Datasets:
metadata
annotations_creators:
- synthetic
language_creators:
- found
language:
- en
multilinguality:
- monolingual
license: apache-2.0
task_categories:
- tabular-classification
- tabular-regression
tags:
- biology
- synthetic
pretty_name: FOF
size_categories:
- 10M<n<100M
dataset_type: tabular
configs:
- config_name: Classification_AGORA_BC-I-100
data_files:
- split: train
path: Classification/AGORA/100/BC-I/X_train_BC-I-100.csv
- split: val
path: Classification/AGORA/100/BC-I/X_val_BC-I-100.csv
- split: test
path: Classification/AGORA/100/BC-I/X_test_BC-I-100.csv
- config_name: Classification_AGORA_BC-II-100
data_files:
- split: train
path: Classification/AGORA/100/BC-II/X_train_BC-II-100.csv
- split: val
path: Classification/AGORA/100/BC-II/X_val_BC-II-100.csv
- split: test
path: Classification/AGORA/100/BC-II/X_test_BC-II-100.csv
- config_name: Classification_AGORA_BC-III-100
data_files:
- split: train
path: Classification/AGORA/100/BC-III/X_train_BC-III-100.csv
- split: val
path: Classification/AGORA/100/BC-III/X_val_BC-III-100.csv
- split: test
path: Classification/AGORA/100/BC-III/X_test_BC-III-100.csv
- config_name: Classification_AGORA_BC-IV-100
data_files:
- split: train
path: Classification/AGORA/100/BC-IV/X_train_BC-IV-100.csv
- split: val
path: Classification/AGORA/100/BC-IV/X_val_BC-IV-100.csv
- split: test
path: Classification/AGORA/100/BC-IV/X_test_BC-IV-100.csv
- config_name: Classification_AGORA_BC-V-100
data_files:
- split: train
path: Classification/AGORA/100/BC-V/X_train_BC-V-100.csv
- split: val
path: Classification/AGORA/100/BC-V/X_val_BC-V-100.csv
- split: test
path: Classification/AGORA/100/BC-V/X_test_BC-V-100.csv
- config_name: Classification_AGORA_MC-I-100
data_files:
- split: train
path: Classification/AGORA/100/MC-I/X_train_MC-I-100.csv
- split: val
path: Classification/AGORA/100/MC-I/X_val_MC-I-100.csv
- split: test
path: Classification/AGORA/100/MC-I/X_test_MC-I-100.csv
- config_name: Classification_AGORA_MC-II-100
data_files:
- split: train
path: Classification/AGORA/100/MC-II/X_train_MC-II-100.csv
- split: val
path: Classification/AGORA/100/MC-II/X_val_MC-II-100.csv
- split: test
path: Classification/AGORA/100/MC-II/X_test_MC-II-100.csv
- config_name: Classification_AGORA_MC-III-100
data_files:
- split: train
path: Classification/AGORA/100/MC-III/X_train_MC-III-100.csv
- split: val
path: Classification/AGORA/100/MC-III/X_val_MC-III-100.csv
- split: test
path: Classification/AGORA/100/MC-III/X_test_MC-III-100.csv
- config_name: Classification_AGORA_BC-I-50
data_files:
- split: train
path: Classification/AGORA/50/BC-I/X_train_BC-I-50.csv
- split: val
path: Classification/AGORA/50/BC-I/X_val_BC-I-50.csv
- split: test
path: Classification/AGORA/50/BC-I/X_test_BC-I-50.csv
- config_name: Classification_AGORA_BC-II-50
data_files:
- split: train
path: Classification/AGORA/50/BC-II/X_train_BC-II-50.csv
- split: val
path: Classification/AGORA/50/BC-II/X_val_BC-II-50.csv
- split: test
path: Classification/AGORA/50/BC-II/X_test_BC-II-50.csv
- config_name: Classification_AGORA_BC-III-50
data_files:
- split: train
path: Classification/AGORA/50/BC-III/X_train_BC-III-50.csv
- split: val
path: Classification/AGORA/50/BC-III/X_val_BC-III-50.csv
- split: test
path: Classification/AGORA/50/BC-III/X_test_BC-III-50.csv
- config_name: Classification_AGORA_BC-IV-50
data_files:
- split: train
path: Classification/AGORA/50/BC-IV/X_train_BC-IV-50.csv
- split: val
path: Classification/AGORA/50/BC-IV/X_val_BC-IV-50.csv
- split: test
path: Classification/AGORA/50/BC-IV/X_test_BC-IV-50.csv
- config_name: Classification_AGORA_BC-V-50
data_files:
- split: train
path: Classification/AGORA/50/BC-V/X_train_BC-V-50.csv
- split: val
path: Classification/AGORA/50/BC-V/X_val_BC-V-50.csv
- split: test
path: Classification/AGORA/50/BC-V/X_test_BC-V-50.csv
- config_name: Classification_AGORA_MC-I-50
data_files:
- split: train
path: Classification/AGORA/50/MC-I/X_train_MC-I-50.csv
- split: val
path: Classification/AGORA/50/MC-I/X_val_MC-I-50.csv
- split: test
path: Classification/AGORA/50/MC-I/X_test_MC-I-50.csv
- config_name: Classification_AGORA_MC-II-50
data_files:
- split: train
path: Classification/AGORA/50/MC-II/X_train_MC-II-50.csv
- split: val
path: Classification/AGORA/50/MC-II/X_val_MC-II-50.csv
- split: test
path: Classification/AGORA/50/MC-II/X_test_MC-II-50.csv
- config_name: Classification_AGORA_MC-III-50
data_files:
- split: train
path: Classification/AGORA/50/MC-III/X_train_MC-III-50.csv
- split: val
path: Classification/AGORA/50/MC-III/X_val_MC-III-50.csv
- split: test
path: Classification/AGORA/50/MC-III/X_test_MC-III-50.csv
- config_name: Classification_CARVEME_BC-I-100
data_files:
- split: train
path: Classification/CARVEME/100/BC-I/X_train_BC-I-100.csv
- split: val
path: Classification/CARVEME/100/BC-I/X_val_BC-I-100.csv
- split: test
path: Classification/CARVEME/100/BC-I/X_test_BC-I-100.csv
- config_name: Classification_CARVEME_BC-II-100
data_files:
- split: train
path: Classification/CARVEME/100/BC-II/X_train_BC-II-100.csv
- split: val
path: Classification/CARVEME/100/BC-II/X_val_BC-II-100.csv
- split: test
path: Classification/CARVEME/100/BC-II/X_test_BC-II-100.csv
- config_name: Classification_CARVEME_BC-III-100
data_files:
- split: train
path: Classification/CARVEME/100/BC-III/X_train_BC-III-100.csv
- split: val
path: Classification/CARVEME/100/BC-III/X_val_BC-III-100.csv
- split: test
path: Classification/CARVEME/100/BC-III/X_test_BC-III-100.csv
- config_name: Classification_CARVEME_BC-IV-100
data_files:
- split: train
path: Classification/CARVEME/100/BC-IV/X_train_BC-IV-100.csv
- split: val
path: Classification/CARVEME/100/BC-IV/X_val_BC-IV-100.csv
- split: test
path: Classification/CARVEME/100/BC-IV/X_test_BC-IV-100.csv
- config_name: Classification_CARVEME_BC-V-100
data_files:
- split: train
path: Classification/CARVEME/100/BC-V/X_train_BC-V-100.csv
- split: val
path: Classification/CARVEME/100/BC-V/X_val_BC-V-100.csv
- split: test
path: Classification/CARVEME/100/BC-V/X_test_BC-V-100.csv
- config_name: Classification_CARVEME_MC-I-100
data_files:
- split: train
path: Classification/CARVEME/100/MC-I/X_train_MC-I-100.csv
- split: val
path: Classification/CARVEME/100/MC-I/X_val_MC-I-100.csv
- split: test
path: Classification/CARVEME/100/MC-I/X_test_MC-I-100.csv
