author: "susu"
date: "2023-05-18"
output: html_document
#1.当前工作目录是什么路径。
getwd()
## [1] "D:/desktop/sxjns h/zuoye chuji"
#2.新建6个向量,基于不同的原子类型。(重点是字符串,数值,逻辑值)
#原子类型包括向量,列表,矩阵,数组,因子,数据帧。
a=c(1,2,3,4,5)#数值型向量
a
## [1] 1 2 3 4 5
b=c("a","b","c")#字符型向量
b
## [1] "a" "b" "c"
c=c(TRUE,TRUE,FALSE)#逻辑型向量
c
## [1] TRUE TRUE FALSE
v4=3L
v4
## [1] 3
##创建整数型向量
v5=2i+1 #复数向量
v5
## [1] 1+2i
#创建字符串向量
d=c("good night")
d
## [1] "good night"
#3.新建一些数据结构,比如矩阵,数组,数据框,列表等,重点是数据框,矩阵。
#矩阵
m1=matrix(1:9,nrow=9)
m1
## [,1]
## [1,] 1
## [2,] 2
## [3,] 3
## [4,] 4
## [5,] 5
## [6,] 6
## [7,] 7
## [8,] 8
## [9,] 9
#数组
m2=array(1:24,c(2,3,4))
m2
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 7 9 11
## [2,] 8 10 12
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 13 15 17
## [2,] 14 16 18
##
## , , 4
##
## [,1] [,2] [,3]
## [1,] 19 21 23
## [2,] 20 22 24
#数据框
m3=data.frame(a=c(1:10),b=paste("q",c(1:10),sep="."),c=1:10>3)#sep是以 分割
m3
## a b c
## 1 1 q.1 FALSE
## 2 2 q.2 FALSE
## 3 3 q.3 FALSE
## 4 4 q.4 TRUE
## 5 5 q.5 TRUE
## 6 6 q.6 TRUE
## 7 7 q.7 TRUE
## 8 8 q.8 TRUE
## 9 9 q.9 TRUE
## 10 10 q.10 TRUE
#列表
m4=list(m1,m2,m3)
m4
## [[1]]
## [,1]
## [1,] 1
## [2,] 2
## [3,] 3
## [4,] 4
## [5,] 5
## [6,] 6
## [7,] 7
## [8,] 8
## [9,] 9
##
## [[2]]
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 7 9 11
## [2,] 8 10 12
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 13 15 17
## [2,] 14 16 18
##
## , , 4
##
## [,1] [,2] [,3]
## [1,] 19 21 23
## [2,] 20 22 24
##
##
## [[3]]
## a b c
## 1 1 q.1 FALSE
## 2 2 q.2 FALSE
## 3 3 q.3 FALSE
## 4 4 q.4 TRUE
## 5 5 q.5 TRUE
## 6 6 q.6 TRUE
## 7 7 q.7 TRUE
## 8 8 q.8 TRUE
## 9 9 q.9 TRUE
## 10 10 q.10 TRUE
#4.在你新建的数据框进行切片操作,比如首先取第1,3行, 然后取第4,6列。
m5=m3[c(1,3),]
m5
## a b c
## 1 1 q.1 FALSE
## 3 3 q.3 FALSE
m3$d=c(2:11)
m3
## a b c d
## 1 1 q.1 FALSE 2
## 2 2 q.2 FALSE 3
## 3 3 q.3 FALSE 4
## 4 4 q.4 TRUE 5
## 5 5 q.5 TRUE 6
## 6 6 q.6 TRUE 7
## 7 7 q.7 TRUE 8
## 8 8 q.8 TRUE 9
## 9 9 q.9 TRUE 10
## 10 10 q.10 TRUE 11
m3$e=c(3:12)
m3
## a b c d e
## 1 1 q.1 FALSE 2 3
## 2 2 q.2 FALSE 3 4
## 3 3 q.3 FALSE 4 5
## 4 4 q.4 TRUE 5 6
## 5 5 q.5 TRUE 6 7
## 6 6 q.6 TRUE 7 8
## 7 7 q.7 TRUE 8 9
## 8 8 q.8 TRUE 9 10
## 9 9 q.9 TRUE 10 11
## 10 10 q.10 TRUE 11 12
m3$f=c(4:13)
m3
## a b c d e f
## 1 1 q.1 FALSE 2 3 4
## 2 2 q.2 FALSE 3 4 5
## 3 3 q.3 FALSE 4 5 6
## 4 4 q.4 TRUE 5 6 7
## 5 5 q.5 TRUE 6 7 8
## 6 6 q.6 TRUE 7 8 9
## 7 7 q.7 TRUE 8 9 10
## 8 8 q.8 TRUE 9 10 11
## 9 9 q.9 TRUE 10 11 12
## 10 10 q.10 TRUE 11 12 13
m6=m3[,c(4,6)]
m6
## d f
## 1 2 4
## 2 3 5
## 3 4 6
## 4 5 7
## 5 6 8
## 6 7 9
## 7 8 10
## 8 9 11
## 9 10 12
## 10 11 13
#5.使用data函数来加载R内置数据集 rivers,并描述它。
data(rivers)
rivers
