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commit 74ff2958ab00ed8f580817b6616ea81c5831dcb4 Author: Andreas Tille <[email protected]> Date: Fri Jan 20 09:12:13 2017 +0100 New upstream version 3.30.8+dfsg --- DESCRIPTION | 6 +- NAMESPACE | 2 + R/decidetests.R | 91 ++++++++++- R/fitFDist.R | 54 ++++-- R/fitFDistRobustly.R | 2 +- inst/doc/changelog.txt | 20 +++ inst/doc/intro.pdf | Bin 43301 -> 43301 bytes man/contrastAsCoef.Rd | 4 +- man/decideTests.Rd | 43 +++-- man/fitfdist.Rd | 5 +- man/plotMDS.Rd | 2 +- tests/limma-Tests.Rout.save | 388 ++++++++++++++++++++++---------------------- 12 files changed, 379 insertions(+), 238 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f017186..2ce7e60 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: limma -Version: 3.30.7 -Date: 2016-12-14 +Version: 3.30.8 +Date: 2017-01-12 Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] @@ -21,4 +21,4 @@ biocViews: ExonArray, GeneExpression, Transcription, MultipleComparison, Normalization, Preprocessing, QualityControl NeedsCompilation: yes -Packaged: 2016-12-14 23:12:22 UTC; biocbuild +Packaged: 2017-01-12 23:15:02 UTC; biocbuild diff --git a/NAMESPACE b/NAMESPACE index f119b94..c24c1b4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -59,6 +59,8 @@ S3method(cbind,MAList) S3method(cbind,RGList) S3method(cbind,EList) S3method(cbind,EListRaw) +S3method(decideTests,default) +S3method(decideTests,MArrayLM) S3method(detectionPValues,default) S3method(detectionPValues,EListRaw) S3method(dim,MAList) diff --git a/R/decidetests.R b/R/decidetests.R index be5bf97..dcbe9a2 100755 --- a/R/decidetests.R +++ b/R/decidetests.R @@ -4,13 +4,14 @@ setClass("TestResults",representation("matrix")) summary.TestResults <- function(object,...) # Gordon Smyth -# 26 Feb 2004. Last modified 31 Jan 2005. +# Created 26 Feb 2004. Last modified 6 Jan 2017. { -# apply(object,2,table) - tab <- array(0,c(3,ncol(object)),dimnames=list(c("-1","0","1"),colnames(object))) - tab[1,] <- colSums(object== -1,na.rm=TRUE) - tab[2,] <- colSums(object== 0,na.rm=TRUE) - tab[3,] <- colSums(object== 1,na.rm=TRUE) + Levels <- attr(object,"levels") + if(is.null(Levels)) Levels <- c(-1L,0L,1L) + nlevels <- length(Levels) + tab <- matrix(0L,nlevels,ncol(object)) + dimnames(tab) <- list(as.character(Levels),colnames(object)) + for (i in 1:nlevels) tab[i,] <- colSums(object==Levels[i],na.rm=TRUE) class(tab) <- "table" tab } @@ -20,12 +21,84 @@ setMethod("show","TestResults",function(object) { printHead([email protected]) }) -decideTests <- function(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0) +decideTests <- function(object,...) UseMethod("decideTests") + +decideTests.default <- function(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0,coefficients=NULL,cor.matrix=NULL,tstat=NULL,df=Inf,genewise.p.value=NULL,...) +# Accept or reject hypothesis tests across genes and contrasts +# Gordon Smyth +# 17 Aug 2004. Last modified 8 Jan 2017. +{ + method <- match.arg(method,c("separate","global","hierarchical","nestedF")) + if(method=="nestedF") stop("nestedF adjust method requires an MArrayLM object",call.=FALSE) + + adjust.method <- match.arg(adjust.method,c("none","bonferroni","holm","BH","fdr","BY")) + if(adjust.method=="fdr") adjust.method <- "BH" + + p <- as.matrix(object) + if(any(p>1) || any(p<0)) stop("object doesn't appear to be a matrix of p-values") + + switch(method, + + separate={ + for (i in 1:ncol(p)) p[,i] <- p.adjust(p[,i],method=adjust.method) + + },global={ + p[] <- p.adjust(p[],method=adjust.method) + + },hierarchical={ + if(is.null(genewise.p.value)) { +# Apply Simes' method by rows to get genewise p-values + genewise.p.value <- rep_len(1,nrow(p)) + ngenes <- nrow(p) + ncontrasts <- ncol(p) + Simes.multiplier <- ncontrasts/(1:ncontrasts) + for (g in 1:ngenes) { + op <- sort(p[g,],na.last=TRUE) + genewise.p.value[g] <- min(op*Simes.multiplier,na.rm=TRUE) + } + } +# Adjust genewise p-values + DEgene <- p.adjust(genewise.p.value,method=adjust.method) <= p.value +# Adjust row-wise p-values + p[!DEgene,] <- 1 + gDE <- which(DEgene) + for (g in gDE) p[g,] <- p.adjust(p[g,],method=adjust.method) +# Adjust p-value cutoff for number of DE genes + nDE <- length(gDE) + a <- switch(adjust.method, + none=1, + bonferroni=1/ngenes, + holm=1/(ngenes-nDE+1), + BH=nDE/ngenes, + BY=nDE/ngenes/sum(1/(1:ngenes)) + ) + p.value <- a*p.value + },nestedF={ + stop("nestedF adjust method requires an MArrayLM object",call.=FALSE) + }) + + isDE <- array(0L,dim(p),dimnames=dimnames(p)) + isDE[p <= p.value] <- 1L + if(is.null(coefficients)) coefficients <- tstat + if(is.null(coefficients)) { + attr(isDE,"levels") <- c(0L,1L) + } else { + attr(isDE,"levels") <- c(-1L,0L,1L) + coefficients <- as.matrix(coefficients) + if( !all(dim(coefficients)==dim(p)) ) stop("dim(object) disagrees with dim(coefficients)") + i <- coefficients<0 + isDE[i] <- -isDE[i] + if(lfc>0) isDE[ abs(coefficients)<lfc ] <- 0L + } + + new("TestResults",isDE) +} + +decideTests.MArrayLM <- function(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0,...) # Accept or reject hypothesis tests across genes and contrasts # Gordon Smyth -# 17 Aug 2004. Last modified 13 August 2010. +# 17 Aug 2004. Last modified 8 Jan 2017. { - if(!is(object,"MArrayLM")) stop("Need MArrayLM object") if(is.null(object$p.value)) object <- eBayes(object) method <- match.arg(method,c("separate","global","hierarchical","nestedF")) adjust.method <- match.arg(adjust.method,c("none","bonferroni","holm","BH","fdr","BY")) diff --git a/R/fitFDist.R b/R/fitFDist.R index 57ea394..df9c43f 100644 --- a/R/fitFDist.R +++ b/R/fitFDist.R @@ -2,12 +2,30 @@ fitFDist <- function(x,df1,covariate=NULL) # Moment estimation of the parameters of a scaled F-distribution. # The numerator degrees of freedom are given, the denominator is to be estimated. # Gordon Smyth and Belinda Phipson -# 8 Sept 2002. Last revised 27 Oct 2012. +# 8 Sept 2002. Last revised 12 Jan 2017. { +# Check x + n <- length(x) + if(n==0) return(list(scale=NA,df2=NA)) + +# Check df1 + ok <- is.finite(df1) & df1 > 1e-15 + if(length(df1)==1L) { + if(!ok) { + return(list(scale=NA,df2=NA)) + } else { + ok <- rep_len(TRUE,n) + } + } else { + if(length(df1) != n) stop("x and df1 have different lengths") + } + # Check covariate - if(!is.null(covariate)) { - if(length(covariate) != length(x)) stop("covariate and x must be of same length") - if(any(is.na(covariate))) stop("NA covariate values not allowed") + if(is.null(covariate)) { + splinedf <- 1L + } else { + if(length(covariate) != n) stop("x and covariate must be of same length") + if(anyNA(covariate)) stop("NA covariate values not allowed") isfin <- is.finite(covariate) if(!all(isfin)) { if(!any(isfin)) @@ -18,22 +36,25 @@ fitFDist <- function(x,df1,covariate=NULL) covariate[covariate == Inf] <- r[2]+1 } } - splinedf <- min(4,length(unique(covariate))) - if(splinedf < 2) covariate <- NULL + splinedf <- min(4L,length(unique(covariate))) + if(splinedf < 2L) covariate <- NULL } + # Remove missing or infinite values and zero degrees of freedom - ok <- is.finite(x) & is.finite(df1) & (x > -1e-15) & (df1 > 1e-15) + ok <- ok & is.finite(x) & (x > -1e-15) + nok <- sum(ok) notallok <- !all(ok) if(notallok) { x <- x[ok] - df1 <- df1[ok] + if(length(df1)>1) df1 <- df1[ok] if(!is.null(covariate)) { covariate2 <- covariate[!ok] covariate <- covariate[ok] } } - n <- length(x) - if(n==0) return(list(scale=NA,df2=NA)) + +# Need enough observations to estimate variance around trend + if(nok <= splinedf) return(list(scale=NA,df2=NA)) # Avoid exactly zero values x <- pmax(x,0) @@ -52,7 +73,7 @@ fitFDist <- function(x,df1,covariate=NULL) if(is.null(covariate)) { emean <- mean(e) - evar <- sum((e-emean)^2)/(n-1) + evar <- sum((e-emean)^2)/(nok-1) } else { if(!requireNamespace("splines",quietly=TRUE)) stop("splines package required but is not available") design <- try(splines::ns(covariate,df=splinedf,intercept=TRUE),silent=TRUE) @@ -60,22 +81,27 @@ fitFDist <- function(x,df1,covariate=NULL) fit <- lm.fit(design,e) if(notallok) { design2 <- predict(design,newx=covariate2) - emean <- rep.int(0,n+length(covariate2)) + emean <- rep_len(0,n) emean[ok] <- fit$fitted emean[!ok] <- design2 %*% fit$coefficients } else { emean <- fit$fitted } - evar <- mean(fit$residuals[-(1:fit$rank)]^2) + evar <- mean(fit$effects[-(1:fit$rank)]^2) } + +# Estimate scale and df2 evar <- evar - mean(trigamma(df1/2)) if(evar > 0) { df2 <- 2*trigammaInverse(evar) s20 <- exp(emean+digamma(df2/2)-log(df2/2)) } else { df2 <- Inf - s20 <- exp(emean) +# Use simple pooled variance, which is MLE of the scale in this case. +# Versions of limma before Jan 2017 returned the limiting value of the evar>0 estimate, which is larger. + s20 <- mean(x) } + list(scale=s20,df2=df2) } diff --git a/R/fitFDistRobustly.R b/R/fitFDistRobustly.R index 673a244..7423322 100644 --- a/R/fitFDistRobustly.R +++ b/R/fitFDistRobustly.R @@ -48,7 +48,7 @@ fitFDistRobustly <- function(x,df1,covariate=NULL,winsor.tail.p=c(0.05,0.1),trac # Avoid zero or negative x values m <- median(x) - if(m<=0) stop("Variances are mostly <= 0") + if(m<=0) stop("Variances are mostly <= 0") i <- (x < m*1e-12) if(any(i)) { nzero <- sum(i) diff --git a/inst/doc/changelog.txt b/inst/doc/changelog.txt index df5ccb6..8ac66bf 100755 --- a/inst/doc/changelog.txt +++ b/inst/doc/changelog.txt @@ -1,3 +1,23 @@ +12 Jan 2016: limma 3.30.8 + +- Bug fix to fitFDist() when 'covariate' is not NULL. The new + estimate for df2 will usually be slightly lower than before. This + affects the eBayes(trend=TRUE) pipeline. The difference will have + an appreciable effect on downstream results when the number of + genes is small. + +- fitFDist() now estimates the scale by mean(x) when df2 is estimated + to be Inf. This will make the results from eBayes() less + conservative than before when df.prior=Inf. + +- Bug fix to fitFDist() when 'x' contains NA values and 'df1' has + length 1. When 'covariate' is non-NULL, fitFDist() will now return + an NA result without an error if there are too few observations to + do the estimation. + + - decideTests() is now an S3 generic function with a default method + and a method for MArrayLM objects. + 14 Dec 2016: limma 3.30.7 - Bug fix for contrastAsCoef() when there is more than one contrast. diff --git a/inst/doc/intro.pdf b/inst/doc/intro.pdf index 9709c3a..4a10eb4 100644 Binary files a/inst/doc/intro.pdf and b/inst/doc/intro.pdf differ diff --git a/man/contrastAsCoef.Rd b/man/contrastAsCoef.Rd index 35f8841..918ca59 100644 --- a/man/contrastAsCoef.Rd +++ b/man/contrastAsCoef.Rd @@ -14,7 +14,8 @@ contrastAsCoef(design, contrast=NULL, first=TRUE) } \details{ -If \code{contrast} doesn't have full column rank, then superfluous columns are dropped. +If the contrasts contained in the columns of \code{contrast} are not linearly dependent, then superfluous columns are dropped until the remaining matrix has full column rank. +The number of retained contrasts is stored in \code{qr$rank} and the retained columns are given by \code{qr$pivot}. } \value{ @@ -42,6 +43,7 @@ y <- rnorm(6) fit1 <- lm(y~0+design) fit2 <- lm(y~0+design2) coef(fit1) +coef(fit1) %*% cont coef(fit2) } diff --git a/man/decideTests.Rd b/man/decideTests.Rd index c260fb0..d5c55bd 100755 --- a/man/decideTests.Rd +++ b/man/decideTests.Rd @@ -1,42 +1,57 @@ \name{decideTests} \alias{decideTests} +\alias{decideTests.default} +\alias{decideTests.MArrayLM} \title{Multiple Testing Across Genes and Contrasts} \description{ -Classify a series of related t-statistics as up, down or not significant. -A number of different multiple testing schemes are offered which adjust for multiple testing down the genes as well as across contrasts for each gene. +Identify which genes are significantly differentially expressed for each contrast from a fit object containing p-values and test statistics. +A number of different multiple testing strategies are offered that adjust for multiple testing down the genes as well as across contrasts for each gene. } \usage{ -decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0) +\method{decideTests}{MArrayLM}(object, method = "separate", adjust.method = "BH", p.value = 0.05, + lfc = 0, \dots) +\method{decideTests}{default}(object, method = "separate", adjust.method = "BH", p.value = 0.05, + lfc = 0, coefficients = NULL, cor.matrix = NULL, tstat = NULL, df = Inf, + genewise.p.value = NULL, \dots) } \arguments{ - \item{object}{\code{MArrayLM} object output from \code{eBayes} or \code{treat} from which the t-statistics may be extracted.} - \item{method}{character string specify how probes and contrasts are to be combined in the multiple testing strategy. Choices are \code{"separate"}, \code{"global"}, \code{"hierarchical"}, \code{"nestedF"} or any partial string.} + \item{object}{a numeric matrix of p-values or an \code{MArrayLM} object from which p-values and t-statistics can be extracted.} + \item{method}{character string specifying how genes and contrasts are to be combined in the multiple testing scheme. Choices are \code{"separate"}, \code{"global"}, \code{"hierarchical"} or \code{"nestedF"}.} \item{adjust.method}{character string specifying p-value adjustment method. Possible values are \code{"none"}, \code{"BH"}, \code{"fdr"} (equivalent to \code{"BH"}), \code{"BY"} and \code{"holm"}. See \code{\link[stats]{p.adjust}} for details.} - \item{p.value}{numeric value between 0 and 1 giving the desired size of the test} - \item{lfc}{minimum log2-fold-change required} + \item{p.value}{numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate.} + \item{lfc}{numeric, minimum absolute log2-fold-change required.} + \item{coefficients}{numeric matrix of coefficients or log2-fold-changes. Of same dimensions as \code{object}.} + \item{cor.matrix}{correlation matrix of coefficients. Square matrix of dimension \code{ncol(object)}.} + \item{tstat}{numeric matrix of t-statistics. Of same dimensions as \code{object}.} + \item{df}{numeric vector of length \code{nrow(object)} giving degrees of freedom for the t-statistics.} + \item{genewise.p.value}{numeric vector of length \code{nrow(object)} containing summary gene-level p-values for use with \code{method="hierarchical"}.} + \item{\dots}{other arguments are not used.} } \value{ An object of class \code{\link[=TestResults-class]{TestResults}}. -This is essentially a numeric matrix with elements \code{-1}, \code{0} or \code{1} depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive respectively. +This is essentially a numeric matrix with elements \code{-1}, \code{0} or \code{1} depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive. If \code{lfc>0} then contrasts are judged significant only when the log2-fold change is at least this large in absolute value. For example, one might choose \code{lfc=log2(1.5)} to restrict to 50\% changes or \code{lfc=1} for 2-fold changes. In this case, contrasts must satisfy both the p-value and the fold-change cutoff to be judged significant. } \details{ -These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. -Rows of \code{tstat} correspond to genes and columns to coefficients or contrasts. +This function can be applied to a matrix of p-values but is more often applied to an \code{MArrayLM} fit object produced by \code{eBayes} or \code{treat}. +In either case, rows of \code{object} correspond to genes and columns to coefficients or contrasts. -The setting \code{method="separate"} is equivalent to using \code{topTable} separately for each coefficient in the linear model fit, and will give the same lists of probes if \code{adjust.method} is the same. +This function applies a multiple testing procedure and a significance level cutoff to the statistics contained in \code{object}. +It implements a number of multiple testing procedures for determining whether each statistic should be considered significantly different from zero. + +The setting \code{method="separate"} is equivalent to using \code{topTable} separately for each coefficient in the linear model fit, and will identify the same probes as significantly differentially expressed if \code{adjust.method} is the same. \code{method="global"} will treat the entire matrix of t-statistics as a single vector of unrelated tests. \code{method="hierarchical"} adjusts down genes and then across contrasts. \code{method="nestedF"} adjusts down genes and then uses \code{classifyTestsF} to classify contrasts as significant or not for the selected genes. Please see the limma User's Guide for a discussion of the statistical properties of these methods. } \note{ -Although this function enables users to set p-value and lfc cutoffs simultaneously, this is not generally recommended. -If the fold changes and p-values are not highly correlated, then the use of a fold change cutoff can increase the false discovery rate above the nominal level. -Users wanting to use fold change thresholding are recommended to use \code{treat} instead of \code{eBayes}, and to leave \code{lfc} at the default value when using \code{decideTests}. +Although this function enables users to set p-value and lfc cutoffs simultaneously, this combination criterion not usually recommended. +Unless the fold changes and p-values are very highly correlated, the addition of a fold change cutoff can increase the family-wise error rate or false discovery rate above the nominal level. +Users wanting to use fold change thresholding are recommended to use \code{treat} instead of \code{eBayes} and to leave \code{lfc} at the default value when using \code{decideTests}. } \seealso{ An overview of multiple testing functions is given in \link{08.Tests}. diff --git a/man/fitfdist.Rd b/man/fitfdist.Rd index 6b2c54f..4ba8fa5 100755 --- a/man/fitfdist.Rd +++ b/man/fitfdist.Rd @@ -18,12 +18,15 @@ fitFDistRobustly(x, df1, covariate=NULL, winsor.tail.p=c(0.05,0.1), trace=FALSE) \item{trace}{logical value indicating whether a trace of the iteration progress should be printed.} } \details{ -\code{fitFDist} implements an algorithm proposed by Smyth (2004). +\code{fitFDist} implements an algorithm proposed by Smyth (2004) and Phipson et al (2016). It estimates \code{scale} and \code{df2} under the assumption that \code{x} is distributed as \code{scale} times an F-distributed random variable on \code{df1} and \code{df2} degrees of freedom. The parameters are estimated using the method of moments, specifically from the mean and variance of the \code{x} values on the log-scale. +When \code{covariate} is supplied, a spline curve trend will be estimated for the \code{x} values and the estimation will be adjusted for this trend (Phipson et al, 2016). + \code{fitFDistRobustly} is similar to \code{fitFDist} except that it computes the moments of the Winsorized values of \code{x}, making it robust against left and right outliers. Larger values for \code{winsor.tail.p} produce more robustness but less efficiency. +When \code{covariate} is supplied, a loess trend is estimated for the \code{x} values. The robust method is described by Phipson et al (2016). As well as estimating the F-distribution for the bulk of the cases, i.e., with outliers discounted, \code{fitFDistRobustly} also returns an estimated F-distribution with reduced df2 that might be appropriate for each outlier case. diff --git a/man/plotMDS.Rd b/man/plotMDS.Rd index 1706924..c43123b 100644 --- a/man/plotMDS.Rd +++ b/man/plotMDS.Rd @@ -19,7 +19,7 @@ Plot samples on a two-dimensional scatterplot so that distances on the plot appr } \arguments{ - \item{x}{any data object which can be coerced to a matrix, such as \code{ExpressionSet} or \code{EList}.} + \item{x}{any data object which can be coerced to a matrix, for example an \code{ExpressionSet} or an \code{EList}.} \item{top}{number of top genes used to calculate pairwise distances.} \item{labels}{character vector of sample names or labels. Defaults to \code{colnames(x)}.} \item{pch}{plotting symbol or symbols. See \code{\link{points}} for possible values. Ignored if \code{labels} is non-\code{NULL}.} diff --git a/tests/limma-Tests.Rout.save b/tests/limma-Tests.Rout.save index 5c3b214..3c3c730 100755 --- a/tests/limma-Tests.Rout.save +++ b/tests/limma-Tests.Rout.save @@ -1,5 +1,5 @@ -R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" +R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) @@ -742,65 +742,65 @@ Array 2 corrected > eb <- ebayes(fit2) > > eb$t - First3 Last3 Last3-First3 - [1,] 9.23328899 0.3132376 -6.3074289 - [2,] -0.62297130 0.7033694 0.9378645 - [3,] -0.63617368 0.7848179 1.0047928 - [4,] -0.28354965 2.0342050 1.6389001 - [5,] 0.09867104 0.5424981 0.3138331 - [6,] -0.84479508 -1.7161311 -0.6161276 - [7,] 1.60397813 -0.3573420 -1.3868627 - [8,] -0.88080565 1.0698285 1.3793067 - [9,] -0.24543647 0.2077673 0.3204634 -[10,] 1.11347326 0.3678166 -0.5272589 + First3 Last3 Last3-First3 + [1,] 10.0852546 0.3421403 -6.8894222 + [2,] -0.6804535 0.7682700 1.0244023 + [3,] -0.6948741 0.8572339 1.0975061 + [4,] -0.3097131 2.2219034 1.7901232 + [5,] 0.1077755 0.5925550 0.3427908 + [6,] -0.9227452 -1.8744804 -0.6729784 + [7,] 1.7519789 -0.3903143 -1.5148301 + [8,] -0.9620785 1.1685428 1.5065768 + [9,] -0.2680832 0.2269382 0.3500330 +[10,] 1.2162147 0.4017554 -0.5759097 > eb$s2.prior -[1] 0.1109952 +[1] 0.09303435 > eb$s2.post - [1] 0.1109952 0.1109952 0.1109952 0.1109952 0.1109952 0.1109952 0.1109952 - [8] 0.1109952 0.1109952 0.1109952 + [1] 0.09303435 0.09303435 0.09303435 0.09303435 0.09303435 0.09303435 + [7] 0.09303435 0.09303435 0.09303435 0.09303435 > eb$df.prior [1] Inf > eb$lods First3 Last3 Last3-First3 - [1,] 35.192546 -4.704301 12.838652 - [2,] -7.009617 -4.662107 -6.386845 - [3,] -7.001350 -4.649212 -6.322593 - [4,] -7.162627 -4.274513 -5.494162 - [5,] -7.197767 -4.683429 -6.772846 - [6,] -6.847709 -4.401419 -6.633922 - [7,] -5.923227 -4.701154 -5.871024 - [8,] -6.816808 -4.592976 -5.881353 - [9,] -7.172653 -4.710147 -6.770768 -[10,] -6.586066 -4.700346 -6.684136 + [1,] 43.333655 -5.116081 16.590631 + [2,] -7.060414 -4.956623 -6.390202 + [3,] -7.050543 -4.907890 -6.313399 + [4,] -7.243125 -3.491841 -5.323150 + [5,] -7.285087 -5.037204 -6.851601 + [6,] -6.867078 -3.971443 -6.685541 + [7,] -5.763144 -5.104190 -5.773625 + [8,] -6.830178 -4.695367 -5.785971 + [9,] -7.255097 -5.138174 -6.849117 +[10,] -6.554648 -5.101136 -6.745563 > eb$p.value First3 Last3 Last3-First3 - [1,] 1.829781e-11 0.75572772 1.747263e-07 - [2,] 5.368393e-01 0.48589978 3.539428e-01 - [3,] 5.282869e-01 0.43718400 3.210366e-01 - [4,] 7.782180e-01 0.04859899 1.090771e-01 - [5,] 9.218923e-01 0.59048539 7.552787e-01 - [6,] 4.032500e-01 0.09387463 5.413009e-01 - [7,] 1.165868e-01 0.72271436 1.731642e-01 - [8,] 3.836849e-01 0.29110994 1.754637e-01 - [9,] 8.073734e-01 0.83646492 7.502852e-01 -[10,] 2.721518e-01 0.71494923 6.009262e-01 + [1,] 1.511047e-12 0.73403652 2.674199e-08 + [2,] 5.001373e-01 0.44683897 3.118009e-01 + [3,] 4.911511e-01 0.39642266 2.789833e-01 + [4,] 7.583869e-01 0.03201123 8.100587e-02 + [5,] 9.147125e-01 0.55681424 7.335508e-01 + [6,] 3.616727e-01 0.06818020 5.048308e-01 + [7,] 8.744151e-02 0.69837505 1.376783e-01 + [8,] 3.417903e-01 0.24950528 1.397766e-01 + [9,] 7.900130e-01 0.82162766 7.281504e-01 +[10,] 2.310317e-01 0.69000264 5.679026e-01 > eb$var.prior -[1] 61.00259109 0.09009399 56.57780753 +[1] 72.8438678 0.6891223 67.6296314 > > ### toptable > > toptable(fit) - logFC t P.Value adj.P.Val B -1 1.77602021 9.23328899 1.829781e-11 1.829781e-10 35.192546 -7 0.30852468 1.60397813 1.165868e-01 5.829340e-01 -5.923227 -10 0.21417623 1.11347326 2.721518e-01 7.669133e-01 -6.586066 -8 -0.16942269 -0.88080565 3.836849e-01 7.669133e-01 -6.816808 -6 -0.16249607 -0.84479508 4.032500e-01 7.669133e-01 -6.847709 -3 -0.12236781 -0.63617368 5.282869e-01 7.669133e-01 -7.001350 -2 -0.11982833 -0.62297130 5.368393e-01 7.669133e-01 -7.009617 -4 -0.05454069 -0.28354965 7.782180e-01 8.970816e-01 -7.162627 -9 -0.04720963 -0.24543647 8.073734e-01 8.970816e-01 -7.172653 -5 0.01897934 0.09867104 9.218923e-01 9.218923e-01 -7.197767 + logFC t P.Value adj.P.Val B +1 1.77602021 10.0852546 1.511047e-12 1.511047e-11 43.333655 +7 0.30852468 1.7519789 8.744151e-02 4.372076e-01 -5.763144 +10 0.21417623 1.2162147 2.310317e-01 7.144818e-01 -6.554648 +8 -0.16942269 -0.9620785 3.417903e-01 7.144818e-01 -6.830178 +6 -0.16249607 -0.9227452 3.616727e-01 7.144818e-01 -6.867078 +3 -0.12236781 -0.6948741 4.911511e-01 7.144818e-01 -7.050543 +2 -0.11982833 -0.6804535 5.001373e-01 7.144818e-01 -7.060414 +4 -0.05454069 -0.3097131 7.583869e-01 8.777922e-01 -7.243125 +9 -0.04720963 -0.2680832 7.900130e-01 8.777922e-01 -7.255097 +5 0.01897934 0.1077755 9.147125e-01 9.147125e-01 -7.285087 > > ### topTable > @@ -809,87 +809,87 @@ Array 2 corrected > fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix)) > topTable(fit2) First3 Last3 Last3.First3 AveExpr F P.Value -A 1.77602021 0.06025114 -1.71576906 0.918135675 42.67587166 2.924856e-19 -D -0.05454069 0.39127869 0.44581938 0.168369004 2.10919528 1.213356e-01 -F -0.16249607 -0.33009728 -0.16760121 -0.246296671 1.82939237 1.605111e-01 -G 0.30852468 -0.06873462 -0.37725930 0.119895035 1.35021957 2.591833e-01 -H -0.16942269 0.20578118 0.37520387 0.018179245 0.96017584 3.828256e-01 -J 0.21417623 0.07074940 -0.14342683 0.142462814 0.68755586 5.028035e-01 -C -0.12236781 0.15095948 0.27332729 0.014295836 0.51032807 6.002986e-01 -B -0.11982833 0.13529287 0.25512120 0.007732271 0.44141085 6.431284e-01 -E 0.01897934 0.10434934 0.08536999 0.061664340 0.15202008 8.589710e-01 -I -0.04720963 0.03996397 0.08717360 -0.003622829 0.05170315 9.496107e-01 +A 1.77602021 0.06025114 -1.71576906 0.918135675 50.91471061 7.727200e-23 +D -0.05454069 0.39127869 0.44581938 0.168369004 2.51638838 8.075072e-02 +F -0.16249607 -0.33009728 -0.16760121 -0.246296671 2.18256779 1.127516e-01 +G 0.30852468 -0.06873462 -0.37725930 0.119895035 1.61088775 1.997102e-01 +H -0.16942269 0.20578118 0.37520387 0.018179245 1.14554368 3.180510e-01 +J 0.21417623 0.07074940 -0.14342683 0.142462814 0.82029274 4.403027e-01 +C -0.12236781 0.15095948 0.27332729 0.014295836 0.60885003 5.439761e-01 +B -0.11982833 0.13529287 0.25512120 0.007732271 0.52662792 5.905931e-01 +E 0.01897934 0.10434934 0.08536999 0.061664340 0.18136849 8.341279e-01 +I -0.04720963 0.03996397 0.08717360 -0.003622829 0.06168476 9.401792e-01 adj.P.Val -A 2.924856e-18 -D 5.350369e-01 -F 5.350369e-01 -G 6.479584e-01 -H 7.656511e-01 -J 8.039105e-01 -C 8.039105e-01 -B 8.039105e-01 -E 9.496107e-01 -I 9.496107e-01 +A 7.727200e-22 +D 3.758388e-01 +F 3.758388e-01 +G 4.992756e-01 +H 6.361019e-01 +J 7.338379e-01 +C 7.382414e-01 +B 7.382414e-01 +E 9.268088e-01 +I 9.401792e-01 > topTable(fit2,coef=3,resort.by="logFC") logFC AveExpr t P.Value adj.P.Val B -D 0.44581938 0.168369004 1.6389001 1.090771e-01 4.386591e-01 -5.494162 -H 0.37520387 0.018179245 1.3793067 1.754637e-01 4.386591e-01 -5.881353 -C 0.27332729 0.014295836 1.0047928 3.210366e-01 5.899046e-01 -6.322593 -B 0.25512120 0.007732271 0.9378645 3.539428e-01 5.899046e-01 -6.386845 -I 0.08717360 -0.003622829 0.3204634 7.502852e-01 7.552787e-01 -6.770768 -E 0.08536999 0.061664340 0.3138331 7.552787e-01 7.552787e-01 -6.772846 -J -0.14342683 0.142462814 -0.5272589 6.009262e-01 7.511577e-01 -6.684136 -F -0.16760121 -0.246296671 -0.6161276 5.413009e-01 7.511577e-01 -6.633922 -G -0.37725930 0.119895035 -1.3868627 1.731642e-01 4.386591e-01 -5.871024 -A -1.71576906 0.918135675 -6.3074289 1.747263e-07 1.747263e-06 12.838652 +D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150 +H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971 +C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399 +B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202 +I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117 +E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601 +J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563 +F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541 +G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625 +A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631 > topTable(fit2,coef=3,resort.by="p") logFC AveExpr t P.Value adj.P.Val B -A -1.71576906 0.918135675 -6.3074289 1.747263e-07 1.747263e-06 12.838652 -D 0.44581938 0.168369004 1.6389001 1.090771e-01 4.386591e-01 -5.494162 -G -0.37725930 0.119895035 -1.3868627 1.731642e-01 4.386591e-01 -5.871024 -H 0.37520387 0.018179245 1.3793067 1.754637e-01 4.386591e-01 -5.881353 -C 0.27332729 0.014295836 1.0047928 3.210366e-01 5.899046e-01 -6.322593 -B 0.25512120 0.007732271 0.9378645 3.539428e-01 5.899046e-01 -6.386845 -F -0.16760121 -0.246296671 -0.6161276 5.413009e-01 7.511577e-01 -6.633922 -J -0.14342683 0.142462814 -0.5272589 6.009262e-01 7.511577e-01 -6.684136 -I 0.08717360 -0.003622829 0.3204634 7.502852e-01 7.552787e-01 -6.770768 -E 0.08536999 0.061664340 0.3138331 7.552787e-01 7.552787e-01 -6.772846 +A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631 +D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150 +G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625 +H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971 +C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399 +B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202 +F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541 +J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563 +I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117 +E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601 > topTable(fit2,coef=3,sort="logFC",resort.by="t") logFC AveExpr t P.Value adj.P.Val B -D 0.44581938 0.168369004 1.6389001 1.090771e-01 4.386591e-01 -5.494162 -H 0.37520387 0.018179245 1.3793067 1.754637e-01 4.386591e-01 -5.881353 -C 0.27332729 0.014295836 1.0047928 3.210366e-01 5.899046e-01 -6.322593 -B 0.25512120 0.007732271 0.9378645 3.539428e-01 5.899046e-01 -6.386845 -I 0.08717360 -0.003622829 0.3204634 7.502852e-01 7.552787e-01 -6.770768 -E 0.08536999 0.061664340 0.3138331 7.552787e-01 7.552787e-01 -6.772846 -J -0.14342683 0.142462814 -0.5272589 6.009262e-01 7.511577e-01 -6.684136 -F -0.16760121 -0.246296671 -0.6161276 5.413009e-01 7.511577e-01 -6.633922 -G -0.37725930 0.119895035 -1.3868627 1.731642e-01 4.386591e-01 -5.871024 -A -1.71576906 0.918135675 -6.3074289 1.747263e-07 1.747263e-06 12.838652 +D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150 +H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971 +C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399 +B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202 +I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117 +E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601 +J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563 +F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541 +G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625 +A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631 > topTable(fit2,coef=3,resort.by="B") logFC AveExpr t P.Value adj.P.Val B -A -1.71576906 0.918135675 -6.3074289 1.747263e-07 1.747263e-06 12.838652 -D 0.44581938 0.168369004 1.6389001 1.090771e-01 4.386591e-01 -5.494162 -G -0.37725930 0.119895035 -1.3868627 1.731642e-01 4.386591e-01 -5.871024 -H 0.37520387 0.018179245 1.3793067 1.754637e-01 4.386591e-01 -5.881353 -C 0.27332729 0.014295836 1.0047928 3.210366e-01 5.899046e-01 -6.322593 -B 0.25512120 0.007732271 0.9378645 3.539428e-01 5.899046e-01 -6.386845 -F -0.16760121 -0.246296671 -0.6161276 5.413009e-01 7.511577e-01 -6.633922 -J -0.14342683 0.142462814 -0.5272589 6.009262e-01 7.511577e-01 -6.684136 -I 0.08717360 -0.003622829 0.3204634 7.502852e-01 7.552787e-01 -6.770768 -E 0.08536999 0.061664340 0.3138331 7.552787e-01 7.552787e-01 -6.772846 +A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631 +D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150 +G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625 +H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971 +C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399 +B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202 +F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541 +J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563 +I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117 +E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601 > topTable(fit2,coef=3,lfc=1) logFC AveExpr t P.Value adj.P.Val B -A -1.715769 0.9181357 -6.307429 1.747263e-07 1.747263e-06 12.83865 +A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063 > topTable(fit2,coef=3,p=0.2) logFC AveExpr t P.Value adj.P.Val B -A -1.715769 0.9181357 -6.307429 1.747263e-07 1.747263e-06 12.83865 +A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063 > topTable(fit2,coef=3,p=0.2,lfc=0.5) logFC AveExpr t P.Value adj.P.Val B -A -1.715769 0.9181357 -6.307429 1.747263e-07 1.747263e-06 12.83865 +A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063 > topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none") logFC AveExpr t P.Value adj.P.Val B -A -1.715769 0.9181357 -6.307429 1.747263e-07 1.747263e-06 12.83865 +A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063 > > designlist <- > list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1))) > out <- selectModel(M,designlist) @@ -1235,37 +1235,37 @@ set1 5 Up 7.3344e-12 > fit <- eBayes(fit,trend=TRUE) > topTable(fit,coef=2) logFC AveExpr t P.Value adj.P.Val B -3 3.488703 1.03931081 4.860410 0.0002436118 0.01647958 0.6722078 -2 3.729512 1.73488969 4.700998 0.0003295917 0.01647958 0.3777787 -4 2.696676 1.74060725 3.280613 0.0053915597 0.17971866 -2.3313104 -1 2.391846 1.72305203 3.009776 0.0092611288 0.23152822 -2.8478458 -5 2.387967 1.63066783 2.786529 0.0144249169 0.26573834 -3.2671364 -33 -1.492317 -0.07525287 -2.735781 0.0159443006 0.26573834 -3.3613142 -80 -1.839760 -0.32802306 -2.594532 0.0210374835 0.30053548 -3.6207072 -95 -1.907074 1.26297763 -2.462009 0.0272186263 0.33449167 -3.8598265 -39 1.366141 -0.27360750 2.409767 0.0301042507 0.33449167 -3.9527943 -70 -1.789476 0.21771869 -2.184062 0.0462410739 0.46241074 -4.3445901 +2 3.729512 1.73488969 4.865697 0.0004854886 0.02902331 0.1596831 +3 3.488703 1.03931081 4.754954 0.0005804663 0.02902331 -0.0144071 +4 2.696676 1.74060725 3.356468 0.0063282637 0.21094212 -2.3434702 +1 2.391846 1.72305203 3.107124 0.0098781268 0.24695317 -2.7738874 +33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835 +5 2.387967 1.63066783 2.773444 0.0179792778 0.29965463 -3.3478204 +80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642 +39 1.366141 -0.27360750 2.451133 0.0320042242 0.37972679 -3.8925860 +95 -1.907074 1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571 +50 1.034777 0.01608433 2.054690 0.0642289403 0.59978803 -4.5350317 > fit$df.prior -[1] 12.17481 +[1] 9.098442 > fit$s2.prior - [1] 0.7108745 0.7186517 0.3976222 0.7224388 0.6531157 0.3014062 0.3169880 - [8] 0.3149772 0.3074632 0.2917431 0.3329334 0.3378027 0.2900500 0.3031741 - [15] 0.3221763 0.2981580 0.2897078 0.2925188 0.2924234 0.3042822 0.2923686 - [22] 0.2897022 0.3251669 0.2929813 0.4922090 0.2902725 0.3018205 0.3029119 - [29] 0.3030051 0.3331358 0.3259651 0.2939051 0.3077824 0.3553515 0.3139985 - [36] 0.3181689 0.3197601 0.4687993 0.3316536 0.2897621 0.2910744 0.2907116 - [43] 0.2907966 0.3265722 0.3240487 0.3241126 0.3003970 0.3064187 0.3645035 - [50] 0.2994391 0.3295512 0.2901076 0.2898658 0.3086659 0.2897209 0.2982976 - [57] 0.3043910 0.2900320 0.3006936 0.2935101 0.3646949 0.3596385 0.3064203 - [64] 0.3027439 0.3076483 0.3363356 0.3504336 0.3496698 0.2897618 0.2898810 - [71] 0.3182290 0.3121707 0.2945001 0.2897549 0.3579410 0.3434376 0.3037970 - [78] 0.3201893 0.3048412 0.3394079 0.3516034 0.3034589 0.3120384 0.3007827 - [85] 0.3013925 0.2902524 0.3527793 0.2969359 0.3033756 0.3170187 0.2978833 - [92] 0.2908437 0.3139422 0.3050183 0.4727609 0.2897104 0.2931671 0.2904177 - [99] 0.3231607 0.2941699 + [1] 0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 + [8] 0.3058098 0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 + [15] 0.3127994 0.2894802 0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 + [22] 0.2812704 0.3157029 0.2844541 0.4778832 0.2818242 0.2930360 0.2940957 + [29] 0.2941862 0.3234399 0.3164779 0.2853510 0.2988244 0.3450090 0.3048596 + [36] 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 0.2826027 0.2822504 + [43] 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 0.3538946 + [50] 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 + [57] 0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 + [64] 0.2939325 0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 + [71] 0.3089669 0.3030850 0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 + [78] 0.3108702 0.2959688 0.3295294 0.3413700 0.2946268 0.3029565 0.2920284 + [85] 0.2926205 0.2818046 0.3425116 0.2882936 0.2945459 0.3077919 0.2892134 + [92] 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 0.2846345 0.2819651 + [99] 0.3137551 0.2856081 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.2518 0.2746 0.3080 0.3425 0.3583 0.7344 + 0.2335 0.2603 0.2997 0.3375 0.3655 0.7812 > > y$E[1,1] <- NA > y$E[1,3] <- NA @@ -1273,39 +1273,39 @@ set1 5 Up 7.3344e-12 > fit <- eBayes(fit,trend=TRUE) > topTable(fit,coef=2) logFC AveExpr t P.Value adj.P.Val B -3 3.488703 1.03931081 4.697008 0.0003209946 0.03209946 0.4203254 -2 3.729512 1.73488969 3.999120 0.0012579276 0.06289638 -0.9004934 -4 2.696676 1.74060725 2.813904 0.0135288060 0.39858921 -3.1693877 -33 -1.492317 -0.07525287 -2.731110 0.0159435682 0.39858921 -3.3232081 -80 -1.839760 -0.32802306 -2.589351 0.0210816889 0.42163378 -3.5833549 -5 2.387967 1.63066783 2.485152 0.0258403123 0.43067187 -3.7715574 -39 1.366141 -0.27360750 2.394886 0.0307776648 0.43968093 -3.9322196 -1 2.638272 1.47993643 2.191607 0.0482424084 0.48242408 -4.0353253 -95 -1.907074 1.26297763 -2.323190 0.0353245811 0.44155726 -4.0580889 -70 -1.789476 0.21771869 -2.198418 0.0447803591 0.48242408 -4.2730579 +3 3.488703 1.03931081 4.604490 0.0007644061 0.07644061 -0.2333915 +2 3.729512 1.73488969 4.158038 0.0016033158 0.08016579 -0.9438583 +4 2.696676 1.74060725 2.898102 0.0145292666 0.44537707 -3.0530813 +33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324 +5 2.387967 1.63066783 2.495395 0.0297982959 0.46902627 -3.7272957 +80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584 +39 1.366141 -0.27360750 2.440729 0.0328318388 0.46902627 -3.8172597 +1 2.638272 1.47993643 2.227507 0.0530016060 0.58890673 -3.9537576 +95 -1.907074 1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439 +50 1.034777 0.01608433 2.063663 0.0635275235 0.60439978 -4.4204731 > fit$df.residual[1] [1] 0 > fit$df.prior -[1] 12.35976 +[1] 