.png){kind=icon}
Return
Single-cell differential expression analysis between conditions within nested settings
.png){kind=icon}
OA
AI
DOI：10.1093/bib/bbaf397.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Differential expression analysis provides insights into fundamental biological processes and with the advent of single-cell transcriptomics, gene expression can now be studied at the level of individual cells. Many analyses treat cells as samples and assume statistical independence. As cells are pseudoreplicates, this assumption does not hold, leading to reduced robustness, reproducibility, and an inflated type 1 error rate. In this study, we investigate various methods for differential expression analysis on single-cell data, conduct extensive benchmarking, and give recommendations for method choice. The tested methods include DESeq2, MAST, DREAM, scVI, the permutation test, distinct, and the t-test. We additionally adapt hierarchical bootstrapping to differential expression analysis on single-cell data and include it in our benchmark. We found that differential expression analysis methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods such as DESeq2 when applied to individual datasets. In addition, they mostly require significantly longer run times. For atlas-level analysis, permutation-based methods excel in performance but show poor runtime, suggesting to use DREAM as a compromise between quality and runtime. Overall, our study offers the community a valuable benchmark of methods across diverse scenarios and offers guidelines on method selection.
Keywords:
differential expression analysis
single-cell atlas
benchmark
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.7
Papers:
5.8K
Citations:
2.7W
Organization
View more organizations
Cited Papers
Data with Hierarchical Structure: Impact of Intraclass Correlation and Sample Size on Type-I ErrorOAAI
OAAIShareSaveMultiplexed droplet single-cell RNA-sequencing using natural genetic variation.png)
NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveMAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing dataGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSavevoom: precision weights unlock linear model analysis tools for RNA-seq read countsGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSaveA practical solution to pseudoreplication bias in single-cell studiesNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSave View more