## [1] 735 320 325 392 524 450 1459 135 465 600 330 336 280 315 870 906 202
## [18] 329 290 1000 600 505 1450 840 1243 890 350 407 286 280 525 720 390 250
## [35] 327 230 265 850 210 630 260 230 360 730 600 306 390 420 291 710 340
## [52] 217 281 352 259 250 470 680 570 350 300 560 900 625 332 2348 1171 3710
## [69] 2315 2533 780 280 410 460 260 255 431 350 760 618 338 981 1306 500 696
## [86] 605 250 411 1054 735 233 435 490 310 460 383 375 1270 545 445 1885 380
## [103] 300 380 377 425 276 210 800 420 350 360 538 1100 1205 314 237 610 360
## [120] 540 1038 424 310 300 444 301 268 620 215 652 900 525 246 360 529 500
## [137] 720 270 430 671 1770
head(rivers)
## [1] 735 320 325 392 524 450
tail(rivers)
## [1] 500 720 270 430 671 1770
str(rivers)
## num [1:141] 735 320 325 392 524 ...
plot(rivers)
length(rivers)
## [1] 141
#6.下载 https://www.ncbi.nlm.nih.gov/sra?term=SRP133642 里面的 RunInfo Table文件读入到R里面,了解这个数据框,多少列,每一列都是什么属性的元素。(参考B站生信小技巧获取runinfo table)
SraRunTable <- read.table("http://www.bio-info-trainee.com/tmp/5years/SraRunTable.txt",fill=TRUE,header = T,sep = "\t")
dim(SraRunTable)
## [1] 768 31
colnames(SraRunTable)
## [1] "BioSample" "Experiment" "MBases" "MBytes"
## [5] "Run" "SRA_Sample" "Sample_Name" "Assay_Type"
## [9] "AssemblyName" "AvgSpotLen" "BioProject" "Center_Name"
## [13] "Consent" "DATASTORE_filetype" "DATASTORE_provider" "InsertSize"
## [17] "Instrument" "LibraryLayout" "LibrarySelection" "LibrarySource"
## [21] "LoadDate" "Organism" "Platform" "ReleaseDate"
## [25] "SRA_Study" "age" "cell_type" "marker_genes"
## [29] "source_name" "strain" "tissue"
class(colnames(SraRunTable))
## [1] "character"
class(SraRunTable)
## [1] "data.frame"
str(SraRunTable)
## 'data.frame': 768 obs. of 31 variables:
## $ BioSample : chr "SAMN08619912" "SAMN08619911" "SAMN08619910" "SAMN08619909" ...
## $ Experiment : chr "SRX3749902" "SRX3749903" "SRX3749904" "SRX3749905" ...
## $ MBases : int 16 16 8 8 11 7 18 5 11 15 ...
## $ MBytes : int 8 8 4 4 5 4 9 3 6 8 ...
## $ Run : chr "SRR6790711" "SRR6790712" "SRR6790713" "SRR6790714" ...
## $ SRA_Sample : chr "SRS3006138" "SRS3006148" "SRS3006137" "SRS3006136" ...
## $ Sample_Name : chr "GSM3025845" "GSM3025846" "GSM3025847" "GSM3025848" ...
## $ Assay_Type : chr "RNA-Seq" "RNA-Seq" "RNA-Seq" "RNA-Seq" ...
## $ AssemblyName : chr "GCF_000001635.20" "GCF_000001635.20" "GCF_000001635.20" "GCF_000001635.20" ...
## $ AvgSpotLen : int 43 43 43 43 43 43 43 43 43 43 ...
## $ BioProject : chr "PRJNA436229" "PRJNA436229" "PRJNA436229" "PRJNA436229" ...
## $ Center_Name : chr "GEO" "GEO" "GEO" "GEO" ...
## $ Consent : chr "public" "public" "public" "public" ...
## $ DATASTORE_filetype: chr "sra" "sra" "sra" "sra" ...
## $ DATASTORE_provider: chr "ncbi" "ncbi" "ncbi" "ncbi" ...
## $ InsertSize : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Instrument : chr "Illumina HiSeq 2000" "Illumina HiSeq 2000" "Illumina HiSeq 2000" "Illumina HiSeq 2000" ...
## $ LibraryLayout : chr "SINGLE" "SINGLE" "SINGLE" "SINGLE" ...
## $ LibrarySelection : chr "cDNA" "cDNA" "cDNA" "cDNA" ...
## $ LibrarySource : chr "TRANSCRIPTOMIC" "TRANSCRIPTOMIC" "TRANSCRIPTOMIC" "TRANSCRIPTOMIC" ...
## $ LoadDate : chr "2018-03-01" "2018-03-01" "2018-03-01" "2018-03-01" ...
## $ Organism : chr "Mus musculus" "Mus musculus" "Mus musculus" "Mus musculus" ...
## $ Platform : chr "ILLUMINA" "ILLUMINA" "ILLUMINA" "ILLUMINA" ...
## $ ReleaseDate : chr "2018-11-23" "2018-11-23" "2018-11-23" "2018-11-23" ...
## $ SRA_Study : chr "SRP133642" "SRP133642" "SRP133642" "SRP133642" ...
## $ age : chr "14 weeks" "14 weeks" "14 weeks" "14 weeks" ...
## $ cell_type : chr "cancer-associated fibroblasts (CAFs)" "cancer-associated fibroblasts (CAFs)" "cancer-associated fibroblasts (CAFs)" "cancer-associated fibroblasts (CAFs)" ...
## $ marker_genes : chr "EpCAM-, CD45-, CD31-, NG2-" "EpCAM-, CD45-, CD31-, NG2-" "EpCAM-, CD45-, CD31-, NG2-" "EpCAM-, CD45-, CD31-, NG2-" ...
## $ source_name : chr "Mammary tumor fibroblast" "Mammary tumor fibroblast" "Mammary tumor fibroblast" "Mammary tumor fibroblast" ...
## $ strain : chr "FVB/N-Tg(MMTVPyVT)634Mul/J" "FVB/N-Tg(MMTVPyVT)634Mul/J" "FVB/N-Tg(MMTVPyVT)634Mul/J" "FVB/N-Tg(MMTVPyVT)634Mul/J" ...
## $ tissue : chr "Mammary tumor fibroblast" "Mammary tumor fibroblast" "Mammary tumor fibroblast" "Mammary tumor fibroblast" ...
sapply(SraRunTable,mode)
## BioSample Experiment MBases MBytes
## "character" "character" "numeric" "numeric"
## Run SRA_Sample Sample_Name Assay_Type
## "character" "character" "character" "character"
## AssemblyName AvgSpotLen BioProject Center_Name
## "character" "numeric" "character" "character"
## Consent DATASTORE_filetype DATASTORE_provider InsertSize
## "character" "character" "character" "numeric"
## Instrument LibraryLayout LibrarySelection LibrarySource
## "character" "character" "character" "character"
## LoadDate Organism Platform ReleaseDate
## "character" "character" "character" "character"
## SRA_Study age cell_type marker_genes
## "character" "character" "character" "character"
## source_name strain tissue
## "character" "character" "character"
#7.下载 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111229 里面的样本信息sample.csv读入到R里面,了解这个数据框,多少列,每一列都是什么属性的元素。
sample <- read.csv('sample.csv')
class(sample)
## [1] "data.frame"
dim(sample)
## [1] 768 12
str(sample)
## 'data.frame': 768 obs. of 12 variables:
## $ Accession : chr "GSM3025845" "GSM3025846" "GSM3025847" "GSM3025848" ...
## $ Title : chr "SS2_15_0048_A1" "SS2_15_0048_A2" "SS2_15_0048_A3" "SS2_15_0048_A4" ...
## $ Sample.Type : chr "SRA" "SRA" "SRA" "SRA" ...
## $ Taxonomy : chr "Mus musculus" "Mus musculus" "Mus musculus" "Mus musculus" ...
## $ Channels : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Platform : chr "GPL13112" "GPL13112" "GPL13112" "GPL13112" ...
## $ Series : chr "GSE111229" "GSE111229" "GSE111229" "GSE111229" ...
## $ Supplementary.Types: chr "SRA Run Selector" "SRA Run Selector" "SRA Run Selector" "SRA Run Selector" ...
## $ Supplementary.Links: chr "https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749902" "https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749903" "https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749904" "https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749905" ...
## $ SRA.Accession : chr "SRX3749902" "SRX3749903" "SRX3749904" "SRX3749905" ...
## $ Contact : chr "Kristian Pietras" "Kristian Pietras" "Kristian Pietras" "Kristian Pietras" ...
## $ Release.Date : chr "Nov 23, 2018" "Nov 23, 2018" "Nov 23, 2018" "Nov 23, 2018" ...
#8.把前面两个步骤的两个表(RunInfo Table 文件,样本信息sample.csv)关联起来,使用merge函数。
merge=merge(sample,SraRunTable,by.x = "Accession",by.y = "Sample_Name")
merge
## Accession Title Sample.Type Taxonomy Channels Platform.x Series
## 1 GSM3025845 SS2_15_0048_A1 SRA Mus musculus 1 GPL13112 GSE111229
## 2 GSM3025846 SS2_15_0048_A2 SRA Mus musculus 1 GPL13112 GSE111229
## 3 GSM3025847 SS2_15_0048_A3 SRA Mus musculus 1 GPL13112 GSE111229
## 4 GSM3025848 SS2_15_0048_A4 SRA Mus musculus 1 GPL13112 GSE111229
## 5 GSM3025849 SS2_15_0048_A5 SRA Mus musculus 1 GPL13112 GSE111229
## 6 GSM3025850 SS2_15_0048_A6 SRA Mus musculus 1 GPL13112 GSE111229
## 7 GSM3025851 SS2_15_0048_A7 SRA Mus musculus 1 GPL13112 GSE111229
## 8 GSM3025852 SS2_15_0048_A8 SRA Mus musculus 1 GPL13112 GSE111229
## 9 GSM3025853 SS2_15_0048_A9 SRA Mus musculus 1 GPL13112 GSE111229
## 10 GSM3025854 SS2_15_0048_A10 SRA Mus musculus 1 GPL13112 GSE111229
## 11 GSM3025855 SS2_15_0048_A11 SRA Mus musculus 1 GPL13112 GSE111229
## 12 GSM3025856 SS2_15_0048_A12 SRA Mus musculus 1 GPL13112 GSE111229
## 13 GSM3025857 SS2_15_0048_A13 SRA Mus musculus 1 GPL13112 GSE111229
## 14 GSM3025858 SS2_15_0048_A14 SRA Mus musculus 1 GPL13112 GSE111229
## 15 GSM3025859 SS2_15_0048_A15 SRA Mus musculus 1 GPL13112 GSE111229
## 16 GSM3025860 SS2_15_0048_A16 SRA Mus musculus 1 GPL13112 GSE111229
## 17 GSM3025861 SS2_15_0048_A17 SRA Mus musculus 1 GPL13112 GSE111229
## 18 GSM3025862 SS2_15_0048_A18 SRA Mus musculus 1 GPL13112 GSE111229
## 19 GSM3025863 SS2_15_0048_A19 SRA Mus musculus 1 GPL13112 GSE111229
## 20 GSM3025864 SS2_15_0048_A20 SRA Mus musculus 1 GPL13112 GSE111229
## 21 GSM3025865 SS2_15_0048_A21 SRA Mus musculus 1 GPL13112 GSE111229
## 22 GSM3025866 SS2_15_0048_A22 SRA Mus musculus 1 GPL13112 GSE111229
## 23 GSM3025867 SS2_15_0048_A23 SRA Mus musculus 1 GPL13112 GSE111229
## Supplementary.Types Supplementary.Links
## 1 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749902
## 2 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749903
## 3 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749904
## 4 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749905
## 5 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749906
## 6 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749907
## 7 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749908
## 8 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749909
## 9 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749910
## 10 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749901
## 11 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749911
## 12 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749912
## 13 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749913
## 14 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749914
## 15 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749915
## 16 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749916
## 17 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749917
## 18 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749918
## 19 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749919
## 20 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749920
## 21 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749921
## 22 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749922
## 23 SRA Run Selector https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRX3749923
## SRA.Accession Contact Release.Date BioSample Experiment MBases MBytes
## 1 SRX3749902 Kristian Pietras Nov 23, 2018 SAMN08619912 SRX3749902 16 8
## 2 SRX3749903 Kristian Pietras Nov 23, 2018 SAMN08619911 SRX3749903 16 8
## 3 SRX3749904 Kristian Pietras Nov 23, 2018 SAMN08619910 SRX3749904 8 4
## 4 SRX3749905 Kristian Pietras Nov 23, 2018 SAMN08619909 SRX3749905 8 4
## 5 SRX3749906 Kristian Pietras Nov 23, 2018 SAMN08619908 SRX3749906 11 5
## 6 SRX3749907 Kristian Pietras Nov 23, 2018 SAMN08619919 SRX3749907 7 4
## 7 SRX3749908 Kristian Pietras Nov 23, 2018 SAMN08619918 SRX3749908 18 9
## 8 SRX3749909 Kristian Pietras Nov 23, 2018 SAMN08619921 SRX3749909 5 3
## 9 SRX3749910 Kristian Pietras Nov 23, 2018 SAMN08619920 SRX3749910 11 6
## 10 SRX3749901 Kristian Pietras Nov 23, 2018 SAMN08619914 SRX3749901 15 8
## 11 SRX3749911 Kristian Pietras Nov 23, 2018 SAMN08619913 SRX3749911 14 7
## 12 SRX3749912 Kristian Pietras Nov 23, 2018 SAMN08619922 SRX3749912 14 7
## 13 SRX3749913 Kristian Pietras Nov 23, 2018 SAMN08619928 SRX3749913 14 7
## 14 SRX3749914 Kristian Pietras Nov 23, 2018 SAMN08619927 SRX3749914 13 7
## 15 SRX3749915 Kristian Pietras Nov 23, 2018 SAMN08619926 SRX3749915 15 7
## 16 SRX3749916 Kristian Pietras Nov 23, 2018 SAMN08619925 SRX3749916 13 7
## 17 SRX3749917 Kristian Pietras Nov 23, 2018 SAMN08619917 SRX3749917 5 3
## 18 SRX3749918 Kristian Pietras Nov 23, 2018 SAMN08619916 SRX3749918 13 7
## 19 SRX3749919 Kristian Pietras Nov 23, 2018 SAMN08619915 SRX3749919 20 10
## 20 SRX3749920 Kristian Pietras Nov 23, 2018 SAMN08619924 SRX3749920 12 6
## 21 SRX3749921 Kristian Pietras Nov 23, 2018 SAMN08619923 SRX3749921 15 8
## 22 SRX3749922 Kristian Pietras Nov 23, 2018 SAMN08622126 SRX3749922 13 6
## 23 SRX3749923 Kristian Pietras Nov 23, 2018 SAMN08622125 SRX3749923 2 1
## Run SRA_Sample Assay_Type AssemblyName AvgSpotLen BioProject Center_Name
## 1 SRR6790711 SRS3006138 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 2 SRR6790712 SRS3006148 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 3 SRR6790713 SRS3006137 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 4 SRR6790714 SRS3006136 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 5 SRR6790715 SRS3006149 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 6 SRR6790716 SRS3006140 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 7 SRR6790717 SRS3006150 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 8 SRR6790718 SRS3006142 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 9 SRR6790719 SRS3006141 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 10 SRR6790720 SRS3006139 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 11 SRR6790721 SRS3006151 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 12 SRR6790722 SRS3006143 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 13 SRR6790723 SRS3006152 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 14 SRR6790724 SRS3006153 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 15 SRR6790725 SRS3006154 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 16 SRR6790726 SRS3006144 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 17 SRR6790727 SRS3006155 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 18 SRR6790728 SRS3006145 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 19 SRR6790729 SRS3006146 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 20 SRR6790730 SRS3006147 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 21 SRR6790731 SRS3006156 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 22 SRR6790732 SRS3007304 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## 23 SRR6790733 SRS3007303 RNA-Seq GCF_000001635.20 43 PRJNA436229 GEO
## Consent DATASTORE_filetype DATASTORE_provider InsertSize Instrument
## 1 public sra ncbi 0 Illumina HiSeq 2000
## 2 public sra ncbi 0 Illumina HiSeq 2000
## 3 public sra ncbi 0 Illumina HiSeq 2000
## 4 public sra ncbi 0 Illumina HiSeq 2000
## 5 public sra ncbi 0 Illumina HiSeq 2000
## 6 public sra ncbi 0 Illumina HiSeq 2000
## 7 public sra ncbi 0 Illumina HiSeq 2000
## 8 public sra ncbi 0 Illumina HiSeq 2000
## 9 public sra ncbi 0 Illumina HiSeq 2000
## 10 public sra ncbi 0 Illumina HiSeq 2000
## 11 public sra ncbi 0 Illumina HiSeq 2000
## 12 public sra ncbi 0 Illumina HiSeq 2000
## 13 public sra