A knockoff calibration method to avoid over-clustering in single-cell RNA-sequencing

Abstract

AbstractStandard single-cell RNA-sequencing (scRNA-seq) pipelines nearly always include unsupervised clustering as a key step in identifying biologically distinct cell types. A follow-up step in these pipelines is to test for differential expression between the identified clusters. When algorithms over-cluster, downstream analyses will produce inflatedP-values resulting in increased false discoveries. In this work, we presentcallback(CalibratedClustering viaKnockoffs): a new method for protecting against over-clustering by controlling for the impact of reusing the same data twice when performing differential expression analysis, commonly known as “double-dipping”. Importantly, our approach can be applied to a wide range of clustering algorithms. Using real and simulated data, we show thatcallbackprovides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies, even on a personal laptop.