
Hi again, sorry to be a bother but one more question. After knowing that lets say 20 genes are being used for the PCA would it be possible to figure out how much each gene contributes? So to develop an equation or know specifically in PC1 the variance in these x genes were the most important or something? 

.kripa
 

> From: dwu@fas.harvard.edu
> To: ttriche@usc.edu; kripa777@hotmail.com
> CC: amackey@virginia.edu; bioconductor@r-project.org
> Date: Mon, 7 May 2012 14:11:33 -0400
> Subject: RE: [BioC] LIMMA: plotMDS
> 
> Hi Kripa,
> 
> It is a good question to ask which the top 500 genes are. We only can know that if "gene.selection" is "common", in which situation, the same top 500 genes were used for calculating the distance matrix among all samples. When "gene.selection" is "pairwise", different sets of 500 genes for different pairs of samples are used for distance matrix calculation.
> 
> If you look at "plotMDS.default" in limma, you will see the above description is consistent with the code. "plotMDS.default" is used by plotMDS.
> 
> The current code does not output the top-500-gene list. However, the code can be edited to output the list when "gene.selection" is "common". Please see the following.
> In the followed example, "mds4[[2]]" is the vector of indexes for the top 500 genes.
> 
> Hope this help.
> 
> Di
> 
> 
> 
> plotMDS.default<-
> function (x, top = 500, labels = colnames(x), col = NULL, cex = 1, 
> dim.plot = c(1, 2), ndim = max(dim.plot), gene.selection = "pairwise", 
> xlab = paste("Dimension", dim.plot[1]), ylab = paste("Dimension", 
> dim.plot[2]), ...) 
> {
> x <- as.matrix(x)
> ok <- is.finite(x)
> if (!all(ok)) 
> x <- x[apply(ok, 1, all), ]
> if (is.null(labels)) 
> labels <- 1:dim(x)[2]
> nprobes <- nrow(x)
> nsamples <- ncol(x)
> if (ndim < 2) 
> stop("Need at least two dim.plot")
> if (nsamples < ndim) 
> stop("Two few samples")
> gene.selection <- match.arg(gene.selection, c("pairwise", 
> "common"))
> cn <- colnames(x)
> dd <- matrix(0, nrow = nsamples, ncol = nsamples, dimnames = list(cn, 
> cn))
> topindex <- nprobes - top + 1
> if (gene.selection == "pairwise") {
> for (i in 2:(nsamples)) for (j in 1:(i - 1)) dd[i, j] = sqrt(mean(sort.int((x[, 
> i] - x[, j])^2, partial = topindex)[topindex:nprobes]))
> }
> else {
> 
> # Same genes used for all comparisons ,"common"
> 
> s <- rowMeans((x - rowMeans(x))^2)
> q <- quantile(s, p = (topindex - 1.5)/(nprobes - 1))
> 
> x <- x[s >= q, ]
> 
> # an extra line
> ind.top.genes<-which(s >= q)
> 
> for (i in 2:(nsamples)) dd[i, 1:(i - 1)] = sqrt(colMeans((x[, 
> i] - x[, 1:(i - 1), drop = FALSE])^2))
> }
> a1 <- cmdscale(as.dist(dd), k = ndim)
> mds <- new("MDS", list(dim.plot = dim.plot, distance.matrix = dd, 
> cmdscale.out = a1, top = top, gene.selection = gene.selection))
> mds$x <- a1[, dim.plot[1]]
> mds$y <- a1[, dim.plot[2]]
> mdsPlot<-plotMDS(mds, labels = labels, col = col, cex = cex, xlab = xlab, 
> ylab = ylab, ...)
> list (mds=mds, ind.top.genes=ind.top.genes) 
> }
> 
> 
> # The example from "plotMDS" documentation
> 
> sd <- 0.3*sqrt(4/rchisq(1000,df=4))
> x <- matrix(rnorm(1000*6,sd=sd),1000,6)
> rownames(x) <- paste("Gene",1:1000)
> x[1:50,4:6] <- x[1:50,4:6] + 2
> # without labels, indexes of samples are plotted.
