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

ABSTRACT:

Background

Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation.

Results

We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies a

SUBMITTER: Subramanian A 

PROVIDER: S-EPMC9793662 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature