R，小作业

From生信菜鸟团

作业 1

根据R包org.Hs.eg.db找到下面ensembl 基因ID 对应的基因名(symbol)
ENSG00000000003.13
ENSG00000000005.5
ENSG00000000419.11
ENSG00000000457.12
ENSG00000000460.15
ENSG00000000938.11

#  copy ensembl基因ID 保存为 e1.txt 
# rm(list = ls())
# options(stringsAsFactors = F) # 最好先run这两行
> content.e1 <- read.table('e1.txt') 
> library(org.Hs.eg.db)
> ?org.Hs.eg.db
> ls("package:org.Hs.eg.db")
 [1] "org.Hs.eg"                "org.Hs.eg.db"             "org.Hs.eg_dbconn"         "org.Hs.eg_dbfile"        
 [5] "org.Hs.eg_dbInfo"         "org.Hs.eg_dbschema"       "org.Hs.egACCNUM"          "org.Hs.egACCNUM2EG"      
 [9] "org.Hs.egALIAS2EG"        "org.Hs.egCHR"             "org.Hs.egCHRLENGTHS"      "org.Hs.egCHRLOC"         
[13] "org.Hs.egCHRLOCEND"       "org.Hs.egENSEMBL"         "org.Hs.egENSEMBL2EG"      "org.Hs.egENSEMBLPROT"    
[17] "org.Hs.egENSEMBLPROT2EG"  "org.Hs.egENSEMBLTRANS"    "org.Hs.egENSEMBLTRANS2EG" "org.Hs.egENZYME"         
[21] "org.Hs.egENZYME2EG"       "org.Hs.egGENENAME"        "org.Hs.egGO"              "org.Hs.egGO2ALLEGS"      
[25] "org.Hs.egGO2EG"           "org.Hs.egMAP"             "org.Hs.egMAP2EG"          "org.Hs.egMAPCOUNTS"      
[29] "org.Hs.egOMIM"            "org.Hs.egOMIM2EG"         "org.Hs.egORGANISM"        "org.Hs.egPATH"           
[33] "org.Hs.egPATH2EG"         "org.Hs.egPFAM"            "org.Hs.egPMID"            "org.Hs.egPMID2EG"        
[37] "org.Hs.egPROSITE"         "org.Hs.egREFSEQ"          "org.Hs.egREFSEQ2EG"       "org.Hs.egSYMBOL"         
[41] "org.Hs.egSYMBOL2EG"       "org.Hs.egUCSCKG"          "org.Hs.egUNIGENE"         "org.Hs.egUNIGENE2EG"     
[45] "org.Hs.egUNIPROT"        
> g2s <- toTable(org.Hs.egSYMBOL)# id转symbol
> head(g2s)
  gene_id symbol
1       1   A1BG
2       2    A2M
3       3  A2MP1
4       9   NAT1
5      10   NAT2
6      11   NATP
> print(content.e1)# 看一眼e1.txt的内容，确认是ensemble id
                  V1
1 ENSG00000000003.13
2  ENSG00000000005.5
3 ENSG00000000419.11
4 ENSG00000000457.12
5 ENSG00000000460.15
6 ENSG00000000938.11
> g2e <- toTable(org.Hs.egENSEMBL)# id转ensemble
> head(g2e)
  gene_id      ensembl_id
1       1 ENSG00000121410
2       2 ENSG00000175899
3       3 ENSG00000256069
4       9 ENSG00000171428
5      10 ENSG00000156006
6      12 ENSG00000196136
> fetch <- function(x) #设函数，以.为分割符将x这个list的第1元素下面的第1元素分割
+ {
+   strsplit(as.character(x), '[.]')[[1]][1]
+ }
> lapply(content.e1$V1, fetch )# fetch对content.e1$V1里的每一个元素做同样的事
[[1]]
[1] "ENSG00000000003"

[[2]]
[1] "ENSG00000000005"

[[3]]
[1] "ENSG00000000419"

[[4]]
[1] "ENSG00000000457"

[[5]]
[1] "ENSG00000000460"

