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 datasets 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 dataset-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 a conventional thresholding approach and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkick’s weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell analysis Python packages.

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps

“…Single-cell analysis identifies neoplastic cells that arose from subtype-specific tumorigenic processes We generated scRNA-seq data on 70,691 (DIS dataset) and 71,374 cells (VAL dataset) (total: 142,065), after filtering for high-quality barcodes using dropkick (Heiser et al, 2021), and See also Figure S1 and Table S1. Article cells from specimens with unconfirmed histology (labeled UNC) were transcriptomically classified (Table S3).…”

“…COLON MAP scRNA-seq, droplet matrix quality control We identified high-quality, cell-containing droplets and their respective barcodes through the joint application of cumulative sum inflection point thresholding, our dropkick QC algorithm (Heiser et al, 2021), and prior-knowledge gene expression profiling. This droplet matrix was processed as an AnnData object using our preprocessing pipeline which utilizes the Scanpy toolkit (Wolf et al, 2018).…”

Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

“…This variant serves the same function as the heuristic droplet filtering section. For automated droplet filtering in Python, dropkick is a machine learning tool that builds a probabilistic model of single-cell barcode transcriptome quality and returns a score for all barcodes in the input scRNA-seq droplet matrix (see step 7 for generating .h5ad from DropEst files) ( Heiser et al., 2020 ). dropkick can be run from the command line or interactively in a Jupyter Notebook.…”

“…Step 21: Automated droplet filtering with the dropkick Python package provides quality control metrics and cell probabilities for each barcode that can be used to subset an unfiltered counts matrix using a gene-based logistic regression model ( Heiser, et al. 2020 ).…”

“…For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018) , Heiser et al. (2020) , and Heiser and Lau (2020) .…”