This library enables computation and retrieval of metrics to benchmark a whole-genome perturbative map created by the pipeline. Metrics that can be computed using this repo are pairwise gene-gene recall for the Reactome, HuMAP, CORUM, SIGNOR, and StringDB datasets which are publicly available (see efaar_benchmarking/benchmark_annotations/LICENSE for terms of use for each source).

By default, we do not filter on perturbation fingerprint, although filtering is available through the parameters to the benchmark function. We compute the metrics for three different random seeds used to generate empirical null entities.

See our bioRxiv paper for the details: https://www.biorxiv.org/content/10.1101/2022.12.09.519400v1

Here are the descriptions for the constants used in the code to configure and control various aspects of the benchmarking process:

BENCHMARK_DATA_DIR: The directory path to the benchmark annotations data. It is obtained using the resources module from the importlib package.

BENCHMARK_SOURCES: A list of benchmark sources, including "Reactome", "HuMAP", "CORUM", "SIGNOR", and "StringDB".

PERT_LABEL_COL: The column name for the gene perturbation labels.

CONTROL_PERT_LABEL: The perturbation label value for the control perturbation units.

PERT_SIG_PVAL_COL: The column name for the perturbation p-value.

PERT_SIG_PVAL_THR: The threshold value for the perturbation p-value.

RECALL_PERC_THRS: A list of tuples of two floats between 0 and 1 representing the threshold pair (lower threshold, upper threshold) for calculating recall.

RANDOM_SEED: The random seed value used for random number generation for sampling the null distribution.

RANDOM_COUNT: The number of runs for benchmarking to compute error in metrics.

N_NULL_SAMPLES: The number of null samples used in benchmarking.

MIN_REQ_ENT_CNT: The minimum required number of entities for benchmarking.

This package is installable via pip.

pip install efaar_benchmarking

from efaar_benchmarking.efaar import embed_by_scvi, align_by_centering, aggregate_by_mean
from efaar_benchmarking.benchmarking import benchmark
from efaar_benchmarking.plotting import plot_recall

adata = load_replogle("genome_wide", "raw")
metadata = adata.obs
embeddings_scvi = embed_by_scvi(adata)
embeddings_aligned = align_by_centering(embeddings_scvi, metadata)
map_data = aggregate_by_mean(embeddings_aligned, metadata)
metrics = benchmark(map_data,
recall_thr_pairs=[(0.01,0.99),(0.02,0.98),(0.03,0.97),(0.04,0.96),(0.05,0.95),(0.06,0.94),(0.07,0.93),(0.08,0.92),(0.09,0.91),(0.1,0.9)])
plot_recall(metrics["summary"])

Reactome:

Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., Senff-Ribeiro, A., Griss, J., Sevilla, C., Matthews, L., Gong, C., et al. (2022). The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692. 10.1093/nar/gkab1028.

CORUM:

Giurgiu, M., Reinhard, J., Brauner, B., Dunger-Kaltenbach, I., Fobo, G., Frishman, G., Montrone, C., and Ruepp, A. (2019). CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res. 47, D559–D563. 10.1093/nar/gky973.

HuMAP:

Drew, K., Wallingford, J.B., and Marcotte, E.M. (2021). hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies. Mol. Syst. Biol. 17, e10016. 10.15252/msb.202010016.

SIGNOR:

Licata, L., Lo Surdo, P., Iannuccelli, M., Palma, A., Micarelli, E., Perfetto, L., Peluso, D., Calderone, A., Castagnoli, L., and Cesareni, G. (2019). SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update. Nucleic Acids Research. 10.1093/nar/gkz949.

StringDB:

von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA, Bork P. STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D433-7. doi: 10.1093/nar/gki005.