[[6]]
[1] "ENSG00000000938"

> trans <- unlist(lapply(content.e1$V1, fetch )) 
> print(trans)
[1] "ENSG00000000003" "ENSG00000000005" "ENSG00000000419" "ENSG00000000457" "ENSG00000000460" "ENSG00000000938"
> content.e1$ensembl_id <- trans # conten.e1增加ensembl_id字段
> print(content.e1)
                  V1      ensembl_id
1 ENSG00000000003.13 ENSG00000000003
2  ENSG00000000005.5 ENSG00000000005
3 ENSG00000000419.11 ENSG00000000419
4 ENSG00000000457.12 ENSG00000000457
5 ENSG00000000460.15 ENSG00000000460
6 ENSG00000000938.11 ENSG00000000938
> head(g2e) # 再看一眼g2e
  gene_id      ensembl_id
1       1 ENSG00000121410
2       2 ENSG00000175899
3       3 ENSG00000256069
4       9 ENSG00000171428
5      10 ENSG00000156006
6      12 ENSG00000196136
> tmp <- merge(content.e1, g2e, by = 'ensembl_id') # 不用by也可以，此时是取交集
> print(tmp)
       ensembl_id                 V1 gene_id
1 ENSG00000000003 ENSG00000000003.13    7105
2 ENSG00000000005  ENSG00000000005.5   64102
3 ENSG00000000419 ENSG00000000419.11    8813
4 ENSG00000000457 ENSG00000000457.12   57147
5 ENSG00000000460 ENSG00000000460.15   55732
6 ENSG00000000938 ENSG00000000938.11    2268
> print(head(g2s)) # 再看一眼g2s
  gene_id symbol
1       1   A1BG
2       2    A2M
3       3  A2MP1
4       9   NAT1
5      10   NAT2
6      11   NATP
> tmp.end <- merge(tmp, g2s) # 注意，没加by
> print(tmp.end)
  gene_id      ensembl_id                 V1   symbol
1    2268 ENSG00000000938 ENSG00000000938.11      FGR
2   55732 ENSG00000000460 ENSG00000000460.15 C1orf112
3   57147 ENSG00000000457 ENSG00000000457.12    SCYL3
4   64102 ENSG00000000005  ENSG00000000005.5     TNMD
5    7105 ENSG00000000003 ENSG00000000003.13   TSPAN6
6    8813 ENSG00000000419 ENSG00000000419.11     DPM1

######原代码如下，太浓缩，不好理解#####
a=read.table('e1.txt')
head(a)
library(org.Hs.eg.db)
ls("package:org.Hs.eg.db")
g2s=toTable(org.Hs.egSYMBOL);head(g2s)
g2e=toTable(org.Hs.egENSEMBL);head(g2e)
head(g2e)
library(stringr)

a$ensembl_id=unlist(lapply(a$V1,function(x){
  strsplit(as.character(x),'[.]')[[1]][1]
})
)
tmp=merge(a,g2e,by='ensembl_id')
tmp=merge(tmp,g2s,by='gene_id')


######修改后，我觉得好理解一些#############


content.e1 <- read.table('e1.txt') 
library(org.Hs.eg.db)

?org.Hs.eg.db
ls("package:org.Hs.eg.db")

g2s <- toTable(org.Hs.egSYMBOL)# id转symbol
head(g2s)

print(content.e1)# 看一眼e1.txt的内容，确认是ensemble id

g2e <- toTable(org.Hs.egENSEMBL)# id转ensemble
head(g2e)

fetch <- function(x) #设函数，以.为分割符将x这个list的第1元素下面的第1元素分割
{
  strsplit(as.character(x), '[.]')[[1]][1]
}

lapply(content.e1$V1, fetch )# fetch对content.e1$V1里的每一个元素做同样的事

trans <- unlist(lapply(content.e1$V1, fetch )) 
print(trans)

content.e1$ensembl_id <- trans # conten.e1增加ensembl_id字段
print(content.e1)

head(g2e) # 再看一眼g2e

tmp <- merge(content.e1, g2e, by = 'ensembl_id') # 不用by也可以，此时是取交集
print(tmp)

print(head(g2s)) # 再看一眼g2s
tmp.end <- merge(tmp, g2s) # 注意，没加by
print(tmp.end)

