Add further important files.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/kofam-2022-01-30-profiles.hmm filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+env

17_apply_to_proteome.py

@@ -0,0 +1,318 @@
+#!/usr/bin/env python3
+
+import polars as pl
+import pandas as pd
+from joblib import load
+import argparse
+import logging
+import os
+import sys
+from io import StringIO
+
+from bird_tool_utils import in_tempdir
+import extern
+from tqdm import tqdm
+
+
+# flatten a column that has lists as entries
+def flatten_columns(df, cols):
+    """Flattens multiple columns in a data frame, cannot specify all columns!"""
+    flattened_cols = {}
+    for col in cols:
+        flattened_cols[col] = pd.DataFrame([(index, value) for (index, values) in df[col].items() for value in values],
+                                           columns=['index', col]).set_index('index')
+    flattened_df = df.drop(cols, axis=1)
+    for col in cols:
+        flattened_df = flattened_df.join(flattened_cols[col])
+    return flattened_df
+
+
+# Returns a data frame with 'query' and 'eggNOG_OGs' columns for gene and the predictive cog that provides
+def read_annotations(annotations_path, predictive_cogs_series):
+    #
+    # equivalent of the following R:
+    # annotations = train_accessions[,fread(cmd=paste('sed s=^.query=query= eggnog1_results/',accession,".faa.emapper.annotations  |grep -v '^\\#'",sep='')),by=accession]
+    # nrow(annotations)
+    #
+    a1 = pd.read_csv(annotations_path, sep="\t", comment='#', header=None)
+    # a1.columns[0] = 'query'
+    # a1[:,0]
+    expected_columns = [
+        "query", "seed_ortholog", "evalue", "score", "eggNOG_OGs", "max_annot_lvl", "COG_category", "Description",
+        "Preferred_name", "GOs", "EC", "KEGG_ko", "KEGG_Pathway", "KEGG_Module", "KEGG_Reaction", "KEGG_rclass",
+        "BRITE", "KEGG_TC", "CAZy", "BiGG_Reaction", "PFAMs"
+    ]
+    if (len(a1.columns) != len(expected_columns)):
+        raise Exception("Unexpected formation (number of columns) in eggnog-mapper .annotations file")
+    a1.columns = expected_columns
+
+    a2 = a1.loc[:, list(['query', 'eggNOG_OGs'])]
+    a2.loc[:, 'eggNOG_OGs'] = a2['eggNOG_OGs'].str.split(",")
+
+    a3 = flatten_columns(a2, ['eggNOG_OGs'])
+    a4 = pd.merge(a3, predictive_cogs_series, on='eggNOG_OGs')
+
+    # dcast - do all at once later
+    # a5 = pd.pivot_table(data=a4, columns=predictive_cogs_series, aggfunc=sum, index=None)
+
+    return a4
+
+
+# Seems to require pandas 1.3.3 (or at least >1.2.2). Dummy first row should be removed first.
+def read_multiple_annotations(to_read, predictive_cogs_series):  # read in a number of annotations
+    # A dummy is needed so the right otherwise the pivot_table at the end fails with a KeyError
+    # when predictive ones aren't found in any genomes.
+    collected = pd.DataFrame({"accession": 'dummy', 'eggNOG_OGs': predictive_cogs_series, 'query': 'dummy'})
+
+    for ann in tqdm(to_read, desc="reading annotation files"):
+        cogs = read_annotations(ann.strip(), predictive_cogs_series)
+        cogs['accession'] = ann
+        # if collected is None:
+        #     collected = cogs
+        # else:
+        collected = pd.concat([collected, cogs])
+    return collected
+
+
+if __name__ == '__main__':
+    data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data')
+
+    parent_parser = argparse.ArgumentParser()
+    parent_parser.add_argument('--debug', help='output debug information', action="store_true")
+    #parent_parser.add_argument('--version', help='output version information and quit',  action='version', version=repeatm.__version__)
+    parent_parser.add_argument('--quiet', help='only output errors', action="store_true")
+
+    parent_parser.add_argument('--working-directory', help='working directory', default=None)
+    parent_parser.add_argument('--protein-fasta', help='protein fasta file', required=True)
+
+    parent_parser.add_argument('--annotation-table', help='Pre-calculated counts of COG / KO families in one or more genomes, in TSV format')
+    # add missing annotations
+    parent_parser.add_argument('--add-missing-annotations', help='when columns are missing, add them as 0 [default: croak]', action="store_true")
