生信人的20個R語言習題及其答案

1. 安裝一些R包：

數據包： ALL, CLL, pasilla, airway
軟件包：limma，DESeq2，clusterProfiler
工具包：reshape2
繪圖包：ggplot2
不同領域的R包使用頻率不一樣，在生物信息學領域，尤其需要掌握bioconductor系列包。

if(F){
source("http:///biocLite.R")
options("repos" = c(CRAN="https://mirrors.tuna./CRAN/"))
options(BioC_mirror="http://mirrors.ustc.edu.cn/bioc/")#修改鏡像，安裝會加速
BiocManager::install("clusterProfiler")
BiocManager::install("ComplexHeatmap")
BiocManager::install("maftools")
BiocManager::install("ggplot2")
BiocManager::install("jmzeng1314/biotrainee")
}
#或者如下：
source("https:///biocLite.R")
options(BioC_mirror="http://mirrors.ustc.edu.cn/bioc/")
BiocManager::install(c('ALL','CLL','pasilla','clusterProfiler')) 
BiocManager::install(c('airway','DESeq2','edgeR','limma'))
install.packages("reshape2", "ggplot2")

2.了解ExpressionSet對象

比如CLL包里面就有data(sCLLex) ，找到它包含的元素，提取其表達矩陣(使用exprs函數)，查看其大小

1. 參考：http://www./bioconductor_China/software/limma.html

2. 參考：https://github.com/bioconductor-china/basic/blob/master/ExpressionSet.md

suppressPackageStartupMessages(library(CLL))
data(sCLLex)
exprSet=exprs(sCLLex)
##sCLLex是依賴于CLL這個package的一個對象
samples=sampleNames(sCLLex)
pdata=pData(sCLLex)
group_list=as.character(pdata[,2])
dim(exprSet)
# [1] 12625    22
exprSet[1:5,1:5]
#            CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL
# 1000_at    5.743132  6.219412  5.523328  5.340477  5.229904
# 1001_at    2.285143  2.291229  2.287986  2.295313  2.662170
# 1002_f_at  3.309294  3.318466  3.354423  3.327130  3.365113
# 1003_s_at  1.085264  1.117288  1.084010  1.103217  1.074243
# 1004_at    7.544884  7.671801  7.474025  7.152482  6.902932

3.了解 str,head,help函數，作用于 第二步提取到的表達矩陣

str(exprSet)
# str: Compactly display the internal structure of an R object, a diagnostic function and an alternative to summary (and to some extent, dput).
head(exprSet)

4. 安裝并了解 hgu95av2.db 包,看看 ls("package:hgu95av2.db") 后 顯示的那些變量

hgu95av2.db是一個注釋包，它為hgu95av2平臺的芯片提供注釋，這個包中有很多注釋文件，如下所示：

BiocManager::install("hgu95av2.db")
library(hgu95av2.db)
ls("package:hgu95av2.db")
#[1] "hgu95av2"              "hgu95av2.db"           "hgu95av2_dbconn"       "hgu95av2_dbfile"       "hgu95av2_dbInfo"       "hgu95av2_dbschema"    
 [7] "hgu95av2ACCNUM"        "hgu95av2ALIAS2PROBE"   "hgu95av2CHR"           "hgu95av2CHRLENGTHS"    "hgu95av2CHRLOC"        "hgu95av2CHRLOCEND"    
[13] "hgu95av2ENSEMBL"       "hgu95av2ENSEMBL2PROBE" "hgu95av2ENTREZID"      "hgu95av2ENZYME"        "hgu95av2ENZYME2PROBE"  "hgu95av2GENENAME"     
[19] "hgu95av2GO"            "hgu95av2GO2ALLPROBES"  "hgu95av2GO2PROBE"      "hgu95av2MAP"           "hgu95av2MAPCOUNTS"     "hgu95av2OMIM"         
[25] "hgu95av2ORGANISM"      "hgu95av2ORGPKG"        "hgu95av2PATH"          "hgu95av2PATH2PROBE"    "hgu95av2PFAM"          "hgu95av2PMID"         
[31] "hgu95av2PMID2PROBE"    "hgu95av2PROSITE"       "hgu95av2REFSEQ"        "hgu95av2SYMBOL"        "hgu95av2UNIGENE"       "hgu95av2UNIPROT"      

5. 理解 head(toTable(hgu95av2SYMBOL)) 的用法，找到 TP53 基因對應的探針ID

?hgu95av2SYMBOL
?toTable
summary(hgu95av2SYMBOL)
#SYMBOL map for chip hgu95av2 (object of class "ProbeAnnDbBimap")
|
| Lkeyname: probe_id (Ltablename: probes)
|    Lkeys: "1000_at", "1001_at", ... (total=12625/mapped=11460)
|
| Rkeyname: symbol (Rtablename: gene_info)
|    Rkeys: "A1BG", "A2M", ... (total=61050/mapped=8585)
|
| direction: L --> R

ids <- toTable(hgu95av2SYMBOL)
View(ids)
library(dplyr)
# 方法1：
filter(ids, symbol=="TP53") #用dplyr包的篩選功能，找到 TP53 基因對應的探針ID
#     probe_id symbol
#1   1939_at   TP53
#2 1974_s_at   TP53
#3  31618_at   TP53

#方法2：
ids[grep("^TP53$", ids$symbol),]
#       probe_id symbol
# 966    1939_at   TP53
# 997  1974_s_at   TP53
# 1420  31618_at   TP53
#方法1，2雖然結果相同，但是定義的對象是不同的

hug95av2SYMBOL是一個R對象，它提供的是芯片生產廠家與基因縮寫之間的映射信息。這個映射的信息主要依據Entrez Gene數據庫。現在我們通過mappedkeys()這個函數，得到映射到基因上的探針信息。

6.理解探針與基因的對應關系，總共多少個基因，基因最多對應多少個探針，是哪些基因，是不是因為這些基因很長，所以在其上面設計多個探針呢？

length(unique(ids$symbol))
# [1] 8585
tail(sort(table(ids$symbol)))
#YME1L1  GAPDH INPP4A    MYB PTGER3  STAT1 
#     7      8      8      8      8      8 
table(sort(table(ids$symbol)))
#  1    2    3    4    5    6    7    8 
# 6555 1428  451  102   22   16    6    5 

image.png

不管是Agilent芯片，還是Affymetrix芯片，上面設計的探針都非常短。最長的如Agilent芯片上的探針，往往都是60bp，但是往往一個基因的長度都好幾Kb。因此一般多個探針對應一個基因，取最大表達值探針來作為基因的表達量。

7.第二步提取到的表達矩陣是12625個探針在22個樣本的表達量矩陣，找到那些不在 hgu95av2.db 包收錄的對應著SYMBOL的探針。

提示：有1165個探針是沒有對應基因名字的。

%in% 邏輯判斷

用法 a %in% table
a值是否包含于table中，為真輸出TURE，否者輸出FALSE

table(rownames(exprSet)) %in% ids$probe_id
# %in% is a more intuitive interface as a binary operator, which returns a logical vector indicating if there is a match or not for its left operand.
n_exprSet <- exprSet[!(rownames(exprSet) %in% ids$probe_id),]
dim(n_exprSet)
# [1] 1165   22
View(n_exprSet)
# These probes are not in the package.

8.過濾表達矩陣，刪除那1165個沒有對應基因名字的探針。

方法1：%in% 邏輯判斷

exprSet <- exprSet[rownames(exprSet) %in% ids$probe_id, ]
dim(exprSet)
[1] 11460    22
View(exprSet)
# These probes are in the package.

方法2 mappedkeys() 映射關系

 length(hgu95av2SYMBOL)
[1] 12625
probe_map <- hgu95av2SYMBOL
length(probe_map)
[1] 12625
#全部的探針數目
# [1] 12625
probe_info <- mappedkeys(probe_map)
length(probe_info)
[1] 11460
#探針與基因產生映射的數目
gene_info <- as.list(probe_map[probe_info])
# 轉化為數據表
length(gene_info)
[1] 11460
gene_symbol <- toTable(probe_map[probe_info])
# 從hgu95av2SYMBOL文件中，取出有映射關系的探針，并生成數據框給gene_symbol
head(gene_symbol)
   probe_id  symbol
1   1000_at   MAPK3
2   1001_at    TIE1
3 1002_f_at CYP2C19
4 1003_s_at   CXCR5
5   1004_at   CXCR5
6   1005_at   DUSP1

mappedkeys用法示例，幫助理解。

library(hgu95av2.db)
  x <- hgu95av2GO
  x
  length(x)
  count.mappedkeys(x)
  x[1:3]
  links(x[1:3])

  ## Keep only the mapped keys
  keys(x) <- mappedkeys(x)
  length(x)
  count.mappedkeys(x)
  x # now it is a submap

  ## The above subsetting can also be achieved with
  x <- hgu95av2GO[mappedkeys(hgu95av2GO)]

  ## mappedkeys() and count.mappedkeys() also work with an environment
  ## or a list
  z <- list(k1=NA, k2=letters[1:4], k3="x")
  mappedkeys(z)
  count.mappedkeys(z)

  ## retrieve the set of primary keys for the ChipDb object named 'hgu95av2.db'
  keys <- keys(hgu95av2.db)
  head(keys)

9.整合表達矩陣，多個探針對應一個基因的情況下，只保留在所有樣本里面平均表達量最大的那個探針。

A. 提示，理解 tapply,by,aggregate,split 函數 , 首先對每個基因找到最大表達量的探針。
B. 然后根據得到探針去過濾原始表達矩陣

ids=ids[match(rownames(exprSet),ids$probe_id),]
head(ids)
exprSet[1:5,1:5]
tmp = by(exprSet,ids$symbol,function(x) rownames(x)[which.max(rowMeans(x))] )
probes = as.character(tmp)
exprSet=exprSet[rownames(exprSet) %in% probes ,]
dim(exprSet)
View(head(exprSet))

10.把過濾后的表達矩陣更改行名為基因的symbol，因為這個時候探針和基因是一對一關系了。

rownames(exprSet)=ids[match(rownames(exprSet),ids$probe_id),2]
exprSet[1:5,1:5]
#  CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL
# MAPK3    5.743132  6.219412  5.523328  5.340477  5.229904
# TIE1     2.285143  2.291229  2.287986  2.295313  2.662170
# CYP2C19  3.309294  3.318466  3.354423  3.327130  3.365113
# CXCR5    1.085264  1.117288  1.084010  1.103217  1.074243
# CXCR5    7.544884  7.671801  7.474025  7.152482  6.902932

library(reshape2)
exprSet_L=melt(exprSet)
colnames(exprSet_L)=c('probe','sample','value')
exprSet_L$group=rep(group_list,each=nrow(exprSet))
head(exprSet_L)
#  probe    sample    value    group
#1   MAPK3 CLL11.CEL 5.743132 progres.
#2    TIE1 CLL11.CEL 2.285143 progres.
#3 CYP2C19 CLL11.CEL 3.309294 progres.
#4   CXCR5 CLL11.CEL 1.085264 progres.
#5   CXCR5 CLL11.CEL 7.544884 progres.
#6   DUSP1 CLL11.CEL 5.083793 progres.
View(head(exprSet))

11. 對第10步得到的表達矩陣進行探索，先畫第一個樣本的所有基因的表達量的boxplot,hist,density ， 然后畫所有樣本的 這些圖

1. 參考：http:///tmp/basic_visualization_for_expression_matrix.html

2. 理解ggplot2的繪圖語法，數據和圖形元素的映射關系

### ggplot2
library(ggplot2)
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_violin()
print(p)
p=ggplot(exprSet_L,aes(value,fill=group))+geom_histogram(bins = 200)+facet_wrap(~sample, nrow = 4)
print(p)
p=ggplot(exprSet_L,aes(value,col=group))+geom_density()+facet_wrap(~sample, nrow = 4)
print(p)
p=ggplot(exprSet_L,aes(value,col=group))+geom_density()
print(p)
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
p=p+stat_summary(fun.y="mean",geom="point",shape=23,size=3,fill="red")
p=p+theme_set(theme_set(theme_bw(base_size=20)))
p=p+theme(text=element_text(face='bold'),axis.text.x=element_text(angle=30,hjust=1),axis.title=element_blank())
print(p)

image.png

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12.理解統計學指標mean,median,max,min,sd,var,mad并計算出每個基因在所有樣本的這些統計學指標，最后按照mad值排序，取top 50 mad值的基因，得到列表。

注意：這個題目出的并不合規，請仔細看。

g_mean <- tail(sort(apply(exprSet,1,mean)),50)
g_median <- tail(sort(apply(exprSet,1,median)),50)
g_max <- tail(sort(apply(exprSet,1,max)),50)
g_min <- tail(sort(apply(exprSet,1,min)),50)
g_sd <- tail(sort(apply(exprSet,1,sd)),50)
g_var <- tail(sort(apply(exprSet,1,var)),50)
g_mad <- tail(sort(apply(exprSet,1,mad)),50)
g_mad
names(g_mad)

