
127 |
+
gene_annotation = get_gene_annotation(soft_file)
|
128 |
+
|
129 |
+
# Preview gene annotation data
|
130 |
+
print("Gene annotation columns and example values:")
|
131 |
+
print(preview_df(gene_annotation))
|
132 |
+
# 1. Observe the IDs used in gene expression data and gene annotation data
|
133 |
+
# In gene expression data, we see probe IDs like '7896748', '7896754', etc.
|
134 |
+
# In gene annotation, these probe IDs are stored in the 'ID' column
|
135 |
+
# Gene symbols are stored in 'gene_assignment' column
|
136 |
+
|
137 |
+
# 2. Extract probe-to-gene mapping
|
138 |
+
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
|
139 |
+
|
140 |
+
# 3. Convert probe-level data to gene-level data using mapping
|
141 |
+
gene_data = apply_gene_mapping(gene_data, mapping_data)
|
142 |
+
|
143 |
+
# Save the processed gene data
|
144 |
+
gene_data.to_csv(out_gene_data_file)
|
145 |
+
|
146 |
+
# Preview the gene_data to verify the conversion
|
147 |
+
print("Gene expression data shape after mapping:", gene_data.shape)
|
148 |
+
print("\nFirst few genes and their expression values:")
|
149 |
+
print(preview_df(gene_data))
|
150 |
+
# 1. Normalize gene symbols and save normalized gene data
|
151 |
+
# Remove "-mRNA" suffix from gene symbols before normalization
|
152 |
+
gene_data.index = gene_data.index.str.replace('-mRNA', '')
|
153 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
154 |
+
gene_data.to_csv(out_gene_data_file)
|
155 |
+
|
156 |
+
# 2. Link clinical and genetic data and trait
|
157 |
+
# First get selected clinical features using the extraction function from previous step
|
158 |
+
selected_clinical = geo_select_clinical_features(
|
159 |
+
clinical_df=clinical_data,
|
160 |
+
trait=trait,
|
161 |
+
trait_row=trait_row,
|
162 |
+
convert_trait=convert_trait,
|
163 |
+
age_row=age_row,
|
164 |
+
convert_age=convert_age,
|
165 |
+
gender_row=gender_row,
|
166 |
+
convert_gender=convert_gender
|
167 |
+
)
|
168 |
+
|
169 |
+
# Debug data structures before linking
|
170 |
+
print("\nPre-linking data shapes:")
|
171 |
+
print("Clinical data shape:", selected_clinical.shape)
|
172 |
+
print("Gene data shape:", gene_data.shape)
|
173 |
+
print("\nClinical data preview:")
|
174 |
+
print(selected_clinical.head())
|
175 |
+
|
176 |
+
# Transpose gene data to match clinical data orientation
|
177 |
+
gene_data_t = gene_data.T
|
178 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
179 |
+
|
180 |
+
# 3. Handle missing values systematically
|
181 |
+
linked_data = handle_missing_values(linked_data, trait)
|
182 |
+
|
183 |
+
# 4. Check for biased features and remove them if needed
|
184 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
185 |
+
|
186 |
+
# 5. Validate data quality and save metadata
|
187 |
+
is_usable = validate_and_save_cohort_info(
|
188 |
+
is_final=True,
|
189 |
+
cohort=cohort,
|
190 |
+
info_path=json_path,
|
191 |
+
is_gene_available=True,
|
192 |
+
is_trait_available=True,
|
193 |
+
is_biased=is_biased,
|
194 |
+
df=linked_data,
|
195 |
+
note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
|
196 |
+
)
|
197 |
+
|
198 |
+
# 6. Save linked data if usable
|
199 |
+
if is_usable:
|
200 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE222788.py
ADDED
