[med-svn] [r-bioc-limma] 01/03: New upstream version 3.30.8+dfsg
Andreas Tille
tille at debian.org
Fri Jan 20 08:36:07 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-bioc-limma.
commit 74ff2958ab00ed8f580817b6616ea81c5831dcb4
Author: Andreas Tille <tille at debian.org>
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(object at .Data)
})
-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
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