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Fig. 4 | Genome Biology

Fig. 4

From: Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Fig. 4

Regularized NB regression removes variation due to sequencing depth, but retains biological heterogeneity. a Distribution of residual mean, across all genes, is centered at 0. b Density of residual gene variance peaks at 1, as would be expected when the majority of genes do not vary across cell types. c Variance of Pearson residuals is independent of gene abundance, demonstrating that the GLM has successfully captured the mean-variance relationship inherent in the data. Genes with high residual variance are exclusively cell-type markers. d In contrast to a regularized NB, a Poisson error model does not fully capture the variance in highly expressed genes. An unconstrained (non-regularized) NB model overfits scRNA-seq data, attributing almost all variation to technical effects. As a result, even cell-type markers exhibit low residual variance. Mean-variance trendline shown in blue for each panel

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