文件介绍

芯片数据分析流程有些复杂，但使用 R 和 Bioconductor包进行分析就简单多了。

本教程将一步一步的展示如何安装 R 和 Bioconductor，通过 GEO数据库下载芯片数据，对数据进行标准化，然后对数据进行质控检查，最后查找差异表达的基因。

# default 是此段代码的命名
# 本段代码是原模板文件自带
#knitr::opts_chunk$set(echo = TRUE)

Installation

options(repos=structure(c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")))
# download the BioC installation routines
source("http://bioconductor.org/biocLite.R")
# Set mirror website
options(BioC_mirror="http://mirrors.ustc.edu.cn/bioc/")
# install the core packages
 biocLite()
# install the GEO libraries

#biocLite("GEOquery")  # hide
#biocLite("ggplot2")   # hide

Getting the data

Download the dataset from GEO

library(GEOquery)

## Loading required package: Biobase

## Loading required package: BiocGenerics

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs

## The following objects are masked from 'package:base':
## 
##     anyDuplicated, append, as.data.frame, cbind, colMeans,
##     colnames, colSums, do.call, duplicated, eval, evalq, Filter,
##     Find, get, grep, grepl, intersect, is.unsorted, lapply,
##     lengths, Map, mapply, match, mget, order, paste, pmax,
##     pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
##     rowMeans, rownames, rowSums, sapply, setdiff, sort, table,
##     tapply, union, unique, unsplit, which, which.max, which.min

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## Setting options('download.file.method.GEOquery'='auto')

## Setting options('GEOquery.inmemory.gpl'=FALSE)

#getGEOSuppFiles("GSE53307")   #hide  对于新数据集，此处需替换

Uncompress tar files

untar("GSE53307/GSE53307_RAW.tar", exdir="data")
cels <- list.files("data/",pattern="[gz]")
sapply(paste("data", cels, sep="/"), gunzip,overwrite=TRUE)    #gunzip 后面可以跟参数

## data/GSM1289208_BS2012070740.CEL.gz data/GSM1289209_BS2012070741.CEL.gz 
##                             5359898                             5359771 
## data/GSM1289210_BS2012070742.CEL.gz data/GSM1289211_BS2012070743.CEL.gz 
##                             5360195                             5360059 
## data/GSM1289212_BS2012070744.CEL.gz data/GSM1289213_BS2012070745.CEL.gz 
##                             5360499                             5360538 
## data/GSM1289214_BS2012070746.CEL.gz data/GSM1289215_BS2012070747.CEL.gz 
##                             5360398                             5360138 
## data/GSM1289216_BS2012070748.CEL.gz data/GSM1289217_BS2012070749.CEL.gz 
##                             5359902                             5359755 
## data/GSM1289218_BS2012070750.CEL.gz data/GSM1289219_BS2012070751.CEL.gz 
##                             5360099                             5359847

cels

##  [1] "GSM1289208_BS2012070740.CEL.gz" "GSM1289209_BS2012070741.CEL.gz"
##  [3] "GSM1289210_BS2012070742.CEL.gz" "GSM1289211_BS2012070743.CEL.gz"
##  [5] "GSM1289212_BS2012070744.CEL.gz" "GSM1289213_BS2012070745.CEL.gz"
##  [7] "GSM1289214_BS2012070746.CEL.gz" "GSM1289215_BS2012070747.CEL.gz"
##  [9] "GSM1289216_BS2012070748.CEL.gz" "GSM1289217_BS2012070749.CEL.gz"
## [11] "GSM1289218_BS2012070750.CEL.gz" "GSM1289219_BS2012070751.CEL.gz"

DOS下利用 dir > XX.txt命令生成 文件列表，然后导入excel中进行处理，只留下文件名单列，文本文件。

use excel to add the group name ，3列 Name,FileName,Target, Name 和 FileName 可以同名，Targe为分组说明

# The simpleaffy package provides routines for handling CEL files including normalisation and loading data with sample information. We are going to load the data into an R object called ‘celfiles’

##biocLite("BiocGenerics")
##biocLite("parallel")
##biocLite("Biobase")


#biocLite("affy")   # hide
#biocLite("genefilter")  # hide


#biocLite("simpleaffy")  # hide

#biocLite("RSQLite")   # hide
##biocLite("gcrma")

library(genefilter)

library(simpleaffy)

celfiles <- read.affy(covdesc="3.txt", path="data")

celfiles

## AffyBatch object
## size of arrays=732x732 features (22 kb)
## cdf=Mouse430A_2 (22690 affyids)
## number of samples=12
## number of genes=22690
## annotation=mouse430a2
## notes=

#biocLite("mouse430a2cdf")    # hide

library(mouse430a2cdf)

AffyBatch object
size of arrays=1164x1164 features (12 kb)
cdf=HG-U133_Plus_2 (54675 affyids)
number of samples=12
number of genes=54675
annotation=hgu133plus2
notes=

celfiles.gcrma <- gcrma(celfiles)

## Adjusting for optical effect............Done.
## Computing affinities

## Loading required package: AnnotationDbi

## Loading required package: stats4

## Loading required package: IRanges

## Loading required package: S4Vectors

## 
## Attaching package: 'S4Vectors'

## The following object is masked from 'package:base':
## 
##     expand.grid

## 
## Attaching package: 'IRanges'

## The following object is masked from 'package:simpleaffy':
## 
##     members

## .Done.
## Adjusting for non-specific binding............Done.
## Normalizing
## Calculating Expression

celfiles.gcrma

## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22690 features, 12 samples 
##   element names: exprs 
## protocolData
##   sampleNames: GSM1289208_BS2012070740.CEL
##     GSM1289209_BS2012070741.CEL ... GSM1289219_BS2012070751.CEL
##     (12 total)
##   varLabels: ScanDate
##   varMetadata: labelDescription
## phenoData
##   sampleNames: GSM1289208_BS2012070740.CEL
##     GSM1289209_BS2012070741.CEL ... GSM1289219_BS2012070751.CEL
##     (12 total)
##   varLabels: sample FileName Target
##   varMetadata: labelDescription
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: mouse430a2

write.exprs(celfiles.gcrma,file="NormalizedData.txt")

eset <- exprs(celfiles.gcrma)

Quality control checks

 # load colour libraries
 library("RColorBrewer")
 # set colour palette
 cols <- brewer.pal(8, "Set1")
 # plot a boxplot of unnormalised intensity values
 boxplot(celfiles, col=cols)

 # plot a boxplot of normalised intensity values, affyPLM is required to interrogate celfiles.gcrma
 library("affyPLM")

## Loading required package: preprocessCore

 boxplot(celfiles.gcrma, col=cols)

 # the boxplots are somewhat skewed by the normalisation algorithm
 # and it is often more informative to look at density plots
 # Plot a density vs log intensity histogram for the unnormalised data
 hist(celfiles, col=cols)

 # Plot a density vs log intensity histogram for the normalised data
 hist(celfiles.gcrma, col=cols)

To take a closer look at the situation on a per-chip level we can use affyPLM. affyPLM allows us to visualise statistical characteristics of the CEL files.

# Perform probe-level metric calculations on the CEL files:
celfiles.qc <- fitPLM(celfiles)
# Create an image of GSM24662.CEL:
 image(celfiles.qc, which=1, add.legend=TRUE)

 # Create an image of GSM524665.CEL
 # There is a spatial artifact present
 image(celfiles.qc, which=10, add.legend=TRUE)

 # affyPLM also provides more informative boxplots
 # RLE (Relative Log Expression) plots should have
 # values close to zero. GSM524665.CEL is an outlier
 RLE(celfiles.qc, main="RLE",col=cols)

 # We can also use NUSE (Normalised Unscaled Standard Errors).
 # The median standard error should be 1 for most genes.
# GSM524665.CEL appears to be an outlier on this plot too
 NUSE(celfiles.qc, main="NUSE",col=cols)

#We can also look at the relationships between the samples using heirarchical clustering:
 
eset <- exprs(celfiles.gcrma)
distance <- dist(t(eset),method="maximum")
clusters <- hclust(distance)
plot(clusters)

Filtering data

## 此处\* 代表斜体 \*\*代表加粗 \*\*\* 代表加粗加斜

Now we have looked at the data, we can go on to analyse it. The first stage of analysis is to filter out uninformative data such as control probesets and other internal controls as well as removing genes with low variance, that would be unlikely to pass statistical tests for differential expression, or are expressed uniformly close to background detection levels. The modifiers to nsFilter below tell nsFilter not to remove probesets without Entrez Gene identifiers, or have duplicated Entrez Gene identifiers.

celfiles.filtered <- nsFilter(celfiles.gcrma, require.entrez=FALSE, remove.dupEntrez=FALSE)
celfiles.filtered $ filter.log

## $numLowVar
## [1] 11313
## 
## $feature.exclude
## [1] 64

From this we conclude 11,313 probesets have been removed for reasons of low variance, and 64 control probesets have been removed as well.