@@ -0,0 +1,166 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE222788"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE222788"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE222788.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE222788.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE222788.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# From background info, this is a NanoString gene profiling study with 730 genes panel
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2.1 Data Availability & Row Keys
|
41 |
+
# From sample characteristics, only treatment group info is available (row 0)
|
42 |
+
# Can infer trait (cancer vs control) from treatment - patients are all cancer cases
|
43 |
+
trait_row = 0
|
44 |
+
age_row = None # Age not recorded
|
45 |
+
gender_row = None # Gender not recorded
|
46 |
+
|
47 |
+
# 2.2 Data Type Conversion Functions
|
48 |
+
def convert_trait(value):
|
49 |
+
# Extract value after colon and strip whitespace
|
50 |
+
if ':' in value:
|
51 |
+
value = value.split(':')[1].strip()
|
52 |
+
# All samples are cancer cases - convert to binary 1
|
53 |
+
return 1
|
54 |
+
|
55 |
+
def convert_age(value):
|
56 |
+
return None # Not used since age data not available
|
57 |
+
|
58 |
+
def convert_gender(value):
|
59 |
+
return None # Not used since gender data not available
|
60 |
+
|
61 |
+
# 3. Save metadata about data availability
|
62 |
+
is_trait_available = trait_row is not None
|
63 |
+
validate_and_save_cohort_info(is_final=False,
|
64 |
+
cohort=cohort,
|
65 |
+
info_path=json_path,
|
66 |
+
is_gene_available=is_gene_available,
|
67 |
+
is_trait_available=is_trait_available)
|
68 |
+
|
69 |
+
# 4. Extract clinical features if trait data available
|
70 |
+
if trait_row is not None:
|
71 |
+
selected_clinical = geo_select_clinical_features(
|
72 |
+
clinical_df=clinical_data,
|
73 |
+
trait=trait,
|
74 |
+
trait_row=trait_row,
|
75 |
+
convert_trait=convert_trait,
|
76 |
+
age_row=age_row,
|
77 |
+
convert_age=convert_age,
|
78 |
+
gender_row=gender_row,
|
79 |
+
convert_gender=convert_gender
|
80 |
+
)
|
81 |
+
|
82 |
+
# Preview the data
|
83 |
+
preview = preview_df(selected_clinical)
|
84 |
+
print("Preview of selected clinical features:")
|
85 |
+
print(preview)
|
86 |
+
|
87 |
+
# Save to CSV
|
88 |
+
selected_clinical.to_csv(out_clinical_data_file)
|
89 |
+
# Extract gene expression data from matrix file
|
90 |
+
gene_data = get_genetic_data(matrix_file)
|
91 |
+
|
92 |
+
# Print first 20 row IDs and shape of data to help debug
|
93 |
+
print("Shape of gene expression data:", gene_data.shape)
|
94 |
+
print("\nFirst few rows of data:")
|
95 |
+
print(gene_data.head())
|
96 |
+
print("\nFirst 20 gene/probe identifiers:")
|
97 |
+
print(gene_data.index[:20])
|
98 |
+
|
99 |
+
# Inspect a snippet of raw file to verify identifier format
|
100 |
+
import gzip
|
101 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
102 |
+
lines = []
|
103 |
+
for i, line in enumerate(f):
|
104 |
+
if "!series_matrix_table_begin" in line:
|
105 |
+
# Get the next 5 lines after the marker
|
106 |
+
for _ in range(5):
|
107 |
+
lines.append(next(f).strip())
|
108 |
+
break
|
109 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
110 |
+
for line in lines:
|
111 |
+
print(line)
