A read count-based method to detect multiplets and their cellular origins from snATAC-seq data

Thibodeau, A.; Eroglu, A.; Lawlor, N.; Nehar-Belaid, D.; Kursawe, R.; Marches, R.; Kuchel, G. A.; Banchereau, J.; Stitzel, M. L.; Cicek, A. E.; Ucar, D.

doi:10.1101/2021.01.04.425250

bioRxiv · 10.1101/2021.01.04.425250

A read count-based method to detect multiplets and their cellular origins from snATAC-seq data

Thibodeau, A.·Eroglu, A.·Lawlor, N.·Nehar-Belaid, D.·Kursawe, R.·Marches, R.·Kuchel, G. A.·Banchereau, J.·Stitzel, M. L.·Cicek, A. E.·Ucar, D.

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Abstract

Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to its sparsity and trinary nature (0 reads: closed chromatin, 1: open in one allele, 2: open in both alleles), yet offers a unique opportunity to infer multiplets when >2 uniquely aligned reads are observed at multiple loci. Here, we implemented the first read count-based multiplet detection method, ATAC-DoubletDetector, that detects multiplets independently of cell-type. Using PBMC and pancreatic islet datasets, ATAC-DoubletDetector captured simulated heterotypic multiplets (different cell-types) with [~]0.60 recall, showing [~]24% improvement over state of the art. ATAC-DoubletDetector detected homotypic multiplets with [~]0.61 recall, representing the first method to detect multiplets originating from the same cell type. Using our novel clustering-based algorithm, multiplets were annotated to their cellular origins with [~]85% accuracy. Application of ATAC-DoubletDetector will improve downstream analysis of snATAC-seq.

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Thibodeau, A., Eroglu, A., Lawlor, N., Nehar-Belaid, D., Kursawe, R., Marches, R., Kuchel, G. A., Banchereau, J., Stitzel, M. L., Cicek, A. E., Ucar, D.. 2021-01-05. A read count-based method to detect multiplets and their cellular origins from snATAC-seq data. https://doi.org/10.1101/2021.01.04.425250

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