Finding differentially expressed probesets

Now we have a filtered dataset, we can send the information to limma for differential gene expression analysis. First of all we need to extract information about the samples

samples <- celfiles.gcrma$Target
# check the results of this  samples
samples

##  [1] "SubIng" "SubIng" "SubIng" "VisEpi" "VisEpi" "VisEpi" "SubAxi"
##  [8] "SubAxi" "SubAxi" "VisMes" "VisMes" "VisMes"

# convert into factors
 samples <- as.factor(samples)

 # check factors have been assigned
samples

##  [1] SubIng SubIng SubIng VisEpi VisEpi VisEpi SubAxi SubAxi SubAxi VisMes
## [11] VisMes VisMes
## Levels: SubAxi SubIng VisEpi VisMes

# set up the experimental design
design <- model.matrix(~0 + samples)
 colnames(design) <- c("SubIng", "VisEpi","SubAxi","VisMes")
 # inspect the experiment design
design

##    SubIng VisEpi SubAxi VisMes
## 1       0      1      0      0
## 2       0      1      0      0
## 3       0      1      0      0
## 4       0      0      1      0
## 5       0      0      1      0
## 6       0      0      1      0
## 7       1      0      0      0
## 8       1      0      0      0
## 9       1      0      0      0
## 10      0      0      0      1
## 11      0      0      0      1
## 12      0      0      0      1
## attr(,"assign")
## [1] 1 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$samples
## [1] "contr.treatment"

At this point we have normalised filtered data, and a description of the data and the samples and experimental design. This can be fed into limma for analysis.

library(limma)

## 
## Attaching package: 'limma'

## The following object is masked from 'package:BiocGenerics':
## 
##     plotMA

#target <- readTargets("3.txt")  
# Reads targets information from file 'affy_targets.txt' and assigns it to targets frame.

# fit the linear model to the filtered expression set
fit <- lmFit(exprs(celfiles.filtered$eset), design)
 # set up a contrast matrix to compare tissues v cell line
contrast.matrix <- makeContrasts(VisEpi_SubIng = VisEpi-SubIng, VisMes_SubIng=VisMes-SubIng,VisEpi_SubAxi=VisEpi-SubAxi,VisMes_SubAxi=VisMes-SubAxi,levels=design)

 # check the contrast matrix
 contrast.matrix

##         Contrasts
## Levels   VisEpi_SubIng VisMes_SubIng VisEpi_SubAxi VisMes_SubAxi
##   SubIng            -1            -1             0             0
##   VisEpi             1             0             1             0
##   SubAxi             0             0            -1            -1
##   VisMes             0             1             0             1

# Now the contrast matrix is combined with the per-probeset linear model fit.
 #huvec_fits <- contrasts.fit(fit, contrast.matrix)
 #huvec_ebFit <- eBayes(huvec_fits)
 
 Vis_fits <- contrasts.fit(fit,contrast.matrix)
 Vis_ebfits <- eBayes(Vis_fits)
 
 
 # return the top 10 results for any given contrast
 # coef=1 is huvec_choroid, coef=2 is huvec_retina
 toptable(Vis_ebfits, number=50, coef=3,adjust="fdr",sort.by="B")

## toptable() is deprecated and will be removed in the future version of limma. Please use topTable() instead.

##                  logFC         t      P.Value    adj.P.Val         B
## 1448377_at   -7.675489 -31.84527 4.084632e-12 4.620944e-08 16.949541
## 1418945_at    6.717054  27.86929 1.722140e-11 9.741288e-08 15.965920
## 1454881_s_at -8.240210 -26.42883 3.049165e-11 1.042400e-07 15.549251
## 1423253_at    5.869459  25.96702 3.685671e-11 1.042400e-07 15.407836
## 1419532_at    6.377121  25.23044 5.021411e-11 1.051779e-07 15.173870
## 1419063_at    5.184754  24.98461 5.578250e-11 1.051779e-07 15.093396
## 1456741_s_at -6.120338 -23.91864 8.906587e-11 1.439432e-07 14.729837
## 1456228_x_at  5.586926  21.72258 2.497098e-10 3.521094e-07 13.898524
## 1434237_at   -7.324556 -21.38325 2.954392e-10 3.521094e-07 13.759158
## 1437718_x_at -4.644284 -21.13023 3.354666e-10 3.521094e-07 13.653186
## 1426442_at   -5.437002 -21.08995 3.423675e-10 3.521094e-07 13.636149
## 1449319_at   -5.047787 -20.36108 4.981510e-10 4.696318e-07 13.319814
## 1423327_at    3.961866  20.04618 5.881008e-10 5.117834e-07 13.178262
## 1433532_a_at  5.336824  19.19148 9.347300e-10 7.129154e-07 12.778338
## 1418755_at    4.108085  19.17125 9.452604e-10 7.129154e-07 12.768584
## 1452418_at   -3.264809 -17.47215 2.526572e-09 1.786444e-06 11.897683
## 1436201_x_at  6.056053  16.70292 4.062621e-09 2.703555e-06 11.467037
## 1455626_at    5.922248  16.59642 4.345711e-09 2.731279e-06 11.405472
## 1419646_a_at  6.264580  15.88519 6.886753e-09 4.100518e-06 10.981555
## 1454651_x_at  5.830354  14.50748 1.778250e-08 9.977912e-06 10.092091
## 1418457_at    2.634644  14.45085 1.852171e-08 9.977912e-06 10.053451
## 1460238_at   -4.596892 -14.36509 1.970562e-08 1.013317e-05  9.994600
## 1424890_at   -4.931935 -14.17119 2.269695e-08 1.116394e-05  9.860064
## 1437685_x_at -3.493464 -13.97058 2.631946e-08 1.240634e-05  9.718659
## 1425995_s_at -4.602885 -13.78997 3.012246e-08 1.363102e-05  9.589393
## 1431475_a_at  3.599702  13.67880 3.275786e-08 1.425345e-05  9.508879
## 1448529_at   -2.211570 -13.41947 3.993241e-08 1.673168e-05  9.318222
## 1417275_at    5.482638  13.36228 4.173428e-08 1.686214e-05  9.275632
## 1418672_at   -1.658643 -13.21800 4.668506e-08 1.821200e-05  9.167291
## 1449033_at   -4.722900 -13.09261 5.150779e-08 1.942359e-05  9.072088
## 1449456_a_at  2.826001  13.03893 5.373592e-08 1.961015e-05  9.031023
## 1439506_at   -2.222314 -12.81294 6.433253e-08 2.274356e-05  8.856139
## 1434342_at    2.415821  12.72307 6.916044e-08 2.370946e-05  8.785665
## 1415939_at   -3.961586 -12.34361 9.434988e-08 2.996775e-05  8.482180
## 1424338_at   -2.299122 -12.33864 9.473939e-08 2.996775e-05  8.478144
## 1418879_at   -2.703162 -12.33074 9.536276e-08 2.996775e-05  8.471718
## 1433855_at   -2.510326 -12.27990 9.948196e-08 3.041728e-05  8.430264
## 1451335_at    3.499658  12.06159 1.194993e-07 3.557621e-05  8.250215
## 1423396_at   -4.435573 -11.95137 1.312336e-07 3.806784e-05  8.158021
## 1422308_a_at -4.405263 -11.89639 1.375487e-07 3.890220e-05  8.111710
## 1420512_at    2.577223  11.75554 1.552856e-07 4.284746e-05  7.992041
## 1417447_at   -4.144418 -11.67801 1.660949e-07 4.473884e-05  7.925551
## 1425505_at   -1.477072 -11.46616 2.000241e-07 5.262494e-05  7.741545
## 1421811_at   -2.526689 -11.43255 2.060654e-07 5.298222e-05  7.712042
## 1436990_s_at  2.255748  11.30079 2.317336e-07 5.825784e-05  7.595524
## 1423136_at   -4.327569 -11.26774 2.387028e-07 5.870532e-05  7.566084
## 1428891_at    2.224485  11.22952 2.470479e-07 5.946496e-05  7.531927
## 1420559_a_at  2.049718  11.18396 2.574084e-07 6.066795e-05  7.491070
## 1448395_at   -2.997288 -11.04337 2.924696e-07 6.699433e-05  7.363926
## 1437324_x_at -3.509240 -11.02989 2.960944e-07 6.699433e-05  7.351650

 write.table(toptable(Vis_ebfits,coef=3,adjust="fdr",sort.by="B",number=10000),file="limma_complete.xls",row.name=T,sep="\t")