 [1] "DUSP5"   "IGFBP4"  "H1FX"    "ENPP2"   "FLNA"    "CLEC2B"  "TSPYL2"  "ZNF266"  "S100A9"  "NR4A2"   "TGFBI"   "ARF6"    "APBB2"   "VCAN"    "RBM38"  
[16] "CAPG"    "PLXNC1"  "RGS2"    "RNASE6"  "VAMP5"   "CYBB"    "GNLY"    "CCL3"    "OAS1"    "ENPP2"   "TRIB2"   "ZNF804A" "H1FX"    "IGH"     "JUND"   
[31] "SLC25A1" "PCDH9"   "VIPR1"   "COBLL1"  "GUSBP11" "S100A8"  "HBB"     "FOS"     "LHFPL2"  "FCN1"    "ZAP70"   "IGLC1"   "LGALS1"  "HBB"     "FOS"    
[46] "SLAMF1"  "TCF7"    "DMD"     "IGF2BP3" "FAM30A" 

13.根據第12步驟得到top 50 mad值的基因列表來取表達矩陣的子集，并且熱圖可視化子表達矩陣。試試看其它5種熱圖的包的不同效果。

## heatmap
library(pheatmap)
choose_gene=names(tail(sort(apply(exprSet,1,mad)),50))
choose_matrix=exprSet[choose_gene,]
choose_matrix=t(scale(t(choose_matrix)))
pheatmap(choose_matrix)

image.png

14.取不同統計學指標mean,median,max,mean,sd,var,mad的各top50基因列表，使用UpSetR包來看他們之間的overlap情況。

## UpSetR
# https://cran./web/packages/UpSetR/README.html
library(UpSetR)
g_all <- unique(c(names(g_mean),names(g_median),names(g_max),names(g_min),
names(g_sd),names(g_var),names(g_mad) ))
dat=data.frame(g_all=g_all,
g_mean=ifelse(g_all %in% names(g_mean) ,1,0),
g_median=ifelse(g_all %in% names(g_median) ,1,0),
g_max=ifelse(g_all %in% names(g_max) ,1,0),
g_min=ifelse(g_all %in% names(g_min) ,1,0),
g_sd=ifelse(g_all %in% names(g_sd) ,1,0),
g_var=ifelse(g_all %in% names(g_var) ,1,0),
g_mad=ifelse(g_all %in% names(g_mad) ,1,0)
)
upset(dat,nsets = 7)

image.png

15.在第二步的基礎上面提取CLL包里面的data(sCLLex) 數據對象的樣本的表型數據。

pdata=pData(sCLLex)
group_list=as.character(pdata[,2])
group_list
# [1] "progres." "stable"   "progres." "progres." "progres." "progres." "stable"   "stable"   "progres." "stable"   "progres." "stable"   "progres." "stable"  
# [15] "stable"   "progres." "progres." "progres." "progres." "progres." "progres." "stable"  
dim(exprSet)
# [1] 8585   22
exprSet[1:5,1:5]
#      CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL
MAPK3    5.743132  6.219412  5.523328  5.340477  5.229904
TIE1     2.285143  2.291229  2.287986  2.295313  2.662170
CYP2C19  3.309294  3.318466  3.354423  3.327130  3.365113
CXCR5    7.544884  7.671801  7.474025  7.152482  6.902932
DUSP1    5.083793  7.610593  7.631311  6.518594  5.059087

16.對所有樣本的表達矩陣進行聚類并且繪圖，然后添加樣本的臨床表型數據信息(更改樣本名)

## hclust
colnames(exprSet)=paste(group_list,1:22,sep='')
# Define nodePar
nodePar <- list(lab.cex = 0.6, pch = c(NA, 19),
                cex = 0.7, col = "blue")
hc=hclust(dist(t(exprSet)))
par(mar=c(5,5,5,10))
plot(as.dendrogram(hc), nodePar = nodePar, horiz = TRUE)

image.png

17.對所有樣本的表達矩陣進行PCA分析并且繪圖，同樣要添加表型信息。

# install.packages("ggfortify")
library(ggfortify)
exprSet <- exprs(sCLLex)
df <- as.data.frame(t(exprSet))
df$group <- group_list
# autoplot uses ggplot2 to draw a particular plot for an object of a particular class in a single command.
autoplot(prcomp(df[,1:(ncol(df)-1)]), data=df, colour = 'group')

