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

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  • Lingfei Wang

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Massachusetts General Hospital Research Institute)

Abstract

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.

Suggested Citation

  • Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26682-1
    DOI: 10.1038/s41467-021-26682-1
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    References listed on IDEAS

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    Cited by:

    1. Chang Su & Zichun Xu & Xinning Shan & Biao Cai & Hongyu Zhao & Jingfei Zhang, 2023. "Cell-type-specific co-expression inference from single cell RNA-sequencing data," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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