## toptable() is deprecated and will be removed in the future version of limma. Please use topTable() instead.

 results = decideTests(Vis_ebfits,p.value = 0.05);vennDiagram(results);plotMA(Vis_ebfits,2);volcanoplot(Vis_ebfits)

 volcanoplot(Vis_ebfits,coef=3,highlight=5,xlab="Log Fold Change",ylab="Log Odds", pch=19, cex=0.67, col = "dark blue")

 x=topTable(Vis_ebfits,number=50000, coef=3,adjust="fdr",sort.by="P"); y <- x[x$adj.P.Val < 0.01,]; y; print("Number of genes in this list:"); length(y$ID)

##                   logFC   AveExpr          t      P.Value    adj.P.Val
## 1448377_at   -7.6754885  7.099929 -31.845273 4.084632e-12 4.620944e-08
## 1418945_at    6.7170540  7.189678  27.869291 1.722140e-11 9.741288e-08
## 1454881_s_at -8.2402099  6.096258 -26.428832 3.049165e-11 1.042400e-07
## 1423253_at    5.8694586  6.363157  25.967016 3.685671e-11 1.042400e-07
## 1419532_at    6.3771211  5.908718  25.230440 5.021411e-11 1.051779e-07
## 1419063_at    5.1847538  4.682831  24.984611 5.578250e-11 1.051779e-07
## 1456741_s_at -6.1203382  6.266611 -23.918637 8.906587e-11 1.439432e-07
## 1456228_x_at  5.5869256  8.259734  21.722575 2.497098e-10 3.521094e-07
## 1434237_at   -7.3245561  5.739062 -21.383249 2.954392e-10 3.521094e-07
## 1437718_x_at -4.6442841  4.742540 -21.130235 3.354666e-10 3.521094e-07
## 1426442_at   -5.4370018  6.126939 -21.089954 3.423675e-10 3.521094e-07
## 1449319_at   -5.0477871  4.532439 -20.361078 4.981510e-10 4.696318e-07
## 1423327_at    3.9618657  5.254291  20.046179 5.881008e-10 5.117834e-07
## 1433532_a_at  5.3368243  9.493537  19.191478 9.347300e-10 7.129154e-07
## 1418755_at    4.1080850  4.944802  19.171248 9.452604e-10 7.129154e-07
## 1452418_at   -3.2648089  9.887167 -17.472149 2.526572e-09 1.786444e-06
## 1436201_x_at  6.0560532  7.270699  16.702919 4.062621e-09 2.703555e-06
## 1455626_at    5.9222476  7.001149  16.596417 4.345711e-09 2.731279e-06
## 1419646_a_at  6.2645800  6.718736  15.885187 6.886753e-09 4.100518e-06
## 1454651_x_at  5.8303541  7.180588  14.507480 1.778250e-08 9.977912e-06
## 1418457_at    2.6346440  3.871217  14.450852 1.852171e-08 9.977912e-06
## 1460238_at   -4.5968925  4.716701 -14.365088 1.970562e-08 1.013317e-05
## 1424890_at   -4.9319352  4.309848 -14.171193 2.269695e-08 1.116394e-05
## 1437685_x_at -3.4934644  5.308318 -13.970580 2.631946e-08 1.240634e-05
## 1425995_s_at -4.6028846  4.620890 -13.789972 3.012246e-08 1.363102e-05
## 1431475_a_at  3.5997016  3.405159  13.678796 3.275786e-08 1.425345e-05
## 1448529_at   -2.2115696  9.947873 -13.419465 3.993241e-08 1.673168e-05
## 1417275_at    5.4826382  6.104669  13.362277 4.173428e-08 1.686214e-05
## 1418672_at   -1.6586432  7.248053 -13.217997 4.668506e-08 1.821200e-05
## 1449033_at   -4.7228996  4.583156 -13.092614 5.150779e-08 1.942359e-05
## 1449456_a_at  2.8260015  5.143290  13.038930 5.373592e-08 1.961015e-05
## 1439506_at   -2.2223143  4.390522 -12.812944 6.433253e-08 2.274356e-05
## 1434342_at    2.4158207  5.337319  12.723068 6.916044e-08 2.370946e-05
## 1415939_at   -3.9615861  5.192550 -12.343608 9.434988e-08 2.996775e-05
## 1424338_at   -2.2991222  6.413550 -12.338642 9.473939e-08 2.996775e-05
## 1418879_at   -2.7031624  5.620922 -12.330740 9.536276e-08 2.996775e-05
## 1433855_at   -2.5103255  5.431757 -12.279898 9.948196e-08 3.041728e-05
## 1451335_at    3.4996578  8.715085  12.061592 1.194993e-07 3.557621e-05
## 1423396_at   -4.4355730  8.058948 -11.951369 1.312336e-07 3.806784e-05
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## 1420512_at    2.5772232  4.577216  11.755537 1.552856e-07 4.284746e-05
## 1417447_at   -4.1444177  3.870834 -11.678011 1.660949e-07 4.473884e-05
## 1425505_at   -1.4770724  9.797096 -11.466155 2.000241e-07 5.262494e-05
## 1421811_at   -2.5266887  8.955620 -11.432550 2.060654e-07 5.298222e-05
## 1436990_s_at  2.2557483 12.424991  11.300791 2.317336e-07 5.825784e-05
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## 1428891_at    2.2244846  7.924754  11.229516 2.470479e-07 5.946496e-05
## 1420559_a_at  2.0497179  2.907983  11.183962 2.574084e-07 6.066795e-05
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## 1427038_at   -3.0913046  8.246281 -10.911358 3.301434e-07 7.262026e-05
## 1422870_at   -1.6433909  7.922658 -10.896918 3.345733e-07 7.262026e-05
## 1448730_at    2.8688501  6.005145  10.878819 3.402169e-07 7.262026e-05
## 1422573_at   -1.2705488  8.096635 -10.817941 3.599699e-07 7.408769e-05
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## 1426154_s_at  8.1175954  9.067815  10.733539 3.894404e-07 7.646593e-05
## 1423294_at   -2.7660823  7.720232 -10.726459 3.920290e-07 7.646593e-05
## 1456084_x_at -3.6856069  8.769302 -10.621813 4.325427e-07 8.293823e-05
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## 1423292_a_at  2.8984889  4.344049  10.278536 6.006893e-07 1.075735e-04
## 1460412_at    1.4896982  3.364960  10.275585 6.024113e-07 1.075735e-04
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## 1431554_a_at -2.0988030  2.711995  -9.957025 8.238577e-07 1.294486e-04
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## 1433720_s_at  2.3420411 10.829359   9.358224 1.518131e-06 1.929744e-04
## 1426657_s_at -1.4662194  9.500554  -9.353056 1.526366e-06 1.929744e-04
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## 1426592_a_at -1.4940247  7.478864  -9.347548 1.535198e-06 1.929744e-04
## 1426530_a_at -1.0148584 10.090677  -9.313891 1.590376e-06 1.977135e-04
## 1416105_at   -1.1753675  7.425582  -9.237923 1.722969e-06 2.118690e-04
## 1449218_at    3.6784149 11.281117   9.202085 1.789626e-06 2.176993e-04
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## 1452127_a_at -1.2609432  6.666351  -9.124030 1.944655e-06 2.315777e-04
## 1448926_at   -2.3597718  6.547531  -9.026182 2.159819e-06 2.537786e-04
## 1455796_x_at -1.4792464  6.705227  -9.019275 2.175950e-06 2.537786e-04
## 1450455_s_at -1.5670062  7.248035  -9.006257 2.206708e-06 2.547397e-04
## 1425137_a_at  2.5087135  9.032383   8.960341 2.319008e-06 2.649993e-04
## 1425475_at   -1.7426384  6.121096  -8.936417 2.379956e-06 2.692444e-04
## 1418328_at    3.5776478  4.718612   8.907456 2.456057e-06 2.751027e-04
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## 1425341_at    1.6692597  5.719628   8.877778 2.536780e-06 2.786271e-04
## 1456691_s_at -1.0772078  6.809195  -8.856690 2.595880e-06 2.823768e-04
## 1449249_at   -1.9881834  5.599175  -8.779976 2.823738e-06 3.042376e-04
## 1422329_a_at  2.2440968  3.671121   8.740964 2.947822e-06 3.146104e-04
## 1438966_x_at -3.1627626  6.992793  -8.712715 3.041345e-06 3.215583e-04
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## 1418907_at   -1.4718938  2.703942  -8.600327 3.446416e-06 3.544482e-04
## 1448816_at   -2.3857605  7.682803  -8.581715 3.518951e-06 3.586477e-04
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## 1451031_at    1.7200426  6.948589   8.538589 3.693425e-06 3.684151e-04
## 1435941_at   -1.3525906  4.278140  -8.534012 3.712483e-06 3.684151e-04
## 1427183_at   -1.3789518 12.151980  -8.498481 3.864092e-06 3.759047e-04
## 1456471_x_at -1.2606431 10.255051  -8.488264 3.908916e-06 3.759047e-04
## 1435833_at   -2.2322979  5.472933  -8.487349 3.912957e-06 3.759047e-04
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## 1417079_s_at -2.0353622  2.713933  -8.464714 4.014381e-06 3.784557e-04
