Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics.
Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics.
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DOI：
10.1186/s12864-021-07502-8
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发表时间：
2021-03-16
期刊：
影响因子：
4.4
通讯作者：
Khodursky A
中科院分区：
文献类型：
--
作者：
Karp PD;Midford PE;Caspi R;Khodursky A
富集或过度代表性分析是转录组学、代谢组学和微生物组数据集的生物信息学研究中使用的常用方法。富集分析背后的关键思想是：给定一组显著表达的基因（或代谢物），使用该组来推断较小的一组受干扰的生物途径或过程，这些基因（或代谢物）在其中发挥作用。富集计算依赖于已定义的生物途径和/或过程的集合，这些集合通常来自途径数据库。尽管富集分析的从业者非常小心地采用统计校正（例如，对于多重测试），他们似乎没有意识到富集结果对计算所使用的途径定义非常敏感。我们发现，替代途径的定义可以改变富集p值高达9个数量级，而统计校正通常只改变富集p值的两个数量级。我们提出了多个例子，其中EcoCyc数据库中使用的较小的途径定义比KEGG数据库中使用的大得多的途径定义产生更强的富集p值;我们证明，为了获得给定的富集p值，基于KEGG的富集分析需要1.3-2.0倍的显着表达的基因，基于EcoCyc的富集分析。KEGG中的大通路是有问题的另一个原因：它们将多个（多达21个）生物过程混淆在一起。当这样的KEGG途径获得高富集p值时，其组分过程中的哪一个受到干扰尚不清楚，因此从大途径富集中得出的生物学结论也存在问题。富集分析中使用的途径数据库的选择对富集结果的影响可能比这些分析中使用的统计校正大得多。在线版本包含补充材料，可在（10.1186/s12864-021-07502-8）获得。
Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses. The online version contains supplementary material available at (10.1186/s12864-021-07502-8).
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影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
30.8
作者：
Weinstein, John N.;Collisson, Eric A.;Mills, Gordon B.;Shaw, Kenna R. Mills;Ozenberger, Brad A.;Ellrott, Kyle;Shmulevich, Ilya;Sander, Chris;Stuart, Joshua M.
通讯作者：
Stuart, Joshua M.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
30.8
作者：
Tavazoie, S;Hughes, JD;Church, GM
通讯作者：
Church, GM
影响因子：
3
作者：
Luo W;Friedman MS;Shedden K;Hankenson KD;Woolf PJ
通讯作者：
Woolf PJ