Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
Abstract 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 no...
Main Authors: | Christoph Hafemeister, Rahul Satija |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-12-01
|
Series: | Genome Biology |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13059-019-1874-1 |
Similar Items
-
Comparison and evaluation of statistical error models for scRNA-seq
by: Saket Choudhary, et al.
Published: (2022-01-01) -
Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey
by: Nicholas Lytal, et al.
Published: (2020-02-01) -
Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers
by: F. William Townes, et al.
Published: (2020-07-01) -
Negative binomial additive model for RNA-Seq data analysis
by: Xu Ren, et al.
Published: (2020-05-01) -
Differentially expressed genes of RNA-seq data are suggested on the intersections of normalization techniques
by: Mohammad Elahimanesh, et al.
Published: (2024-03-01)