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## 1426261_s_at -1.5014277  9.337173  -8.380371 4.418041e-06 4.029258e-04
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## 1422904_at   -2.5031485  6.618896  -8.309160 4.793015e-06 4.303443e-04
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## 1431530_a_at -1.5551259  5.743055  -8.129468 5.900701e-06 5.095774e-04
## 1423278_at   -1.6702236  4.729804  -8.080659 6.247205e-06 5.354139e-04
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## 1454674_at    1.3300593  2.740763   7.991242 6.940232e-06 5.816612e-04
## 1425468_at    2.6680637  6.072564   7.991141 6.941064e-06 5.816612e-04
## 1448664_a_at -1.5463508  3.930116  -7.970492 7.112629e-06 5.889745e-04
## 1449315_at   -1.8127995  5.426116  -7.968140 7.132459e-06 5.889745e-04
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## 1426208_x_at -2.0808906 10.866311  -7.930036 7.462156e-06 6.073336e-04
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## 1416468_at   -1.7367898 11.421302  -7.871837 7.997836e-06 6.416987e-04
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## 1439036_a_at -2.3929826  9.368874  -7.845369 8.255039e-06 6.528289e-04
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## 1420664_s_at  1.1548675  9.400716   7.824508 8.464029e-06 6.603694e-04
## 1437621_x_at -1.1448075 11.996382  -7.810291 8.609718e-06 6.671352e-04
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## 1421882_a_at  2.7377327  3.931380   7.639147 1.059215e-05 7.883485e-04
## 1420621_a_at -1.0541595 11.719197  -7.620741 1.083299e-05 7.965863e-04
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## 1417040_a_at -1.4639666  6.065146  -7.610767 1.096596e-05 8.003734e-04
## 1450377_at   -1.5756605  2.686671  -7.596745 1.115587e-05 8.090148e-04
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## 1416316_at    3.2951115  5.379022   7.576862 1.143125e-05 8.180922e-04
## 1450286_at   -2.0478526  7.305392  -7.568193 1.155360e-05 8.180922e-04
## 1423281_at   -1.2564761  4.777584  -7.565881 1.158646e-05 8.180922e-04
## 1449824_at   -3.3838954  5.115611  -7.561948 1.164261e-05 8.180922e-04
## 1448550_at   -1.8286411  8.235776  -7.552413 1.177993e-05 8.226318e-04
## 1450652_at    1.5554304  8.652790   7.502129 1.253360e-05 8.698933e-04
## 1455206_at   -0.9017819 10.924477  -7.488395 1.274835e-05 8.791730e-04
## 1419309_at   -1.5319930  8.506383  -7.483696 1.282273e-05 8.791730e-04
## 1437863_at   -1.5475774  7.155868  -7.470824 1.302889e-05 8.879264e-04
## 1454714_x_at -1.2533334 12.292363  -7.455873 1.327282e-05 8.991339e-04
## 1456573_x_at -1.0492595  8.372305  -7.445381 1.344693e-05 9.055069e-04
## 1451047_at    1.7399404  9.796267   7.435103 1.361989e-05 9.064348e-04
## 1421841_at   -1.2468699  3.656449  -7.435040 1.362096e-05 9.064348e-04
## 1420842_at   -1.4546604  8.371622  -7.417035 1.392976e-05 9.215633e-04
## 1452179_at   -0.8165858  8.991757  -7.404773 1.414436e-05 9.258969e-04
## 1436845_at   -1.7945856  5.339632  -7.403947 1.415895e-05 9.258969e-04
## 1455061_a_at  1.2345322  9.761179   7.380834 1.457364e-05 9.419072e-04
## 1436448_a_at -1.2072859  7.023062  -7.377091 1.464203e-05 9.419072e-04
## 1450185_a_at -1.3277936  2.795851  -7.376462 1.465356e-05 9.419072e-04
## 1428372_at   -1.0869343  7.128990  -7.327812 1.557518e-05 9.954916e-04
## 1423135_at    1.1679371  8.239127   7.319483 1.573911e-05 9.994564e-04
## 1417877_at   -1.6818198  8.292776  -7.315714 1.581390e-05 9.994564e-04
## 1423437_at   -2.1516482  6.404660  -7.265277 1.685243e-05 1.056881e-03
## 1418157_at   -2.0625374  7.017250  -7.262610 1.690934e-05 1.056881e-03
## 1425163_at   -3.0234619  3.384183  -7.256156 1.704795e-05 1.059689e-03
## 1424783_a_at -1.4287252  9.662038  -7.241900 1.735843e-05 1.073092e-03
## 1419648_at   -0.8928712  8.500788  -7.197854 1.835674e-05 1.125607e-03
## 1419762_at    2.9540076  6.457758   7.195536 1.841096e-05 1.125607e-03
## 1427052_at    1.8603700  8.783335   7.191473 1.850640e-05 1.125607e-03
## 1431028_a_at  1.7225258  7.102283   7.180724 1.876150e-05 1.135021e-03
## 1426677_at   -0.7554694  9.187357  -7.175533 1.888603e-05 1.136477e-03
## 1420629_a_at  0.9812186  6.786670   7.168498 1.905625e-05 1.138435e-03
## 1424214_at    2.4989506  6.184761   7.165887 1.911982e-05 1.138435e-03
## 1426522_at    1.3007487 12.454139   7.151146 1.948315e-05 1.150156e-03
## 1416204_at    1.4979828  8.597448   7.149667 1.952002e-05 1.150156e-03
## 1417552_at    1.2484004  8.691417   7.129451 2.003140e-05 1.174172e-03
## 1421999_at   -1.9561723  7.787894  -7.097289 2.087489e-05 1.217308e-03
## 1423363_at   -1.2653185  6.413666  -7.067833 2.168093e-05 1.252435e-03
## 1424407_s_at -0.7130667  8.869003  -7.063463 2.180333e-05 1.252435e-03
## 1422444_at   -1.2620889  9.463306  -7.060665 2.188209e-05 1.252435e-03
## 1423307_s_at  1.1191767  8.457242   7.059319 2.192011e-05 1.252435e-03
## 1418804_at   -1.3083286  9.982083  -7.054164 2.206628e-05 1.254451e-03
## 1452320_at   -2.1733460  2.739067  -7.039382 2.249130e-05 1.267002e-03
## 1453839_a_at  2.2096147  8.581060   7.038703 2.251103e-05 1.267002e-03
## 1449079_s_at  1.1926311  9.401792   7.033310 2.266840e-05 1.269543e-03
## 1435162_at    2.6824358  4.301396   7.014695 2.322081e-05 1.294074e-03
## 1423574_s_at -0.9333407  6.747700  -6.998331 2.371837e-05 1.315323e-03
## 1427516_a_at -1.8819554  4.632796  -6.977368 2.437255e-05 1.343482e-03
## 1416910_at    0.9530495 11.832911   6.974496 2.446366e-05 1.343482e-03
## 1429054_at    0.8027204  9.379225   6.950624 2.523536e-05 1.373189e-03
## 1417403_at    2.7978762  9.737667   6.950259 2.524736e-05 1.373189e-03
## 1450686_at   -0.7513701  9.524475  -6.939118 2.561662e-05 1.386607e-03
## 1430385_a_at -1.3186559  4.467916  -6.920569 2.624429e-05 1.402939e-03
## 1450024_at   -0.8850471  7.306914  -6.919188 2.629170e-05 1.402939e-03
## 1460409_at   -0.8903163  8.541593  -6.915623 2.641443e-05 1.402939e-03
## 1456226_x_at -0.8155419 10.229453  -6.915333 2.642445e-05 1.402939e-03
## 1426851_a_at -2.2240693  6.254935  -6.909000 2.664415e-05 1.402939e-03
## 1428401_at   -0.6568739  3.740918  -6.908475 2.666242e-05 1.402939e-03
## 1418604_at   -1.1362706  2.455795  -6.886151 2.745315e-05 1.435724e-03
## 1416564_at   -1.9794075  6.471850  -6.880342 2.766300e-05 1.435724e-03
## 1427482_a_at  2.3434062  6.165579   6.880254 2.766621e-05 1.435724e-03
## 1419470_at   -1.1400214  8.568118  -6.872713 2.794124e-05 1.442612e-03
## 1436031_at   -1.3965923  6.567594  -6.869645 2.805397e-05 1.442612e-03
## 1423109_s_at  1.2376584  9.867509   6.859821 2.841823e-05 1.454730e-03
## 1438932_at   -1.0182817  6.358483  -6.834423 2.938374e-05 1.497379e-03
## 1427442_a_at -0.8188439 12.786812  -6.822272 2.985802e-05 1.514725e-03
## 1417497_at   -1.6468352  5.687893  -6.807008 3.046554e-05 1.538646e-03
## 1417411_at    2.0329671  4.571517   6.803529 3.060585e-05 1.538862e-03
## 1426852_x_at -2.3560474  6.225903  -6.775998 3.174088e-05 1.583084e-03
## 1450021_at   -0.7240344 10.918141  -6.775419 3.176523e-05 1.583084e-03
## 1423908_at    0.7773649  9.498962   6.771780 3.191874e-05 1.583757e-03
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## 1426410_at   -1.7998280  4.633114  -6.743730 3.312901e-05 1.629515e-03
