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

By Christoph Hafemeister, Rahul Satija. Genome Biology, 2019

3,813 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6ecf1dfa2688d949a309e2cc336ddd929a69b12b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Comparison and evaluation of statistical error models for scRNA-seq (2021, methodology): “procedure from [9] (v1 regularization).”
Best practices for single-cell analysis across modalities (2023, methodology): “A recent benchmark 44 recommends using the built-in cell cycle labelling and correction functions in Scanpy 4 or Seurat 45 as a baseline, which compare the mean expression values to a reference signature.”
Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis (2019, methodology): “The slingshot wrapper function was performed with the UMAP dimensionality reduction and cluster labels as in Seurat objects to identify the trajectory.”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, positive): “Briefly, we find that exclusion of zero inflation from the model results in approximately similar performance, which is consistent with findings in (Hafemeister & Satija, 2019; Townes et al , 2019).”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Finally, utility packages such as Seurat and Giotto provide deconvolution methods [79, 81].”
Cell2location maps fine-grained cell types in spatial transcriptomics (2022, methodology): “Before integration, both snRNA-seq reference and synthetic spatial datasets were subjected to SCTransform normalization (Seurat::SCTransform) 56 .”
Comprehensive analysis of single cell ATAC-seq data with SnapATAC (2021, methodology): “Pair-end reads are converted into fragments and only those that meet the following criteria are kept: (1) properly paired (according to SMA ﬂ ag value); (2) uniquely mapped (MAPQ >30); (3) insert distance within [50 – 1000 bp].”
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, methodology): “Recently, Pearson residuals based on a negative binomial null model have also been independently proposed as the sctransform method [60].”
Integrated analysis of multimodal single-cell data (2020, positive): “We apply SCTransform (Hafemeister and Satija, 2019) to normalize gene expression data, and we apply a CLR transformation to normalize protein data within each cell.”