Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations. However, the sparsity and technical noise inherent to single-cell data present statistical challenges. Here, we systematically benchmark existing methods and find that they produce large numbers of false discoveries. We develop a mixed-effects framework that accounts for biological and technical sources of variation and dramatically reduces false discovery rates.