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## 1451041_at   -1.2046703  7.773609  -5.819350 1.196823e-04 3.813989e-03
## 1438115_a_at -1.2250631  7.885607  -5.816282 1.202167e-04 3.820116e-03
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## 1448672_a_at -0.7587107  7.497770  -5.761093 1.302794e-04 3.983382e-03
## 1428319_at   -1.1179876  5.952299  -5.754267 1.315851e-04 4.012459e-03
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## 1427954_at   -0.8609290  4.307570  -5.725920 1.371581e-04 4.137786e-03
## 1422502_at    0.8599102  7.745952   5.720439 1.382644e-04 4.145969e-03
## 1425143_a_at  0.7877764 11.702874   5.719741 1.384059e-04 4.145969e-03
## 1448024_at   -2.1749834  8.668689  -5.719136 1.385288e-04 4.145969e-03
## 1426965_at   -0.9993286  6.783716  -5.716797 1.390048e-04 4.149240e-03
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## 1417437_at    0.6678120  6.952895   5.691216 1.443255e-04 4.263065e-03
## 1429183_at   -2.2047246  7.831189  -5.675781 1.476404e-04 4.298698e-03
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## 1455042_at   -0.8587974  9.493770  -5.668235 1.492906e-04 4.298698e-03
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## 1427658_at   -0.8675823  4.995997  -5.640320 1.555673e-04 4.385759e-03
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## 1453913_a_at  0.9410959  7.802194   5.637432 1.562327e-04 4.385759e-03
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## 1432273_a_at  1.1475164  6.892644   5.614994 1.615054e-04 4.459587e-03
## 1424184_at    1.2322821 11.537980   5.614507 1.616221e-04 4.459587e-03
## 1448448_a_at  0.7422594  8.411786   5.611525 1.623374e-04 4.468426e-03
## 1427255_s_at  0.8332451  6.001400   5.595720 1.661861e-04 4.563259e-03
## 1448649_at   -1.1967722 12.440884  -5.592494 1.669835e-04 4.574053e-03
## 1421960_at    0.8208084  7.205513   5.588769 1.679092e-04 4.588302e-03
## 1423891_at   -1.0707178  4.092910  -5.574817 1.714258e-04 4.673108e-03
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## 1423242_at    0.6894372  9.490940   5.566013 1.736851e-04 4.711990e-03
## 1437671_x_at -1.0402545 11.674498  -5.560515 1.751121e-04 4.739337e-03
## 1417212_at    1.1899161  9.135893   5.556246 1.762286e-04 4.758172e-03
## 1455315_at   -0.8960847  4.618397  -5.551234 1.775490e-04 4.763670e-03
## 1418980_a_at  1.7653788  4.879989   5.550638 1.777068e-04 4.763670e-03
## 1448433_a_at  1.0777003  9.933588   5.548713 1.782172e-04 4.763670e-03
## 1427957_at    2.5425687  7.335217   5.548083 1.783846e-04 4.763670e-03
## 1448859_at    4.0293520  7.368414   5.547508 1.785376e-04 4.763670e-03
## 1417520_at   -0.7327756  2.549051  -5.544599 1.793134e-04 4.773112e-03
## 1425608_at   -1.6044810  5.761361  -5.542577 1.798550e-04 4.776289e-03
## 1456292_a_at -0.6490739 13.235791  -5.534470 1.820431e-04 4.823077e-03
## 1435767_at   -2.6798330  3.412757  -5.529169 1.834892e-04 4.850031e-03
## 1419485_at   -1.8913017  4.466251  -5.520441 1.858968e-04 4.902216e-03
## 1433883_at   -0.6178797 11.779580  -5.518530 1.864287e-04 4.904808e-03
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## 1425177_at    1.0492854  6.960262   5.503055 1.907937e-04 4.974237e-03
## 1452178_at    1.1055761  5.794656   5.502972 1.908173e-04 4.974237e-03
## 1448870_at   -0.8289029  8.425605  -5.502940 1.908264e-04 4.974237e-03
## 1429947_a_at  2.2287146  5.514020   5.498392 1.921302e-04 4.987000e-03
## 1417604_at   -1.0299179  6.979559  -5.497290 1.924476e-04 4.987000e-03
## 1437396_at   -0.7721644  3.756076  -5.496628 1.926385e-04 4.987000e-03
## 1449799_s_at -1.8327840  9.288696  -5.492402 1.938617e-04 5.007209e-03
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## 1417851_at    4.3827875  7.189118   5.475672 1.987861e-04 5.087934e-03
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## 1452692_a_at  0.6131813 11.958046   5.422258 2.154188e-04 5.309441e-03
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## 1418280_at   -1.1420395  8.191819  -5.382904 2.286163e-04 5.504543e-03
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## 1434341_x_at  0.9441276  9.517414   5.366022 2.345386e-04 5.621472e-03
## 1434542_at   -1.1351332  9.307244  -5.361693 2.360832e-04 5.643916e-03
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## 1455156_at   -0.5592860  7.943194  -5.357820 2.374747e-04 5.653100e-03
## 1433508_at   -0.7639271 10.254657  -5.355942 2.381521e-04 5.653100e-03
## 1424567_at   -1.5739150  6.230386  -5.355377 2.383567e-04 5.653100e-03
## 1455800_x_at  0.6277387 11.678918   5.350904 2.399805e-04 5.679707e-03
## 1426291_at   -0.7307476  3.755161  -5.345332 2.420197e-04 5.716010e-03
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## 1426950_at   -0.9233218  6.353612  -5.329065 2.480793e-04 5.834763e-03
## 1426237_at   -0.7396422  5.683605  -5.324772 2.497053e-04 5.856526e-03
## 1423816_at   -0.9471367  6.710951  -5.323824 2.500658e-04 5.856526e-03
## 1424364_a_at  0.9743416 11.469844   5.322534 2.505576e-04 5.856526e-03
## 1423771_at   -1.2348062  9.774800  -5.307379 2.564093e-04 5.974954e-03
## 1450668_s_at  0.6055805 11.496588   5.306686 2.566806e-04 5.974954e-03
## 1427206_at    0.7098681  7.457394   5.303084 2.580942e-04 5.995523e-03
## 1434971_x_at  0.5809925  8.757129   5.296600 2.606595e-04 6.036860e-03
## 1415992_at    0.7861291  5.224277   5.295039 2.612813e-04 6.036860e-03
## 1421883_at    0.7358855  2.432063   5.293758 2.617927e-04 6.036860e-03
## 1452173_at    1.0827075  8.847472   5.292853 2.621548e-04 6.036860e-03
## 1418318_at   -2.3935233  4.673755  -5.290379 2.631468e-04 6.036860e-03
## 1428803_at   -0.6622193  2.946551  -5.288639 2.638469e-04 6.036860e-03
## 1426375_s_at  0.7010237  8.376812   5.288548 2.638834e-04 6.036860e-03
## 1422411_s_at -1.3051371  9.459561  -5.286832 2.645761e-04 6.036860e-03
## 1449146_at   -0.9347059  7.285411  -5.286584 2.646763e-04 6.036860e-03
## 1448293_at   -0.7055832  9.383816  -5.279755 2.674525e-04 6.087908e-03
## 1426258_at    1.2823818  9.322960   5.266196 2.730561e-04 6.202979e-03
## 1426562_a_at -1.4214936  4.916599  -5.260039 2.756418e-04 6.249170e-03
## 1449040_a_at  0.7481167  9.855480   5.256473 2.771516e-04 6.270831e-03
## 1423084_at   -1.1102475  9.920537  -5.248343 2.806256e-04 6.328197e-03
## 1419417_at   -1.3186629  6.349880  -5.247729 2.808901e-04 6.328197e-03
## 1419012_at   -1.3435184  4.900566  -5.246400 2.814629e-04 6.328197e-03
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## 1448318_at    0.9198346 11.746378   5.239892 2.842861e-04 6.368572e-03
