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。

演示源代码 基因芯片分析流程

对于每一个芯片，数据表中存储着探针组和对应探针组标准化之后的基因表达量值。表头中提供了表达量值标准化所用的算法。本教程中使用 GC-RMA 算法进行数据标准化，关于 GC-RMA 算法的详细细节可以参考这篇文献。

# 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

此处装载Bioconductor及其镜像

在 R 环境中安装 Bioconductor 包

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

如果你没有管理员权限，你需要将这些包安装到你个人库目录中。安装 Bioconductor 需要一段时间，GEOquery 包也需要安装，GEOquery 是 NCBI 存储标准化的转录组数据的基因表达综合数据库 GEO 的接口程序。

Getting the data

Download the dataset from GEO 首先，我们从 GEO 数据库下载原始数据，导入 GEOquery 包，用它下载原始数据。

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

下载完成后，打开文件管理器，在工作文件夹里可以看到当前文件夹下生成了一个 GSEXXX命名的文件夹

Uncompress tar files

利用以下命令可以对RAW.tar文件解压缩，其中文件均为CEL(Affymetrix array 数据原始格式)文件。先解包数据，再解压数据。这些操作也可以直接在 R 命令终端中或直接在windows中进行

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"

芯片实验信息整理

在对数据进行分析之前，我们需要先整理好实验设计信息。这其实就是一个文本文件，包含芯片名字、此芯片上杂交的样本名字。为了方便在 R 中 使用 simpleaffy 包读取实验信息文本文件，需要先编辑好格式：

将这个文本文件用编辑器打开，现在其中只有一列 CEL 文件名，最终的实验信息文本需要包含三列数据（用 tab 分隔），分别是 Name, FileName, Target。本教程中 Name 和 FileName 这两栏是相同的，当然 Name 这一栏可以用更加容易理解的名字代替。

Target 这一栏数据是芯片上的样本标签，这些标签定义了那些数据是生物学重复以便后面的分析。

这个实验信息文本文件最终格式是这样的：

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

注意：每栏之间是使用 Tab 进行分隔的，而不是空格!

载入数据并对其进行标准化

需要先安装 simpleaffy 包，simpleaffy 包提供了处理 CEL 数据的程序，可以对 CEL 数据进行标准化同时导入实验信息（即前一步中整理好的实验信息文本文件内容），导入数据到 R 变量 celfiles 中：

# 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)

你可以通过输入 ‘celfiles’ 来确定数据导入成功并添加芯片注释（第一次输入 ‘celfiles’ 的时候会进行注释）

显示 celfiles 如果是这样的则为正确

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=

现在我们需要对数据进行标准化，使用 GC-RMA 算法对 GEO 数据库中的数据进行标准化，第一次运行的时候需要下载一些其他的必要文件

如果你想看标准化之后的数据，输入 celfiles.gcrma， 你会发现提示已经不是 AffyBatch object 了，而是 ExpressionSet object，是已经标准化了的数据：

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 数据质量控制

再进行下一步的数据分析之前，我们有必要对数据质量进行检查，确保没有其他的问题。首先，可以通过对标准化之前和之后的数据画箱线图来检查 GC-RMA 标准化的效果：

 # 载入色彩包 load colour libraries
 library("RColorBrewer")
 # 设置调色板 set colour palette
 cols <- brewer.pal(8, "Set1")
 # 对标准化之前的探针信号强度做箱线图 plot a boxplot of unnormalised intensity values
 boxplot(celfiles, col=cols)

 # 对标准化之后的探针信号强度做箱线图，需要先安装 affyPLM 包，以便解析 celfiles.gcrma 数据 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)

 # 标准化前后的箱线图会有些变化 the boxplots are somewhat skewed by the normalisation algorithm
 # 但是密度曲线图看起来更直观一些 and it is often more informative to look at density plots
 # 对标准化之前的数据做密度曲线图 Plot a density vs log intensity histogram for the unnormalised data
 hist(celfiles, col=cols)

 # 对标准化之后的数据做密度曲线图 Plot a density vs log intensity histogram for the normalised data
 hist(celfiles.gcrma, col=cols)

通过这些图我们可以看出这12张芯片数据之间差异不大，标准化处理将所有芯片信号强度标准化到具有类似分布特征的区间内。为了更详细地了解芯片探针信号强度，我们可以使用 affyPLM 对单个芯片 CEL 数据进行可视化。

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.

