TITLE: “ClusterDE: a post-clustering differentially expressed (DE) gene identification method robust to false-positive inflation caused by double-dipping”
ABSTRACT: In typical single-cell RNA-seq data analysis, first, a clustering algorithm is applied to cluster cells; then, a statistical method is used to identify the differentially expressed (DE) genes between the cell clusters. However, this common procedure uses the same data twice, an issue known as “double dipping”: the same gene expression data are used to define cell clusters and DE genes, leading to false-positive DE genes even when the cell clusters are spurious. To overcome this challenge, we propose ClusterDE, a post-clustering DE method for controlling the false discovery rate (FDR) regardless of clustering quality. The core idea of ClusterDE is to generate in silico negative control data with only one cluster, which can be used in contrast to real data for evaluating the whole clustering+DE procedure. Using comprehensive simulation and real data analysis, we show that ClusterDE can not only has solid FDR control but also finds cell-type marker genes that are biologically meaningful. ClusterDE is fast, transparent, and adaptive to a wide range of clustering methods and statistical tests.