> 
> mds <- plotMDS(x, col=c(rep("black",3), rep("red",3)) )
> mds4<-plotMDS.default(x, col=c(rep("black",3), rep("red",3)) , gene.selection="common")
> 
> > mds4[[1]]
> > length(mds4[[2]])
> [1] 500
> 
> 
> 
> 
> 
> 
> ----
> Di Wu
> Postdoctoral fellow
> Harvard University, Statistics Department
> Harvard Medical School
> Science Center, 1 Oxford Street, Cambridge, MA 02138-2901 USA
> 
> ________________________________________
> From: bioconductor-bounces@r-project.org [bioconductor-bounces@r-project.org] On Behalf Of Tim Triche, Jr. [tim.triche@gmail.com]
> Sent: Monday, May 07, 2012 1:48 PM
> To: Kripa R
> Cc: amackey@virginia.edu; bioconductor@r-project.org
> Subject: Re: [BioC] LIMMA: plotMDS
> 
> also http://www.bioconductor.org/packages/2.11/bioc/vignettes/pcaMethods/inst/doc/pcaMethods.pdf<http://www.bioconductor.org/packages/2.11/bioc/vignettes/pcaMethods/inst/doc/pcaMethods.pdf>
> 
> if you are willing to simply truncate loadings at some arbitrary value (not
> always the worst idea of all time mind you)
> 
> superPC and the preconditioned lasso turn this strategy into a method for
> obtaining sparse classifiers, FWIW.
> 
> 
> On Mon, May 7, 2012 at 10:46 AM, Tim Triche, Jr. <tim.triche@gmail.com>wrote:
> 
> > try using the 'sparcl' package or something like this:
> > http://www.biomedcentral.com/1471-2105/11/296
> >
> > the point is to threshold vigorously enough that most of the loadings are
> > forced to zero
> >
> >
> >
> > On Mon, May 7, 2012 at 10:16 AM, Kripa R <kripa777@hotmail.com> wrote:
> >
> >>
> >> Maybe this is very basic, but how do you identify those 500 genes for the
> >> heatmap?
> >>
> >> .kripa
> >>
> >> From: amackey@virginia.edu
> >> Date: Mon, 7 May 2012 11:47:16 -0400
> >> Subject: Re: [BioC] LIMMA: plotMDS
> >> To: kripa777@hotmail.com
> >> CC: bioconductor@r-project.org
> >>
> >> All genes contribute to all principle components; some just more than
> >> others. I've found it useful (or perhaps just entertaining) to see
> >> heatmaps of the first N (say 10) component loadings, constrained to the 500
> >> genes whose variances contribute most to those components.
> >>
> >>
> >> -Aaron
> >>
> >> On Sun, May 6, 2012 at 7:21 PM, Kripa R <kripa777@hotmail.com> wrote:
> >>
> >>
> >>
> >>
> >> Hi I was wondering how I can figure out which genes correspond to each of
> >> the principle components ($cmdscale.out). Gene.selection is set to pairwise
> >> for the top 500 but I'd like to know exactly which genes are being
> >> considered for cmdscale.out[,3], for example.
> >>
> >>
> >>
> >>
> >>
> >> Thanks for the help!
> >>
> >>
> >>
> >> .kripa
> >>
> >> [[alternative HTML version deleted]]
> >>
> >>
> >>
> >> _______________________________________________
> >>
> >> Bioconductor mailing list
> >>
> >> Bioconductor@r-project.org
> >>
> >> https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>
> >> Search the archives:
> >> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>
> >>
> >>
> >> [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> Bioconductor mailing list
> >> Bioconductor@r-project.org
> >> https://stat.ethz.ch/mailman/listinfo/bioconductor
> >> Search the archives:
> >> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>
> >
> >
> >
> > --
> > *A model is a lie that helps you see the truth.*
> > *
> > *
> > Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
> >
> >
> 
> 
> --
> *A model is a lie that helps you see the truth.*
> *
> *
> Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
> 
> [[alternative HTML version deleted]]
> 
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