# 从 CEL 文件读取探针信号强度: Perform probe-level metric calculations on the CEL files:
celfiles.qc <- fitPLM(celfiles)
# 对芯片 1 信号进行可视化 Create an image of GSM24662.CEL:
 image(celfiles.qc, which=1, add.legend=TRUE)

 # 对芯片 10 进行可视化 Create an image of GSM524665.CEL
 # 这张芯片数据有些人为误差 There is a spatial artifact present
 image(celfiles.qc, which=10, add.legend=TRUE)

 # affyPLM 包还可以画箱线图 affyPLM also provides more informative boxplots
 # RLE (Relative Log Expression) plots should have
 # 所有的值都应该接近于零。 GSM524665.CEL 芯片数据由于有人为误差而例外。 values close to zero. GSM524665.CEL is an outlier
 RLE(celfiles.qc, main="RLE",col=cols)

 # 也可以用 NUSE (Normalised Unscaled Standard Errors)作图比较. We can also use NUSE (Normalised Unscaled Standard Errors).
 # 对于绝大部分基因，标准差的中位数应该是1。 The median standard error should be 1 for most genes.
# 芯片 GSM524665.CEL 在这个图中，同样是一个例外 GSM524665.CEL appears to be an outlier on this plot too
 NUSE(celfiles.qc, main="NUSE",col=cols)

# 我们还可以通过层次聚类来查看样本之间的关系： 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

现在我们可以对数据进行分析了，分析的第一步就是要过滤掉数据中的无用数据，例如作为内参的探针数据，基因表达无明显变化的数据（在差异表达统计时也会被过滤掉），信号值与背景信号差不多的探针数据。 下面的 nsFilter 参数是为了不删除没有 Entrez Gene ID 的位点，保留有重复 Entrez Gene ID 的位点：

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

# 哪些位点被过滤掉了？为什么？
celfiles.filtered $ filter.log
## $numLowVar
## [1] 11313
## 
## $feature.exclude
## [1] 64

我们可以看出有 11,313 个探针位点因为无明显表达差异（LowVar）被过滤掉，有 64 个探针位点因为是内参而被过滤掉。

查找有表达差异的探针位点 Finding differentially expressed probesets

现在有了过滤之后的数据，我们就可以用 limma 包进行差异表达分析了。首先，我们要提取样本的信息：

samples <- celfiles.gcrma$Target
# 检查数据的分组信息 check the results of this  samples
samples
##  [1] "SubIng" "SubIng" "SubIng" "VisEpi" "VisEpi" "VisEpi" "SubAxi"
##  [8] "SubAxi" "SubAxi" "VisMes" "VisMes" "VisMes"
# 将分组数据转换为因子类型变量 convert into factors
 samples <- as.factor(samples)

 # 检查转换的因子变量 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
# 设置实验分组 set up the experimental design
design <- model.matrix(~0 + samples)
 colnames(design) <- c("SubIng", "VisEpi","SubAxi","VisMes")
 #  检查实验分组 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"

现在我们将芯片数据进行了标准化和过滤，也有样品分组和实验分组信息，可以将数据导入 limma 包进行差异表达分析了：

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.

#  将线性模型拟合到过滤之后的表达数据集上 fit the linear model to the filtered expression set
fit <- lmFit(exprs(celfiles.filtered$eset), design)
 # 建立对比矩阵以比较组织和细胞系 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)

 # 检查对比矩阵 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
# 现在将对比矩阵与线性模型拟合，比较不同细胞系的表达数据 Now the contrast matrix is combined with the per-probeset linear model fit.

