ABSTRACT
A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.
Footnotes
Author contributions
Conceptualization: G.L., A.S.; Methodology: G.L.; Software: G.L., M.S.; Validation: A.I., R.C.V.T., A.D., M.C.-T., S.S., M.-E.T.-P.; Formal analysis: G.L., M.S., M.R., R.C.V.T., A.D., M.C.-T., S.S., M.-E.T.-P.; Resources: A.I.; Data curation: G.L., A.I., M.L.R.T.S.; Writing - original draft: G.L., A.S.; Writing - review & editing: G.L., R.C.V.T., M.C.-T., F.J.T., S.S., M.-E.T.-P., A.S.; Visualization: G.L.; Supervision: A.S.; Project administration: A.S.; Funding acquisition: F.J.T., A.S.
Funding
Work in the Scialdone lab is funded by the Helmholtz Association. Work in the Torres-Padilla laboratory is funded by the Helmholtz Association, Helmholtz Zentrum München Small Molecule projects (Developmental projects) and the Deutsche Forschungsgemeinschaft (German Research Foundation; CRC 1064). A.I. was a recipient of a long-term European Molecular Biology Organization fellowship (ALTF 383-2016). G.L. was funded by the Bundesministerium für Bildung und Forschung project MechML (01IS18053A). M.S. was supported by the Helmholtz Association under the joint research school ‘Munich School for Data Science – MUDS’ and by an Add-on Fellowship for Interdisciplinary Life Science from the Joachim Herz Stiftung. A.D. was funded by the Deutsche Forschungsgemeinschaft (DFG STR 1385/5-1).
Data availability
Raw data for the mouse embryonic stem cells scRNA-seq dataset are available through ArrayExpress, under accession number E-MTAB-11610.
