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if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Homo.sapiens")## Bioconductor version 3.14 (BiocManager 1.30.16), R 4.1.1 (2021-08-10)## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
## re-install: 'Homo.sapiens'library(Glimma)
library(edgeR)## Loading required package: limmalibrary(limma)
library(Homo.sapiens)## Loading required package: AnnotationDbi## Warning: package 'AnnotationDbi' was built under R version 4.1.2## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following object is masked from 'package:limma':
##
## plotMA## 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, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors## Warning: package 'S4Vectors' was built under R version 4.1.2##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: OrganismDbi## Loading required package: GenomicFeatures## Loading required package: GenomeInfoDb## Loading required package: GenomicRanges## Warning: package 'GenomicRanges' was built under R version 4.1.2## Loading required package: GO.db## ## Loading required package: org.Hs.eg.db## ## Loading required package: TxDb.Hsapiens.UCSC.hg19.knownGenelibrary(stringr)
library(knitr)#setwd("/Users/gaohuizi/desktop/510project-gao/whitefemale")file_wf <- c("whitefemale/WFNS1.htseq.counts",
"whitefemale/WFNS2.htseq.counts",
"whitefemale/WFNS3.htseq.counts",
"whitefemale/WFNS4.htseq.counts",
"whitefemale/WFNS5.htseq.counts",
"whitefemale/WFNS6.htseq.counts",
"whitefemale/WFNS7.htseq.counts",
"whitefemale/WFNS8.htseq.counts",
"whitefemale/WFNS9.htseq.counts",
"whitefemale/WFNS10.htseq.counts",
"whitefemale/WFNS11.htseq.counts",
"whitefemale/WFNS12.htseq.counts",
"whitefemale/WFNS13.htseq.counts",
"whitefemale/WFNS14.htseq.counts",
"whitefemale/WFNS15.htseq.counts",
"whitefemale/WFNS16.htseq.counts",
"whitefemale/WFNS17.htseq.counts",
"whitefemale/WFNS18.htseq.counts",
"whitefemale/WFNS19.htseq.counts",
"whitefemale/WFNS20.htseq.counts",
"whitefemale/WFNS21.htseq.counts",
"whitefemale/WFNS22.htseq.counts",
"whitefemale/WFNS23.htseq.counts",
"whitefemale/WFNS24.htseq.counts",
"whitefemale/WFNS25.htseq.counts",
"whitefemale/WFNS26.htseq.counts",
"whitefemale/WFNS27.htseq.counts",
"whitefemale/WFNS28.htseq.counts",
"whitefemale/WFNS29.htseq.counts",
"whitefemale/WFNS30.htseq.counts",
"whitefemale/WFNS31.htseq.counts",
"whitefemale/WFNS32.htseq.counts",
"whitefemale/WFS1.htseq.counts",
"whitefemale/WFS2.htseq.counts",
"whitefemale/WFS3.htseq.counts",
"whitefemale/WFS4.htseq.counts",
"whitefemale/WFS5.htseq.counts",
"whitefemale/WFS6.htseq.counts",
"whitefemale/WFS7.htseq.counts",
"whitefemale/WFS8.htseq.counts",
"whitefemale/WFS9.htseq.counts",
"whitefemale/WFS10.htseq.counts",
"whitefemale/WFS11.htseq.counts",
"whitefemale/WFS12.htseq.counts",
"whitefemale/WFS13.htseq.counts",
"whitefemale/WFS14.htseq.counts",
"whitefemale/WFS15.htseq.counts",
"whitefemale/WFS16.htseq.counts",
"whitefemale/WFS17.htseq.counts",
"whitefemale/WFS18.htseq.counts",
"whitefemale/WFS19.htseq.counts",
"whitefemale/WFS20.htseq.counts",
"whitefemale/WFS21.htseq.counts",
"whitefemale/WFS22.htseq.counts",
"whitefemale/WFS23.htseq.counts",
"whitefemale/WFS24.htseq.counts",
"whitefemale/WFS25.htseq.counts",
"whitefemale/WFS26.htseq.counts",
"whitefemale/WFS27.htseq.counts",
"whitefemale/WFS28.htseq.counts",
"whitefemale/WFS29.htseq.counts",
"whitefemale/WFS30.htseq.counts",
"whitefemale/WFS31.htseq.counts",
"whitefemale/WFS32.htseq.counts",
"whitefemale/WFS33.htseq.counts",
"whitefemale/WFS34.htseq.counts",
"whitefemale/WFS35.htseq.counts",
"whitefemale/WFS36.htseq.counts",
"whitefemale/WFS37.htseq.counts",
"whitefemale/WFS38.htseq.counts",
"whitefemale/WFS39.htseq.counts",
"whitefemale/WFS40.htseq.counts",
"whitefemale/WFS41.htseq.counts",
"whitefemale/WFS42.htseq.counts",
"whitefemale/WFS43.htseq.counts",
"whitefemale/WFS44.htseq.counts")read.delim(file_wf[1], header=FALSE)getwd()## [1] "/Users/gaohuizi/Desktop/510project-gao"x <- readDGE(file_wf, columns=c(1,2))## Meta tags detected: __no_feature, __ambiguous, __too_low_aQual, __not_aligned, __alignment_not_uniqueclass(x)## [1] "DGEList"
## attr(,"package")
## [1] "edgeR"dim(x)## [1] 60487 76samplenames<-colnames(x)
sampleNames## standardGeneric for "sampleNames" defined from package "Biobase"
##
## function (object)
## standardGeneric("sampleNames")
## <bytecode: 0x7fc403a41548>
## <environment: 0x7fc401f27dd0>
## Methods may be defined for arguments: object
## Use showMethods(sampleNames) for currently available ones.colnames(x) <- samplenamesgroup <- c(rep("WFNS",32), rep("WFS",44))x$samples$group <- group
x$samplesif (!require("gsubfn"))
install.packages("gsubfn")## Loading required package: gsubfn## Loading required package: proto## Warning in doTryCatch(return(expr), name, parentenv, handler): unable to load shared object '/Library/Frameworks/R.framework/Resources/modules//R_X11.so':
## dlopen(/Library/Frameworks/R.framework/Resources/modules//R_X11.so, 6): Library not loaded: /opt/X11/lib/libSM.6.dylib
## Referenced from: /Library/Frameworks/R.framework/Versions/4.1/Resources/modules/R_X11.so
## Reason: image not found## Could not load tcltk. Will use slower R code instead.library("gsubfn")geneid <- rownames(x)
geneid <-gsub("\\.[0-9]*$","", geneid)
head(geneid)## [1] "ENSG00000000005" "ENSG00000000419" "ENSG00000000457" "ENSG00000000460"
## [5] "ENSG00000000938" "ENSG00000000971"genes <- select(Homo.sapiens, keys=geneid, columns=c("SYMBOL", "TXCHROM"),
keytype="ENSEMBL")## 'select()' returned 1:many mapping between keys and columnshead(genes)genes <- genes[!duplicated(genes$ENSEMBL),]
x$genes <- genes
x## An object of class "DGEList"
