Abstract
High-throughput single-cell RNA-Seq (scRNA-Seq) methods can efficiently generate expression profiles for thousands of cells, and promise to enable the comprehensive molecular characterization of all cell types and states present in heterogeneous tissues. However, compared to bulk RNA-Seq, single-cell expression profiles are extremely noisy and only capture a fraction of transcripts present in the cell. Here, we describe an algorithm to smooth scRNA-Seq data, with the goal of significantly improving the signal-to-noise ratio of each profile, while largely preserving biological expression heterogeneity. The algorithm is based on the observation that across platforms, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on variance-stabilized and partially smoothed expression profiles, and then aggregating their UMI counts. For multiple datasets, the application of our algorithm resulted in more stable cell type-specific expression profiles, and recovered correlations between co-expressed genes. More generally, smoothing improved the results of commonly used dimensionality reduction and clustering methods, greatly facilitating the identification of cell subsets and clusters of co-expressed genes. Our work implies that there exists a quantitative relationship between the number of cells profiled and the potential accuracy with which individual cell types or states can be characterized, and helps unlock the full potential of scRNA-Seq to elucidate molecular processes in healthy and disease tissues.