|
112 |
+
# Looking at gene identifiers like "A2M-mRNA", "ABCB1-mRNA", etc.
|
113 |
+
# These are already human gene symbols with "-mRNA" suffix
|
114 |
+
# No mapping needed, just need to remove the "-mRNA" suffix
|
115 |
+
requires_gene_mapping = False
|
116 |
+
# 1. Normalize gene symbols and save normalized gene data
|
117 |
+
# Remove "-mRNA" suffix from gene symbols before normalization
|
118 |
+
gene_data.index = gene_data.index.str.replace('-mRNA', '')
|
119 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
120 |
+
gene_data.to_csv(out_gene_data_file)
|
121 |
+
|
122 |
+
# 2. Link clinical and genetic data and trait
|
123 |
+
# First get selected clinical features using the extraction function from previous step
|
124 |
+
selected_clinical = geo_select_clinical_features(
|
125 |
+
clinical_df=clinical_data,
|
126 |
+
trait=trait,
|
127 |
+
trait_row=trait_row,
|
128 |
+
convert_trait=convert_trait,
|
129 |
+
age_row=age_row,
|
130 |
+
convert_age=convert_age,
|
131 |
+
gender_row=gender_row,
|
132 |
+
convert_gender=convert_gender
|
133 |
+
)
|
134 |
+
|
135 |
+
# Debug data structures before linking
|
136 |
+
print("\nPre-linking data shapes:")
|
137 |
+
print("Clinical data shape:", selected_clinical.shape)
|
138 |
+
print("Gene data shape:", gene_data.shape)
|
139 |
+
print("\nClinical data preview:")
|
140 |
+
print(selected_clinical.head())
|
141 |
+
|
142 |
+
# Transpose gene data to match clinical data orientation
|
143 |
+
gene_data_t = gene_data.T
|
144 |
+
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
|
145 |
+
|
146 |
+
# 3. Handle missing values systematically
|
147 |
+
linked_data = handle_missing_values(linked_data, trait)
|
148 |
+
|
149 |
+
# 4. Check for biased features and remove them if needed
|
150 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
151 |
+
|
152 |
+
# 5. Validate data quality and save metadata
|
153 |
+
is_usable = validate_and_save_cohort_info(
|
154 |
+
is_final=True,
|
155 |
+
cohort=cohort,
|
156 |
+
info_path=json_path,
|
157 |
+
is_gene_available=True,
|
158 |
+
is_trait_available=True,
|
159 |
+
is_biased=is_biased,
|
160 |
+
df=linked_data,
|
161 |
+
note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
|
162 |
+
)
|
163 |
+
|
164 |
+
# 6. Save linked data if usable
|
165 |
+
if is_usable:
|
166 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE223409.py
ADDED
@@ -0,0 +1,173 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE223409"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE223409"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE223409.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE223409.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE223409.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# Based on the background info, this appears to be an EVs study with specific gene treatment
|
38 |
+
# and likely contains gene expression data
|
39 |
+
is_gene_available = True
|
40 |
+
|
41 |
+
# 2. Variable Availability and Data Type Conversion
|
42 |
+
# Looking at sample characteristics:
|
43 |
+
# - Can infer trait data from treatment groups in row 1
|
44 |
+
# - No age data
|
45 |
+
# - No gender data
|
46 |
+
|
47 |
+
# Row indices for variables
|
48 |
+
trait_row = 1 # Can infer from treatment groups
|
49 |
+
age_row = None # Age not available
|
50 |
+
gender_row = None # Gender not available
|
51 |
+
|
52 |
+
def convert_trait(value: str) -> int:
|
53 |
+
"""Convert treatment value to binary trait."""
