Differential expression analyses for single-cell RNA sequencing typically use empirical Bayes methods such as DESeq2, edgeR, limma, and MAST. These approaches perform univariate statistical testing by modeling gene expression with generalized linear models and borrow strength across genes to stabilize variance estimates. In this talk, I will introduce a framework that borrows strength also for the estimation of the gene expression itself by predicting a gene of interest from the other genes. Our R package, conformeR, combines counterfactual prediction with conformal prediction to leverage the multivariate structure of the data and increase statistical power. This is joint work with Justine Leclerc.