We are analyzing https://link.springer.com/article/10.1186/s13059-019-1874-1.

Title:
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression | Genome Biology
Description:
Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
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28 years and 1 months (reg. 1997-05-29).

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Keywords {🔍}

data, genes, gene, model, cell, singlecell, sequencing, depth, expression, cells, variance, residuals, pubmed, regression, umi, article, pearson, normalization, additional, regularized, counts, parameter, parameters, google, scholar, analysis, variation, models, methods, fig, biological, dataset, values, negative, technical, results, count, observed, file, cas, rnaseq, binomial, scrnaseq, differential, central, total, error, similar, heterogeneity, procedure,

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ec{_}id=nature-20150723{&}spmailingid=49156958{&}spuserid=njyzmja5otgyodus1{&}spjobid=722865381{&}spreportid=nziyody1mzgxs0 /nrg/journal/vaop/ncurrent/full/nrg3833 /nature/journal/v523/n7561/full/nature14590 umi-based scrna-seq experiment single-cell rna-seq denoising single-cell rna-seq data single-cell mrna quantification single-cell rna-sequencing experiments cell-type-specific batch effects umi-based scrna-seq dataset single-cell transcriptomic characterization scaling-factor-based normalization schemes single-cell rna-seq popular scrna-seq packages bulk rna-seq workflows multifactor rna-seq experiments bulk rna-seq data overfit scrna-seq data current scrna-seq experiments normalize single-cell data single-cell analytical workflows bulk rna-seq normalization exclusively cell-type markers rna-seq read counts large scrna-seq datasets single-cell count data single-cell sequencing data article download pdf cell-based size factors linear size/scaling factors single y-axis range modeling single-cell data single-cell gene expression placing data-driven constraints single-cell umi datasets single-cell toolkit seurat deep-learning methods designed including cell-cycle state set user-defined parameters rna-seq data scaled log-normalized expression exclusively represent markers bulk rna-seq [12] bulk rna-seq scrna-seq data [5] scrna-seq data [6–11] scrna-seq data equal-sized groups based single-cell transcriptomics inflated negative binomial

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         headline:Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
         description:Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
         datePublished:2019-12-23T00:00:00Z
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            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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      headline:Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
      description:Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
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         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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