This repository contains R source code used to conduct the analysis in our manuscript, "Confronting false discoveries in single-cell differential expression."

A brief overview of the main computational analyses that were conducted, and the location of the corresponding source code, is given below.

• First, differentially expressed genes were identified in single-cell and matching bulk datasets, respectively, using code in the directories R/analysis/run_de and R/analysis/run_bulk_de. Code in R/analysis/run_spike_in_de was used to analyze a lone single-cell dataset in which the ERCC mixture of synthetic mRNAs was spiked in alongside each individual cell.
  • A list of all single-cell and bulk datasets analyzed in this study is provided in R/functions/datasets.R. Datasets containing multiple comparisons of two experimental groups were split into each possible comparison using the functions in R/functions/get_comparisons.R and R/functions/get_bulk_comparisons.R.
  • Code used to run differential expression analyses is provided in R/functions/run_DE.R
• The concordance between the single-cell and bulk DE results was then quantified using code in the R/analysis/bulk_concordance directory.
  • Code used to calculate the AUCC and fold-change correlation is provided in R/functions/calculate_overlap.R
• Gene set enrichment analysis of differential expression results was performed for both the single-cell and bulk datasets using code in the R/analysis/run_GSEA directory.
• False-positive and false-negative DE calls were obtained for the single-cell data, using the bulk data as a reference, using code in the R/analysis/extract_FPs directory.
• A number of summary statistics were obtained for each dataset (e.g., number of replicates, number of cell types) or each gene within each dataset (e.g., mean expression, delta-variance), using code in the directories R/analysis/confounds, R/analysis/delta_variance, and R/analysis/expr_summary.
• The relationships between mean expression, the variance of gene expression, and the delta-variance in 'pseudo-replicates' were interrogated using code in R/analysis/mean_variance.
• The effect of between-replicate variance was interrogated with simulation studies using the code in R/analysis/simulations. The code in this directory was used to generate synthetic gene expression data, perform DE analysis, and analyse the properties of DE genes.
• DE analysis was performed between random groups of control samples using code in the R/analysis/control_only directory. This code was also used to analyze a spatial transcriptomics dataset.
• The performance of generalized linear mixed models was assessed in downsampled datasets using code in the R/analysis/downsample_cells directory.
• Finally, the computational resources (wall time, peak RAM usage) used by each method were extracted using code in the R/analysis/time_RAM directory.