## 1452280_at   -0.9527139  5.431559  -5.237314 2.854127e-04 6.372954e-03
## 1418520_at    1.0081845  7.255602   5.236713 2.856760e-04 6.372954e-03
## 1425404_a_at -0.7059321  5.527132  -5.235583 2.861717e-04 6.372954e-03
## 1418028_at   -2.1092059  4.105247  -5.233448 2.871113e-04 6.381317e-03
## 1428074_at    0.9609122  7.255348   5.230604 2.883676e-04 6.396671e-03
## 1416855_at   -1.2115893 10.274207  -5.227482 2.897533e-04 6.414831e-03
## 1415891_at    0.7482526 11.813162   5.222735 2.918739e-04 6.420048e-03
## 1452473_at   -1.2514092  3.310617  -5.222126 2.921473e-04 6.420048e-03
## 1417141_at    1.9212315  9.928747   5.221943 2.922294e-04 6.420048e-03
## 1428745_a_at  0.8036227  7.133167   5.221877 2.922589e-04 6.420048e-03
## 1426044_a_at  2.2784236  5.067657   5.218674 2.937014e-04 6.439231e-03
## 1425204_s_at -0.9913401  5.869524  -5.216300 2.947751e-04 6.444641e-03
## 1449122_at   -0.8062662  6.511275  -5.214564 2.955630e-04 6.444641e-03
## 1452654_at    0.7942493  6.659306   5.214357 2.956571e-04 6.444641e-03
## 1451744_a_at  0.6204425  8.618977   5.207024 2.990109e-04 6.490258e-03
## 1417430_at   -1.4014859  7.155706  -5.206247 2.993685e-04 6.490258e-03
## 1449070_x_at -1.2802735 11.122701  -5.206025 2.994709e-04 6.490258e-03
## 1416326_at   -0.6475306 12.224650  -5.203176 3.007869e-04 6.506314e-03
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## 1426576_at   -0.7566089  7.370423  -5.192816 3.056242e-04 6.579581e-03
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## 1428402_at   -0.8294347  2.609626  -5.185871 3.089131e-04 6.597613e-03
## 1427483_at   -1.0044293  4.792482  -5.185500 3.090900e-04 6.597613e-03
## 1416053_at   -1.9352507  3.544792  -5.183836 3.098839e-04 6.602104e-03
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## 1438676_at    1.7913724  8.551051   5.175142 3.140680e-04 6.666137e-03
## 1422185_a_at -0.8931843  8.383608  -5.170714 3.162220e-04 6.699288e-03
## 1421921_at   -0.8498209  4.792329  -5.169436 3.168467e-04 6.699975e-03
## 1425742_a_at -0.7072588 11.801763  -5.165834 3.186143e-04 6.717429e-03
## 1416382_at   -0.6137920 12.076972  -5.165335 3.188597e-04 6.717429e-03
## 1424948_x_at  0.5974918 11.235307   5.157365 3.228111e-04 6.788033e-03
## 1416269_at    0.5806214 12.791011   5.153694 3.246486e-04 6.814006e-03
## 1424040_at   -0.7651087  8.667104  -5.150998 3.260047e-04 6.829798e-03
## 1456590_x_at  0.5308109 11.618304   5.148860 3.270849e-04 6.839763e-03
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## 1422483_a_at  0.7584413 13.081122   5.140488 3.313502e-04 6.901982e-03
## 1421534_at   -1.2559170  6.079787  -5.138265 3.324930e-04 6.901982e-03
## 1432558_a_at  1.4015075  4.612907   5.136543 3.333808e-04 6.901982e-03
## 1449020_at   -0.6560110  8.046401  -5.136390 3.334598e-04 6.901982e-03
## 1425955_at   -1.0574504  3.249805  -5.135382 3.339807e-04 6.901982e-03
## 1434937_at   -0.8205683 11.184776  -5.134705 3.343310e-04 6.901982e-03
## 1417399_at   -1.0205863 10.546413  -5.128119 3.377603e-04 6.952495e-03
## 1450659_at    2.0259949  5.761110   5.127648 3.380069e-04 6.952495e-03
## 1418383_at   -1.5701573  8.175462  -5.120855 3.415862e-04 7.013367e-03
## 1415733_a_at  0.5422024  9.641788   5.118416 3.428810e-04 7.027197e-03
## 1420477_at   -0.6025518  9.489812  -5.115753 3.443003e-04 7.043524e-03
## 1426094_at   -1.4729869  7.104571  -5.114538 3.449504e-04 7.044087e-03
## 1448696_at   -0.9111517 10.303067  -5.109266 3.477846e-04 7.089166e-03
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## 1416630_at   -0.6823235 10.063990  -5.099974 3.528409e-04 7.166408e-03
## 1421344_a_at -1.0035325  6.126222  -5.096422 3.547941e-04 7.183314e-03
## 1434628_a_at -1.9028365  5.910715  -5.096041 3.550040e-04 7.183314e-03
## 1427347_s_at -0.8916077 10.006223  -5.095002 3.555781e-04 7.183314e-03
## 1422785_at   -0.8031753  8.675266  -5.087783 3.595927e-04 7.251465e-03
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## 1450034_at    0.8152106 10.255435   5.074694 3.669942e-04 7.370182e-03
## 1422810_at    0.7335489  6.540852   5.073925 3.674342e-04 7.370182e-03
## 1435327_at   -0.9620738 11.495779  -5.071539 3.688027e-04 7.383028e-03
## 1425638_at   -1.3868366  7.625578  -5.069923 3.697322e-04 7.383028e-03
## 1450968_at    0.7238668 12.469626   5.069402 3.700324e-04 7.383028e-03
## 1448028_at    0.6992526  7.398426   5.066503 3.717081e-04 7.403404e-03
## 1452895_at   -1.1276570  9.821980  -5.063490 3.734585e-04 7.425195e-03
## 1418885_a_at  0.6311953 11.823790   5.060553 3.751727e-04 7.446191e-03
## 1449942_a_at -0.6390371  9.110534  -5.058121 3.765987e-04 7.459258e-03
## 1439389_s_at -0.7973328 11.561787  -5.057183 3.771498e-04 7.459258e-03
## 1428090_at    0.5745478  9.410255   5.054531 3.787138e-04 7.477118e-03
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## 1424968_at    0.8592194  5.301190   5.048662 3.821990e-04 7.507622e-03
## 1452626_a_at  0.9181736  7.363596   5.048577 3.822497e-04 7.507622e-03
## 1448471_a_at -0.6043272  9.662510  -5.038155 3.885234e-04 7.607342e-03
## 1422412_x_at -1.2890259  4.746536  -5.037857 3.887039e-04 7.607342e-03
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## 1435326_at   -0.8686259 10.878551  -5.032356 3.920611e-04 7.624357e-03
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## 1426918_at   -0.7200923 10.113281  -5.022233 3.983191e-04 7.716069e-03
## 1439435_x_at  0.6363313 11.308467   5.018658 4.005542e-04 7.739756e-03
## 1421871_at   -0.9250883 11.392357  -5.018090 4.009102e-04 7.739756e-03
## 1425396_a_at  3.7370451  6.070627   5.016407 4.019682e-04 7.746962e-03
## 1430320_at   -0.8227462  2.565672  -5.014559 4.031334e-04 7.756204e-03
## 1439241_x_at -0.5200408  6.651043  -5.011875 4.048316e-04 7.767723e-03
## 1431805_a_at -1.2357738  3.051387  -5.011195 4.052630e-04 7.767723e-03
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## 1424334_at   -0.7473087  5.453953  -4.998905 4.131452e-04 7.855313e-03
## 1459931_a_at  0.5241389  3.003376   4.995645 4.152637e-04 7.882346e-03
## 1425084_at    3.0175765  4.531446   4.991821 4.177624e-04 7.916493e-03
## 1419061_at   -0.5990685  2.468506  -4.990413 4.186869e-04 7.920744e-03
## 1423907_a_at  0.5315204 12.642969   4.985997 4.215993e-04 7.948372e-03
## 1423369_at   -0.7186731 11.089414  -4.985373 4.220126e-04 7.948372e-03
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## 1426952_at   -0.9637878  7.909815  -4.978830 4.263718e-04 8.012532e-03
## 1417046_at    0.5344523  6.822361   4.972841 4.304041e-04 8.074895e-03
## 1425853_s_at -1.6316165  3.054940  -4.966297 4.348555e-04 8.144902e-03
## 1427746_x_at  0.6436246 11.350846   4.965146 4.356437e-04 8.146177e-03
## 1434589_x_at -0.6082085  9.768909  -4.963804 4.365645e-04 8.148498e-03
## 1416056_a_at  0.5735989 12.748518   4.962867 4.372084e-04 8.148498e-03
## 1417374_at    1.4661960  9.189128   4.961280 4.383018e-04 8.155441e-03
## 1422558_at   -1.1357299  6.120298  -4.958592 4.401596e-04 8.176560e-03
## 1425042_s_at -0.8774289  6.946142  -4.953594 4.436369e-04 8.227647e-03
## 1424271_at   -0.7133194  7.842003  -4.949801 4.462953e-04 8.263402e-03
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## 1450388_s_at -0.7473527  8.834855  -4.943488 4.507572e-04 8.318787e-03