|
54 |
+
if pd.isna(value):
|
55 |
+
return None
|
56 |
+
value = value.split(': ')[-1].lower()
|
57 |
+
# Consider control/PBS as non-cancer (0) and treated as cancer (1)
|
58 |
+
if 'control' in value or 'pbs' in value:
|
59 |
+
return 0
|
60 |
+
return 1
|
61 |
+
|
62 |
+
# Age and gender conversion functions not needed since data unavailable
|
63 |
+
convert_age = None
|
64 |
+
convert_gender = None
|
65 |
+
|
66 |
+
# 3. Save metadata
|
67 |
+
is_trait_available = trait_row is not None
|
68 |
+
validate_and_save_cohort_info(
|
69 |
+
is_final=False,
|
70 |
+
cohort=cohort,
|
71 |
+
info_path=json_path,
|
72 |
+
is_gene_available=is_gene_available,
|
73 |
+
is_trait_available=is_trait_available
|
74 |
+
)
|
75 |
+
|
76 |
+
# 4. Clinical feature extraction
|
77 |
+
if trait_row is not None:
|
78 |
+
clinical_features = geo_select_clinical_features(
|
79 |
+
clinical_df=clinical_data,
|
80 |
+
trait=trait,
|
81 |
+
trait_row=trait_row,
|
82 |
+
convert_trait=convert_trait,
|
83 |
+
age_row=age_row,
|
84 |
+
convert_age=convert_age,
|
85 |
+
gender_row=gender_row,
|
86 |
+
convert_gender=convert_gender
|
87 |
+
)
|
88 |
+
print("Preview of extracted clinical features:")
|
89 |
+
print(preview_df(clinical_features))
|
90 |
+
clinical_features.to_csv(out_clinical_data_file)
|
91 |
+
# Extract gene expression data from matrix file
|
92 |
+
gene_data = get_genetic_data(matrix_file)
|
93 |
+
|
94 |
+
# Print first 20 row IDs and shape of data to help debug
|
95 |
+
print("Shape of gene expression data:", gene_data.shape)
|
96 |
+
print("\nFirst few rows of data:")
|
97 |
+
print(gene_data.head())
|
98 |
+
print("\nFirst 20 gene/probe identifiers:")
|
99 |
+
print(gene_data.index[:20])
|
100 |
+
|
101 |
+
# Inspect a snippet of raw file to verify identifier format
|
102 |
+
import gzip
|
103 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
104 |
+
lines = []
|
105 |
+
for i, line in enumerate(f):
|
106 |
+
if "!series_matrix_table_begin" in line:
|
107 |
+
# Get the next 5 lines after the marker
|
108 |
+
for _ in range(5):
|
109 |
+
lines.append(next(f).strip())
|
110 |
+
break
|
111 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
112 |
+
for line in lines:
|
113 |
+
print(line)
|
114 |
+
requires_gene_mapping = True
|
115 |
+
# Get file paths using library function
|
116 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
117 |
+
|
118 |
+
# Extract gene annotation from SOFT file
|
119 |
+
gene_annotation = get_gene_annotation(soft_file)
|
120 |
+
|
121 |
+
# Preview gene annotation data
|
122 |
+
print("Gene annotation columns and example values:")
|
123 |
+
print(preview_df(gene_annotation))
|
124 |
+
# Get mapping between probe IDs and gene symbols from annotation data
|
125 |
+
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
|
126 |
+
|
127 |
+
# Apply mapping to convert probe data to gene expression
|
128 |
+
gene_data = apply_gene_mapping(gene_data, mapping_data)
|
129 |
+
|
130 |
+
# Preview the result
|
131 |
+
print("Shape of mapped gene expression data:", gene_data.shape)
|
132 |
+
print("\nFirst few rows of mapped data:")
|
133 |
+
print(gene_data.head())
|
134 |
+
# 1. Normalize gene symbols and save normalized gene data
|
135 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
136 |
+
gene_data.to_csv(out_gene_data_file)
|
137 |
+
|
138 |
+
# 2. Link clinical and genetic data and trait
|
139 |
+
# First get selected clinical features using the extraction function from previous step
|
140 |
+
selected_clinical = geo_select_clinical_features(
|
141 |
+
clinical_df=clinical_data,
|
142 |
+
trait=trait,
|
143 |
+
trait_row=trait_row,
|
