Experimental Design and Differential Inference for Comparative Single-cell RNA-sequencing Studies

Music City Center 

Single-cell RNA-sequencing (scRNA-seq) experiments are becoming increasingly complicated with multiple treatment or biological conditions. However, guidelines on experimental designs and rigorous statistical methods for comparative scRNA-seq studies with cells collected from multiple conditions
are still lacking. For a confounded design, the batch effects, cell-type effects and condition effects can never be distinguished. Therefore, we mathematically derive the requirements for a valid design for a comparative scRNA-seq study. Moreover, existing methods for identifying differentially expressed genes
and differential cell-type abundance between conditions have to be multi-stage approaches. Because multi-stage approaches ignore uncertainties in previous stages and may propagate errors from earlier stages to later stages, they can suffer from high error rates. Here, we introduce DIFseq, a hierarchical
model that accounts for all uncertainties and hence rigorously quantifies the condition effects on both cellular composition and cell-type-specific gene expression levels. DIFseq substantially outperforms state-of-the-art methods for both simulated and real data.

Single-cell RNA-sequencing experiments

Differential gene expression

Differential abundance

Experimental design

Model identifiability

Integrative analysis 

Section on Statistics in Genomics and Genetics