scDrug: From scRNA-seq to Drug Repositioning

The scDrug constructed a workflow for comprehensive analysis on single-cell RNA sequencing (scRNA-seq) data. It provided a powerful tool with various functions, from fundamental data analysis to drug response prediction, and treatment suggestions.

The scDrug went through three parts on raw scRNA-seq data investigation: Single-Cell Data Analysis, Drug Response Prediction, and Treatment Selection.

• Single-Cell Data Analysis performed data preprocessing, clustering, cell type annotation, Gene Set Enrichment Analysis (GSEA), and survival analysis.

• Drug Response Prediction estimated the half maximal inhibitory concentration (IC50) of cell clusters, and reported the cell death percentages as drug kill efficacy.

• Treatment Selection listed treatment combinations of given cell clusters.

Download and Installation

1. Clone the repository to local directory, e.g., ./scDrug.

   git clone https://github.com/ailabstw/scDrug.git ./scDrug
   

2. Build the Docker image tagged sc-drug.

   docker build -t sc-drug ./scDrug
   

3. Run the Docker container named scDrug with /docker/path mounted to /server/path to access files within the Docker container.

   docker run -it --name scDrug -v /server/path:/docker/path --privileged sc-drug
   

4. In the Docker container scDrug, pull the Docker image cibersortx/fractions used in treatment selection.

   /etc/init.d/docker start
   docker pull cibersortx/fractions
   

   Note 1: Get CONTAINER_ID with command docker ps -a and start the container with docker start -i CONTAINER_ID. Note 2: If docker-in-docker cannot be operated on the user's computer, the user can pull and run the CIBERSORTx container outside the scDrug container as long as a shared folder is mounted on both containers for file sharing.

Usage

Note: Refer to example for a detail illustration of the usage for the scDrug.

Single-Cell Data Analysis

Single-Cell Data Analysis took the scRNA-seq data in a 10x-Genomics-formatted mtx directory or a CSV file as input, performed fundamental data analysis, and output a Scanpy Anndata object scanpyobj.h5ad, a UMAP umap_cluster.png and differentially expressed genes (DEGs) cluster_DEGs.csv of the clustering result, and a gene expression profile (GEP) file GEP.txt.

Optionally, Single-Cell Data Analysis carried out batch correction, cell type annotation and Gene Set Enrichment Analysis (GSEA), and provided additional UMAPs showing batch effects and cell types (umap_batch.png and umap_cell_type.png), and the GSEA result GSEA_results.csv. For cell type annotation, we used scMatch: a single-cell gene expression profile annotation tool using reference datasets.

Furthermore, Single-Cell Data Analysis could take previously produced Anndata as input and applied sub-clustering on specified clusters.

• Run python3 single_cell_analysis.py -h to show the help messages as follow for Single-Cell Data Analysis.

usage: single_cell_analysis.py [-h] -i INPUT [-f FORMAT] [-o OUTPUT] [-r RESOLUTION] [--impute] [--auto-resolution] [-m METADATA]
                               [-b BATCH] [-c CLUSTERS] [--cname CNAME] [--GEP] [--annotation] [--gsea] [--cpus CPUS] [--survival]
                               [--tcga TCGA] [--id ID] [--prefix PREFIX] [--not_treated]

scRNA-seq data analysis

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        path to input 10x directory or CSV file
  -f FORMAT, --format FORMAT
                        input format, 10x (default) | csv | h5ad (Anndata object for subclustering with --clusters CLUSTERS)
  -o OUTPUT, --output OUTPUT
                        path to output directory, default='./'
  -r RESOLUTION, --resolution RESOLUTION
                        resolution for clustering, default=0.6
  --impute              do imputation. default: no
  --auto-resolution     automatically determine resolution for clustering
  -m METADATA, --metadata METADATA
                        path to metadata CSV file for batch correction (index as input in first column)
  -b BATCH, --batch BATCH
                        column in metadata (or adata.obs) for batch correction, e.g. 'PatientID'
  -c CLUSTERS, --clusters CLUSTERS
                        perform single cell analysis only on specified clusters, e.g. '1,3,8,9'
  --cname CNAME         which variable should be used when selecting clusters; required when clusters are provided. Default:
                        'louvain'
  --GEP                 generate Gene Expression Profile file.
  --annotation          perform cell type annotation
  --gsea                perform gene set enrichment analysis (GSEA)
  --cpus CPUS           number of CPU used for auto-resolution and annotation, default=1
  --survival            perform survival analysis
  --tcga TCGA           path to TCGA data
  --id ID               Specify TCGA project id in the format "TCGA-xxxx", e.g., "TCGA-LIHC"
  --prefix PREFIX       Any prefix before matrix.mtx, genes.tsv and barcodes.tsv.
  --not_treated         only consider untreated samples from TCGA for survival analysis.

• Apply Single-Cell Data Analysis with batch correction, clustering resolution 1.0, cell type annotation and GSEA.

python3 single_cell_analysis.py --input INPUT --metadata METADATA --batch BATCH --resolution 1.0 --annotation --gsea

• Single-Cell Data Analysis for sub-clustering on specified clusters at automatically determined resolution run under 4 cpus.

python3 single_cell_analysis.py -f h5ad --input scanpyobj.h5ad --clusters CLUSTERS --auto-resolution --cpus 4

Drug Response Prediction

Drug Response Prediction examined scanpyobj.h5ad generated in Single-Cell Data Analysis, reported clusterwise IC50 and cell death percentages to drugs in the GDSC database via CaDRReS-Sc (a recommender system framework for in silico drug response prediction), or drug sensitivity AUC in the PRISM database from [DepMap Portal PRISM-19Q4] (https://doi.org/10.1038/s43018-019-0018-6). The output the prediction results are IC50_prediction.csv and drug_kill_prediction.csv while using parameter --model GDSC, and AUC_prediction.csv whlie using parameter --model PRISM.

