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I wish to adapt the r language function fgseaL, https://github.com/ctlab/fgsea , to perform rapidGSEA, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1244-x , computation of inter-class deviation per gene and the subsequent gene rank sorting operation on 9 different phenotype labels as illustrated in the diagram immediately below:

enter image description here

I thought of applying the R-language rank() function on the Expression Data Matrix D. If that is not correct, what sequence of R language commands should we apply to the Expression Data Matrix D to calculate a key value sorted deviation measure across 8 labeled human leukemia groups and a healthy labeled normal control group prior to running fgseaL?

I show below how fgseaL finds the correlation matrix between the R language variable , mat, which corresponds to the Expression Data Matrix D and the R language variable , labels , which is a vector of gene phenotype labels

    tmatSc <- scale(t(mat))
    labelsSc <- scale(labels)[, 1]

    minSize <- max(minSize, 1)

    pathwaysFiltered <- lapply(pathways, function(p) { as.vector(na.omit(fmatch(p, rownames(mat)))) })
    pathwaysSizes <- sapply(pathwaysFiltered, length)

    toKeep <- which(minSize <= pathwaysSizes & pathwaysSizes <= maxSize)
    m <- length(toKeep)

    if (m == 0) {
        return(data.table(pathway=character(),
                          pval=numeric(),
                          padj=numeric(),
                          ES=numeric(),
                          NES=numeric(),
                          nMoreExtreme=numeric(),
                          size=integer(),
                          leadingEdge=list()))
    }

    pathwaysFiltered <- pathwaysFiltered[toKeep]
    pathwaysSizes <- pathwaysSizes[toKeep]

    corRanks <- var(tmatSc, labelsSc)[,1]
    ranksOrder <- order(corRanks, decreasing=T)
    ranksOrderInv <- invPerm(ranksOrder)
    stats <- corRanks[ranksOrder]

    pathwaysReordered <- lapply(pathwaysFiltered, function(x) ranksOrderInv[x])

    gseaStatRes <- do.call(rbind,
                           lapply(pathwaysReordered, calcGseaStat,
                                  stats=stats,
                                  returnLeadingEdge=TRUE))

I found a problem with the algorithm shown immediately below.
correcttest <- data.frame(names = row.names(normal))
correcttest <- cbind(correcttest3, normal)
correcttest <- cbind(correcttest3, ALL3m)
rownames(correcttest) <- correcttest$names
correcttest$names <- NULL
correctlabelnormal <- rep(0:0, 73)
correctlabelALL3m <- rep(1:1, 122)
correctlabel <- as.vector(c(correctlabelnormal,correctlabelALL3m))
s <- apply(correcttest, 1, function(x) coef(lm(x~correctlabel))[2])
o <- rank(s)
o <- max(o) - o + 1
res <- fgseaL(df,o,correctlabel,nperm = 2000,minSize = 1, maxSize=50000)
empty data table (0 rows) of 8 columns:   pathway,pval,padj,ES,NES,nMoreExtreme,size

I found the binary phenotype labeled group fgseaL test results below looked satisfactory.    
correcttest <- data.frame(names = row.names(normal))
correcttest <- cbind(correcttest3, normal)
correcttest <- cbind(correcttest3, ALL3m)
rownames(correcttest) <- correcttest$names
correcttest$names <- NULL
correctlabelnormal <- rep(0:0, 73)
correctlabelALL3m <- rep(1:1, 122)
correctlabel <- as.vector(c(correctlabelnormal,correctlabelALL3m))
fgseaL(df,correcttest,correctlabel,nperm = 2000,minSize = 1, maxSize=50000)
       pathway        pval        padj         ES       NES nMoreExtreme  size
1: Gene.Symbol 0.003940887 0.003940887 -0.2460126 -1.180009            3 45714
                                 leadingEdge
1: AKIRIN2,LRRC20,HSPA5,HSPA5,DTWD2,ZFYVE28,

Thank you for your consideration.

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    $\begingroup$ It might be worth to use the package provided by the authors in github. This way you don't need to implement it again $\endgroup$ – llrs Aug 8 '17 at 10:14
  • $\begingroup$ @Llopis, Thank you for the good suggestion. I will try installing it later today.. $\endgroup$ – Frank Aug 8 '17 at 10:54
  • $\begingroup$ @Llopis, Can we run rapidGSEA without the CUDA enhancemnets since I am not using a computer with a NVIDIA cpu? Thank you. $\endgroup$ – Frank Aug 12 '17 at 14:49
  • $\begingroup$ I think you could be able to do so, but I am not the maintainer neither the author of the code. You could also check the source code. But that probably is better to check and ask as another question $\endgroup$ – llrs Aug 12 '17 at 18:11
  • $\begingroup$ @Llopis, How do I install the NVIDIA c++ compiler to compile cudaGSEA? I already installed the NVIDIA CUDA Toolkit. Thank you. $\endgroup$ – Frank Aug 12 '17 at 19:09
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You rank the fit coefficient rather than the original score matrix. So, given a score matrix, D:

D = matrix(c(22,20,9,8,46,22,18,10,3,18,3,29,2,1,5,45,43,47,17,5,14,44,21,36), byrow=T, ncol=6)
cl = c(0,0,0,1,1,1)
s = apply(m, 1, function(x) coef(lm(x~cl))[2]) # [1]
o = rank(s)
o = max(o) - o + 1 # [2]

o is then the rank of each row.