 Vis_fits <- contrasts.fit(fit,contrast.matrix)
  # 使用经验贝叶斯算法计算差异表达基因的显著性
 Vis_ebfits <- eBayes(Vis_fits)
 
 
 #  返回对应比对矩阵 top 10 的结果 return the top 10 results for any given contrast
 # coef=1 是 第一个 比对矩阵, coef=2 是 第二个 比对矩阵
 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
## 1422308_a_at -4.4052634  4.409563 -11.896395 1.375487e-07 3.890220e-05
## 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
## 1423136_at   -4.3275686  7.263693 -11.267741 2.387028e-07 5.870532e-05
## 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
## 1448395_at   -2.9972881  5.002243 -11.043370 2.924696e-07 6.699433e-05
## 1437324_x_at -3.5092398  4.502582 -11.029888 2.960944e-07 6.699433e-05
## 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
## 1425506_at   -1.4399072  9.681889 -10.817285 3.601894e-07 7.408769e-05
## 1418715_at    2.1622155  7.388945  10.751188 3.830684e-07 7.646593e-05
## 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
## 1418603_at   -2.1718004  3.381947 -10.307581 5.840233e-07 1.075735e-04
## 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
## 1422789_at   -2.6159577  5.485018 -10.263635 6.094385e-07 1.075735e-04
## 1448211_at   -2.9354753  7.249059 -10.231042 6.290611e-07 1.075735e-04
## 1421433_at    3.0983372  5.690619  10.230226 6.295611e-07 1.075735e-04
## 1449433_at    2.3696527  4.321141  10.221570 6.348911e-07 1.075735e-04
## 1449867_at    2.8547318  3.496831  10.205389 6.449868e-07 1.075735e-04
## 1426513_at   -1.6528072  7.067604 -10.202827 6.466011e-07 1.075735e-04
## 1460302_at   -3.2334082  4.126534 -10.070216 7.364419e-07 1.195352e-04
## 1449252_at   -2.2058172  3.610226 -10.065833 7.396329e-07 1.195352e-04
## 1420465_s_at  6.1607265 12.094813   9.999853 7.895272e-07 1.258017e-04
## 1431554_a_at -2.0988030  2.711995  -9.957025 8.238577e-07 1.294486e-04
## 1416529_at   -1.7821978 11.485804  -9.884022 8.861585e-07 1.349094e-04
## 1423577_at   -1.2025631  7.285257  -9.879273 8.903842e-07 1.349094e-04
## 1450723_at   -3.7190695  5.249579  -9.874796 8.943873e-07 1.349094e-04
## 1456584_x_at -1.2840384  8.377148  -9.685436 1.082943e-06 1.612017e-04
## 1450992_a_at -1.5277196  6.019356  -9.668936 1.101303e-06 1.618057e-04
## 1425464_at   -1.8717983  6.950738  -9.565877 1.223885e-06 1.775105e-04
## 1434776_at   -2.1283963  4.159094  -9.530643 1.269118e-06 1.805568e-04
## 1457776_at   -1.8406888  4.162749  -9.524786 1.276809e-06 1.805568e-04
## 1460187_at   -3.3729390  4.205143  -9.507816 1.299385e-06 1.806124e-04
## 1426869_at   -2.5367526  5.811162  -9.500586 1.309133e-06 1.806124e-04
## 1449314_at   -2.2829530  5.784812  -9.464883 1.358449e-06 1.851582e-04
## 1423608_at    1.5932109 11.689947   9.453081 1.375190e-06 1.852086e-04
## 1427299_at   -1.6509568  7.633987  -9.437758 1.397257e-06 1.859667e-04
## 1416832_at   -2.0911711  6.899971  -9.389288 1.469625e-06 1.929744e-04
## 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
## 1455149_at   -1.1854758  5.294292  -9.351159 1.529402e-06 1.929744e-04
## 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
## 1418057_at   -1.3193540  6.526424  -9.187501 1.817544e-06 2.187434e-04
## 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
## 1428781_at   -3.4064923  4.294581  -8.881975 2.525192e-06 2.786271e-04
## 