Emerging single-cell sequencing protocols allow researchers to study multiple layers of epigenetic regulation while resolving tissue heterogeneity. However, despite the rising popularity of such single-cell epigenomic assays, a lack of user-friendly computational tools for flexible quality control and genome-wide data exploration hinders their broad adoption. We introduce the Single-Cell Informatics (sincei) toolkit, a command-line interface for the exploration of data from a wide range of single-cell (epi)genomics protocols directly from aligned reads stored in BAM format. Sincei provides tools for preprocessing, feature identification, signal aggregation, dimensionality reduction, clustering and visualization. Results are stored in the AnnData format for seamless compatibility with a wide variety of tools. Additionally, sincei includes a Python API that enables advanced use cases, such as generating cell embeddings using Generalized Principal Component Analysis (GLM-PCA). We show that sincei resolves cellular heterogeneity and improves interpretation of genomic regions by using single-cell histone modification signal across species, tissues and protocols.

Co-authors: Fernando Sancho Gómez, Soufiane Mourragui