## $samples
## files group lib.size
## whitefemale/WFNS1.htseq whitefemale/WFNS1.htseq.counts WFNS 50172162
## whitefemale/WFNS2.htseq whitefemale/WFNS2.htseq.counts WFNS 53737770
## whitefemale/WFNS3.htseq whitefemale/WFNS3.htseq.counts WFNS 95236738
## whitefemale/WFNS4.htseq whitefemale/WFNS4.htseq.counts WFNS 44608020
## whitefemale/WFNS5.htseq whitefemale/WFNS5.htseq.counts WFNS 82184353
## norm.factors
## whitefemale/WFNS1.htseq 1
## whitefemale/WFNS2.htseq 1
## whitefemale/WFNS3.htseq 1
## whitefemale/WFNS4.htseq 1
## whitefemale/WFNS5.htseq 1
## 71 more rows ...
##
## $counts
## Samples
## Tags whitefemale/WFNS1.htseq whitefemale/WFNS2.htseq
## ENSG00000000005.5 2 1
## ENSG00000000419.11 1128 2084
## ENSG00000000457.12 1348 513
## ENSG00000000460.15 386 708
## ENSG00000000938.11 559 659
## Samples
## Tags whitefemale/WFNS3.htseq whitefemale/WFNS4.htseq
## ENSG00000000005.5 0 0
## ENSG00000000419.11 1327 621
## ENSG00000000457.12 1032 493
## ENSG00000000460.15 1277 156
## ENSG00000000938.11 1809 515
## Samples
## Tags whitefemale/WFNS5.htseq whitefemale/WFNS6.htseq
## ENSG00000000005.5 8 16
## ENSG00000000419.11 1040 1832
## ENSG00000000457.12 670 810
## ENSG00000000460.15 151 449
## ENSG00000000938.11 3275 2597
## Samples
## Tags whitefemale/WFNS7.htseq whitefemale/WFNS8.htseq
## ENSG00000000005.5 0 2
## ENSG00000000419.11 2094 1020
## ENSG00000000457.12 1211 752
## ENSG00000000460.15 359 169
## ENSG00000000938.11 1776 561
## Samples
## Tags whitefemale/WFNS9.htseq whitefemale/WFNS10.htseq
## ENSG00000000005.5 921 7
## ENSG00000000419.11 641 714
## ENSG00000000457.12 615 440
## ENSG00000000460.15 313 59
## ENSG00000000938.11 654 1831
## Samples
## Tags whitefemale/WFNS11.htseq whitefemale/WFNS12.htseq
## ENSG00000000005.5 1 0
## ENSG00000000419.11 1418 1098
## ENSG00000000457.12 639 1255
## ENSG00000000460.15 179 613
## ENSG00000000938.11 1241 1734
## Samples
## Tags whitefemale/WFNS13.htseq whitefemale/WFNS14.htseq
## ENSG00000000005.5 0 0
## ENSG00000000419.11 2246 2337
## ENSG00000000457.12 931 1456
## ENSG00000000460.15 856 896
## ENSG00000000938.11 3833 3091
## Samples
## Tags whitefemale/WFNS15.htseq whitefemale/WFNS16.htseq
## ENSG00000000005.5 0 4
## ENSG00000000419.11 837 888
## ENSG00000000457.12 1038 589
## ENSG00000000460.15 148 104
## ENSG00000000938.11 241 1069
## Samples
## Tags whitefemale/WFNS17.htseq whitefemale/WFNS18.htseq
## ENSG00000000005.5 0 6
## ENSG00000000419.11 1272 1673
## ENSG00000000457.12 689 1163
## ENSG00000000460.15 198 615
## ENSG00000000938.11 584 1629
## Samples
## Tags whitefemale/WFNS19.htseq whitefemale/WFNS20.htseq
## ENSG00000000005.5 0 0
## ENSG00000000419.11 1072 1798
## ENSG00000000457.12 1118 310
## ENSG00000000460.15 375 162
## ENSG00000000938.11 1080 276
## Samples
## Tags whitefemale/WFNS21.htseq whitefemale/WFNS22.htseq
## ENSG00000000005.5 8 0
## ENSG00000000419.11 1916 1642
## ENSG00000000457.12 995 476
## ENSG00000000460.15 501 600
## ENSG00000000938.11 4402 909
## Samples
## Tags whitefemale/WFNS23.htseq whitefemale/WFNS24.htseq
## ENSG00000000005.5 0 18
## ENSG00000000419.11 1373 663
## ENSG00000000457.12 747 507
## ENSG00000000460.15 216 108
## ENSG00000000938.11 1589 473
## Samples
## Tags whitefemale/WFNS25.htseq whitefemale/WFNS26.htseq
## ENSG00000000005.5 1 15
## ENSG00000000419.11 1170 1220
## ENSG00000000457.12 426 876
## ENSG00000000460.15 222 250
## ENSG00000000938.11 1893 422
## Samples
## Tags whitefemale/WFNS27.htseq whitefemale/WFNS28.htseq
## ENSG00000000005.5 2 7
## ENSG00000000419.11 815 727
## ENSG00000000457.12 944 667
## ENSG00000000460.15 312 123
## ENSG00000000938.11 537 1212
## Samples
## Tags whitefemale/WFNS29.htseq whitefemale/WFNS30.htseq
## ENSG00000000005.5 4 0
## ENSG00000000419.11 1319 2450
## ENSG00000000457.12 1706 1941
## ENSG00000000460.15 407 751
## ENSG00000000938.11 1255 1376
## Samples
## Tags whitefemale/WFNS31.htseq whitefemale/WFNS32.htseq
## ENSG00000000005.5 1 2
## ENSG00000000419.11 864 2160
## ENSG00000000457.12 717 1583
## ENSG00000000460.15 181 689
## ENSG00000000938.11 908 358
## Samples
## Tags whitefemale/WFS1.htseq whitefemale/WFS2.htseq
## ENSG00000000005.5 0 2
## ENSG00000000419.11 1292 914
## ENSG00000000457.12 757 474
## ENSG00000000460.15 339 80
## ENSG00000000938.11 746 2132
## Samples
## Tags whitefemale/WFS3.htseq whitefemale/WFS4.htseq
## ENSG00000000005.5 1 10
## ENSG00000000419.11 1249 286
## ENSG00000000457.12 345 656
## ENSG00000000460.15 110 504
## ENSG00000000938.11 4762 177
## Samples
## Tags whitefemale/WFS5.htseq whitefemale/WFS6.htseq
## ENSG00000000005.5 149 0
## ENSG00000000419.11 1057 1201
## ENSG00000000457.12 709 779
## ENSG00000000460.15 339 226
## ENSG00000000938.11 884 342
## Samples
## Tags whitefemale/WFS7.htseq whitefemale/WFS8.htseq
## ENSG00000000005.5 7 1
## ENSG00000000419.11 1530 2362
## ENSG00000000457.12 973 920
## ENSG00000000460.15 196 1130
## ENSG00000000938.11 3241 746
## Samples
## Tags whitefemale/WFS9.htseq whitefemale/WFS10.htseq
## ENSG00000000005.5 1 151
## ENSG00000000419.11 1115 2527
## ENSG00000000457.12 863 3507
## ENSG00000000460.15 334 2071
## ENSG00000000938.11 423 2272
## Samples
## Tags whitefemale/WFS11.htseq whitefemale/WFS12.htseq
## ENSG00000000005.5 5 13
## ENSG00000000419.11 1003 1552
## ENSG00000000457.12 832 1505
## ENSG00000000460.15 221 261
## ENSG00000000938.11 1342 1472
## Samples
## Tags whitefemale/WFS13.htseq whitefemale/WFS14.htseq
## ENSG00000000005.5 0 2
## ENSG00000000419.11 1082 1312
## ENSG00000000457.12 926 858
## ENSG00000000460.15 330 319
## ENSG00000000938.11 488 1052
## Samples
## Tags whitefemale/WFS15.htseq whitefemale/WFS16.htseq
## ENSG00000000005.5 1 5
## ENSG00000000419.11 3565 1592
## ENSG00000000457.12 1506 1144
## ENSG00000000460.15 737 308
