Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr

• Published in: Nature communications Vol. 12; no. 1; pp. 6395 - 13
• Main Author: Wang, Lingfei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.11.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2114; 631/114/2404; 631/114/2415; 631/1647/2163; 631/553/2711; Clustered Regularly Interspaced Short Palindromic Repeats - genetics; CRISPR; Gene expression; Gene Expression Profiling; Gene regulation; Gene Regulatory Networks - genetics; Gene sequencing; Humanities and Social Sciences; Humans; multidisciplinary; Networks; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Software; Statistics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-26682-1

Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions. Normalisr removes technical bias in single-cell RNA-seq and detects gene differential and coexpression accurately and efficiently. It also infers gene regulatory and co-expression networks from conventional and CRISPR screen single-cell RNA-seq datasets.