- config_name: Classification_CARVEME_MC-II-100
data_files:
- split: train
path: Classification/CARVEME/100/MC-II/X_train_MC-II-100.csv
- split: val
path: Classification/CARVEME/100/MC-II/X_val_MC-II-100.csv
- split: test
path: Classification/CARVEME/100/MC-II/X_test_MC-II-100.csv
- config_name: Classification_CARVEME_MC-III-100
data_files:
- split: train
path: Classification/CARVEME/100/MC-III/X_train_MC-III-100.csv
- split: val
path: Classification/CARVEME/100/MC-III/X_val_MC-III-100.csv
- split: test
path: Classification/CARVEME/100/MC-III/X_test_MC-III-100.csv
- config_name: Classification_CARVEME_BC-I-50
data_files:
- split: train
path: Classification/CARVEME/50/BC-I/X_train_BC-I-50.csv
- split: val
path: Classification/CARVEME/50/BC-I/X_val_BC-I-50.csv
- split: test
path: Classification/CARVEME/50/BC-I/X_test_BC-I-50.csv
- config_name: Classification_CARVEME_BC-II-50
data_files:
- split: train
path: Classification/CARVEME/50/BC-II/X_train_BC-II-50.csv
- split: val
path: Classification/CARVEME/50/BC-II/X_val_BC-II-50.csv
- split: test
path: Classification/CARVEME/50/BC-II/X_test_BC-II-50.csv
- config_name: Classification_CARVEME_BC-III-50
data_files:
- split: train
path: Classification/CARVEME/50/BC-III/X_train_BC-III-50.csv
- split: val
path: Classification/CARVEME/50/BC-III/X_val_BC-III-50.csv
- split: test
path: Classification/CARVEME/50/BC-III/X_test_BC-III-50.csv
- config_name: Classification_CARVEME_BC-IV-50
data_files:
- split: train
path: Classification/CARVEME/50/BC-IV/X_train_BC-IV-50.csv
- split: val
path: Classification/CARVEME/50/BC-IV/X_val_BC-IV-50.csv
- split: test
path: Classification/CARVEME/50/BC-IV/X_test_BC-IV-50.csv
- config_name: Classification_CARVEME_BC-V-50
data_files:
- split: train
path: Classification/CARVEME/50/BC-V/X_train_BC-V-50.csv
- split: val
path: Classification/CARVEME/50/BC-V/X_val_BC-V-50.csv
- split: test
path: Classification/CARVEME/50/BC-V/X_test_BC-V-50.csv
- config_name: Classification_CARVEME_MC-I-50
data_files:
- split: train
path: Classification/CARVEME/50/MC-I/X_train_MC-I-50.csv
- split: val
path: Classification/CARVEME/50/MC-I/X_val_MC-I-50.csv
- split: test
path: Classification/CARVEME/50/MC-I/X_test_MC-I-50.csv
- config_name: Classification_CARVEME_MC-II-50
data_files:
- split: train
path: Classification/CARVEME/50/MC-II/X_train_MC-II-50.csv
- split: val
path: Classification/CARVEME/50/MC-II/X_val_MC-II-50.csv
- split: test
path: Classification/CARVEME/50/MC-II/X_test_MC-II-50.csv
- config_name: Classification_CARVEME_MC-III-50
data_files:
- split: train
path: Classification/CARVEME/50/MC-III/X_train_MC-III-50.csv
- split: val
path: Classification/CARVEME/50/MC-III/X_val_MC-III-50.csv
- split: test
path: Classification/CARVEME/50/MC-III/X_test_MC-III-50.csv
- config_name: Regression_AGORA_GR-I-100
data_files:
- split: train
path: Regression/AGORA/100/GR-I/X_train_GR-I.csv
- split: val
path: Regression/AGORA/100/GR-I/X_val_GR-I.csv
- split: test
path: Regression/AGORA/100/GR-I/X_test_GR-I.csv
- config_name: Regression_AGORA_GR-II-100
data_files:
- split: train
path: Regression/AGORA/100/GR-II/X_train_GR-II.csv
- split: val
path: Regression/AGORA/100/GR-II/X_val_GR-II.csv
- split: test
path: Regression/AGORA/100/GR-II/X_test_GR-II.csv
- config_name: Regression_AGORA_GR-III-100
data_files:
- split: train
path: Regression/AGORA/100/GR-III/X_train_GR-III.csv
- split: val
path: Regression/AGORA/100/GR-III/X_val_GR-III.csv
- split: test
path: Regression/AGORA/100/GR-III/X_test_GR-III.csv
- config_name: Regression_AGORA_GR-I-50
data_files:
- split: train
path: Regression/AGORA/50/GR-I/X_train_GR-I.csv
- split: val
path: Regression/AGORA/50/GR-I/X_val_GR-I.csv
- split: test
path: Regression/AGORA/50/GR-I/X_test_GR-I.csv
- config_name: Regression_AGORA_GR-II-50
data_files:
- split: train
path: Regression/AGORA/50/GR-II/X_train_GR-II.csv
- split: val
path: Regression/AGORA/50/GR-II/X_val_GR-II.csv
- split: test
path: Regression/AGORA/50/GR-II/X_test_GR-II.csv
- config_name: Regression_AGORA_GR-III-50
data_files:
- split: train
path: Regression/AGORA/50/GR-III/X_train_GR-III.csv
- split: val
path: Regression/AGORA/50/GR-III/X_val_GR-III.csv
- split: test
path: Regression/AGORA/50/GR-III/X_test_GR-III.csv
- config_name: Regression_CARVEME_GR-I-100
data_files:
- split: train
path: Regression/CARVEME/100/GR-I/X_train_GR-I.csv
- split: val
path: Regression/CARVEME/100/GR-I/X_val_GR-I.csv
- split: test
path: Regression/CARVEME/100/GR-I/X_test_GR-I.csv
- config_name: Regression_CARVEME_GR-II-100
data_files:
- split: train
path: Regression/CARVEME/100/GR-II/X_train_GR-II.csv
- split: val
path: Regression/CARVEME/100/GR-II/X_val_GR-II.csv
- split: test
path: Regression/CARVEME/100/GR-II/X_test_GR-II.csv
- config_name: Regression_CARVEME_GR-III-100
data_files:
- split: train
path: Regression/CARVEME/100/GR-III/X_train_GR-III.csv
- split: val
path: Regression/CARVEME/100/GR-III/X_val_GR-III.csv
- split: test
path: Regression/CARVEME/100/GR-III/X_test_GR-III.csv
- config_name: Regression_CARVEME_GR-I-50
data_files:
- split: train
path: Regression/CARVEME/50/GR-I/X_train_GR-I.csv
- split: val
path: Regression/CARVEME/50/GR-I/X_val_GR-I.csv
- split: test
path: Regression/CARVEME/50/GR-I/X_test_GR-I.csv
- config_name: Regression_CARVEME_GR-II-50
data_files:
- split: train
path: Regression/CARVEME/50/GR-II/X_train_GR-II.csv
- split: val
path: Regression/CARVEME/50/GR-II/X_val_GR-II.csv
- split: test
path: Regression/CARVEME/50/GR-II/X_test_GR-II.csv
- config_name: Regression_CARVEME_GR-III-50
data_files:
- split: train
path: Regression/CARVEME/50/GR-III/X_train_GR-III.csv
- split: val
path: Regression/CARVEME/50/GR-III/X_val_GR-III.csv
- split: test
path: Regression/CARVEME/50/GR-III/X_test_GR-III.csv
- config_name: Transfer_AGORA_TL-I-100
data_files:
- split: train
path: Transfer Learning/AGORA/100/TL-I/X_train_TL-I.csv
- split: val
path: Transfer Learning/AGORA/100/TL-I/X_val_TL-I.csv
- split: test
path: Transfer Learning/AGORA/100/TL-I/X_test_TL-I.csv
- config_name: Transfer_AGORA_TL-II-100
data_files:
- split: train
path: Transfer Learning/AGORA/100/TL-II/X_train_TL-II.csv
- split: val
path: Transfer Learning/AGORA/100/TL-II/X_val_TL-II.csv
- split: test
path: Transfer Learning/AGORA/100/TL-II/X_test_TL-II.csv