## ENSG00000000938.11 1543 1408
## Samples
## Tags whitefemale/WFS17.htseq whitefemale/WFS18.htseq
## ENSG00000000005.5 4 0
## ENSG00000000419.11 1974 770
## ENSG00000000457.12 2270 617
## ENSG00000000460.15 452 189
## ENSG00000000938.11 1406 917
## Samples
## Tags whitefemale/WFS19.htseq whitefemale/WFS20.htseq
## ENSG00000000005.5 5 0
## ENSG00000000419.11 1432 1672
## ENSG00000000457.12 1258 837
## ENSG00000000460.15 456 510
## ENSG00000000938.11 606 615
## Samples
## Tags whitefemale/WFS21.htseq whitefemale/WFS22.htseq
## ENSG00000000005.5 2 24
## ENSG00000000419.11 1451 557
## ENSG00000000457.12 1330 1030
## ENSG00000000460.15 385 583
## ENSG00000000938.11 431 288
## Samples
## Tags whitefemale/WFS23.htseq whitefemale/WFS24.htseq
## ENSG00000000005.5 0 1
## ENSG00000000419.11 1280 1008
## ENSG00000000457.12 456 644
## ENSG00000000460.15 205 235
## ENSG00000000938.11 81 2964
## Samples
## Tags whitefemale/WFS25.htseq whitefemale/WFS26.htseq
## ENSG00000000005.5 0 2
## ENSG00000000419.11 2788 1003
## ENSG00000000457.12 1439 388
## ENSG00000000460.15 1701 74
## ENSG00000000938.11 1121 3617
## Samples
## Tags whitefemale/WFS27.htseq whitefemale/WFS28.htseq
## ENSG00000000005.5 1 10
## ENSG00000000419.11 1332 3310
## ENSG00000000457.12 1569 1839
## ENSG00000000460.15 303 890
## ENSG00000000938.11 1207 405
## Samples
## Tags whitefemale/WFS29.htseq whitefemale/WFS30.htseq
## ENSG00000000005.5 2 6
## ENSG00000000419.11 885 1528
## ENSG00000000457.12 823 943
## ENSG00000000460.15 164 196
## ENSG00000000938.11 592 6334
## Samples
## Tags whitefemale/WFS31.htseq whitefemale/WFS32.htseq
## ENSG00000000005.5 1 3
## ENSG00000000419.11 2298 1409
## ENSG00000000457.12 1110 546
## ENSG00000000460.15 521 437
## ENSG00000000938.11 1148 409
## Samples
## Tags whitefemale/WFS33.htseq whitefemale/WFS34.htseq
## ENSG00000000005.5 0 1
## ENSG00000000419.11 1460 1347
## ENSG00000000457.12 859 1415
## ENSG00000000460.15 480 228
## ENSG00000000938.11 1320 386
## Samples
## Tags whitefemale/WFS35.htseq whitefemale/WFS36.htseq
## ENSG00000000005.5 0 0
## ENSG00000000419.11 857 754
## ENSG00000000457.12 694 1400
## ENSG00000000460.15 254 253
## ENSG00000000938.11 2262 1108
## Samples
## Tags whitefemale/WFS37.htseq whitefemale/WFS38.htseq
## ENSG00000000005.5 0 1
## ENSG00000000419.11 1591 645
## ENSG00000000457.12 955 731
## ENSG00000000460.15 407 138
## ENSG00000000938.11 2864 185
## Samples
## Tags whitefemale/WFS39.htseq whitefemale/WFS40.htseq
## ENSG00000000005.5 2 0
## ENSG00000000419.11 1230 1570
## ENSG00000000457.12 916 341
## ENSG00000000460.15 629 253
## ENSG00000000938.11 941 326
## Samples
## Tags whitefemale/WFS41.htseq whitefemale/WFS42.htseq
## ENSG00000000005.5 1 129
## ENSG00000000419.11 1355 2988
## ENSG00000000457.12 776 1117
## ENSG00000000460.15 618 491
## ENSG00000000938.11 575 1671
## Samples
## Tags whitefemale/WFS43.htseq whitefemale/WFS44.htseq
## ENSG00000000005.5 15 4
## ENSG00000000419.11 1747 2765
## ENSG00000000457.12 1104 2658
## ENSG00000000460.15 440 577
## ENSG00000000938.11 816 1216
## 60482 more rows ...
##
## $genes
## ENSEMBL SYMBOL TXCHROM
## 1 ENSG00000000005 TNMD chrX
## 2 ENSG00000000419 DPM1 chr20
## 3 ENSG00000000457 SCYL3 chr1
## 4 ENSG00000000460 C1orf112 chr1
## 5 ENSG00000000938 FGR chr1
## 60482 more rows ...cpm <- cpm(x)
lcpm <- cpm(x, log=TRUE)L <- mean(x$samples$lib.size) * 1e-6
M <- median(x$samples$lib.size) * 1e-6
c(L, M)## [1] 75.58755 68.58721summary(lcpm)## whitefemale/WFNS1.htseq whitefemale/WFNS2.htseq whitefemale/WFNS3.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.24008
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.24008
## Median :-4.43002 Median :-4.4717 Median :-4.75791
## Mean :-2.35879 Mean :-2.3439 Mean :-2.31935
## 3rd Qu.: 0.03284 3rd Qu.: 0.1451 3rd Qu.:-0.07328
## Max. :17.81943 Max. :18.1956 Max. :17.63510
## whitefemale/WFNS4.htseq whitefemale/WFNS5.htseq whitefemale/WFNS6.htseq
## Min. :-5.2401 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.3547 Median :-4.6942 Median :-4.8186
## Mean :-2.3350 Mean :-2.4737 Mean :-2.5757
## 3rd Qu.: 0.2262 3rd Qu.:-0.2486 3rd Qu.:-0.4604
## Max. :17.8626 Max. :18.2850 Max. :18.4000
## whitefemale/WFNS7.htseq whitefemale/WFNS8.htseq whitefemale/WFNS9.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.240
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.240
## Median :-4.71982 Median :-5.2401 Median :-4.530
## Mean :-2.39791 Mean :-2.4332 Mean :-2.443
## 3rd Qu.: 0.02077 3rd Qu.:-0.1648 3rd Qu.:-0.262
## Max. :18.11950 Max. :17.6035 Max. :17.858
## whitefemale/WFNS10.htseq whitefemale/WFNS11.htseq whitefemale/WFNS12.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.32411 Median :-4.5157 Median :-4.5556
## Mean :-2.35926 Mean :-2.4622 Mean :-2.1385
## 3rd Qu.: 0.05027 3rd Qu.:-0.2858 3rd Qu.: 0.4936
## Max. :17.50665 Max. :17.8507 Max. :17.4972
## whitefemale/WFNS13.htseq whitefemale/WFNS14.htseq whitefemale/WFNS15.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.24008
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.24008