144 |
+
convert_trait=convert_trait,
|
145 |
+
age_row=age_row,
|
146 |
+
convert_age=convert_age,
|
147 |
+
gender_row=gender_row,
|
148 |
+
convert_gender=convert_gender
|
149 |
+
)
|
150 |
+
|
151 |
+
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
|
152 |
+
|
153 |
+
# 3. Handle missing values systematically
|
154 |
+
linked_data = handle_missing_values(linked_data, trait)
|
155 |
+
|
156 |
+
# 4. Check for biased features and remove them if needed
|
157 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
158 |
+
|
159 |
+
# 5. Validate data quality and save metadata
|
160 |
+
is_usable = validate_and_save_cohort_info(
|
161 |
+
is_final=True,
|
162 |
+
cohort=cohort,
|
163 |
+
info_path=json_path,
|
164 |
+
is_gene_available=True,
|
165 |
+
is_trait_available=True,
|
166 |
+
is_biased=is_biased,
|
167 |
+
df=linked_data,
|
168 |
+
note="Gene expression data from extracellular vesicles in pancreatic cancer study"
|
169 |
+
)
|
170 |
+
|
171 |
+
# 6. Save linked data if usable
|
172 |
+
if is_usable:
|
173 |
+
linked_data.to_csv(out_data_file)
|
p3/preprocess/Pancreatic_Cancer/code/GSE236951.py
ADDED
@@ -0,0 +1,165 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Path Configuration
|
2 |
+
from tools.preprocess import *
|
3 |
+
|
4 |
+
# Processing context
|
5 |
+
trait = "Pancreatic_Cancer"
|
6 |
+
cohort = "GSE236951"
|
7 |
+
|
8 |
+
# Input paths
|
9 |
+
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
|
10 |
+
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE236951"
|
11 |
+
|
12 |
+
# Output paths
|
13 |
+
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE236951.csv"
|
14 |
+
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE236951.csv"
|
15 |
+
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE236951.csv"
|
16 |
+
json_path = "./output/preprocess/3/Pancreatic_Cancer/cohort_info.json"
|
17 |
+
|
18 |
+
# Get file paths
|
19 |
+
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
|
20 |
+
|
21 |
+
# Extract background info and clinical data
|
22 |
+
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
|
23 |
+
|
24 |
+
# Get unique values per clinical feature
|
25 |
+
sample_characteristics = get_unique_values_by_row(clinical_data)
|
26 |
+
|
27 |
+
# Print background info
|
28 |
+
print("Dataset Background Information:")
|
29 |
+
print(f"{background_info}\n")
|
30 |
+
|
31 |
+
# Print sample characteristics
|
32 |
+
print("Sample Characteristics:")
|
33 |
+
for feature, values in sample_characteristics.items():
|
34 |
+
print(f"Feature: {feature}")
|
35 |
+
print(f"Values: {values}\n")
|
36 |
+
# 1. Gene Expression Data Availability
|
37 |
+
# The series summary indicates nanostring gene expression analysis of ~700 immune related genes
|
38 |
+
is_gene_available = True
|
39 |
+
|
40 |
+
# 2.1 Data Availability
|
41 |
+
# Disease status in row 0, gender in row 2, age in row 3
|
42 |
+
trait_row = 0
|
43 |
+
gender_row = 2
|
44 |
+
age_row = 3
|
45 |
+
|
46 |
+
# 2.2 Data Type Conversion Functions
|
47 |
+
def convert_trait(x: str) -> Optional[int]:
|
48 |
+
if not isinstance(x, str):
|
49 |
+
return None
|
50 |
+
x = x.lower().split(': ')[-1]
|
51 |
+
if 'pancreatic' in x:
|
52 |
+
return 1
|
53 |
+
elif 'colon' in x or 'benign' in x:
|
54 |
+
return 0
|
55 |
+
return None
|
56 |
+
|
57 |
+
def convert_gender(x: str) -> Optional[int]:
|
58 |
+
if not isinstance(x, str):
|
59 |
+
return None
|
60 |
+
x = x.lower().split(': ')[-1]
|
61 |
+
if 'female' in x:
|
62 |
+
return 0
|
63 |
+
elif 'male' in x:
|
64 |
+
return 1
|
65 |
+
return None
|
66 |
+
|
67 |
+
def convert_age(x: str) -> Optional[float]:
|
68 |
+
if not isinstance(x, str):