• Run python3 drug_response_prediction.py -h to show the help messages as follow for Drug Response Prediction.

usage: drug_response_prediction.py [-h] -i INPUT [-o OUTPUT] [-c CLUSTERS] [-m MODEL] [--n_drugs N_DRUGS]

Drug response prediction

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        path to input Anndata object (h5ad file)
  -o OUTPUT, --output OUTPUT
                        path to output directory, default='./'
  -c CLUSTERS, --clusters CLUSTERS
                        perform sensitivity prediction on specified clusters, e.g. '1,3,8,9', default='All'
  -m MODEL, --model MODEL
                        the sensitivity screening is from GDSC ic50/PRISM auc, e.g. GDSC, PRISM
  --n_drugs N_DRUGS     the number of drugs to visualize for each cluster

• Predict drug response on specified clusters (here for default all clusters) with Drug Response Prediction.

python3 drug_response_prediction.py --input scanpyobj.h5ad

Treatment Selection

In Treatment Selection, we first imputed cell fractions of bulk GEPs from the LINCS L1000 database with single-cell GEP GEP.txt created in Single-Cell Data Analysis via Docker version of CIBERSORTx Cell Fractions, which enumerated the proportions of distinct cell subpopulations in tissue expression profiles. Then, we selected treatment combinations from the LINCS L1000 database with the CIBERSORTx result, and generated plots and a dataframe to show the drug effect.

Impute Cell Fractions

Impute Cell Fractions took the reference sample file GEP.txt as input to run CIBERSORTx Cell Fractions with bulk GEP of user specified or automatically determined cell type, and output CIBERSORTx result files to the output directory, including CIBERSORTx_Adjusted.txt. The cell type for bulk GEP involved A375 (malignant melanoma), A549 (non-small cell lung carcinoma), HCC515 (non-small cell lung adenocarcinoma), HEPG2 (hepatocellular carcinoma), HT29 (colorectal adenocarcinoma), MCF7 (breast adenocarcinoma), PC3 (prostate adenocarcinoma), YAPC (Pancreatic carcinoma).

• Run python3 CIBERSORTx_fractions.py -h to show the help messages as follow for Impute Cell Fractions.

usage: CIBERSORTx_fractions.py [-h] -i INPUT [-o OUTPUT] [-l LINCS] [-c CLUSTERS] -u USERNAME -t TOKEN
                               [--celltype CELLTYPE] [--develop]

impute the fractions of previous identified cell subsets under each bulk sample in the LINCS L1000 database.

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        path to input single-cell GEP file
  -o OUTPUT, --output OUTPUT
                        path to output directory, default='./'
  -l LINCS, --lincs LINCS
                        path to LINCS data directory
  -c CLUSTERS, --clusters CLUSTERS
                        perform combined treatment prediction on specified clusters, e.g. '1,3,8,9'
  -u USERNAME, --username USERNAME
                        email address registered on CIBERSORTx website
  -t TOKEN, --token TOKEN
                        token obtained from CIBERSORTx website
  --celltype CELLTYPE   choose a cell line from the options. If no name is provided, we will automatically
                        determine the cell type. Options: A375 (malignant melanoma), A549 (non-small cell lung
                        carcinoma), HCC515 (non-small cell lung adenocarcinoma), HEPG2 (hepatocellular carcinoma),
                        HT29 (colorectal adenocarcinoma), MCF7 (breast adenocarcinoma), PC3 (prostate
                        adenocarcinoma), YAPC (Pancreatic carcinoma)
  --develop             Only for development version.

• Impute Cell Fractions via CIBERSORTx Cell Fractions with single-cell GEP GEP.txt and LINCS L1000 bulk GEP of automatically determined cell type.

python3 CIBERSORTx_fractions.py --input GEP.txt --username USERNAME --token TOKEN

Note: To obtain USERNAME and TOKEN, register and request for access to CIBERSORTx Docker on CIBERSORTx website.

Select Treatment Combinations

Select Treatment Combinations takes the CIBERSORTx result CIBERSORTx_Results.txt and the L1000 instance info file as input, selects treatment combinations for the given cell type from the LINCS L1000 database, and output the report of the identified treatment combinations (treatment_combinations.pdf).

• Run python3 treatment_selection.py -h to show the help messages as follow for Select Treatment Combinations.

usage: treatment_selection.py [-h] -i INPUT [-o OUTDIR] [-t THRESHOLD] [-c CON_THRESHOLD] --celltype CELLTYPE
                              [--metadata METADATA]

Select treatment combination from the LINCS L1000 database.

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        CIBERSORTx output file.
  -o OUTDIR, --outdir OUTDIR
                        path to output directory, default='./'
  -t THRESHOLD, --threshold THRESHOLD
                        Sensitivity threshold. Range: [-1,0), default:-0.9
  -c CON_THRESHOLD, --con_threshold CON_THRESHOLD
                        Consistency threshold. Range: [-1,0), default:-0.75
  --celltype CELLTYPE   Same as the cell type for decomposition. Options: A375 | A549 | HEPG2 | HT29 | MCF7 | PC3 | YAPC
  --metadata METADATA   the L1000 instance info file, e.g., 'GSE70138_Broad_LINCS_inst_info_2017-03-06.txt'

• Select Treatment Combinations with the L1000 metadata.

python3 treatment_selection.py --input CIBERSORTx_Adjusted.txt --celltype CELLTYPE --metadata METADATA