## Median :-4.42767 Median :-4.4792 Median :-4.51052
## Mean :-2.34135 Mean :-2.2349 Mean :-2.43437
## 3rd Qu.: 0.09332 3rd Qu.: 0.1634 3rd Qu.:-0.07494
## Max. :18.30714 Max. :17.7108 Max. :17.93567
## whitefemale/WFNS16.htseq whitefemale/WFNS17.htseq whitefemale/WFNS18.htseq
## Min. :-5.2401 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.4194 Median :-5.2401 Median :-4.4443
## Mean :-2.1698 Mean :-2.5921 Mean :-2.2950
## 3rd Qu.: 0.4906 3rd Qu.:-0.5892 3rd Qu.: 0.1723
## Max. :17.5787 Max. :18.1858 Max. :17.7606
## whitefemale/WFNS19.htseq whitefemale/WFNS20.htseq whitefemale/WFNS21.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.57337 Median :-5.2401 Median :-4.6701
## Mean :-2.37652 Mean :-2.6906 Mean :-2.2854
## 3rd Qu.: 0.05212 3rd Qu.:-0.7542 3rd Qu.: 0.1911
## Max. :18.23622 Max. :18.3183 Max. :17.8603
## whitefemale/WFNS22.htseq whitefemale/WFNS23.htseq whitefemale/WFNS24.htseq
## Min. :-5.240 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.240 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-5.240 Median :-4.5613 Median :-4.3376
## Mean :-2.507 Mean :-2.2046 Mean :-2.4685
## 3rd Qu.:-0.390 3rd Qu.: 0.4453 3rd Qu.:-0.1886
## Max. :18.419 Max. :17.8096 Max. :18.1729
## whitefemale/WFNS25.htseq whitefemale/WFNS26.htseq whitefemale/WFNS27.htseq
## Min. :-5.2401 Min. :-5.24008 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.24008 1st Qu.:-5.2401
## Median :-4.5747 Median :-4.48538 Median :-4.4311
## Mean :-2.4890 Mean :-2.30754 Mean :-2.2054
## 3rd Qu.:-0.3757 3rd Qu.: 0.03792 3rd Qu.: 0.3783
## Max. :17.7866 Max. :17.74987 Max. :17.8361
## whitefemale/WFNS28.htseq whitefemale/WFNS29.htseq whitefemale/WFNS30.htseq
## Min. :-5.2401 Min. :-5.2401 Min. :-5.24008
## 1st Qu.:-5.2401 1st Qu.:-5.2401 1st Qu.:-5.24008
## Median :-4.4853 Median :-4.6096 Median :-4.55642
## Mean :-2.2905 Mean :-2.2400 Mean :-2.36727
## 3rd Qu.: 0.4178 3rd Qu.: 0.3323 3rd Qu.:-0.03976
## Max. :18.1328 Max. :17.8542 Max. :18.23535
## whitefemale/WFNS31.htseq whitefemale/WFNS32.htseq whitefemale/WFS1.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.54523 Median :-4.2921 Median :-4.4994
## Mean :-2.38272 Mean :-2.1605 Mean :-2.4818
## 3rd Qu.:-0.03509 3rd Qu.: 0.4633 3rd Qu.:-0.3731
## Max. :18.27184 Max. :17.6961 Max. :18.1474
## whitefemale/WFS2.htseq whitefemale/WFS3.htseq whitefemale/WFS4.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.240
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.240
## Median :-4.37161 Median :-4.5671 Median :-3.989
## Mean :-2.36678 Mean :-2.3867 Mean :-2.717
## 3rd Qu.: 0.01382 3rd Qu.:-0.1406 3rd Qu.:-0.857
## Max. :17.61455 Max. :17.4865 Max. :18.695
## whitefemale/WFS5.htseq whitefemale/WFS6.htseq whitefemale/WFS7.htseq
## Min. :-5.2401 Min. :-5.2401 Min. :-5.240077
## 1st Qu.:-5.2401 1st Qu.:-5.2401 1st Qu.:-5.240077
## Median :-4.4727 Median :-4.6251 Median :-4.782374
## Mean :-2.2927 Mean :-2.4834 Mean :-2.384327
## 3rd Qu.: 0.1905 3rd Qu.:-0.3735 3rd Qu.:-0.007851
## Max. :17.6028 Max. :17.8770 Max. :17.519844
## whitefemale/WFS8.htseq whitefemale/WFS9.htseq whitefemale/WFS10.htseq
## Min. :-5.2401 Min. :-5.240 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.240 1st Qu.:-5.2401
## Median :-4.2678 Median :-4.560 Median :-4.6208
## Mean :-2.1810 Mean :-2.306 Mean :-2.3737
## 3rd Qu.: 0.4599 3rd Qu.: 0.192 3rd Qu.:-0.1426
## Max. :17.8385 Max. :18.116 Max. :17.8059
## whitefemale/WFS11.htseq whitefemale/WFS12.htseq whitefemale/WFS13.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.73140 Median :-4.4121 Median :-4.5361
## Mean :-2.34085 Mean :-2.2220 Mean :-2.2518
## 3rd Qu.: 0.04708 3rd Qu.: 0.4377 3rd Qu.: 0.5272
## Max. :17.87827 Max. :17.7441 Max. :17.7035
## whitefemale/WFS14.htseq whitefemale/WFS15.htseq whitefemale/WFS16.htseq
## Min. :-5.2401 Min. :-5.24008 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.24008 1st Qu.:-5.2401
## Median :-4.5640 Median :-4.78838 Median :-4.6142
## Mean :-2.1692 Mean :-2.40509 Mean :-2.2278
## 3rd Qu.: 0.5517 3rd Qu.:-0.05841 3rd Qu.: 0.4693
## Max. :17.8467 Max. :18.18542 Max. :17.7121
## whitefemale/WFS17.htseq whitefemale/WFS18.htseq whitefemale/WFS19.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.53419 Median :-5.2401 Median :-4.2633
## Mean :-2.29650 Mean :-2.5535 Mean :-2.5477
## 3rd Qu.: 0.08658 3rd Qu.:-0.3891 3rd Qu.:-0.6557
## Max. :17.72512 Max. :18.1696 Max. :19.2194
## whitefemale/WFS20.htseq whitefemale/WFS21.htseq whitefemale/WFS22.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.2401
## Median :-4.62394 Median :-4.3101 Median :-3.6568
## Mean :-2.30645 Mean :-2.2257 Mean :-2.3637
## 3rd Qu.:-0.02176 3rd Qu.: 0.4518 3rd Qu.:-0.1837
## Max. :18.00774 Max. :18.0988 Max. :19.0689
## whitefemale/WFS23.htseq whitefemale/WFS24.htseq whitefemale/WFS25.htseq
## Min. :-5.2401 Min. :-5.2401 Min. :-5.24008
## 1st Qu.:-5.2401 1st Qu.:-5.2401 1st Qu.:-5.24008
## Median :-5.2401 Median :-4.5994 Median :-4.70648
## Mean :-2.5374 Mean :-2.2495 Mean :-2.30396
## 3rd Qu.:-0.5724 3rd Qu.: 0.4413 3rd Qu.:-0.07385
## Max. :17.8981 Max. :17.9386 Max. :17.77817
## whitefemale/WFS26.htseq whitefemale/WFS27.htseq whitefemale/WFS28.htseq
## Min. :-5.24008 Min. :-5.2401 Min. :-5.240
## 1st Qu.:-5.24008 1st Qu.:-5.2401 1st Qu.:-5.240
## Median :-4.43838 Median :-4.2341 Median :-4.263
## Mean :-2.39134 Mean :-2.1987 Mean :-2.635
## 3rd Qu.:-0.04351 3rd Qu.: 0.2741 3rd Qu.:-0.927
## Max. :17.60441 Max. :17.8004 Max. :19.074
## whitefemale/WFS29.htseq whitefemale/WFS30.htseq whitefemale/WFS31.htseq
## Min. :-5.240 Min. :-5.2401 Min. :-5.24008
## 1st Qu.:-5.240 1st Qu.:-5.2401 1st Qu.:-5.24008
## Median :-5.240 Median :-4.7524 Median :-4.75048
## Mean :-2.452 Mean :-2.3087 Mean :-2.37028
## 3rd Qu.:-0.237 3rd Qu.: 0.1531 3rd Qu.: 0.07422
## Max. :18.014 Max. :17.7842 Max. :17.87255
## whitefemale/WFS32.htseq whitefemale/WFS33.htseq whitefemale/WFS34.htseq
## Min. :-5.2401 Min. :-5.24008 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.24008 1st Qu.:-5.2401
## Median :-4.3191 Median :-4.68596 Median :-5.2401
## Mean :-2.2378 Mean :-2.37099 Mean :-2.5348
## 3rd Qu.: 0.3011 3rd Qu.:-0.06533 3rd Qu.:-0.3107
## Max. :17.7022 Max. :17.98864 Max. :17.7935
## whitefemale/WFS35.htseq whitefemale/WFS36.htseq whitefemale/WFS37.htseq
## Min. :-5.24008 Min. :-5.24008 Min. :-5.2401
## 1st Qu.:-5.24008 1st Qu.:-5.24008 1st Qu.:-5.2401
## Median :-4.60404 Median :-4.66196 Median :-4.6151
## Mean :-2.36756 Mean :-2.33730 Mean :-2.2889
## 3rd Qu.: 0.03329 3rd Qu.: 0.06173 3rd