|
69 |
+
return None
|
70 |
+
try:
|
71 |
+
return float(x.split(': ')[-1].split()[0])
|
72 |
+
except:
|
73 |
+
return None
|
74 |
+
|
75 |
+
# 3. Save Metadata
|
76 |
+
validate_and_save_cohort_info(
|
77 |
+
is_final=False,
|
78 |
+
cohort=cohort,
|
79 |
+
info_path=json_path,
|
80 |
+
is_gene_available=is_gene_available,
|
81 |
+
is_trait_available=trait_row is not None
|
82 |
+
)
|
83 |
+
|
84 |
+
# 4. Clinical Feature Extraction
|
85 |
+
clinical_features = geo_select_clinical_features(
|
86 |
+
clinical_df=clinical_data,
|
87 |
+
trait=trait,
|
88 |
+
trait_row=trait_row,
|
89 |
+
convert_trait=convert_trait,
|
90 |
+
age_row=age_row,
|
91 |
+
convert_age=convert_age,
|
92 |
+
gender_row=gender_row,
|
93 |
+
convert_gender=convert_gender
|
94 |
+
)
|
95 |
+
|
96 |
+
print("Preview of extracted clinical features:")
|
97 |
+
print(preview_df(clinical_features))
|
98 |
+
|
99 |
+
clinical_features.to_csv(out_clinical_data_file)
|
100 |
+
# Extract gene expression data from matrix file
|
101 |
+
gene_data = get_genetic_data(matrix_file)
|
102 |
+
|
103 |
+
# Print first 20 row IDs and shape of data to help debug
|
104 |
+
print("Shape of gene expression data:", gene_data.shape)
|
105 |
+
print("\nFirst few rows of data:")
|
106 |
+
print(gene_data.head())
|
107 |
+
print("\nFirst 20 gene/probe identifiers:")
|
108 |
+
print(gene_data.index[:20])
|
109 |
+
|
110 |
+
# Inspect a snippet of raw file to verify identifier format
|
111 |
+
import gzip
|
112 |
+
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
|
113 |
+
lines = []
|
114 |
+
for i, line in enumerate(f):
|
115 |
+
if "!series_matrix_table_begin" in line:
|
116 |
+
# Get the next 5 lines after the marker
|
117 |
+
for _ in range(5):
|
118 |
+
lines.append(next(f).strip())
|
119 |
+
break
|
120 |
+
print("\nFirst few lines after matrix marker in raw file:")
|
121 |
+
for line in lines:
|
122 |
+
print(line)
|
123 |
+
# The gene identifiers are already human gene symbols (like A2M, ABCB1, ABL1, etc.)
|
124 |
+
# No mapping is needed
|
125 |
+
requires_gene_mapping = False
|
126 |
+
# 1. Normalize gene symbols and save normalized gene data
|
127 |
+
gene_data = normalize_gene_symbols_in_index(gene_data)
|
128 |
+
gene_data.to_csv(out_gene_data_file)
|
129 |
+
|
130 |
+
# 2. Link clinical and genetic data and trait
|
131 |
+
# First get selected clinical features using the extraction function from previous step
|
132 |
+
selected_clinical = geo_select_clinical_features(
|
133 |
+
clinical_df=clinical_data,
|
134 |
+
trait=trait,
|
135 |
+
trait_row=trait_row,
|
136 |
+
convert_trait=convert_trait,
|
137 |
+
age_row=age_row,
|
138 |
+
convert_age=convert_age,
|
139 |
+
gender_row=gender_row,
|
140 |
+
convert_gender=convert_gender
|
141 |
+
)
|
142 |
+
|
143 |
+
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
|
144 |
+
|
145 |
+
# 3. Handle missing values systematically
|
146 |
+
linked_data = handle_missing_values(linked_data, trait)
|
147 |
+
|
148 |
+
# 4. Check for biased features and remove them if needed
|
149 |
+
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
|
150 |
+
|
151 |
+
# 5. Validate data quality and save metadata
|
152 |
+
is_usable = validate_and_save_cohort_info(
|
153 |
+
is_final=True,
|
154 |
+
cohort=cohort,
|
155 |
+
info_path=json_path,
|
156 |
+
is_gene_available=True,
|
157 |
+
is_trait_available=True,
|
158 |
+
is_biased=is_biased,
|
159 |
+
df=linked_data,
|
160 |
+
note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
|
161 |
+
)
|
162 |
+
|
163 |
+
# 6. Save linked data if usable
|
164 |
+
if is_usable:
|
165 |
+
linked_data.to_csv(out_data_file)
|