image.png

18.根據表達矩陣及樣本分組信息進行批量T檢驗，得到檢驗結果表格

## t.test
dat = exprSet
group_list=as.factor(group_list)
group1 = which(group_list == levels(group_list)[1])
group2 = which(group_list == levels(group_list)[2])
dat1 = dat[, group1]
dat2 = dat[, group2]
dat = cbind(dat1, dat2)
pvals = apply(exprSet, 1, function(x){
  t.test(as.numeric(x)~group_list)$p.value
})
p.adj = p.adjust(pvals, method = "BH")
avg_1 = rowMeans(dat1)
avg_2 = rowMeans(dat2)
log2FC = avg_2-avg_1
DEG_t.test = cbind(avg_1, avg_2, log2FC, pvals, p.adj)
DEG_t.test=DEG_t.test[order(DEG_t.test[,4]),]
DEG_t.test=as.data.frame(DEG_t.test)
head(DEG_t.test)
#           avg_1    avg_2     log2FC        pvals     p.adj
36129_at 7.875615 8.791753  0.9161377 1.629755e-05 0.2057566
37676_at 6.622749 7.965007  1.3422581 4.058944e-05 0.2436177
33791_at 7.616197 5.786041 -1.8301554 6.965416e-05 0.2436177
39967_at 4.456446 2.152471 -2.3039752 8.993339e-05 0.2436177
34594_at 5.988866 7.058738  1.0698718 9.648226e-05 0.2436177
32198_at 4.157971 3.407405 -0.7505660 2.454557e-04 0.3516678

19.使用limma包對表達矩陣及樣本分組信息進行差異分析，得到差異分析表格，重點看logFC和P值，畫個火山圖(就是logFC和-log10(P值)的散點圖)。

# DEG by limma
suppressMessages(library(limma))
design <- model.matrix(~0+factor(group_list))
colnames(design)=levels(factor(group_list))
rownames(design)=colnames(exprSet)
design
contrast.matrix<-makeContrasts(paste0(unique(group_list),collapse = "-"),levels = design)
contrast.matrix
##這個矩陣聲明，我們要把progres.組跟stable進行差異分析比較
##step1
fit <- lmFit(exprSet,design)
##step2
fit2 <- contrasts.fit(fit, contrast.matrix) ##這一步很重要，大家可以自行看看效果
fit2 <- eBayes(fit2) ## default no trend !!!
##eBayes() with trend=TRUE
##step3
tempOutput = topTable(fit2, coef=1, n=Inf)
nrDEG = na.omit(tempOutput)
#write.csv(nrDEG2,"limma_notrend.results.csv",quote = F)
head(nrDEG)

## volcano plot
DEG=nrDEG
logFC_cutoff <- with(DEG,mean(abs( logFC)) + 2*sd(abs( logFC)) )
DEG$change = as.factor(ifelse(DEG$P.Value < 0.05 & abs(DEG$logFC) > logFC_cutoff,
                              ifelse(DEG$logFC > logFC_cutoff ,'UP','DOWN'),'NOT')
)
this_tile <- paste0('Cutoff for logFC is ',round(logFC_cutoff,3),
                    '\nThe number of up gene is ',nrow(DEG[DEG$change =='UP',]) ,
                    '\nThe number of down gene is ',nrow(DEG[DEG$change =='DOWN',])
)

g = ggplot(data=DEG, aes(x=logFC, y=-log10(P.Value), color=change)) +
  geom_point(alpha=0.4, size=1.75) +
  theme_set(theme_set(theme_bw(base_size=20)))+
  xlab("log2 fold change") + ylab("-log10 p-value") +
  ggtitle( this_tile ) + theme(plot.title = element_text(size=15,hjust = 0.5))+
  scale_colour_manual(values = c('blue','black','red')) ## corresponding to the levels(res$change)
print(g)

image.png

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20.對T檢驗結果的P值和limma包差異分析的P值畫散點圖，看看哪些基因相差很大。

### different P values
head(nrDEG)
head(DEG_t.test)
DEG_t.test=DEG_t.test[rownames(nrDEG),]
plot(DEG_t.test[,3],nrDEG[,1])
plot(DEG_t.test[,4],nrDEG[,4])
plot(-log10(DEG_t.test[,4]),-log10(nrDEG[,4]))

rownames(exprSet)=ids[match(rownames(exprSet),ids$probe_id),2]
exprSet[1:5,1:5]
exprSet['GAPDH',]
exprSet['ACTB',]
exprSet['DLEU1',]
library(ggplot2)
library(ggpubr)
my_comparisons <- list(
  c("stable", "progres.")
)
dat=data.frame(group=group_list,
               sampleID= names(exprSet['DLEU1',]),
               values= as.numeric(exprSet['DLEU1',]))
ggboxplot(
  dat, x = "group", y = "values",
  color = "group",
  add = "jitter"
)+
  stat_compare_means(comparisons = my_comparisons, method = "t.test")

## heatmap
library(pheatmap)
choose_gene=head(rownames(nrDEG),25)
choose_matrix=exprSet[choose_gene,]
choose_matrix=t(scale(t(choose_matrix)))
pheatmap(choose_matrix)