Qu.: 0.2138
## Max. :18.06670 Max. :17.80973 Max. :17.6272
## whitefemale/WFS38.htseq whitefemale/WFS39.htseq whitefemale/WFS40.htseq
## Min. :-5.2401 Min. :-5.24008 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.24008 1st Qu.:-5.2401
## Median :-5.2401 Median :-4.62057 Median :-5.2401
## Mean :-2.5727 Mean :-2.43901 Mean :-2.5201
## 3rd Qu.:-0.5653 3rd Qu.:-0.07567 3rd Qu.:-0.4886
## Max. :17.8924 Max. :18.05029 Max. :17.8325
## whitefemale/WFS41.htseq whitefemale/WFS42.htseq whitefemale/WFS43.htseq
## Min. :-5.2401 Min. :-5.24008 Min. :-5.2401
## 1st Qu.:-5.2401 1st Qu.:-5.24008 1st Qu.:-5.2401
## Median :-4.6529 Median :-4.75179 Median :-4.5918
## Mean :-2.3713 Mean :-2.36094 Mean :-2.2394
## 3rd Qu.:-0.1043 3rd Qu.: 0.06979 3rd Qu.: 0.2416
## Max. :18.2217 Max. :17.94979 Max. :17.6199
## whitefemale/WFS44.htseq
## Min. :-5.2401
## 1st Qu.:-5.2401
## Median :-4.5866
## Mean :-2.3681
## 3rd Qu.:-0.1794
## Max. :17.8613table(rowSums(x$counts==0)==9)##
## FALSE TRUE
## 60110 377keep.exprs <- filterByExpr(x, group=group)
x <- x[keep.exprs,, keep.lib.sizes=FALSE]
dim(x)## [1] 21559 76lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
nsamples <- ncol(x)
col <- brewer.pal(nsamples, "Paired")## Warning in brewer.pal(nsamples, "Paired"): n too large, allowed maximum for palette Paired is 12
## Returning the palette you asked for with that many colorspar(mfrow=c(1,2))
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
lcpm <- cpm(x, log=TRUE)
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
x <- calcNormFactors(x, method = "TMM")
x$samples$norm.factors## [1] 1.0864683 1.0995992 1.2015306 1.1281621 0.8881155 0.7931739 0.9731491
## [8] 1.0718366 0.9985034 1.0687453 1.0225463 1.3626677 0.9789331 1.1951005
## [15] 0.9891228 1.1621874 0.9212492 1.1858691 0.9970874 0.6843201 1.1593694
## [22] 0.8725546 1.1386858 0.8870856 0.9558430 1.1201807 1.2472777 1.0794770
## [29] 1.2587726 0.9945056 0.9504877 1.2709627 0.9567161 1.0505931 0.9435556
## [36] 0.3030472 1.2162580 0.9590334 1.0343686 1.2312309 1.0474225 1.0980503
## [43] 1.0394273 1.1541164 1.1565538 1.2213398 0.9839400 1.2091710 1.1262339
## [50] 0.8803225 0.4764993 1.0792561 1.1219468 0.4379288 0.9467069 1.0982377
## [57] 1.1602744 0.9763699 1.1763853 0.4965849 1.0047268 1.0428736 1.0366464
## [64] 1.1754805 1.0443877 0.8691458 1.0113301 1.0897997 1.0977749 0.9225881
## [71] 0.9617719 0.9526412 0.9968807 1.0126526 1.2075802 1.0263590x2 <- x
x2$samples$norm.factors <- 1
x2$counts[,1] <- ceiling(x2$counts[,1]*0.05)
x2$counts[,2] <- x2$counts[,2]*5par(mfrow=c(1,2))
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")
x2 <- calcNormFactors(x2)
x2$samples$norm.factors## [1] 0.06503721 5.36646401 1.21109899 1.18070024 0.95280213 0.79810587
## [7] 0.97692466 1.08663341 1.00611964 1.14645339 1.04074496 1.41406061
## [13] 1.00406207 1.18597596 1.02041104 1.26151760 0.89631605 1.20421925
## [19] 0.97918706 0.68075260 1.21567093 0.87784525 1.16410591 0.89025736
## [25] 0.94062934 1.13567928 1.29238865 1.15211264 1.27419014 0.99585286
## [31] 0.96438267 1.22867087 0.96568453 1.12350429 1.01410718 0.30953729
## [37] 1.23583326 0.93605531 1.08919564 1.21721071 1.07750424 1.11038468
## [43] 1.07563281 1.21774322 1.16767422 1.29537817 0.97966343 1.23132925
## [49] 1.13959068 0.88913916 0.49217053 1.05286285 1.13054732 0.44164091
## [55] 0.92524858 1.18581540 1.16613837 1.03178296 1.20517851 0.47713817
## [61] 0.94723947 1.11943172 1.07828090 1.20207385 1.07818732 0.83768825
## [67] 1.01888159 1.09274675 1.11513114 0.90294689 0.96835007 0.96047268
## [73] 0.98195284 1.01632319 1.26238797 1.03419695
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")
lcpm <- cpm(x, log=TRUE)
par(mfrow=c(1,2))
group## [1] "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS"
## [11] "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS"
## [21] "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS" "WFNS"
## [31] "WFNS" "WFNS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS"
## [41] "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS"
## [51] "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS"
## [61] "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS" "WFS"
## [71] "WFS" "WFS" "WFS" "WFS" "WFS" "WFS"levels(group) <- brewer.pal(nlevels(group), "Set1")## Warning in brewer.pal(nlevels(group), "Set1"): minimal value for n is 3, returning requested palette with 3 different levelscol.group <- as.character(group)
col.group <- c("purple","orange")[group]
plotMDS(lcpm, labels=group, col=col.group)
title(main="A. Sample groups")
glMDSPlot(lcpm,groups = group)design <- model.matrix(~0+group)
colnames(design) <- gsub("group", "", colnames(design))
design## WFNS WFS
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 6 1 0
## 7 1 0
## 8 1 0
## 9 1 0
## 10 1 0
## 11 1 0
## 12 1 0
## 13 1 0
## 14 1 0
## 15 1 0
## 16 1 0
## 17 1 0
## 18 1 0
## 19 1 0
## 20 1 0
## 21 1 0
## 22 1 0
## 23 1 0
## 24 1 0
## 25 1 0
## 26 1 0
## 27 1 0
## 28 1 0
## 29 1 0
## 30 1 0
## 31 1 0
## 32 1 0
## 33 0 1
## 34 0 1
## 35 0 1
## 36 0 1
## 37 0 1
## 38 0 1
## 39 0 1
## 40 0 1
## 41 0 1
## 42 0 1
## 43 0 1
## 44 0 1
## 45 0 1
## 46 0 1
## 47 0 1
## 48 0 1
## 49 0 1
## 50 0 1
## 51 0 1
## 52 0 1
## 53 0 1
## 54 0 1
## 55 0 1
## 56 0 1
## 57 0 1
## 58 0 1
## 59 0 1
## 60 0 1
## 61 0 1
## 62 0 1
## 63 0 1
## 64 0 1
## 65 0 1
## 66 0 1
## 67 0 1
## 68 0 1
## 69 0 1
## 70 0 1
## 71 0 1
## 72 0 1
## 73 0 1
## 74 0 1
## 75 0 1
## 76 0 1
## attr(,"assign")
## [1] 1 1
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"contr.matrix <- makeContrasts(
WFNSVsWFS = WFNS-WFS,
levels = colnames(design))
contr.matrix## Contrasts
## Levels WFNSVsWFS
## WFNS 1
## WFS -1par(mfrow=c(1,2))
v <- voom(x, design, plot=TRUE)
v## An object of class "EList"