- config_name: Transfer_AGORA_TL-I-50
data_files:
- split: train
path: Transfer Learning/AGORA/50/TL-I/X_train_TL-I.csv
- split: val
path: Transfer Learning/AGORA/50/TL-I/X_val_TL-I.csv
- split: test
path: Transfer Learning/AGORA/50/TL-I/X_test_TL-I.csv
- config_name: Transfer_AGORA_TL-II-50
data_files:
- split: train
path: Transfer Learning/AGORA/50/TL-II/X_train_TL-II.csv
- split: val
path: Transfer Learning/AGORA/50/TL-II/X_val_TL-II.csv
- split: test
path: Transfer Learning/AGORA/50/TL-II/X_test_TL-II.csv
- config_name: Transfer_CARVEME_TL-I-100
data_files:
- split: train
path: Transfer Learning/CARVEME/100/TL-I/X_train_TL-I.csv
- split: val
path: Transfer Learning/CARVEME/100/TL-I/X_val_TL-I.csv
- split: test
path: Transfer Learning/CARVEME/100/TL-I/X_test_TL-I.csv
- config_name: Transfer_CARVEME_TL-II-100
data_files:
- split: train
path: Transfer Learning/CARVEME/100/TL-II/X_train_TL-II.csv
- split: val
path: Transfer Learning/CARVEME/100/TL-II/X_val_TL-II.csv
- split: test
path: Transfer Learning/CARVEME/100/TL-II/X_test_TL-II.csv
- config_name: Transfer_CARVEME_TL-I-50
data_files:
- split: train
path: Transfer Learning/CARVEME/50/TL-I/X_train_TL-I.csv
- split: val
path: Transfer Learning/CARVEME/50/TL-I/X_val_TL-I.csv
- split: test
path: Transfer Learning/CARVEME/50/TL-I/X_test_TL-I.csv
- config_name: Transfer_CARVEME_TL-II-50
data_files:
- split: train
path: Transfer Learning/CARVEME/50/TL-II/X_train_TL-II.csv
- split: val
path: Transfer Learning/CARVEME/50/TL-II/X_val_TL-II.csv
- split: test
path: Transfer Learning/CARVEME/50/TL-II/X_test_TL-II.csv
- config_name: Generative_AGORA_100
data_files:
- split: train
path: Generative/AGORA/100/GEN/df_train_AG-100.csv
- split: test
path: Generative/AGORA/100/GEN/df_test_AG-100.csv
- config_name: Generative_AGORA_50
data_files:
- split: train
path: Generative/AGORA/50/GEN/df_train_AG-50.csv
- split: test
path: Generative/AGORA/50/GEN/df_test_AG-50.csv
- config_name: Generative_CARVEME_100
data_files:
- split: train
path: Generative/CARVEME/100/GEN/df_train_CM-100.csv
- split: test
path: Generative/CARVEME/100/GEN/df_test_CM-100.csv
- config_name: Generative_CARVEME_50
data_files:
- split: train
path: Generative/CARVEME/50/GEN/df_train_CM-50.csv
- split: test
path: Generative/CARVEME/50/GEN/df_test_CM-50.csv
Friend or Foe
Welcome to the Friend or Foe Collection!
This file contains the description and general structure of the Friend or Foe v.1 collection. The tree-like structure of this folder can be found in tree.txt.
This folder has 5 subfolders related to different ML tasks, namely Classification, Regression, Transfer Learning, Generative Modeling, and Clustering. Each subfolder stores datasets that address a specific task of evaluating the microbial interactions suitable for the machine learning pipeline. All datasets are stored as large sparse tables in .csv format with corresponding indices for the chemical compounds (features) 424 for AGORA and 499 for CARVEME. In addition, pre-processed values of the environmental matrices with the provided train/val/test split are stored as .npy files to simplify the ML pipeline.
In addition, we provide full mapping of indices to real names of compounds in compounds.json. Note that for the clustering datasets, we changed the indices as we selected specific compounds, so the provided compounds.json is different from the one for supervised learning, transfer learning, and generative modeling. For clustering, we also provide names of species used in a specific dataset as species.json. Species used for other tasks can be found in the utils folder.
The utils subfolder contains all the metabolic models that we used for the friend or foe construction. All metabolic models are stored in .mat format, the number also identifies the species. In addition, we provide the environment results of the metabolic models in raw .csv files that collect the environments for specific interactions. A General info subfolder contains information about organism names and abbreviations of environmental compounds (to convert the abbreviation to a name, see Agora_env_compounds.xlsx for AGORA and the Bigg database at http://bigg.ucsd.edu for CARVEME).
Getting started
from huggingface_hub import hf_hub_download
import pandas as pd
REPO_ID = "powidla/Friend-Or-Foe"
X_train_ID = "Classification/AGORA/100/BC-I/X_train_BC-I-100.csv"
X_val_ID = "Classification/AGORA/100/BC-I/X_val_BC-I-100.csv"
X_test_ID = "Classification/AGORA/100/BC-I/X_test_BC-I-100.csv"
y_train_ID = "Classification/AGORA/100/BC-I/y_train_BC-I-100.csv"
y_val_ID = "Classification/AGORA/100/BC-I/y_val_BC-I-100.csv"
y_test_ID = "Classification/AGORA/100/BC-I/y_test_BC-I-100.csv"
X_train = pd.read_csv(
hf_hub_download(repo_id=REPO_ID, filename=X_train_ID, repo_type="dataset")
)
X_val = pd.read_csv(
hf_hub_download(repo_id=REPO_ID, filename=X_val_ID, repo_type="dataset")
)
X_test = pd.read_csv(
hf_hub_download(repo_id=REPO_ID, filename=X_test_ID, repo_type="dataset")
)
y_train = pd.read_csv(
hf_hub_download(repo_id=REPO_ID, filename=y_train_ID, repo_type="dataset")
)
y_val = pd.read_csv(
hf_hub_download(repo_id=REPO_ID, filename=y_val_ID, repo_type="dataset")
)
y_test = pd.read_csv(
hf_hub_download(repo_id=REPO_ID, filename=y_test_ID, repo_type="dataset")
)
Citation Information
@article {Solowiej-Wedderburn2024.07.03.601864,
author = {Solowiej-Wedderburn, Josephine and Pentz, Jennifer T. and Lizana, Ludvig and Schr{\"o}der, Bj{\"o}rn and Lind, Peter and Libby, Eric},
title = {Competition and cooperation: The plasticity of bacteria interactions across environments},
elocation-id = {2024.07.03.601864},
year = {2024},
doi = {10.1101/2024.07.03.601864},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Bacteria live in diverse communities, forming complex networks of interacting species. A central question in bacterial ecology is why some species engage in cooperative interactions, whereas others compete. But this question often neglects the role of the environment. Here, we use genome-scale metabolic networks from two different open-access collections (AGORA and CarveMe) to assess pairwise interactions of different microbes in varying environmental conditions (provision of different environmental compounds). By scanning thousands of environments for 10,000 pairs of bacteria from each collection, we found that most pairs were able to both compete and cooperate depending on the availability of environmental resources. This approach allowed us to determine commonalities between environments that could facilitate the potential for cooperation or competition between a pair of species. Namely, cooperative interactions, especially obligate, were most common in less diverse environments. Further, as compounds were removed from the environment, we found interactions tended to degrade towards obligacy. However, we also found that on average at least one compound could be removed from an environment to switch the interaction from competition to facultative cooperation or vice versa. Together our approach indicates a high degree of plasticity in microbial interactions to the availability of environmental resources.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2024/07/03/2024.07.03.601864},
eprint = {https://www.biorxiv.org/content/early/2024/07/03/2024.07.03.601864.full.pdf},
journal = {bioRxiv}
}