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
## 1419684_at    2.5878345  8.859994   8.676948 3.164380e-06 3.314688e-04
## 1427320_at   -1.4639829  7.801927  -8.620009 3.371467e-06 3.499212e-04
## 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
## 1416527_at   -1.1100457  8.821119  -8.562360 3.596130e-06 3.632412e-04
## 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
## 1424099_at    1.8525703 11.290234   8.485561 3.920866e-06 3.759047e-04
## 1425967_a_at  1.6744344  3.906529   8.468488 3.997274e-06 3.784557e-04
## 1417079_s_at -2.0353622  2.713933  -8.464714 4.014381e-06 3.784557e-04
## 1423436_at   -2.1477411  9.653877  -8.413816 4.252968e-06 3.957216e-04
## 1416236_a_at  1.9311598  6.693696   8.410819 4.267483e-06 3.957216e-04
## 1426261_s_at -1.5014277  9.337173  -8.380371 4.418041e-06 4.029258e-04
## 1419693_at   -1.6310193  7.761254  -8.373882 4.450864e-06 4.029258e-04
## 1418190_at    2.1492815  8.714366   8.373654 4.452022e-06 4.029258e-04
## 1422904_at   -2.5031485  6.618896  -8.309160 4.793015e-06 4.303443e-04
## 1422596_at   -4.4089325  3.961529  -8.286879 4.917284e-06 4.380255e-04
## 1417781_at   -1.5438458  5.962859  -8.268021 5.025179e-06 4.441395e-04
## 1453678_at   -1.4130343  4.562712  -8.194850 5.468632e-06 4.795863e-04
## 1423405_at   -1.6849070 10.860741  -8.146822 5.782540e-06 5.032144e-04
## 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
## 1450869_at   -3.3711212  5.959836  -8.011195 6.778699e-06 5.765972e-04
## 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
## 1427300_at    2.5945413  5.201124   7.956015 7.235640e-06 5.931651e-04
## 1426208_x_at -2.0808906 10.866311  -7.930036 7.462156e-06 6.073336e-04
## 1423978_at    1.2963636  6.163787   7.899158 7.741385e-06 6.255592e-04
## 1416468_at   -1.7367898 11.421302  -7.871837 7.997836e-06 6.416987e-04
## 1451718_at    2.2247349  7.742224   7.852467 8.185202e-06 6.521069e-04
## 1439036_a_at -2.3929826  9.368874  -7.845369 8.255039e-06 6.528289e-04
## 1421035_a_at -0.9585137  4.411129  -7.839861 8.309676e-06 6.528289e-04
## 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
## 1418545_at   -1.8167791  5.324306  -7.790310 8.819057e-06 6.787074e-04
## 1452432_at   -1.5053393  6.715680  -7.782447 8.902935e-06 6.805332e-04
## 1448415_a_at  1.4681820  5.487271   7.766870 9.071647e-06 6.887755e-04
## 1452048_at    0.7549328 10.172678   7.727149 9.517657e-06 7.176132e-04
## 1452318_a_at -1.4826331  8.703800  -7.721901 9.578325e-06 7.176132e-04
## 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
## 1427122_at   -1.7953219  2.695367  -7.619936 1.084366e-05 7.965863e-04
## 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
## 1423516_a_at -1.8740081  8.639407  -7.587330 1.128537e-05 8.131933e-04
## 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
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## 1437341_x_at  1.2214238  9.886715   6.018657 8.985046e-05 3.089600e-03
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## 1448153_at    0.6789245 13.338616   5.778709 1.269734e-04 3.914034e-03
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## 1449040_a_at  0.7481167  9.855480   5.256473 2.771516e-04 6.270831e-03
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## 1426044_a_at  2.2784236  5.067657   5.218674 2.937014e-04 6.439231e-03
