Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

Abstract

Advancements in RNA sequencing have expanded our ability to study gene expression profiles of biological samples in bulk tissue and single cells. Deconvolution of bulk data with single-cell references provides the ability to study relative cell-type proportions, but most methods assume a reference is present for every cell type in bulk data. This is not true in all circumstances—cell types can be missing in single-cell profiles for many reasons. In this study, we examine the impact of missing cell types on deconvolution methods. Using paired single-cell and single-nucleus data, we simulate realistic scenarios where cell types are missing since single-nucleus RNA sequencing is able to capture cell types that would otherwise be missing in a single-cell counterpart. Single-nucleus sequencing captures cell types absent in single-cell counterparts, allowing us to study their effects on deconvolution. We evaluate three different methods and find that performance is influenced by both the number and similarity of missing cell types. Additionally, missing cell-type profiles can be recovered from residuals using a simple non-negative matrix factorization strategy. We also analyzed real bulk data of cancerous and non-cancerous samples. We observe results consistent with simulation, namely that expression patterns from cell types likely to be missing appear present in residuals. We expect our results to provide a starting point for those developing new deconvolution methods and help improve their to better account for the presence of missing cell types. Our results suggest that deconvolution methods should consider the possibility of missing cell types.