Download the RxRx19a Data

Create a directory called 'data' in the home directory of the repo but running: mkdir data

Go to https://www.rxrx.ai/rxrx19a, scroll to the bottom of the page, and download the "Metadata" and "Deep Learning Embeddings." Put these files (metadata.csv and embeddings.csv) into the data directory.

Set up the Environment

Create a Python3 virtual environment and install the packages from the provided requirements.txt file.

This can be done by running the following commands:
python3 -m venv env
source env/bin/activate
python3 -m pip install -r requirements.txt

If that does not work, just make sure these packages are installed:

pandas
numpy
imbalanced-learn
scikit-learn
matplotlib
shap

Running the Pipeline

These scripts run using the SLURM Workload Manager. To perform these analyses without SLURM, you must replace sbatch with bash in all the bash scripts. When running with SLURM, you may need to adjust the bash scripts resource requests or other arguments.

Determine the Noise vs. Biological Signal in each Feature

Run this command to split the data by experiment and calculate SHAP values for each feature when predicting plate (batch effect/noise) vs disease condition (biological signal):
bash bash_scripts/split_n_get_shaps.sh data/embeddings.csv data/metadata.csv

If you want to normalize the deep learning embeddings to remove batch effects (we strongly recommend this) then add a final argument n when running, like so:
bash bash_scripts/split_n_get_shaps.sh data/embeddings.csv data/metadata.csv n

If you want to run with just feature dropping, instead add the argument f. If you want to use both, add b instead.

If feature dropping, run this command to view plots of the two SHAP values for each feature plotted against each other:
bash bash_scripts/run_shap_plots.sh

Look at each experiments' plot to determine a cutoff for the disease condition shap that will remove the features that have small SHAP values for disease condition but have large shap values for plate. Record these four cut off values because we will use them in subsequent scripts.

Ensure that Removing Noisy Features does not Remove Biological Signal

Run this command to perform a sensitivity test on each experiment except replace the four numbers with your determined SHAP cut off values for the HRCE-1, HRCE-2, VERO-1, and VERO-2 experiments, respectively. You can skip this step if you are only normalizing and not feature dropping.
bash bash_scripts/run_all_sensitivity_tests.sh 0.0002 0.0022 0.002 0.00015

Identify Potentially Effective Treatments

Run this command to identify treatments that might be effective against SARS-CoV-2, using two different methods:
bash bash_scripts/identify_effective_treatments.sh 0.0002 0.0022 0.002 0.00015

If you want to identify effective compounds without feature dropping, run the above script with all cutoffs as 0 like so:
bash bash_scripts/identify_effective_treatments.sh n

Results

Cosine Similarity vs RandomForest Predicted Probability

HRCE-1 (Mock)

SHAP Value Cutoff: 0.0002

Rank	Drug (Concentration)	Cosine Similarity	Drug (Concentration)	RandomForest Predicted Probability
1	Remdesivir (GS-5734) 0.3	0.9942	Remdesivir (GS-5734) 10.0	0.5558
2	GS-441524 10.0	0.9903	Quercetin 3.0	0.4971
3	Remdesivir (GS-5734) 1.0	0.9869	Remdesivir (GS-5734) 3.0	0.4567
4	budipine 3.0	0.9742	Pacritinib 1.0	0.4446
5	Remdesivir (GS-5734) 3.0	0.9737	GS-441524 10.0	0.4338
6	Quercetin 3.0	0.9730	Remdesivir (GS-5734) 0.3	0.4021
7	GS-441524 3.0	0.9701	GS-441524 3.0	0.3946
8	Remdesivir (GS-5734) 10.0	0.9685	Remdesivir (GS-5734) 1.0	0.3779
9	paricalcitol 0.01	0.9670	Aloxistatin 0.3	0.3487
10	Calcipotriene 0.3	0.9668	proscillaridin-a 0.01	0.3462
11	Aloxistatin 1.0	0.9641	narasin 0.3	0.3362
12	Cinnarizine 3.0	0.9640	narasin 1.0	0.3346
13	Darunavir 1.0	0.9636	Aloxistatin 10.0	0.3333
14	Calcipotriene 0.1	0.9636	Rapamycin 1.0	0.3333
15	Aloxistatin 0.1	0.9612	proscillaridin-a 0.03	0.3329
16	(R)-baclofen 0.01	0.9602	narasin 0.1	0.3271
17	paricalcitol 0.1	0.9581	Duvelisib 3.0	0.3196
18	Aloxistatin 0.3	0.9557	Gilteritinib 3.0	0.3142
19	Aloxistatin 10.0	0.9551	silmitasertib 3.0	0.3067
20	paricalcitol 0.03	0.9523	Zotarolimus 1.0	0.2983

HRCE-2 (Mock)