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## 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
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## 1424040_at   -0.7651087  8.667104  -5.150998 3.260047e-04 6.829798e-03
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## 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
## 1425731_at    0.7167627  4.453197   5.049323 3.818046e-04 7.507622e-03
## 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
## 1419758_at   -0.8439180  7.343085  -5.036804 3.893442e-04 7.607342e-03
## 1437052_s_at -2.2207972  8.023632  -5.035267 3.902808e-04 7.612495e-03
## 1435326_at   -0.8686259 10.878551  -5.032356 3.920611e-04 7.624357e-03
## 1426571_at   -1.4763966  6.052962  -5.032069 3.922369e-04 7.624357e-03
## 1420944_at   -1.6891810  7.169251  -5.023578 3.974815e-04 7.713050e-03
## 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
## 1417968_a_at -0.8613765  8.742859  -5.009970 4.060412e-04 7.767723e-03
## 1418758_a_at -0.8037486  8.236415  -5.009283 4.064786e-04 7.767723e-03
## 1428115_a_at -0.4963049  6.206445  -5.008133 4.072116e-04 7.768608e-03
## 1451655_at    1.8723907  6.109080   5.002494 4.108271e-04 7.824389e-03
## 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
## 1452207_at   -0.9527035  9.868168  -4.985007 4.222551e-04 7.948372e-03
## 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
## 1416337_at    0.5425297 12.188743   4.944352 4.501435e-04 8.318787e-03
## 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
## 1434489_at   -0.7892694  4.739051  -4.788410 5.765424e-04 9.768391e-03
## 1459890_s_at  0.6973542  8.649441   4.788136 5.767953e-04 9.768391e-03
## 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
## 1416698_a_at -0.6708698 10.967449  -4.774456 5.895511e-04 9.878441e-03
## 1456088_at   -0.5587435  9.913453  -4.773684 5.902790e-04 9.878441e-03
## 1424683_at    1.7622189  8.529018   4.764920 5.986178e-04 9.991432e-03
## 1448632_at    0.8038983  9.648954   4.764733 5.987970e-04 9.991432e-03
##                         B
## 1448377_at   16.949540867
## 1418945_at   15.965920366
## 1454881_s_at 15.549251360
## 1423253_at   15.407835730
## 1419532_at   15.173870421
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## 1434237_at   13.759157716
## 1437718_x_at 13.653185678
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## 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
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## 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)

分析结果的各列数据含义：

第一列是探针组在表达矩阵中的行号； 第二列“ID” 是探针组的 AffymatrixID； 第三列“logFC”是两组表达值之间以2为底对数化的的变化倍数（Fold change, FC），由于基因表达矩阵本身已经取了对数，这里实际上只是两组基因表达值均值之差； 第四列“AveExpr”是该探针组所在所有样品中的平均表达值； 第五列“t”是贝叶斯调整后的两组表达值间 T 检验中的 t 值； 第六列“P.Value”是贝叶斯检验得到的 P 值； 第七列“adj.P.Val”是调整后的 P 值； 第八列“B”是经验贝叶斯得到的标准差的对数化值。 如果要设置一个倍数变化阈值，并查看不同阈值返回了多少基因，可以使用 topTable 的 lfc 参数，参数设置为 5,4,3,2 时返回的基因个数：

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
# 提取表达量倍数变化超过 3 的探针列表 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))

注释差异分析结果的基因 ID Annotating the results with associated gene symbols

为了将探针集注释上基因 ID 我们需要先安装一些数据库的包和注释的包，之后可以提取 topTable 中的探针 ID 并注释上基因 ID：

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!