8.971891 > fit$s2.prior - [1] 0.7245758 0.9965185 0.4417532 1.0037410 0.8738246 0.3006625 0.3199347 - [8] 0.3175766 0.3084128 0.2878407 0.3375034 0.3425540 0.2857778 0.3029544 - [15] 0.3258534 0.2963955 0.2852604 0.2892364 0.2887441 0.3043789 0.2886711 - [22] 0.2852486 0.3291690 0.2898777 0.4748285 0.2859269 0.3021138 0.3036285 - [29] 0.3027364 0.3377157 0.3300432 0.2907249 0.3088128 0.3598916 0.3164146 - [36] 0.3213019 0.3231250 0.4563979 0.3361549 0.2852951 0.2872259 0.2864913 - [43] 0.2868366 0.3307052 0.3279369 0.3280075 0.2993443 0.3070975 0.3685088 - [50] 0.2980866 0.3339193 0.2858607 0.2854174 0.3099154 0.2852511 0.2965803 - [57] 0.3045183 0.2857518 0.2997325 0.2906102 0.3686865 0.3847458 0.3070996 - [64] 0.3023988 0.3086449 0.3410451 0.3551517 0.3544080 0.2852948 0.2854358 - [71] 0.3213712 0.3142182 0.2915214 0.2852871 0.3623552 0.3482564 0.3037564 - [78] 0.3236131 0.3050938 0.3441932 0.3562867 0.3043882 0.3140578 0.2998489 - [85] 0.3006447 0.2860677 0.3574227 0.2953492 0.3032142 0.3199704 0.2960316 - [92] 0.2866624 0.3163475 0.3053198 0.5604622 0.2852416 0.2901352 0.2863025 - [99] 0.3269520 0.2910794 + [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052 + [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679 + [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412 + [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204 + [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977 + [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311 + [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262 + [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975 + [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804 + [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094 + [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441 + [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616 + [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663 + [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484 + [99] 0.3164981 0.2817725 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. - 0.2495 0.2749 0.3091 0.3512 0.3649 0.9233 + 0.2296 0.2581 0.3003 0.3453 0.3652 0.9158 > > ### voom > @@ -1346,40 +1346,40 @@ set1 5 Up 7.3344e-12 > go <- goana(fit,FDR=0.8,geneid=EB) > topGO(go,n=10,truncate.term=30) Term Ont N Up Down P.Up -GO:0006915 apoptotic process BP 5 4 1 0.003446627 -GO:0012501 programmed cell death BP 5 4 1 0.003446627 +GO:0009653 anatomical structure morpho... BP 10 1 4 0.936627742 +GO:0048856 anatomical structure develo... BP 23 4 6 0.844070086 +GO:0032502 developmental process BP 23 4 6 0.844070086 GO:0055082 cellular chemical homeostas... BP 2 0 2 1.000000000 -GO:0006897 endocytosis BP 3 3 0 0.005046382 -GO:0048856 anatomical structure develo... BP 23 3 5 0.845083285 -GO:0032502 developmental process BP 23 3 5 0.845083285 -GO:0008219 cell death BP 6 4 1 0.009129968 -GO:0019725 cellular homeostasis BP 3 0 2 1.000000000 -GO:0048232 male gamete generation BP 3 0 2 1.000000000 -GO:0007283 spermatogenesis BP 3 0 2 1.000000000 +GO:0006915 apoptotic process BP 5 4 1 0.009503355 +GO:0012501 programmed cell death BP 5 4 1 0.009503355 +GO:0006897 endocytosis BP 3 3 0 0.010952381 +GO:0031988 membrane-bounded vesicle CC 19 4 5 0.691026648 +GO:0016485 protein processing BP 7 1 3 0.849770470 +GO:0005615 extracellular space CC 13 5 4 0.143422146 P.Down -GO:0006915 0.309695910 -GO:0012501 0.309695910 -GO:0055082 0.004242424 +GO:0009653 0.008224876 +GO:0048856 0.009014340 +GO:0032502 0.009014340 +GO:0055082 0.009090909 +GO:0006915 0.416247633 +GO:0012501 0.416247633 GO:0006897 1.000000000 -GO:0048856 0.006651547 -GO:0032502 0.006651547 -GO:0008219 0.360560422 -GO:0019725 0.012294372 -GO:0048232 0.012294372 -GO:0007283 0.012294372 +GO:0031988 0.020081679 +GO:0016485 0.020760307 +GO:0005615 0.023836810 > topGO(go,n=10,truncate.term=30,sort="down") Term Ont N Up Down P.Up P.Down -GO:0055082 cellular chemical homeostas... BP 2 0 2 1.0000000 0.004242424 -GO:0048856 anatomical structure develo... BP 23 3 5 0.8450833 0.006651547 -GO:0032502 developmental process BP 23 3 5 0.8450833 0.006651547 -GO:0019725 cellular homeostasis BP 3 0 2 1.0000000 0.012294372 -GO:0048232 male gamete generation BP 3 0 2 1.0000000 0.012294372 -GO:0007283 spermatogenesis BP 3 0 2 1.0000000 0.012294372 -GO:0009653 anatomical structure morpho... BP 10 0 3 1.0000000 0.020760307 -GO:0031988 membrane-bounded vesicle CC 19 1 4 0.9851064 0.023153004 -GO:0007276 gamete generation BP 4 0 2 1.0000000 0.023749721 -GO:0032504 multicellular organism repr... BP 4 0 2 1.0000000 0.023749721 +GO:0009653 anatomical structure morpho... BP 10 1 4 0.9366277 0.008224876 +GO:0048856 anatomical structure develo... BP 23 4 6 0.8440701 0.009014340 +GO:0032502 developmental process BP 23 4 6 0.8440701 0.009014340 +GO:0055082 cellular chemical homeostas... BP 2 0 2 1.0000000 0.009090909 +GO:0031988 membrane-bounded vesicle CC 19 4 5 0.6910266 0.020081679 +GO:0016485 protein processing BP 7 1 3 0.8497705 0.020760307 +GO:0005615 extracellular space CC 13 5 4 0.1434221 0.023836810 +GO:0031982 vesicle CC 20 4 5 0.7361976 0.025464546 +GO:0009887 animal organ morphogenesis BP 3 0 2 1.0000000 0.025788497 +GO:0019725 cellular homeostasis BP 3 0 2 1.0000000 0.025788497 > > proc.time() user system elapsed - 2.88 0.18 3.08 + 3.13 0.26 4.14 -- Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-med/r-bioc-limma.git _______________________________________________ debian-med-commit mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/debian-med-commit
