Table 1.

Summary of expected outputs from each step in the protocol

Key step	Steps number	Output format	Output file
Gene count differences between tumor and normal cells	6–8	These steps will generate a table containing 2 columns, 1st with cell IDs and 2nd with total number of genes expressed per cell. The table will be further used to calculate the average number of genes for tumor and normal cells.	A text file.
Signature scoring and tumor/normal comparisons	9–15	These steps will generate a table with cell IDs along the columns and names of gene sets along the rows. Each entry in the table represents the signature score calculated by a tool. In total 5 tables will be generated, 1 for each tool.	An Rdata object.
	16–18	These steps will generate a table containing gene sets along the rows and signature scoring tools along the columns. Each entry in the table represents ES score per gene set. Based on the ES values, percentage of up and down regulated gene sets will be calculated.	An Rdata object
	19	Correlation between gene set size and ES will be calculated in this step. In total 5 numbers will be generated per dataset.	A text file
Detection Sensitivity	20–23	These steps will generate a table containing differentially expressed genes along the rows. The columns will contain log2FC, p-value and FDR for each DEG.	A text file
	24	This step will generate up and down gold standard gene sets.	2 .gmt files
	25	This step will score gene sets generated in step 24. The output format is same as described in steps 9–15.	An Rdata object per tool.
	26–27	These steps will generate same output as in steps 16–18 for data generated in step 25.	An Rdata file
Detection Specificity	28–30	These steps will generate a gene expression matrix containing genes along the rows and cell IDs along the columns. Each entry in the matrix will represent gene expression level per cell.	An Rdata object
	31	This step will generate same output as in steps 9–15 using the data generated in steps 28–30.	An Rdata object per tool.
	32–33	These steps will generate same output as in steps 16–18 for the data generated in step 31.	An Rdata object
Consensus calling	34–36	These steps will generate a table containing names of signature scoring tools in column 1, ES direction in column 2, sensitivity, specificity and accuracy information in columns 3–5.	An Rdata object
Impact of dropouts on single cell scoring	37	This step will generate 4 scRNAseq expression matrices with different coverages.	An Rdata object
	38	This step will generate same output as in steps 9–15 for the datasets generated in step 37.	An Rdata object per tool.
	39–40	These steps will generate the same output as in steps 16–18 for the data generated in step 38.	An Rdata object