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Today, I will talk to you about the example analysis of the RobustRankAggreg package of multiple data sets integration artifact, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

RobustRankAggreg package appears frequently in various data mining articles. There is no need for me to say more. You can check out the references to it. Basically, they are all GEO database mining articles:

RobustRankAggreg article

For example, published in peerJ's BIOINFORMATICS AND GENOMICS article Identification of key candidate genes and biological pathways in bladder cancer:

4 GEO data sets

The author takes these four data sets and follows the standard processes of difference analysis, volcano map, heat map and so on independently, and basically reads the public database mining series tweets of my expression chip in Shengxin skill tree.

Interpretation of GEO data storage rules and downloads, one article is enough to interpret SRA database rules, one article is enough to download the expression matrix from GEO database, one article is enough for GSEA analysis (stand-alone version + R language version) to do difference analysis based on grouping information-this article is not enough to explain the results of difference analysis.

You can also easily analyze these data sets: GSE7476, GSE13507, GSE37815 and GSE65635, and then the author uses the RobustRankAggreg package to integrate the results of the difference analysis of the four data sets, as follows:

The integrated DEGs were screened using the RRA package (corrected P

1 or − logFC < − 1). The RRA method is based on the assumption that each gene in each dataset is randomly arranged.If the gene ranks high in all datasets, the associated P-value is lower, the possibility of differential gene expression is greater.Through rank analysis, 343integrated DEGs, consisting of 111upregulated genes and 232downregulated genes, were identified by the RRA method

And the difference multiple between up-regulated and down-regulated genes of top20 is visualized by heat map, as follows:

Thermographic visualization of the difference multiple between up-regulated and down-regulated genes in top20

Of course, not only the mRNA expression chip, but also others, such as the circRNA chip, are also published in 2018: a circRNA-miRNA-mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma.

Just download 3 GEO data sets, do the difference analysis, and use the RobustRankAggreg package to integrate, and finally only identify 6 circRNA.

CircRNA chip integration

We don't have to interpret hundreds of articles one by one, anyway, independent data sets do their own difference analysis, and then use RobustRankAggreg packages to integrate the differential genes in multiple data sets.

RobustRankAggreg package instruction manual

This RobustRankAggreg package is super simple, but it's interesting that it's not on the bioconductor list, probably because it wasn't created for data analysis in the field of bioinformatics in the first place. Because it's not in bioconductor, its sample tutorial is a mess and requires a little background to understand. The point is just the aggregateRanks function:

Options (BioC_mirror= "https://mirrors.tuna.tsinghua.edu.cn/bioconductor/")

Options ("repos" = c (CRAN= "https://mirrors.tuna.tsinghua.edu.cn/CRAN/")))

# https://bioconductor.org/packages/release/bioc/html/GEOquery.html

If (! requireNamespace ("BiocManager", quietly = TRUE))

Install.packages ("BiocManager")

BiocManager::install ("RobustRankAggreg", ask = Ferri update = F)

Library (RobustRankAggreg)

? aggregateRanks

Generally speaking, the function of normal R package can call its help document through the question mark, and the aggregateRanks function is no exception. Let's take a look at the sample code directly:

Set.seed (1234567)

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