ReportingTools package
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Jason Hackney ▴ 160
@jason-hackney-5882
Last seen 7.1 years ago
Hi Hena, > I am using the package ReportingTools and would like to know if is it possible to publish t statistics in the report for differential expression analysis using limma? This depends quite a bit on what version of ReportingTools you are using. For the current release version, there are a couple of ways to do this. The most straightforward way is to write a custom function and override the toReportDF method for MArrayLM objects. There is a helper function in the package, ReportingTools:::.marrayLM.to.data.frame that might serve as a template for getting the t-statistic. Here's kind of a rough sketch that I would use, drawn heavily from that helper function, but missing some error checking: my.df.fn <- function (object, eSet = NULL, n = 1000, pvalueCutoff = 0.01, lfc = 0, adjust.method = "BH", coef = NULL, make.plots = FALSE, factor = NULL, ...) { dat <- topTable(object, number = n, p.value = pvalueCutoff, lfc = lfc, coef = coef, adjust.method = adjust.method) selection <- rownames(dat) padj <- apply(object$p.value, 2, p.adjust, method = adjust.method) padj <- padj[selection, coef] object <- object[selection, coef] if (!missing(eSet) & !is.null(eSet)) { eSet <- eSet[selection, ] fdata <- ReportingTools:::annotate.genes(featureNames(eSet), annotation(eSet), keytype = "PROBEID", cols = list(ProbeId = "PROBEID", EntrezId = "ENTREZID", Symbol = "SYMBOL", GeneName = "GENENAME")) } ret <- data.frame(fdata, Image = rep(NA, nrow(fdata)), object$t, padj, stringsAsFactors = FALSE) t.cols <- (ncol(fdata) + 2):(ncol(fdata) + 1 + ncol(object$t)) colnames(ret)[t.cols] <- paste(colnames(object), "t-Statistic") pv.cols <- (ncol(fdata) + ncol(object$coef) + 2):ncol(ret) colnames(ret)[pv.cols] <- paste(colnames(object), "Adjusted p-Value") return(ret) } Once you have your function for coercing an MArrayLM object to a data.frame, then you can pass it to the publish call in the .toDF argument: publish(my.fit, my.report, my.eset, n = 100, coef = 2, factor = my.eset$groups, .toDF = my.df.fn) The rest should work as expected. Let me know if that helps, or if you have any further problems. Cheers, Jason > Thanks! > > > > Hena Ramay, PhD > Bioinformatician > Swiss Institute of Bioinformatics > Lausanne, Switzerland -- Jason A. Hackney, Ph.D. Bioinformatics and Computational Biology Genentech hackney.jason@gene.com 650-467-5084 [[alternative HTML version deleted]]
ReportingTools ReportingTools • 723 views
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hena ramay ▴ 10
@hena-ramay-6228
Last seen 2.3 years ago
Thank you very much Jason! This really helps me in doing what I want to do. I will contact you again if I have questions! Hena On Tue, Nov 5, 2013 at 6:59 PM, Jason Hackney <hackney.jason@gene.com>wrote: > Hi Hena, > > I am using the package ReportingTools and would like to know if is it > possible to publish t statistics in the report for differential expression > analysis using limma? > > > This depends quite a bit on what version of ReportingTools you are using. > For the current release version, there are a couple of ways to do this. The > most straightforward way is to write a custom function and override the > toReportDF method for MArrayLM objects. There is a helper function in the > package, ReportingTools:::.marrayLM.to.data.frame that might serve as a > template for getting the t-statistic. > > Here's kind of a rough sketch that I would use, drawn heavily from that > helper function, but missing some error checking: > > my.df.fn <- function (object, eSet = NULL, n = 1000, pvalueCutoff = 0.01, > lfc = 0, adjust.method = "BH", coef = NULL, make.plots = FALSE, > factor = NULL, ...) > { > dat <- topTable(object, number = n, p.value = pvalueCutoff, > lfc = lfc, coef = coef, adjust.method = adjust.method) > > selection <- rownames(dat) > > padj <- apply(object$p.value, 2, p.adjust, method = adjust.method) > padj <- padj[selection, coef] > > object <- object[selection, coef] > if (!missing(eSet) & !is.null(eSet)) { > eSet <- eSet[selection, ] > fdata <- ReportingTools:::annotate.genes(featureNames(eSet), > annotation(eSet), > keytype = "PROBEID", cols = list(ProbeId = "PROBEID", > EntrezId = "ENTREZID", Symbol = "SYMBOL", GeneName = > "GENENAME")) > } > > ret <- data.frame(fdata, Image = rep(NA, nrow(fdata)), > object$t, padj, stringsAsFactors = FALSE) > t.cols <- (ncol(fdata) + 2):(ncol(fdata) + 1 + ncol(object$t)) > colnames(ret)[t.cols] <- paste(colnames(object), "t-Statistic") > > pv.cols <- (ncol(fdata) + ncol(object$coef) + 2):ncol(ret) > colnames(ret)[pv.cols] <- paste(colnames(object), > "Adjusted p-Value") > return(ret) > } > > Once you have your function for coercing an MArrayLM object to a > data.frame, then you can pass it to the publish call in the .toDF argument: > > publish(my.fit, my.report, my.eset, n = 100, coef = 2, factor = > my.eset$groups, > .toDF = my.df.fn) > > The rest should work as expected. Let me know if that helps, or if you > have any further problems. > > Cheers, > > Jason > > > Thanks! > > > > Hena Ramay, PhD > Bioinformatician > Swiss Institute of Bioinformatics > Lausanne, Switzerland > > > > -- > Jason A. Hackney, Ph.D. > Bioinformatics and Computational Biology > Genentech > hackney.jason@gene.com > 650-467-5084 > > [[alternative HTML version deleted]]
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