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在半合成 RNA-seq 数据模拟中忽略标准化的影响会产生人为的假阳性。

Neglecting the impact of normalization in semi-synthetic RNA-seq data simulations generates artificial false positives.

机构信息

Univ. Bordeaux, INSERM Bordeaux Population Health Research Center, U1219, INRIA SISTM, Bordeaux, F-33000, France.

Vaccine Research Institute, Créteil, F-94000, France.

出版信息

Genome Biol. 2024 Oct 30;25(1):281. doi: 10.1186/s13059-024-03231-9.

DOI:10.1186/s13059-024-03231-9
PMID:39478633
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11523660/
Abstract

A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.

摘要

最近的一项研究报告称，在分析大型人群样本时，流行的差异表达方法得出了夸张的假阳性结果。我们重现了差异表达分析的模拟结果，并发现了数据生成过程中的一个注意事项。数据并非真正在零假设下生成，导致基准方法的比较出现错误。我们提供了经过修正的模拟结果，证明了 dearseq 的良好性能，并反驳了之前研究中提出的 Wilcoxon 秩和检验具有优越性的观点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddc8/11523660/984f841cd53f/13059_2024_3231_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddc8/11523660/944d14967d7b/13059_2024_3231_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddc8/11523660/984f841cd53f/13059_2024_3231_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddc8/11523660/944d14967d7b/13059_2024_3231_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ddc8/11523660/984f841cd53f/13059_2024_3231_Fig2_HTML.jpg

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本文引用的文献

1
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Genome Biol. 2022 Mar 15;23(1):79. doi: 10.1186/s13059-022-02648-4.
2
dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate.DearSeq：一种用于RNA测序差异分析的方差成分评分检验，可有效控制错误发现率。
NAR Genom Bioinform. 2020 Nov 19;2(4):lqaa093. doi: 10.1093/nargab/lqaa093. eCollection 2020 Dec.
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Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package.
使用NOISeq R/Bioc软件包对RNA测序中的差异表达进行数据质量感知分析。
Nucleic Acids Res. 2015 Dec 2;43(21):e140. doi: 10.1093/nar/gkv711. Epub 2015 Jul 16.
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.使用DESeq2对RNA测序数据的倍数变化和离散度进行适度估计。
Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8.
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Error estimates for the analysis of differential expression from RNA-seq count data.RNA-seq 计数数据差异表达分析的误差估计。
PeerJ. 2014 Sep 23;2:e576. doi: 10.7717/peerj.576. eCollection 2014.
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voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.voom：精确权重为RNA测序读数计数解锁线性模型分析工具。
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.edgeR：一个用于数字基因表达数据差异表达分析的 Bioconductor 包。
Bioinformatics. 2010 Jan 1;26(1):139-40. doi: 10.1093/bioinformatics/btp616. Epub 2009 Nov 11.