scMoC: single-cell multi-omics clustering

Bioinformatics Advances, vol. 2, pp. vbac011

Abstract

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: lustering (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. Availability and implementation: The data used in this manuscript is publicly available, and we refer to the original manuscript for their description and availability. For convience sci-CAR data is available at NCBI GEO under the accession number of GSE117089. SNARE-seq data is available at NCBI GEO under the accession number of GSE126074. The 10X multiome data is available at the following link https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-no-cell-sorting-3-k-1-standard-2-0-0. Supplementary information: online.

Authors 4

1. Mostafa Eltager

   Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , Delft 2628XE, The Netherlands

2. Tamim Abdelaal Aachen Division of Image Processing (LKEB) Department of Radiology, Leiden University Medical Center Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , Delft 2628XE, The Netherlands

   Division of Image Processing, Department of Radiology, Leiden University Medical Center , Leiden, 2333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center , Leiden 2333ZC, The Netherlands

3. Ahmed Essam Mahfouz Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , Delft 2628XE, The Netherlands

   Department of Human Genetics, Leiden University Medical Center , Leiden 2333ZC, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center , Leiden 2333ZC, The Netherlands

4. Marcel J. T. Reinders corresponding Aachen Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology , Delft 2628XE, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center , Leiden 2333ZC, The Netherlands