ncbi 0 Illumina HiSeq 2000
## 14 public sra ncbi 0 Illumina HiSeq 2000
## 15 public sra ncbi 0 Illumina HiSeq 2000
## 16 public sra ncbi 0 Illumina HiSeq 2000
## 17 public sra ncbi 0 Illumina HiSeq 2000
## 18 public sra ncbi 0 Illumina HiSeq 2000
## 19 public sra ncbi 0 Illumina HiSeq 2000
## 20 public sra ncbi 0 Illumina HiSeq 2000
## 21 public sra ncbi 0 Illumina HiSeq 2000
## 22 public sra ncbi 0 Illumina HiSeq 2000
## 23 public sra ncbi 0 Illumina HiSeq 2000
## LibraryLayout LibrarySelection LibrarySource LoadDate Organism Platform.y
## 1 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 2 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 3 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 4 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 5 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 6 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 7 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 8 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 9 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 10 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 11 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 12 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 13 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 14 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 15 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 16 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 17 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 18 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 19 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 20 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 21 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 22 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## 23 SINGLE cDNA TRANSCRIPTOMIC 2018-03-01 Mus musculus ILLUMINA
## ReleaseDate SRA_Study age cell_type
## 1 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 2 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 3 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 4 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 5 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 6 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 7 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 8 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 9 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 10 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 11 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 12 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 13 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 14 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 15 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 16 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 17 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 18 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 19 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 20 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 21 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 22 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## 23 2018-11-23 SRP133642 14 weeks cancer-associated fibroblasts (CAFs)
## marker_genes source_name strain
## 1 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 2 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 3 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 4 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 5 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 6 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 7 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 8 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 9 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 10 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 11 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 12 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 13 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 14 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 15 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 16 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 17 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 18 