## $genes
## ENSEMBL SYMBOL TXCHROM
## 2 ENSG00000000419 DPM1 chr20
## 3 ENSG00000000457 SCYL3 chr1
## 4 ENSG00000000460 C1orf112 chr1
## 5 ENSG00000000938 FGR chr1
## 6 ENSG00000000971 CFH chr1
## 21554 more rows ...
##
## $targets
## files group lib.size
## whitefemale/WFNS1.htseq whitefemale/WFNS1.htseq.counts WFNS 54462510
## whitefemale/WFNS2.htseq whitefemale/WFNS2.htseq.counts WFNS 58887213
## whitefemale/WFNS3.htseq whitefemale/WFNS3.htseq.counts WFNS 114297236
## whitefemale/WFNS4.htseq whitefemale/WFNS4.htseq.counts WFNS 50284300
## whitefemale/WFNS5.htseq whitefemale/WFNS5.htseq.counts WFNS 72952233
## norm.factors
## whitefemale/WFNS1.htseq 1.0864683
## whitefemale/WFNS2.htseq 1.0995992
## whitefemale/WFNS3.htseq 1.2015306
## whitefemale/WFNS4.htseq 1.1281621
## whitefemale/WFNS5.htseq 0.8881155
## 71 more rows ...
##
## $E
## Samples
## Tags whitefemale/WFNS1.htseq whitefemale/WFNS2.htseq
## ENSG00000000419.11 4.372999 5.145603
## ENSG00000000457.12 4.629948 3.124338
## ENSG00000000460.15 2.827133 3.588742
## ENSG00000000938.11 3.360803 3.485346
## ENSG00000000971.14 5.100473 3.921826
## Samples
## Tags whitefemale/WFNS3.htseq whitefemale/WFNS4.htseq
## ENSG00000000419.11 3.537849 3.627574
## ENSG00000000457.12 3.175279 3.294870
## ENSG00000000460.15 3.482461 1.637983
## ENSG00000000938.11 3.984729 3.357792
## ENSG00000000971.14 6.106326 7.217076
## Samples
## Tags whitefemale/WFNS5.htseq whitefemale/WFNS6.htseq
## ENSG00000000419.11 3.834181 4.375313
## ENSG00000000457.12 3.200213 3.198384
## ENSG00000000460.15 1.054294 2.347893
## ENSG00000000938.11 5.488619 4.878623
## ENSG00000000971.14 7.091383 5.625993
## Samples
## Tags whitefemale/WFNS7.htseq whitefemale/WFNS8.htseq
## ENSG00000000419.11 4.628903 4.195708
## ENSG00000000457.12 3.839092 3.756195
## ENSG00000000460.15 2.086361 1.605789
## ENSG00000000938.11 4.391335 3.333790
## ENSG00000000971.14 5.850213 7.606919
## Samples
## Tags whitefemale/WFNS9.htseq whitefemale/WFNS10.htseq
## ENSG00000000419.11 3.435025 3.9724725
## ENSG00000000457.12 3.375334 3.2746805
## ENSG00000000460.15 2.402041 0.3865001
## ENSG00000000938.11 3.463969 5.3304923
## ENSG00000000971.14 7.552469 6.3318702
## Samples
## Tags whitefemale/WFNS11.htseq whitefemale/WFNS12.htseq
## ENSG00000000419.11 4.581996 3.696450
## ENSG00000000457.12 3.432647 3.889178
## ENSG00000000460.15 1.599686 2.856051
## ENSG00000000938.11 4.389715 4.355435
## ENSG00000000971.14 7.201876 6.089362
## Samples
## Tags whitefemale/WFNS13.htseq whitefemale/WFNS14.htseq
## ENSG00000000419.11 4.522043 4.169955
## ENSG00000000457.12 3.251991 3.487494
## ENSG00000000460.15 3.130889 2.787364
## ENSG00000000938.11 5.293026 4.573295
## ENSG00000000971.14 5.728012 6.434082
## Samples
## Tags whitefemale/WFNS15.htseq whitefemale/WFNS16.htseq
## ENSG00000000419.11 3.883044 3.955818
## ENSG00000000457.12 4.193384 3.363938
## ENSG00000000460.15 1.387418 0.867949
## ENSG00000000938.11 2.088978 4.223311
## ENSG00000000971.14 6.620828 7.840565
## Samples
## Tags whitefemale/WFNS17.htseq whitefemale/WFNS18.htseq
## ENSG00000000419.11 4.877965 3.781463
## ENSG00000000457.12 3.993922 3.257066
## ENSG00000000460.15 2.197511 2.338426
## ENSG00000000938.11 3.755575 3.743024
## ENSG00000000971.14 3.762961 6.067429
## Samples
## Tags whitefemale/WFNS19.htseq whitefemale/WFNS20.htseq
## ENSG00000000419.11 4.065306 5.453379
## ENSG00000000457.12 4.125894 2.919251
## ENSG00000000460.15 2.551213 1.985097
## ENSG00000000938.11 4.076028 2.751937
## ENSG00000000971.14 5.857108 3.831860
## Samples
## Tags whitefemale/WFNS21.htseq whitefemale/WFNS22.htseq
## ENSG00000000419.11 4.406237 5.268830
## ENSG00000000457.12 3.461256 3.483485
## ENSG00000000460.15 2.472085 3.817173
## ENSG00000000938.11 5.606086 4.416082
## ENSG00000000971.14 7.137754 5.351609
## Samples
## Tags whitefemale/WFNS23.htseq whitefemale/WFNS24.htseq
## ENSG00000000419.11 4.262478 4.105662
## ENSG00000000457.12 3.384766 3.718974
## ENSG00000000460.15 1.597060 1.493261
## ENSG00000000938.11 4.473194 3.618930
## ENSG00000000971.14 5.785194 6.945877
## Samples
## Tags whitefemale/WFNS25.htseq whitefemale/WFNS26.htseq
## ENSG00000000419.11 4.247930 4.309830
## ENSG00000000457.12 2.791422 3.832184
## ENSG00000000460.15 1.852682 2.025241
## ENSG00000000938.11 4.941860 2.779381
## ENSG00000000971.14 4.407616 5.769316
## Samples
## Tags whitefemale/WFNS27.htseq whitefemale/WFNS28.htseq
## ENSG00000000419.11 3.702555 3.616986
## ENSG00000000457.12 3.914421 3.492807
## ENSG00000000460.15 2.318727 1.058550
## ENSG00000000938.11 3.101135 4.353952
## ENSG00000000971.14 5.823194 6.188167
## Samples
## Tags whitefemale/WFNS29.htseq whitefemale/WFNS30.htseq
## ENSG00000000419.11 3.927207 4.305967
## ENSG00000000457.12 4.298256 3.970063
## ENSG00000000460.15 2.232088 2.600736
## ENSG00000000938.11 3.855478 3.473896
## ENSG00000000971.14 7.459607 5.129089
## Samples
## Tags whitefemale/WFNS31.htseq whitefemale/WFNS32.htseq
## ENSG00000000419.11 3.897446 4.388438
## ENSG00000000457.12 3.628559 3.940190
## ENSG00000000460.15 1.645550 2.740695
## ENSG00000000938.11 3.969067 1.797118
## ENSG00000000971.14 5.213521 7.614391
## Samples
## Tags whitefemale/WFS1.htseq whitefemale/WFS2.htseq
## ENSG00000000419.11 4.585004 4.2503450
## ENSG00000000457.12 3.814158 3.3037699
## ENSG00000000460.15 2.656324 0.7444225
## ENSG00000000938.11 3.793054 5.4718357
## ENSG00000000971.14 6.730225 5.2340835
## Samples
## Tags whitefemale/WFS3.htseq whitefemale/WFS4.htseq
## ENSG00000000419.11 4.3813233 2.791960
## ENSG00000000457.12 2.5267300 3.988220
## ENSG00000000460.15 0.8820907 3.608279
## ENSG00000000938.11 6.3116915 2.101244