+    
+    parent_parser.add_argument('--threads', help='number of threads to use', default=1, type=int)
+    
+    eggnog_parser = parent_parser.add_mutually_exclusive_group()
+    eggnog_parser.add_argument('--eggnog-data-dir',
+                               help='eggnog data directory e.g. ~/m/db/eggnog-mapper/2.1.3')
+    eggnog_parser.add_argument('--eggnog-annotation-file', help='eggnog .annotation file')
+
+    kofam_parser = parent_parser.add_mutually_exclusive_group()
+    kofam_parser.add_argument('--kofam-hmm-path',
+                               help='path to kofam hmm',
+                               default=os.path.join(data_dir, 'kofam-2022-01-30-profiles.hmm'))
+    kofam_parser.add_argument('--kofam-tsv-file', help='hmmsearch output file')
+
+    # whitelist default to 'data/COGs_with_more_than_50_percent_remaining.tsv'
+    parent_parser.add_argument('--whitelist', help='whitelist of COGs to use', default=os.path.join(data_dir, 'COGs_with_more_than_50_percent_remaining.tsv'))
+
+    parent_parser.add_argument('--modal-keggs', help='modal keggs', default=os.path.join(data_dir, 'ModalKEGGs.tsv'))
+    # ./12_apply_model.py --model the.model -x the.x --training-data-header <(head the.training-data-header)
+    parent_parser.add_argument('--models', nargs='+', help='models to use')
+
+    parent_parser.add_argument('--training-data-header', help='header of training data', default=os.path.join(data_dir, 'training_data_header.tsv'))
+    
+    parent_parser.add_argument('--output-predictions', help='output predictions')
+    parent_parser.add_argument('--output-annotations', help='output annotations, and exit, do not apply models')
+    args = parent_parser.parse_args()
+
+    # Setup logging
+    if args.debug:
+        loglevel = logging.DEBUG
+    elif args.quiet:
+        loglevel = logging.ERROR
+    else:
+        loglevel = logging.INFO
+    logging.basicConfig(level=loglevel, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%d/%m/%Y %I:%M:%S %p')
+
+    # Read training data header
+    eg_data = pl.read_csv(args.training_data_header, separator="\t", has_header=True)
+    header = eg_data.select(pl.exclude(['accession', 'false_negative_rate', 'false_positive_rate'])).columns
+    # Blacklist these as they aren't in the current ancestral file, not sure why
+    header = list([h for h in header if h not in ['COG0411', 'COG0459', 'COG0564', 'COG1344', 'COG4177']])
+
+    if not args.annotation_table:
+        if not args.eggnog_data_dir and not args.eggnog_annotation_file:
+            raise Exception("Either --annotations, --eggnog-data-dir or --eggnog-annotation-file must be specified")
+        if not args.kofam_hmm_path and not args.kofam_tsv_file:
+            raise Exception("Either --annotations, --kofam-hmm-path or --kofam-tsv-file must be specified")
+    
+    if not args.output_predictions and not args.output_annotations:
+        raise Exception("Either --output-predictions or --output-annotations must be specified")
+
+    # Gather abs paths so chdir to working directory doesn't break things
+    working_directory = None
+    if args.working_directory:
+        working_directory = os.path.abspath(args.working_directory)
+    protein_fasta = os.path.abspath(args.protein_fasta)
+    if args.eggnog_data_dir:
+        eggnog_data_dir = os.path.abspath(args.eggnog_data_dir)
+    if args.eggnog_annotation_file:
+        eggnog_annotation_file = os.path.abspath(args.eggnog_annotation_file)
+    if args.kofam_hmm_path:
+        kofam_hmm_path = os.path.abspath(args.kofam_hmm_path)
+    if args.kofam_tsv_file:
+        kofam_tsv_file = os.path.abspath(args.kofam_tsv_file)
+
+    modal_keggs = os.path.abspath(args.modal_keggs)
+
+    if args.annotation_table:
+        if args.output_annotations:
+            logging.error("Cannot specify --annotation-table and --output-annotations")
+            sys.exit(1)
+        logging.info("Reading annotation table ..")
+        raw = pl.read_csv(args.annotation_table, separator='\t')
+        if args.add_missing_annotations:
+            added_column_count = 0
+            for h in header:
+                if h not in raw.columns:
