Automated quality control and cell identification of droplet-based single-cell data using dropkick.

ABSTRACT: A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventio

SUBMITTER: Heiser CN 

PROVIDER: S-EPMC8494217 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature