miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

Abstract

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC.

Evolutionarily conservative and non-conservative regulatory networks during primate interneuron development revealed by single-cell RNA and ATAC sequencing

“…Human samples were mapped to the GRCh38 (hg38) and Macaque samples were mapped to the (v5.0). Gene-cell data matrix was analyzed by Seurat (v4.0.4), and we used MiQC (v1.1.3) 104 (posterior.cutoff = 0.95, model.slot = “flexmix_model”, model.type = “spline”) to remove low quality genes. Cells expressing more than 20% of mitochondrial genes were also excluded.…”

Physiological hypoxia improves growth and functional differentiation of human intestinal epithelial organoids

“…Aligning with others, we find that the oxygen level directly affects transcriptional regulation. The transcription factor FOXO3, with multiple roles in cellular stress responses ( 49 ), regulates the adaptation to hypoxia by reducing mitochondrial mass and oxygen consumption during HIF-1 activation ( 50 ), while AP-1 proteins, including JUN, JUNB, and FOS, are activated by hypoxia and to cooperate with HIF-1 to increase gene expression ( 51 ). Reduced oxygen concentration may induce histone modifications, and we found increased expression of histone demethylases (KDMs) ( 52 ) when reducing oxygen from 20% to 2% in colonoids.…”

“…Downstream analysis was conducted primarily using the Seurat R package ( 48 ). Cells with low counts (<200) or classified as low quality by the miQC R package were excluded ( 49 ). Features with counts lower than 500 or higher than 40000 were also excluded.…”

Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics

“…The scater package [28] encourages the use of diagnostic plots and sample-specific QC. More recently, probabilistic mixture modeling has been favored for data-driven quality control at the level of samples or sample sets, either in combination with other QC approaches [15] or standalone as in miQC [29]. However, no approach performs quality control explicitly considering the biological variability of QC metrics at the cell type or cell-state level.…”