+                    raw = raw.with_columns(pl.lit(0).alias(h))
+                    added_column_count = 0
+            if added_column_count > 0:
+                logging.info("Added {} extra 0 count columns to annotation table".format(added_column_count))
+        d5 = raw.to_pandas()[header]
+        accessions = list(raw['accession'])
+    else:
+        logging.info("Reading or calculating annotations not from a table ..")
+        with in_tempdir():
+            logging.info("Created temp working directory {}".format(os.getcwd()))
+            if args.working_directory:
+                logging.info("Changing working directory to {}".format(working_directory))
+                if not os.path.exists(working_directory):
+                    logging.info("Creating working directory {}".format(working_directory))
+                    os.makedirs(working_directory)
+                os.chdir(working_directory)
+
+            logging.info("Reading in COG whitelist")
+            whitelist1 = open(args.whitelist).read().splitlines()
+            logging.info("Found {} COGs in the whitelist".format(len(whitelist1)))
+
+            # Run eggnog-mapper if needed
+            predictive_cogs_series = pd.Series(["{}@1|root".format(c) for c in whitelist1], name='eggNOG_OGs')
+            if args.eggnog_annotation_file:
+                logging.info("Reading EGNOG annotations from supplied file")
+                cog_annotations = read_annotations(eggnog_annotation_file, predictive_cogs_series)
+            else:
+                logging.info("Running eggnog-mapper")
+                eggnog_output = 'eggnog_output'
+                extern.run(
+                    f'EGGNOG_DATA_DIR={eggnog_data_dir} emapper.py -m diamond -i {protein_fasta} --target_orthologs one2one --query_cover 50.0 --evalue 0.0000001 --cpu {args.threads} -o {eggnog_output}'
+                )
+                cog_annotations = read_multiple_annotations(
+                    [eggnog_output+'.emapper.annotations'],
+                    predictive_cogs_series)
+
+            # Run hmmsearch in required
+            if args.kofam_tsv_file:
+                hmmsearch_tblout = kofam_tsv_file
+            else:
+                # $ ls proteomes/*faa |parallel hmmsearch --tblout kegg_annotations/annotations/{/}.hmmsearch_tblout.csv -o /dev/null --notextw --cpu 1 ~/m/db/kofam/2022-01-30/profiles.hmm {}
+                logging.info("Running hmmsearch")
+                hmmsearch_tblout = 'hmmsearch_tblout.csv'
+                extern.run(
+                    f'hmmsearch --tblout {hmmsearch_tblout} -o /dev/null --notextw --cpu {args.threads} {kofam_hmm_path} {protein_fasta}'
+                )
+
+            # and then we take the best hit for each protein
+            # $ ls kegg_annotations/annotations/*_tblout.csv |parallel -j10 ./7_process_kegg_hmmsearch.py --input-tblout {} --output-csv kegg_annotations/best_hits/{/}.best_hits.csv &>7_process_kegg_hmmsearch.py.log
+            hits = extern.run("sed 's/  */\t/g' {} |cut -f1-6 |grep -v '^#'".format(hmmsearch_tblout))
+            hmmsearch_best_hits_df = pd.read_csv(StringIO(hits), sep='\t', header=None)
+            hmmsearch_best_hits_df = hmmsearch_best_hits_df.loc[:, [0, 2, 4, 5]]
+            logging.info("Read in {} annotations".format(len(hmmsearch_best_hits_df)))
+
+            hmmsearch_best_hits_df.columns = ['query', 'target', 'evalue', 'score']
+            hmmsearch_best_hits_df2 = hmmsearch_best_hits_df.groupby('query', as_index=False).apply(
+                lambda x: x.nsmallest(1, 'evalue')).reset_index().loc[:, ['query', 'target', 'evalue', 'score']]
+            logging.info("Found {} unique HMM annotations".format(len(hmmsearch_best_hits_df2)))
+
+            # Process eggNOG annotations
+            cog_annotations.loc[:, 'eggNOG_OGs'] = [x.replace('@1|root', '') for x in cog_annotations['eggNOG_OGs']]
+            # Remove rows with dummy accession
+            # cog_annotations = cog_annotations[cog_annotations['accession'] != 'dummy']
+            # fixed_accessions = []
+            # r = re.compile(r'.*/(.*)_protein.faa.gz.emapper.annotations')
+            # for acc in cog_annotations['accession']:
+            #     m = r.match(acc)
+            #     if m:
+            #         fixed_accessions.append(m.group(1))
+            #     else:
+            #         raise Exception("Regex {} failed to match accession {}".format(r, acc))
+            # cog_annotations.loc[:, 'accession'] = fixed_accessions
+            logging.info("Read in {} cog_annotations".format(len(cog_annotations)))
+
+            # Read in modal KEGGs for each COG grouping
+            kegg_pairings = pd.read_csv(modal_keggs, sep="\t", header=None, names=['cog', 'kegg'])
+
+            kegg_cog = pd.merge(hmmsearch_best_hits_df, cog_annotations, on='query')
+            kegg_cog.loc[:, 'cog=kegg'] = kegg_cog['eggNOG_OGs'] + '=' + kegg_cog['target']
+            kegg_pairings.loc[:, 'cog=kegg'] = kegg_pairings['cog'] + '=' + kegg_pairings['kegg']
+
+            medianed = pd.merge(kegg_cog.loc[:, ['cog=kegg']], kegg_pairings, on='cog=kegg')
+            medianed.columns = ['cog=kegg', 'cog', 'kegg']
+            medianed['accession'] = 'query_genome'
+
+            wide_table = pd.pivot_table(medianed.loc[:, ['accession','cog']],
+                                        index='accession',
+                                        aggfunc=len,
+                                        columns='cog',
+                                        values='cog',
+                                        fill_value=0)
+
+            whitelist = ['accession'] + whitelist1
+            # do intersect because some COGs are in whitelist but not in any genomes
+            wide_table2 = wide_table.loc[:, wide_table.columns.intersection(whitelist)]
+
+            # # Read in data
+            # # d = pl.read_csv('TableAncestralRoot1.tsv',sep="\t")
+            # d = pd.read_csv(args.x, sep="\t")
+            # logging.info("Read in input data of shape {}".format(d.shape))
+
+            # # Collapse counts of each COG subfamily
+            # d2 = d
+            # d2['COG'] = d2['COG'].str.split('_').str[0]
+            # d3 = d2.groupby('COG').sum()
+            # d4 = d3.transpose()
+
+            wide_table2_melt_plus = pd.concat([wide_table2.melt(), pd.DataFrame({'cog': header, 'value': 0})])
+            wide_table2_melt_plus2 = pl.DataFrame(wide_table2_melt_plus).groupby('cog').sum()
+            wide_table2_melt_plus2 = wide_table2_melt_plus2.with_columns(pl.lit('query_genome').alias('accession'))
+            wide_table2_melt_plus_pivot = wide_table2_melt_plus2.pivot(index='accession', columns='cog', values='value', aggregate_function='sum')
+
+        # Reorder columns to be the same as the training dataset
+        d5 = wide_table2_melt_plus_pivot.to_pandas()[header]
+
+        if args.output_annotations:
+            d6 = d5
+            d6['accession'] = args.protein_fasta
+            d6.to_csv(args.output_annotations, sep="\t", index=False)
+            logging.info("Wrote annotation table to {}".format(args.output_annotations))
+
+    all_results = []
+    for model_path in args.models:
+        logging.info("Loading model {}".format(model_path))
+        model = load(model_path)
+        logging.info("Loaded model {}".format(model_path))
+
+        doing_perceptron = 'Perceptron' in model_path
+
+        preds = model.predict(d5)
+        # if doing_perceptron:
+        #     probas = pl.lit(-1.0)
+        # else:
+        #     probas = model.predict_proba(d5)[:,1]
+
+        results = pl.DataFrame({
+            'node': accessions if args.annotation_table else args.protein_fasta,
+            'preds': preds})
+        results = results.select(
+            pl.col('node'),
+            pl.col('preds').alias('prediction').cast(pl.Int64),
+            # pl.col('proba').alias('probability').cast(pl.Float64),
+            pl.lit(model_path).alias('model'))
+        if doing_perceptron:
+            results = results.with_columns(pl.lit(-1.0).alias('probability').cast(pl.Float64))
+        else:
+            results = results.with_columns(pl.lit(model.predict_proba(d5)[:, 1]).alias('probability').cast(pl.Float64))
+        all_results.append(results)
+
+    pl.concat(all_results).write_csv(args.output_predictions, separator="\t")
+    logging.info("Wrote predictions to {}".format(args.output_predictions))

