Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose–response study designs

The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose–response study designs used in safety assessments. To benchmark DGEA methods for dose–response scRNAseq experiments, Michigan State University researchers proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Their Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose–response study designs. Collectively, this benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.

Flow diagram of the simulation, benchmarking, and experimental data evaluation strategy

Briefly, SplattDR was developed to simulate dose–response scRNAseq data and validated based on experimental dose–response data. Simulated datasets were generated varying diverse parameters 10 times and then used to assess the performance of each test method. Each test method was also assessed using experimental data from the hepatic snRNAseq dose response dataset obtained from male mice gavage every 4 days for 28 days with 0.01, 0.03, 0.1, 0.3, 1, 3, 10 or 30 μg/kg TCDD. Related figures for each analysis from the main body are noted.