Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr.Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity.  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.  On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.
Normalisr
Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr.Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity.  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.  On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.
Normalisr