SHAP Value Cutoff: 0.001

Rank	Drug (Concentration)	Cosine Similarity	Drug (Concentration)	RandomForest Predicted Probability
1	trihexyphenidyl 3.0	0.9889	Remdesivir (GS-5734) 10.0	0.3880
2	Remdesivir (GS-5734) 0.3	0.9885	clofazimine 0.3	0.3758
3	Remdesivir (GS-5734) 1.0	0.9875	Remdesivir (GS-5734) 0.3	0.3520
4	celiprolol 0.1	0.9874	Remdesivir (GS-5734) 3.0	0.3380
5	GS-441524 3.0	0.9870	Remdesivir (GS-5734) 1.0	0.3295
6	Obeticholic acid 0.3	0.9864	abiraterone 3.0	0.3254
7	telmesteine 3.0	0.9854	cloranolol 3.0	0.3212
8	GS-441524 10.0	0.9839	GS-441524 3.0	0.3190
9	eperisone 3.0	0.9833	digitoxin 0.03	0.3171
10	carazolol 0.01	0.9830	Mebendazole 1.0	0.3104
11	Remdesivir (GS-5734) 3.0	0.9825	Aloxistatin 0.3	0.3005
12	ISO-1 0.1	0.9821	YK-4-279 3.0	0.3000
13	Vismodegib 3.0	0.9820	digoxin 0.3	0.2979
14	Obeticholic acid 3.0	0.9819	salinomycin 0.03	0.2971
15	Erythromycin ethylsuccinate 0.01	0.9817	Indocyanine green 3.0	0.2958
16	Bromantane 3.0	0.9817	Silodosin 3.0	0.2954
17	alprenolol 0.1	0.9816	Eravacycline (dihydrochloride) 3.0	0.2929
18	Aloxistatin 0.3	0.9812	bupranolol 0.03	0.2908
19	Forodesine 3.0	0.9809	Ponatinib 0.1	0.2883
20	SIN-1 chloride 3.0	0.9803	Ciclopirox 3.0	0.2867

VERO-1 (Mock)

SHAP Value Cutoff: 0.001

Rank	Drug (Concentration)	Cosine Similarity	Drug (Concentration)	RandomForest Predicted Probability
1	GS-441524 3.0	0.9836	GS-441524 3.0	0.4944
2	GS-441524 10.0	0.9818	GS-441524 10.0	0.4506
3	Remdesivir (GS-5734) 10.0	0.9488	Remdesivir (GS-5734) 10.0	0.3281
4	Hydroxychloroquine Sulfate 10.0	0.7515	Chloroquine 10.0	0.2850
5	Chloroquine 3.0	0.7446	Hydroxychloroquine Sulfate 30.0	0.2616
6	Chloroquine 10.0	0.7114	Chloroquine 30.0	0.2594
7	solithromycin 10.0	0.6778	Hydroxychloroquine Sulfate 10.0	0.2366
8	Imiquimod 10.0	0.6511	Thymoquinone 10.0	0.2056
9	Lopinavir 10.0	0.6038	Pacritinib 10.0	0.1975
10	GS-441524 1.0	0.6029	Arbidol 10.0	0.1653
11	solithromycin 3.0	0.5920	Pacritinib 3.0	0.1512
12	Remdesivir (GS-5734) 3.0	0.5850	Thymoquinone 3.0	0.1500
13	Hydroxychloroquine Sulfate 30.0	0.5800	Quinine 10.0	0.1469
14	Chloroquine 30.0	0.5612	Hydroxychloroquine Sulfate 100.0	0.1434
15	Ritonavir 10.0	0.5325	Chloroquine 3.0	0.1409
16	Hydroxychloroquine Sulfate 3.0	0.5243	Chloroquine 100.0	0.1388
17	Quinine 10.0	0.5174	solithromycin 10.0	0.0981
18	Chloroquine 1.0	0.5065	ML-9 10.0	0.0853
19	Thymoquinone 1.0	0.4993	Lopinavir 10.0	0.0816
20	Aloxistatin 10.0	0.4957	Ritonavir 10.0	0.0722

VERO-2 (Mock)

SHAP Value Cutoff: 0.00125

Rank	Drug (Concentration)	Cosine Similarity	Drug (Concentration)	RandomForest Predicted Probability
1	GS-441524 3.0	0.9898	GS-441524 3.0	0.3825
2	GS-441524 10.0	0.9866	Chloroquine 30.0	0.3553
3	Hydroxychloroquine Sulfate 10.0	0.9406	Hydroxychloroquine Sulfate 30.0	0.3519
4	Chloroquine 10.0	0.9366	Chloroquine 10.0	0.3512
5	Chloroquine 3.0	0.8977	Hydroxychloroquine Sulfate 10.0	0.3181
6	Hydroxychloroquine Sulfate 3.0	0.8292	Arbidol 10.0	0.3062
7	Hydroxychloroquine Sulfate 30.0	0.8103	GS-441524 10.0	0.3009
8	Quinine 10.0	0.8047	Pacritinib 10.0	0.2950
9	Remdesivir (GS-5734) 10.0	0.8028	Thymoquinone 10.0	0.2841
10	solithromycin 10.0	0.8007	Chloroquine 3.0	0.2562
11	Chloroquine 30.0	0.7932	Quinine 10.0	0.2394
12	Lopinavir 10.0	0.7684	Pacritinib 3.0	0.2231
13	Chloroquine 1.0	0.7633	Hydroxychloroquine Sulfate 100.0	0.1966
14	Arbidol 10.0	0.7567	solithromycin 10.0	0.1884
15	GS-441524 1.0	0.7371	Chloroquine 100.0	0.1872
16	Aloxistatin 3.0	0.6163	Remdesivir (GS-5734) 10.0	0.1459
17	Quinine 3.0	0.6041	Hydroxychloroquine Sulfate 3.0	0.1450
18	ML-9 10.0	0.5757	ML-9 10.0	0.0841
19	Hydroxychloroquine Sulfate 1.0	0.5638	GS-441524 1.0	0.0806
20	Cobicistat 10.0	0.5599	Chloroquine 1.0	0.0800