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 19 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 20 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 21 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 22 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## 23 EpCAM-, CD45-, CD31-, NG2- Mammary tumor fibroblast FVB/N-Tg(MMTVPyVT)634Mul/J
## tissue
## 1 Mammary tumor fibroblast
## 2 Mammary tumor fibroblast
## 3 Mammary tumor fibroblast
## 4 Mammary tumor fibroblast
## 5 Mammary tumor fibroblast
## 6 Mammary tumor fibroblast
## 7 Mammary tumor fibroblast
## 8 Mammary tumor fibroblast
## 9 Mammary tumor fibroblast
## 10 Mammary tumor fibroblast
## 11 Mammary tumor fibroblast
## 12 Mammary tumor fibroblast
## 13 Mammary tumor fibroblast
## 14 Mammary tumor fibroblast
## 15 Mammary tumor fibroblast
## 16 Mammary tumor fibroblast
## 17 Mammary tumor fibroblast
## 18 Mammary tumor fibroblast
## 19 Mammary tumor fibroblast
## 20 Mammary tumor fibroblast
## 21 Mammary tumor fibroblast
## 22 Mammary tumor fibroblast
## 23 Mammary tumor fibroblast
## [ reached 'max' / getOption("max.print") -- omitted 745 rows ]
dim(merge)
## [1] 768 42
#9.对前面读取的 RunInfo Table 文件在R里面探索其MBases列,包括箱线图(boxplot)和五分位数(fivenum),还有频数图(hist),以及密度图(density) 。
data=SraRunTable$MBases
data
## [1] 16 16 8 8 11 7 18 5 11 15 14 14 14 13 15 13 5 13 20 12 15 13 2 18 13 8 18 7 8
## [30] 5 12 6 8 11 8 12 13 9 5 14 7 9 9 4 12 12 13 12 54 12 12 3 12 13 15 12 13 9
## [59] 19 13 20 16 12 22 11 14 20 13 14 14 14 18 16 12 18 9 12 16 17 16 8 9 16 16 17 11 6
## [88] 15 12 8 14 3 15 13 8 27 18 4 19 2 10 7 18 16 20 20 12 18 20 9 16 20 14 17 24 5
## [117] 10 17 14 15 10 12 21 11 16 7 17 9 17 18 16 19 13 11 11 16 10 13 17 12 23 19 17 22 21
## [146] 13 23 13 4 15 31 11 20 28 26 19 18 14 10 21 8 16 22 11 15 24 15 24 9 16 15 16 16 14
## [175] 21 11 19 19 15 21 13 13 11 20 3 16 19 14 20 20 25 15 11 13 12 8 15 13 15 9 17 14 19
## [204] 16 19 17 12 16 7 16 16 8 12 14 13 4 15 11 19 8 19 10 8 11 11 12 19 17 16 7 12 19
## [233] 13 13 16 10 17 18 15 4 20 20 15 7 19 7 25 10 20 14 23 9 19 16 16 14 15 16 11 4 6
## [262] 14 16 6 13 6 11 9 11 5 13 11 7 12 10 9 5 9 4 8 5 9 6 16 7 11 9 2 14 10
## [291] 19 11 18 15 26 18 20 22 15 18 22 11 14 13 13 17 15 13 24 16 10 3 15 16 5 10 10 11 19
## [320] 6 13 24 15 10 5 7 6 14 12 6 14 12 12 19 15 3 4 4 5 8 11 16 8 7 18 19 8 16
## [349] 7 15 12 11 11 15 19 7 10 13 4 5 11 7 7 9 13 9 9 8 6 8 16 9 11 2 5 6 9
## [378] 10 10 7 14 6 10 4 1 4 9 5 11 7 14 5 4 15 15 17 17 9 10 10 11 10 14 0 16 14
## [407] 9 12 8 6 13 2 14 9 11 2 6 4 12 11 10 8 4 7 7 10 14 8 11 15 11 8 6 7 17
## [436] 6 14 18 21 11 14 23 9 21 13 11 12 20 11 14 17 12 16 19 8 5 11 7 16 10 13 16 20 10
## [465] 18 16 4 17 13 7 7 14 10 8 15 5 17 9 7 14 16 15 20 1 19 18 16 14 25 20 25 19 20
## [494] 12 14 19 4 3 16 3 16 25 9 17 6 11 15 8 6 6 19 8 16 8 16 18 19 6 9 14 9 11
## [523] 18 12 17 21 15 16 17 13 22 16 13 19 18 14 26 23 24 27 23 10 12 11 16 16 19 13 23 22 11
## [552] 19 9 15 19 4 17 7 24 12 25 18 20 19 13 8 12 17 14 7 20 12 20 18 22 21 12 13 10 6
## [581] 13 13 9 2 14 17 11 15 7 4 9 14 7 7 12 8 19 17 14 3 7 9 18 9 13 3 18 5 21
## [610] 8 4 16 18 14 9 14 10 13 13 13 20 19 15 4 12 6 12 2 2 8 7 6 22 19 15 18 20 13
## [639] 5 24 17 74 12 10 16 6 4 1 5 5 13 8 9 2 12 6 17 7 10 9 11 8 2 7 6 6 6
## [668] 13 4 16 2 1 5 17 20 5 5 14 10 10 25 24 21 12 8 13 12 61 14 36 10 4 9 13 16 3
## [697] 10 13 11 2 8 12 10 3 2 7 14 11 11 5 7 6 4 7 9 1 2 11 11 2 17 13 14 6 15
## [726] 3 7 10 19 15 11 10 4 14 9 15 10 12 12 9 16 16 11 4 2 10 11 2 4 6 13 4 11 14
## [755] 10 9 13 3 4 10 10 11 14 2 11 15 11 3
#箱线图
library(ggplot2)
library(reshape2)
library(ggsignif)
boxplot(data)
fivenum(data)
## [1] 0 8 12 16 74
hist(data)
density(data)
##
## Call:
## density.default(x = data)
##
## Data: data (768 obs.); Bandwidth 'bw' = 1.423
##
## x y
## Min. :-4.269 Min. :0.0000000
## 1st Qu.:16.366 1st Qu.:0.0000353
## Median :37.000 Median :0.0003001
## Mean :37.000 Mean :0.0121039
## 3rd Qu.:57.634 3rd Qu.:0.0142453
## Max. :78.269 Max. :0.0665647
plot(density(data,na.rm=T))
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
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原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。