作业 2

#根据R包hgu133a.db找到下面探针对应的基因名(symbol)
1053_at
117_at
121_at
1255_g_at
1316_at
1320_at
1405_i_at
1431_at
1438_at
1487_at
1494_f_at
1598_g_at
160020_at
1729_at
177_at
#########
> rm(list = ls())
> options(stringsAsFactors = F) 
> content.e2 <- read.table('e2.txt') 
> print(content.e2)
          V1
1    1053_at
2     117_at
3     121_at
4  1255_g_at
5    1316_at
6    1320_at
7  1405_i_at
8    1431_at
9    1438_at
10   1487_at
11 1494_f_at
12 1598_g_at
13 160020_at
14   1729_at
15    177_at
> 
> library(hgu133a.db)
> ids <- toTable(hgu133aSYMBOL)
> head(ids)
   probe_id symbol
1   1053_at   RFC2
2    117_at  HSPA6
3    121_at   PAX8
4 1255_g_at GUCA1A
5   1316_at   THRA
6   1320_at PTPN21
> 
> print(content.e2)
          V1
1    1053_at
2     117_at
3     121_at
4  1255_g_at
5    1316_at
6    1320_at
7  1405_i_at
8    1431_at
9    1438_at
10   1487_at
11 1494_f_at
12 1598_g_at
13 160020_at
14   1729_at
15    177_at
> 
> colnames(content.e2) <- "probe_id"
> print(content.e2)
    probe_id
1    1053_at
2     117_at
3     121_at
4  1255_g_at
5    1316_at
6    1320_at
7  1405_i_at
8    1431_at
9    1438_at
10   1487_at
11 1494_f_at
12 1598_g_at
13 160020_at
14   1729_at
15    177_at
> 
> tmp <- merge(ids, content.e2)
> print(tmp)
    probe_id symbol
1    1053_at   RFC2
2     117_at  HSPA6
3     121_at   PAX8
4  1255_g_at GUCA1A
5    1316_at   THRA
6    1320_at PTPN21
7  1405_i_at   CCL5
8    1431_at CYP2E1
9    1438_at  EPHB3
10   1487_at  ESRRA
11 1494_f_at CYP2A6
12 1598_g_at   GAS6
13 160020_at  MMP14
14   1729_at  TRADD
15    177_at   PLD1

作业 3

###找到R包CLL内置的数据集的表达矩阵里面的TP53基因的表达量，并且绘制在 progres.-stable分组的boxplot图

> rm(list = ls())
> options(stringsAsFactors = F) 
> suppressPackageStartupMessages(library(CLL))
> data(sCLLex) # 官网读包的说明书
> sCLLex
ExpressionSet (storageMode: lockedEnvironment)
assayData: 12625 features, 22 samples 
  element names: exprs 
protocolData: none
phenoData
  sampleNames: CLL11.CEL CLL12.CEL ... CLL9.CEL (22 total)
  varLabels: SampleID Disease
  varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu95av2 
> 
> exprSet <- exprs(sCLLex) 
> pd <- pData(sCLLex) # progres.-stable分组信息，在Rstudio右上角Environment中点pd看分组信息
> library(hgu95av2.db)
> ids <- toTable(hgu95av2SYMBOL) # 在Rstudio右上角Environment中点击ids变量
> #然后在左上边窗口的搜索框中输入TP53,取当中对应的探针，例如1939_at
> 
> expr.1939 <- exprSet['1939_at', ] #看图表达量差异最大，通常保留，好说明问题 
> expr.1974 <- exprSet['1974_s_at', ]
> expr.31618 <- exprSet['31618_at', ]
> # 变量对分组
> boxplot(expr.1939 ~ pd$Disease)
> boxplot(expr.1974 ~ pd$Disease)
> boxplot(expr.31618 ~ pd$Disease)