scMoC: single-cell multi-omics clustering

scMoC: single-cell multi-omics clustering

Eltager, M.A.M.E. (TU Delft Pattern Recognition and Bioinformatics)
Abdelaal, T.R.M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)

2022

Motivation: Single-cell multi-omics assays simultaneously measure different molecular features from the same cell. A key question is how to benefit from the complementary data available and perform cross-modal clustering of cells. Results: We propose Single-Cell Multi-omics Clustering (scMoC), an approach to identify cell clusters from data with comeasurements of scRNA-seq and scATAC-seq from the same cell. We overcome the high sparsity of the scATAC-seq data by using an imputation strategy that exploits the less-sparse scRNA-seq data available from the same cell. Subsequently, scMoC identifies clusters of cells by merging clusterings derived from both data domains individually. We tested scMoC on datasets generated using different protocols with variable data sparsity levels. We show that scMoC (i) is able to generate informative scATAC-seq data due to its RNA-guided imputation strategy and (ii) results in integrated clusters based on both RNA and ATAC information that are biologically meaningful either from the RNA or from the ATAC perspective.

OA-Fund TU Delft

http://resolver.tudelft.nl/uuid:9934c4c4-4079-4dcc-8f83-7cb4bb9b3928

https://doi.org/10.1093/bioadv/vbac011

2635-0041

Bioinformatics Advances, 2 (1), 1-8

Institutional Repository

journal article

© 2022 M.A.M.E. Eltager, T.R.M. Abdelaal, A.M.E.T.A. Mahfouz, M.J.T. Reinders