## ENSG00000000971.14 6.0500196 6.467394
## Samples
## Tags whitefemale/WFS5.htseq whitefemale/WFS6.htseq
## ENSG00000000419.11 4.014932 4.140712
## ENSG00000000457.12 3.439149 3.516496
## ENSG00000000460.15 2.375758 1.733458
## ENSG00000000938.11 3.757208 2.330051
## ENSG00000000971.14 5.300806 4.209871
## Samples
## Tags whitefemale/WFS7.htseq whitefemale/WFS8.htseq
## ENSG00000000419.11 3.8704131 4.614548
## ENSG00000000457.12 3.2176629 3.254724
## ENSG00000000460.15 0.9090113 3.551195
## ENSG00000000938.11 4.9530716 2.952448
## ENSG00000000971.14 5.7391293 6.960819
## Samples
## Tags whitefemale/WFS9.htseq whitefemale/WFS10.htseq
## ENSG00000000419.11 4.087030 4.030253
## ENSG00000000457.12 3.717607 4.502985
## ENSG00000000460.15 2.349417 3.743218
## ENSG00000000938.11 2.689773 3.876822
## ENSG00000000971.14 6.161846 4.699917
## Samples
## Tags whitefemale/WFS11.htseq whitefemale/WFS12.htseq
## ENSG00000000419.11 3.433773 3.788907
## ENSG00000000457.12 3.164254 3.744556
## ENSG00000000460.15 1.254111 1.219197
## ENSG00000000938.11 3.853654 3.712581
## ENSG00000000971.14 5.609358 5.596992
## Samples
## Tags whitefemale/WFS13.htseq whitefemale/WFS14.htseq
## ENSG00000000419.11 3.963087 4.088731
## ENSG00000000457.12 3.738583 3.476304
## ENSG00000000460.15 2.251442 2.050301
## ENSG00000000938.11 2.815150 3.770234
## ENSG00000000971.14 5.409190 6.383138
## Samples
## Tags whitefemale/WFS15.htseq whitefemale/WFS16.htseq
## ENSG00000000419.11 5.140630 4.243590
## ENSG00000000457.12 3.897726 3.767014
## ENSG00000000460.15 2.867240 1.875639
## ENSG00000000938.11 3.932731 4.066456
## ENSG00000000971.14 5.564592 4.690324
## Samples
## Tags whitefemale/WFS17.htseq whitefemale/WFS18.htseq
## ENSG00000000419.11 3.872901 4.164243
## ENSG00000000457.12 4.074424 3.844887
## ENSG00000000460.15 1.747404 2.140646
## ENSG00000000938.11 3.383524 4.416156
## ENSG00000000971.14 5.438000 6.648231
## Samples
## Tags whitefemale/WFS19.htseq whitefemale/WFS20.htseq
## ENSG00000000419.11 4.270080 4.450671
## ENSG00000000457.12 4.083250 3.452826
## ENSG00000000460.15 2.620232 2.738647
## ENSG00000000938.11 3.030125 3.008495
## ENSG00000000971.14 6.848397 5.528128
## Samples
## Tags whitefemale/WFS21.htseq whitefemale/WFS22.htseq
## ENSG00000000419.11 3.955674 3.493498
## ENSG00000000457.12 3.830098 4.379799
## ENSG00000000460.15 2.042933 3.559259
## ENSG00000000938.11 2.205562 2.543097
## ENSG00000000971.14 5.521292 5.443138
## Samples
## Tags whitefemale/WFS23.htseq whitefemale/WFS24.htseq
## ENSG00000000419.11 4.9654653 3.764938
## ENSG00000000457.12 3.4774448 3.118980
## ENSG00000000460.15 2.3259683 1.666526
## ENSG00000000938.11 0.9917019 5.320516
## ENSG00000000971.14 4.5476084 5.599722
## Samples
## Tags whitefemale/WFS25.htseq whitefemale/WFS26.htseq
## ENSG00000000419.11 4.832118 4.3379146
## ENSG00000000457.12 3.878197 2.9688605
## ENSG00000000460.15 4.119436 0.5862583
## ENSG00000000938.11 3.518059 6.1878670
## ENSG00000000971.14 6.326516 5.7172002
## Samples
## Tags whitefemale/WFS27.htseq whitefemale/WFS28.htseq
## ENSG00000000419.11 3.333902 5.419161
## ENSG00000000457.12 3.570071 4.571426
## ENSG00000000460.15 1.199535 3.524800
## ENSG00000000938.11 3.191789 2.389886
## ENSG00000000971.14 5.103253 6.640433
## Samples
## Tags whitefemale/WFS29.htseq whitefemale/WFS30.htseq
## ENSG00000000419.11 4.502275 3.964409
## ENSG00000000457.12 4.397551 3.268387
## ENSG00000000460.15 2.073870 1.004894
## ENSG00000000938.11 3.922598 6.015523
## ENSG00000000971.14 5.398865 7.132970
## Samples
## Tags whitefemale/WFS31.htseq whitefemale/WFS32.htseq
## ENSG00000000419.11 4.568024 4.828815
## ENSG00000000457.12 3.518540 3.461925
## ENSG00000000460.15 2.428070 3.140987
## ENSG00000000938.11 3.567082 3.045567
## ENSG00000000971.14 5.843842 4.331466
## Samples
## Tags whitefemale/WFS33.htseq whitefemale/WFS34.htseq
## ENSG00000000419.11 4.116730 4.433441
## ENSG00000000457.12 3.351837 4.504468
## ENSG00000000460.15 2.512876 1.873422
## ENSG00000000938.11 3.971352 2.631696
## ENSG00000000971.14 6.476484 5.942570
## Samples
## Tags whitefemale/WFS35.htseq whitefemale/WFS36.htseq
## ENSG00000000419.11 3.636685 3.175682
## ENSG00000000457.12 3.332523 4.068031
## ENSG00000000460.15 1.884214 1.602147
## ENSG00000000938.11 5.036394 3.730698
## ENSG00000000971.14 6.608118 6.045842
## Samples
## Tags whitefemale/WFS37.htseq whitefemale/WFS38.htseq
## ENSG00000000419.11 4.381474 3.874335
## ENSG00000000457.12 3.645414 4.054775
## ENSG00000000460.15 2.415958 1.653803
## ENSG00000000938.11 5.229370 2.075337
## ENSG00000000971.14 6.318358 4.618879
## Samples
## Tags whitefemale/WFS39.htseq whitefemale/WFS40.htseq
## ENSG00000000419.11 4.183755 5.390527
## ENSG00000000457.12 3.758718 3.189260
## ENSG00000000460.15 3.216789 2.759360
## ENSG00000000938.11 3.797544 3.124457
## ENSG00000000971.14 6.571683 6.980685
## Samples
## Tags whitefemale/WFS41.htseq whitefemale/WFS42.htseq
## ENSG00000000419.11 4.178003 4.977085
## ENSG00000000457.12 3.374236 3.557938
## ENSG00000000460.15 3.046023 2.372927
## ENSG00000000938.11 2.942066 4.138807
## ENSG00000000971.14 6.303922 6.424552
## Samples
## Tags whitefemale/WFS43.htseq whitefemale/WFS44.htseq
## ENSG00000000419.11 4.442303 4.353617
## ENSG00000000457.12 3.780404 4.296689
## ENSG00000000460.15 2.454225 2.093969
## ENSG00000000938.11 3.344536 3.168813
## ENSG00000000971.14 6.113781 5.832901
## 21554 more rows ...
##
## $weights
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1.976821 2.026294 2.319106 1.923732 2.149424 2.238567 2.219834 1.991974