## 1460411_s_at -0.7912759  6.363611  -4.942072 4.517643e-04 8.322558e-03
## 1429038_at   -0.9811010  7.041627  -4.941134 4.524329e-04 8.322558e-03
## 1460583_at   -0.9889987  5.958547  -4.939247 4.537805e-04 8.323780e-03
## 1448427_at    0.6722748 11.582729   4.938981 4.539708e-04 8.323780e-03
## 1422478_a_at  1.2102954 11.134385   4.936775 4.555528e-04 8.334625e-03
## 1424358_at   -0.7658891  8.374940  -4.935651 4.563613e-04 8.334625e-03
## 1421045_at    0.9127126  5.965121   4.935080 4.567725e-04 8.334625e-03
## 1454716_x_at  0.5970353 13.029879   4.930702 4.599386e-04 8.378882e-03
## 1425675_s_at -0.6162176  5.897470  -4.927796 4.620528e-04 8.394291e-03
## 1423277_at   -0.9538641  4.025703  -4.927500 4.622685e-04 8.394291e-03
## 1433603_at    0.7002680 11.717458   4.920177 4.676444e-04 8.478304e-03
## 1418651_at   -0.7560038  6.901083  -4.918274 4.690520e-04 8.490217e-03
## 1417185_at    0.7320384 12.763411   4.917200 4.698491e-04 8.491059e-03
## 1448213_at   -0.8838950 12.876936  -4.907710 4.769490e-04 8.592586e-03
## 1416164_at   -1.2041752  9.587889  -4.907661 4.769861e-04 8.592586e-03
## 1417316_at    0.9007594  9.628713   4.903702 4.799820e-04 8.632809e-03
## 1422504_at    1.5207079  3.026331   4.901087 4.819714e-04 8.654829e-03
## 1423831_at   -0.7385414  6.798867  -4.892760 4.883662e-04 8.755764e-03
## 1448469_at   -1.3420946 10.309803  -4.890978 4.897460e-04 8.766608e-03
## 1422571_at    1.2193676  6.382225   4.889073 4.912259e-04 8.779208e-03
## 1418492_at   -2.0911094  6.333518  -4.886581 4.931685e-04 8.800024e-03
## 1419442_at   -1.2157859  9.262959  -4.870858 5.056156e-04 9.007921e-03
## 1448477_at   -0.6244266  4.888837  -4.868333 5.076450e-04 9.029856e-03
## 1448214_at    1.1952100  8.219519   4.865196 5.101784e-04 9.060673e-03
## 1451970_at   -0.8287361  7.161710  -4.858486 5.156424e-04 9.143358e-03
## 1460198_a_at  0.5601133 11.287809   4.854540 5.188845e-04 9.186449e-03
## 1419458_at   -0.9169380  4.338192  -4.846655 5.254278e-04 9.285267e-03
## 1425526_a_at  1.3208230  6.897687   4.845842 5.261077e-04 9.285267e-03
## 1439267_x_at  0.5284565 12.959448   4.833855 5.362360e-04 9.449281e-03
## 1419251_at   -0.6729207  6.696917  -4.831920 5.378901e-04 9.463687e-03
## 1417780_at   -1.3897813  6.891954  -4.827279 5.418797e-04 9.507892e-03
## 1424808_at   -1.2319798  6.463434  -4.825360 5.435378e-04 9.507892e-03
## 1454838_s_at -1.0118616  8.240736  -4.822298 5.461956e-04 9.507892e-03
## 1460351_at   -0.8827525 12.034454  -4.821274 5.470880e-04 9.507892e-03
## 1424226_at    1.0897624  6.548219   4.820951 5.473690e-04 9.507892e-03
## 1426511_at   -0.7377009  2.912079  -4.819981 5.482159e-04 9.507892e-03
## 1434777_at   -1.0020586  6.440086  -4.819643 5.485118e-04 9.507892e-03
## 1428762_at   -0.8595772  8.072839  -4.819269 5.488387e-04 9.507892e-03
## 1416779_at   -0.7956188 12.368257  -4.819039 5.490394e-04 9.507892e-03
## 1417100_at   -1.3038336  8.199499  -4.818785 5.492622e-04 9.507892e-03
## 1436955_at    1.2259509  6.268884   4.818345 5.496474e-04 9.507892e-03
## 1431058_at   -0.7340602  4.623459  -4.816100 5.516180e-04 9.527412e-03
## 1438680_at   -1.4608512  5.797901  -4.814874 5.526968e-04 9.531493e-03
## 1417892_a_at  0.8966080  6.740914   4.811080 5.560508e-04 9.574737e-03
## 1425985_s_at -1.3874106  4.777229  -4.807498 5.592362e-04 9.602359e-03
## 1426655_a_at -0.8033118  5.809780  -4.807368 5.593525e-04 9.602359e-03
## 1420657_at    1.3921993  7.185637   4.805787 5.607651e-04 9.612024e-03
## 1419975_at    0.6052667  7.324692   4.803330 5.629678e-04 9.635144e-03
## 1418109_at   -0.7941269  3.099257  -4.802386 5.638173e-04 9.635144e-03
## 1425436_x_at  2.2593054  2.894636   4.799387 5.665222e-04 9.666765e-03
## 1419310_s_at  0.7472588  5.619480   4.797408 5.683152e-04 9.670302e-03
## 1418117_at    0.5909222  5.623473   4.797272 5.684391e-04 9.670302e-03
## 1451190_a_at  1.2309603  9.072167   4.788872 5.761171e-04 9.768391e-03
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## 1423346_at   -0.6004943 11.834690  -4.783876 5.807358e-04 9.820425e-03
## 1423895_a_at -0.6027000 10.459223  -4.780042 5.843071e-04 9.828751e-03
## 1425507_at    0.5381084  8.086849   4.779964 5.843798e-04 9.828751e-03
## 1416357_a_at -1.2277169  8.507230  -4.779813 5.845209e-04 9.828751e-03
## 1451152_a_at -1.5108796  8.596884  -4.779618 5.847034e-04 9.828751e-03
## 1435232_x_at  0.5408076  9.884766   4.777929 5.862847e-04 9.840710e-03
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## 1448632_at    0.8038983  9.648954   4.764733 5.987970e-04 9.991432e-03
##                         B
## 1448377_at   16.949540867
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## 1420477_at    0.120590190
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## 1448696_at    0.110182399
## 1415941_s_at  0.100376971
## 1416630_at    0.095263450
## 1421344_a_at  0.089557893
## 1434628_a_at  0.088946517
## 1427347_s_at  0.087276351
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## 1448471_a_at -0.004291992
## 1422412_x_at -0.004772148
## 1419758_at   -0.006472665
## 1437052_s_at -0.008955301
## 1435326_at   -0.013657842
## 1426571_at   -0.014120842
## 1420944_at   -0.027844355
## 1426918_at   -0.030019283
## 1439435_x_at -0.035800446
## 1421871_at   -0.036718293
## 1425396_a_at -0.039441287
## 1430320_at   -0.042431643
## 1439241_x_at -0.046774596
## 1431805_a_at -0.047874839
## 1417968_a_at -0.049856871
## 1418758_a_at -0.050969039
## 1428115_a_at -0.052830386
## 1451655_at   -0.061962359
## 1424334_at   -0.067774920
## 1459931_a_at -0.073058516
## 1425084_at   -0.079255872
## 1419061_at   -0.081539225
## 1423907_a_at -0.088699752
## 1423369_at   -0.089712089
## 1452207_at   -0.090305292
## 1426952_at   -0.100327136
## 1417046_at   -0.110049750
## 1425853_s_at -0.120677382
## 1427746_x_at -0.122547651
## 1434589_x_at -0.124728519
## 1416056_a_at -0.126250733
## 1417374_at   -0.128830452
## 1422558_at   -0.133198920
## 1425042_s_at -0.141326247
## 1424271_at   -0.147496351
## 1416337_at   -0.156363043
## 1450388_s_at -0.157770085
## 1460411_s_at -0.160074777
## 1429038_at   -0.161602040
## 1460583_at   -0.164673484
## 1448427_at   -0.165106545
## 1422478_a_at -0.168698882
## 1424358_at   -0.170530016
## 1421045_at   -0.171460164
## 1454716_x_at -0.178593258
## 1425675_s_at -0.183329006
## 1423277_at   -0.183810976
## 1433603_at   -0.195750344
## 1418651_at   -0.198853660
## 1417185_at   -0.200606788
## 1448213_at   -0.216092769
## 1416164_at   -0.216173070
## 1417316_at   -0.222637704
## 1422504_at   -0.226908027
## 1423831_at   -0.240516317
## 1448469_at   -0.243429005
## 1422571_at   -0.246544061
## 1418492_at   -0.250618594
## 1419442_at   -0.276349812
## 1448477_at   -0.280484649
## 1448214_at   -0.285623215
## 1451970_at   -0.296619181
## 1460198_a_at -0.303088641
## 1419458_at   -0.316022602
## 1425526_a_at -0.317357159
## 1439267_x_at -0.337036785
## 1419251_at   -0.340215148
## 1417780_at   -0.347841249
## 1424808_at   -0.350994138
## 1454838_s_at -0.356027795
## 1460351_at   -0.357712335
## 1424226_at   -0.358242205
## 1426511_at   -0.359837624
## 1434777_at   -0.360394456
## 1428762_at   -0.361009200
## 1416779_at   -0.361386442
## 1417100_at   -0.361805152
## 1436955_at   -0.362528660
## 1431058_at   -0.366221555
## 1438680_at   -0.368237647
## 1417892_a_at -0.374480329
## 1425985_s_at -0.380374495
## 1426655_a_at -0.380589047
## 1420657_at   -0.383191565
## 1419975_at   -0.387236551
## 1418109_at   -0.388792281
## 1425436_x_at -0.393730255
## 1419310_s_at -0.396990530
## 1418117_at   -0.397215409
## 1451190_a_at -0.411057639
## 1434489_at   -0.411818968
## 1459890_s_at -0.412271449
## 1423346_at   -0.419295473
## 1423895_a_at -0.425620101
## 1425507_at   -0.425748525
## 1416357_a_at -0.425997515
## 1451152_a_at -0.426319576
## 1435232_x_at -0.429105776
## 1416698_a_at -0.434837156
## 1456088_at   -0.436109989
## 1424683_at   -0.450580401
## 1448632_at   -0.450889148