README.md

@@ -3,4 +3,14 @@ license: gpl-3.0
 language:
 - en
 library_name: sklearn
----
+---
+
+Prediction of aerobicity (whether an bacteria or archaeon is aerobic) based on gene copy numbers. The prediction problem is posed as a 2-class problem (the prediction is either aerobic or anaerobic).
+
+To apply the predictor:
+
+```
+$ cd aerobicity
+$ mamba env create -p env -f env-apply.yml
+$ 
+```

data/COGs_with_more_than_50_percent_remaining.tsv

@@ -0,0 +1,2700 @@
+COG0001
+COG0002
+COG0003
+COG0004
+COG0005
+COG0007
+COG0008
+COG0010
+COG0012
+COG0013
+COG0015
+COG0016
+COG0017
+COG0018
+COG0019
+COG0022
+COG0023
+COG0025
+COG0026
+COG0027
+COG0028
+COG0029
+COG0030
+COG0033
+COG0035
+COG0036
+COG0038
+COG0039
+COG0041
+COG0042
+COG0043
+COG0044
+COG0045
+COG0046
+COG0047
+COG0048
+COG0051
+COG0053
+COG0055
+COG0056
+COG0057
+COG0058
+COG0059
+COG0060
+COG0061
+COG0062
+COG0064
+COG0065
+COG0066
+COG0067
+COG0068
+COG0069
+COG0070
+COG0072
+COG0073
+COG0074
+COG0075
+COG0076
+COG0077
+COG0078
+COG0079
+COG0081
+COG0082
+COG0083
+COG0084
+COG0085
+COG0088
+COG0090
+COG0092
+COG0093
+COG0094
+COG0095
+COG0096
+COG0098
+COG0100
+COG0101
+COG0102
+COG0104
+COG0106
+COG0107
+COG0108
+COG0109
+COG0110
+COG0112
+COG0114
+COG0115
+COG0116
+COG0118
+COG0119
+COG0120
+COG0121
+COG0122
+COG0123
+COG0124
+COG0128
+COG0129
+COG0130
+COG0132
+COG0133
+COG0134
+COG0136
+COG0137
+COG0139
+COG0140
+COG0141
+COG0142
+COG0143
+COG0144
+COG0145
+COG0146
+COG0147
+COG0148
+COG0151
+COG0152
+COG0153
+COG0155
+COG0156
+COG0157
+COG0158
+COG0159
+COG0160
+COG0161
+COG0162
+COG0163
+COG0165
+COG0166
+COG0167
+COG0168
+COG0170
+COG0171
+COG0172
+COG0173
+COG0174
+COG0175
+COG0176
+COG0181
+COG0182
+COG0183
+COG0184
+COG0188
+COG0190
+COG0191
+COG0192
+COG0193
+COG0194
+COG0195
+COG0196
+COG0197
+COG0199
+COG0201
+COG0202
+COG0205
+COG0206
+COG0207
+COG0208
+COG0209
+COG0213
+COG0214
+COG0215
+COG0216
+COG0217
+COG0218
+COG0220
+COG0221
+COG0224
+COG0225
+COG0229
+COG0231
+COG0232
+COG0233
+COG0234
+COG0235
+COG0239
+COG0240
+COG0241
+COG0242
+COG0243
+COG0244
+COG0246
+COG0247
+COG0248
+COG0250
+COG0252
+COG0253
+COG0254
+COG0258
+COG0259
+COG0260
+COG0262
+COG0263
+COG0266
+COG0268
+COG0269
+COG0270
+COG0271
+COG0272
+COG0274
+COG0276
+COG0277
+COG0278
+COG0279
+COG0280
+COG0281
+COG0282
+COG0283
+COG0285
+COG0286
+COG0287
+COG0288
+COG0289
+COG0292
+COG0293
+COG0294
+COG0296
+COG0297
+COG0298
+COG0300
+COG0301
+COG0302
+COG0303
+COG0305
+COG0306
+COG0309
+COG0310
+COG0311
+COG0312
+COG0313
+COG0314
+COG0317
+COG0320
+COG0321
+COG0322
+COG0324
+COG0326
+COG0327
+COG0328
+COG0329
+COG0330
+COG0331
+COG0332
+COG0334
+COG0335
+COG0337
+COG0338
+COG0339
+COG0341
+COG0342
+COG0343
+COG0344
+COG0345
+COG0347
+COG0349
+COG0350
+COG0352
+COG0353
+COG0354
+COG0356
+COG0358
+COG0359
+COG0362
+COG0363
+COG0364
+COG0365
+COG0366
+COG0368
+COG0369
+COG0370
+COG0371
+COG0372
+COG0373
+COG0374
+COG0375
+COG0376
+COG0377
+COG0378
+COG0379
+COG0380
+COG0381
+COG0382
+COG0383
+COG0384
+COG0385
+COG0386
+COG0387
+COG0388
+COG0389
+COG0390
+COG0391
+COG0397
+COG0399
+COG0400
+COG0402
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data/kofam-2022-01-30-profiles.hmm

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data/training_data_header.tsv

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env-apply.yml

@@ -0,0 +1,21 @@
+channels:
+  - conda-forge
+  - bioconda
+  - defaults
+dependencies:
+  - python>=3.10
+  - pandas
+  - ipython
+  - polars
+  - scikit-learn==1.2.2
+  - joblib==1.2.0
+  - xgboost==1.7.4
+  - jupyterlab
+  - shap
+  - matplotlib
+  # Below for 17_apply
+  - extern
+  - eggnog-mapper==2.1.3 # Pinned for repeatability
+  - hmmer
+  - bird_tool_utils_python
+  - pyarrow