[1] This fits each row as a linear model of cl and extracts the cl coefficient.

[2] This converts the ranking to be the same as shown in the figure. I don't know if this is important, but I would assume so given how GSEA methods tend to work.

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  • $\begingroup$ Thank you for your excellent answer. I just accepted it. Could I pass a R language nine(9) valued phenotype label vector to where you specify cl = c(0,0,0,1,1,1) or does cl have to be a binary phenotype label? The reason I ask this question is that we have 8 human leukemia groups and 1 healthy normal control group. $\endgroup$ – Frank Aug 7 '17 at 13:32
  • $\begingroup$ The method itself is only designed for two-group comparisons, so if you have more phenotypes you'll either have to do one comparison at a time or come up with a different method (I'm not sure what at multi-factor GSEA output would even mean in such cases). $\endgroup$ – Devon Ryan Aug 7 '17 at 13:34
  • $\begingroup$ Thank you for your reply to my comment. In the original Bioinformatics Stack Exchange question above , I have added some fgseaL source code which calculates the correlation matrix between the Expression Data Matrix D and the R language variable , labels , which is a vector of gene phenotype labels. Could I ask you to explain the purpose of this fgseaL source code extract? $\endgroup$ – Frank Aug 7 '17 at 13:59
  • $\begingroup$ @Frank Please post that as a new question, though please read the preprint linked to from github first, since I assume that goes over the method. $\endgroup$ – Devon Ryan Aug 7 '17 at 14:03
  • $\begingroup$ I will post the new question later today. Also, I will try out your rank the linear regression fit coefficient method on our data and report the results back to you later today. Thanks. $\endgroup$ – Frank Aug 7 '17 at 14:52
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I finally found another answer to my question. Please read this great article in May 12 2017 BioMed Central (BMC) Bioinformatics article titled Ranking metrics in gene set enrichment analysis: do they matter?.

Also, please read this blog , Diving into Genetics and Genomics: Gene Set Enrichment Analysis (GSEA) explained.

After reading these two articles, my choice for the best rapidGSEA local ranking measure is Minimum Significant Difference (i.e., MSD), because it has the best overall false positive rate (i.e., FPR) for larger sample sizes.

Finally, it is important to realize that fgseaL's phenotype labeling can hypothetically be emulated by GSEA with either one of it's sixteen possible ranking metrics or a custom ranking metric.

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This weekend, I  installed the R language packages rapidGSEA and the Broad Institute GSEA , the Nvidia CUDA Toolkit 6.5-- Custom installation option  and MINGW makefiles on my son's Windows 8.1 notebook computer. This step was very tricky to accomplish because I had to maneuver around the "NVIDIA Installer cannot continue. The NVIDIA graphics driver is not compatible with this version of Windows" installation error message which prevents the NVIDIA compilers from being installed correctly. My objective is to run rapidGSEA without the CUDA enhancements since I am not using a computer with a NVIDIA GPU. I did this step because rapidGSEA and Broad Institute GSEA have a significantly richer set of gene set enrichment analytics than fgseaL as shown below:

The GSEA method takes five default arguments and three optional arguments GSEA <- function(exprsData, labelList, geneSets, numPermutations, metricString, dumpFileName="", checkInput=TRUE, doublePrecision=FALSE) {...}

exprsData, labelList and geneSets refer to the data obtained in the previous section. numPermutations denotes the number of permutations in the resampling test, metricString denotes the local ranking measure (one of the following):

naive_diff_of_classes
naive_ratio_of_classes
naive_log2_ratio_of_classes
stable_diff_of_classes
stable_ratio_of_classes
stable_log2_ratio_of_classes
onepass_signal2noise
onepass_t_test
twopass_signal2noise
twopass_t_test
stable_signal2noise
stable_t_test
overkill_signal2noise
overkill_t_test
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  • $\begingroup$ Glad you got it working. Not sure what else I'm supposed to comment on here. $\endgroup$ – Devon Ryan Aug 14 '17 at 6:47
  • $\begingroup$ @Devon Ryan, If metricString denotes the local ranking measure we are requesting that rapidGSEA use when it is invoked, which metricString value should I choose so that rapidGSEA operates in a manner most closely resembling fgseaL? Thank you. $\endgroup$ – Frank Aug 14 '17 at 17:11
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    $\begingroup$ Don't try to make one work like the other. If you start making your own method then you have to get it through peer review. It doesn't appear you have the background necessary to do that. Use rapidGSEA or fgseaL, stop trying to merge them. $\endgroup$ – Devon Ryan Aug 15 '17 at 7:11

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