## [1] "Number of genes in this list:"

## [1] 0

 x=topTable(Vis_ebfits,number=50000, coef=3,adjust="fdr",sort.by="P"); y <- x[x$adj.P.Val < 0.01 & (x$logFC > 2 | x$logFC < -2) & x$AveExpr > 10,]; y; print("Number of genes in this list:"); length(y$ID)

##                  logFC  AveExpr         t      P.Value    adj.P.Val
## 1436990_s_at  2.255748 12.42499 11.300791 2.317336e-07 5.825784e-05
## 1420465_s_at  6.160727 12.09481  9.999853 7.895272e-07 1.258017e-04
## 1433720_s_at  2.342041 10.82936  9.358224 1.518131e-06 1.929744e-04
## 1449218_at    3.678415 11.28112  9.202085 1.789626e-06 2.176993e-04
## 1426208_x_at -2.080891 10.86631 -7.930036 7.462156e-06 6.073336e-04
## 1417404_at    2.489987 10.60414  6.757525 3.252775e-05 1.606928e-03
## 1418536_at    2.091256 10.72265  5.489544 1.946935e-04 5.017239e-03
##                      B
## 1436990_s_at 7.5955240
## 1420465_s_at 6.3681322
## 1433720_s_at 5.7065302
## 1449218_at   5.5393854
## 1426208_x_at 4.0796624
## 1417404_at   2.5620332
## 1418536_at   0.7102646

## [1] "Number of genes in this list:"

## [1] 0

 results <- decideTests(Vis_ebfits, p.value=0.000005); heatDiagram(results, Vis_ebfits$coef, primary=1)

To impose a fold change cut off, and see how many genes are returned you can use the lfc modifier for topTable, here we show the results for fold changes of 5,4,3 and 2 in terms of the number of probesets.

nrow(topTable(Vis_ebfits, coef=3, number=10000, lfc=5))

## [1] 22

nrow(topTable(Vis_ebfits, coef=3, number=10000, lfc=4))

## [1] 49

nrow(topTable(Vis_ebfits, coef=3, number=10000, lfc=3))

## [1] 111

nrow(topTable(Vis_ebfits, coef=3, number=10000, lfc=2))

## [1] 270

# Get a list for probesets with a four fold change or more

probeset.list <- toptable(Vis_ebfits, coef=3, number=200, lfc=5,genelist = Vis_ebfits$genes,adjust.method = "BH",sort.by = "B")


 biocLite("mouse430a2.db")  # hide
library(mouse430a2.db)
#library(annotate)


#heatmap(exprs(probeset.list))

Annotating the results with associated gene symbols

In order to annotate the probesets into gene symbols we need to install and load the associated database package and the annotate package, then we can extract the probeset ID’s from the topTable results, and match the symbols

library(annotate)

## Loading required package: XML

gene.symbols <- getSYMBOL(rownames(probeset.list), "mouse430a2") #此处用 rownames(probeset.list) 代替 probeset.list$ID，因为目前这个版本的limma似乎不会生成  ID列名
results <- cbind(probeset.list, gene.symbols)
head(results)

##                  logFC         t      P.Value    adj.P.Val        B
## 1448377_at   -7.675489 -31.84527 4.084632e-12 4.620944e-08 16.94954
## 1418945_at    6.717054  27.86929 1.722140e-11 9.741288e-08 15.96592
## 1454881_s_at -8.240210 -26.42883 3.049165e-11 1.042400e-07 15.54925
## 1423253_at    5.869459  25.96702 3.685671e-11 1.042400e-07 15.40784
## 1419532_at    6.377121  25.23044 5.021411e-11 1.051779e-07 15.17387
## 1419063_at    5.184754  24.98461 5.578250e-11 1.051779e-07 15.09340
##              gene.symbols
## 1448377_at           Slpi
## 1418945_at           Mmp3
## 1454881_s_at        Upk3b
## 1423253_at            Mpz
## 1419532_at          Il1r2
## 1419063_at          Ugt8a

write.table(results, "results.txt", sep="\t", quote=FALSE)

Finished! All done~~~ Let’s do better next time!