RT Journal Article
SR Electronic
T1 Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.09.467903
DO 10.1101/2021.11.09.467903
A1 Pinar Demetci
A1 Rebecca Santorella
A1 Björn Sandstede
A1 Ritambhara Singh
YR 2021
UL http://biorxiv.org/content/early/2021/11/11/2021.11.09.467903.abstract
AB Integrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple (M ≥ 2) single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.Available at: http://rsinghlab.github.io/SCOT.Competing Interest StatementThe authors have declared no competing interest.