## [2,] 1.675162 1.730710 2.159680 1.616887 1.884906 2.011277 1.984288 1.690784
## [3,] 1.098148 1.130736 1.498791 1.066533 1.232093 1.336346 1.312198 1.107186
## [4,] 1.861535 1.915969 2.270294 1.804956 2.053323 2.160819 2.138094 1.877731
## [5,] 2.356300 2.349675 2.243295 2.360088 2.324853 2.293906 2.301674 2.354407
## [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## [1,] 2.030656 1.854612 2.029763 2.220258 2.275005 2.343136 2.004246 2.009425
## [2,] 1.735951 1.546899 1.734877 1.984939 2.073532 2.218884 1.704312 1.710499
## [3,] 1.133924 1.030241 1.133271 1.312740 1.397608 1.587363 1.115002 1.118574
## [4,] 1.920665 1.732636 1.919703 2.138607 2.208504 2.309291 1.891745 1.897826
## [5,] 2.349065 2.362925 2.349190 2.301497 2.275243 2.214607 2.352787 2.352052
## [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## [1,] 1.818740 2.333803 2.078042 1.7818005 2.248181 1.807768 2.139095 1.7351532
## [2,] 1.511871 2.190439 1.792493 1.4762379 2.025342 1.501186 1.870871 1.4351166
## [3,] 1.012624 1.542016 1.168730 0.9951156 1.349986 1.007226 1.222312 0.9752781
## [4,] 1.696426 2.291942 1.971786 1.6585396 2.173560 1.685365 2.041559 1.6127448
## [5,] 2.360890 2.229223 2.341210 2.3585789 2.289610 2.360204 2.327506 2.3550314
## [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
## [1,] 2.053800 2.053138 2.064061 2.030462 2.230619 2.336756 2.017266 2.293516
## [2,] 1.763848 1.763048 1.776269 1.735717 2.000637 2.198204 1.719883 2.104938
## [3,] 1.150884 1.150398 1.158428 1.133782 1.326065 1.554570 1.124146 1.431353
## [4,] 1.945607 1.944893 1.956682 1.920455 2.151170 2.298135 1.906256 2.232200
## [5,] 2.345859 2.345950 2.344008 2.349093 2.297188 2.225198 2.350944 2.264377
## [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40]
## [1,] 1.972227 1.897175 2.039911 1.790498 2.093895 2.115180 2.299673 2.271806
## [2,] 1.713619 1.631474 1.792068 1.526034 1.853383 1.882761 2.147495 2.104061
## [3,] 1.119508 1.073622 1.167482 1.019033 1.209087 1.229528 1.482237 1.428949
## [4,] 1.696068 1.613742 1.774206 1.509099 1.835778 1.864808 2.134272 2.090579
## [5,] 2.362867 2.358202 2.359811 2.342043 2.354853 2.351420 2.293224 2.307269
## [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
## [1,] 2.095706 2.359851 2.258888 2.316079 2.125158 2.181128 2.287984 2.218742
## [2,] 1.855843 2.304763 2.082345 2.184131 1.896497 1.965850 2.128885 2.021252
## [3,] 1.210798 1.759267 1.405101 1.530414 1.239180 1.295685 1.459283 1.344789
## [4,] 1.838210 2.299069 2.067004 2.172521 1.878502 1.949381 2.115805 2.006470
## [5,] 2.354562 2.212632 2.313132 2.280258 2.349825 2.338785 2.299622 2.328140
## [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56]
## [1,] 2.348793 1.816970 2.161349 2.177458 2.261394 1.916375 1.784026 2.160904
## [2,] 2.260122 1.552691 1.940385 1.960155 2.086948 1.653164 1.519620 1.939844
## [3,] 1.659878 1.032400 1.274592 1.290960 1.409701 1.085345 1.015806 1.274145
## [4,] 2.251739 1.535387 1.924183 1.943746 2.071564 1.635137 1.502775 1.923647
## [5,] 2.243580 2.347222 2.343532 2.339839 2.311990 2.359499 2.340782 2.343634
## [,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64]
## [1,] 2.276952 1.917991 2.346118 2.182418 1.7484477 2.277032 2.273448 1.917649
## [2,] 2.111943 1.654993 2.253434 1.967854 1.4871607 2.112066 2.106575 1.654606
## [3,] 1.438542 1.086331 1.645409 1.297348 0.9996819 1.438692 1.432005 1.086122
## [4,] 2.098993 1.636941 2.244935 1.951365 1.4707631 2.099115 2.093589 1.636559
## [5,] 2.304950 2.359608 2.247602 2.338415 2.3337419 2.304914 2.306528 2.359585
## [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
## [1,] 2.219364 2.064094 2.121567 2.215745 2.176644 1.833846 2.112020 1.7174149
## [2,] 2.022100 1.819463 1.891627 2.017167 1.959032 1.569437 1.878381 1.4588959
## [3,] 1.345611 1.185761 1.235702 1.340831 1.290029 1.040908 1.226479 0.9861099
## [4,] 2.007310 1.802260 1.873568 2.002423 1.942635 1.552190 1.860479 1.4428751
## [5,] 2.327966 2.358691 2.350398 2.328977 2.340047 2.350482 2.351927 2.3237325
## [,73] [,74] [,75] [,76]
## [1,] 2.166010 2.266241 2.199030 2.349297
## [2,] 1.946061 2.095555 1.993757 2.261384
## [3,] 1.279286 1.418635 1.318901 1.662620
## [4,] 1.929800 2.080403 1.976996 2.252995
## [5,] 2.342466 2.309787 2.333682 2.242824
## 21554 more rows ...
##
## $design
## WFNS WFS
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 71 more rows ...
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend")
summary(decideTests(efit))## WFNSVsWFS
## Down 0
## NotSig 21559
## Up 0tfit <- treat(vfit, lfc=1)
dt <- decideTests(tfit)
summary(dt)## WFNSVsWFS
## Down 0
## NotSig 21559
## Up 0de.common <- which(dt[,1]!=0)
length(de.common)## [1] 0head(tfit$genes$SYMBOL[de.common], n=76)## character(0)vennDiagram(dt[,1], circle.col=c("turquoise", "salmon"))
write.fit(tfit, dt, file="results.txt")WFNS.vs.WFS <- topTreat(tfit, coef=1, n=Inf)
head(WFNS.vs.WFS)plotMD(tfit, column=1, status=dt[,1], main=colnames(tfit)[1],
xlim=c(-8,13))
glMDPlot(tfit, coef=1, status=dt, main=colnames(tfit)[1],
side.main="ENSEMBL", counts=lcpm, groups=group, launch=TRUE)if (!require("gplots"))
install.packages("gplots")## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:IRanges':
##
## space## The following object is masked from 'package:S4Vectors':
##
## space## The following object is masked from 'package:stats':
##
## lowesslibrary("gplots")# get heatmap of log-CPM values
library(gplots)WFNS.vs.WFS.topgenes <- WFNS.vs.WFS$ENSEMBL[1:50]
i <- which(v$genes$ENSEMBL %in% WFNS.vs.WFS.topgenes)
mycol <- colorpanel(1000,"blue","white","red")
par(cex.main=0.8,mar=c(1,1,1,1))
heatmap.2(lcpm[i,], scale="row", labRow=v$genes$SYMBOL[i], labCol=group, col=mycol, cexRow=1,cexCol=0.2, margins = c(8,6), main="HeatMap")