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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

    as
    1. Lingfei Wang & Tom Michoel, 2017. "Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-26, August.
    2. Jong Kyoung Kim & Aleksandra A. Kolodziejczyk & Tomislav Ilicic & Sarah A. Teichmann & John C. Marioni, 2015. "Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression," Nature Communications, Nature, vol. 6(1), pages 1-9, December.
    3. Xiuwei Zhang & Chenling Xu & Nir Yosef, 2019. "Simulating multiple faceted variability in single cell RNA sequencing," Nature Communications, Nature, vol. 10(1), pages 1-16, December.
    4. Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    5. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    6. Wei Vivian Li & Jingyi Jessica Li, 2018. "An accurate and robust imputation method scImpute for single-cell RNA-seq data," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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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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