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

Standard 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 inflated P -values resulting in increased false discoveries. In this work, we present callback ( Cal i b r a ted C lustering via K nockoffs): 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 that callback provides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies, even on a personal laptop. ### Competing Interest Statement SR holds equity in Amgen. SR and PSW receive research funding from Microsoft. AKS reports compensation for consulting and/or scientific advisory board membership from Honeycomb Biotechnologies, Cellarity, Ochre Bio, Relation Therapeutics, Fog Pharma, Bio-Rad Laboratories, IntrECate Biotherapeutics, Passkey Therapeutics and Dahlia Biosciences unrelated to this work. All other authors have declared that no competing interests exist.