This document depends on the following packages:
library(devtools)
library(Biobase)To install these packages you can use the code (or if you are compiling the document, remove the eval=FALSE from the chunk.)
install.packages("devtools")
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("Biobase"))con=url("http://bowtie-bio.sourceforge.net/recount/ExpressionSets/bodymap_eset.RData")
load(file=con)
close(con)This kind of object in R contains the three tables
bm = bodymap.eset
bm## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 52580 features, 19 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: ERS025098 ERS025092 ... ERS025091 (19 total)
## varLabels: sample.id num.tech.reps ... race (6 total)
## varMetadata: labelDescription
## featureData
## featureNames: ENSG00000000003 ENSG00000000005 ... LRG_99 (52580
## total)
## fvarLabels: gene
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:These are usually high dimensional measurements. In this case it is RNA-sequencing data. Each row is a gene and each column is a sample.
exp_data = exprs(bm)
dim(exp_data)## [1] 52580 19head(exp_data,n=5)## ERS025098 ERS025092 ERS025085 ERS025088 ERS025089
## ENSG00000000003 1354 216 215 924 725
## ENSG00000000005 712 134 4 1495 119
## ENSG00000000419 450 547 516 529 808
## ENSG00000000457 188 368 196 386 156
## ENSG00000000460 66 29 1 26 11
## ERS025082 ERS025081 ERS025096 ERS025099 ERS025086
## ENSG00000000003 125 796 1954 815 249
## ENSG00000000005 20 7 0 0 16
## ENSG00000000419 680 744 369 636 486
## ENSG00000000457 259 436 288 187 553
## ENSG00000000460 9 25 42 12 22
## ERS025084 ERS025087 ERS025093 ERS025083 ERS025095
## ENSG00000000003 4614 6548 5030 1450 781
## ENSG00000000005 975 923 696 15 24
## ENSG00000000419 1200 2823 2159 933 870
## ENSG00000000457 1543 1194 876 655 862
## ENSG00000000460 213 80 57 13 78
## ERS025097 ERS025094 ERS025090 ERS025091
## ENSG00000000003 69 1888 983 3
## ENSG00000000005 0 20 4 0
## ENSG00000000419 621 1026 1185 765
## ENSG00000000457 242 569 609 782
## ENSG00000000460 17 351 46 73This is the “meta data” or “phenotype data” that is what you typically associate the genomic data with. Each column is a variable and each row is a sample.
pheno_data = pData(bm)
dim(pheno_data)## [1] 19 6head(pheno_data)## sample.id num.tech.reps tissue.type gender age race
## ERS025098 ERS025098 2 adipose F 73 caucasian
## ERS025092 ERS025092 2 adrenal M 60 caucasian
## ERS025085 ERS025085 2 brain F 77 caucasian
## ERS025088 ERS025088 2 breast F 29 caucasian
## ERS025089 ERS025089 2 colon F 68 caucasian
## ERS025082 ERS025082 2 heart M 77 caucasianThis is information about the genes or other genomic features you are measuring. Each column is a variable and each row is a gene/feature.
feature_data = fData(bm)
dim(fData(bodymap.eset))## [1] 52580 1fData(bodymap.eset)[1:10,,1]## [1] ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457
## [5] ENSG00000000460 ENSG00000000938 ENSG00000000971 ENSG00000001036
## [9] ENSG00000001084 ENSG00000001167
## 52580 Levels: ENSG00000000003 ENSG00000000005 ... LRG_99Here is the session information
devtools::session_info()## setting value
## version R version 3.2.1 (2015-06-18)
## system x86_64, darwin10.8.0
## ui RStudio (0.99.447)
## language (EN)
## collate en_US.UTF-8
## tz America/New_York
##
## package * version date
## acepack 1.3-3.3 2014-11-24
## annotate 1.46.1 2015-07-11
## AnnotationDbi * 1.30.1 2015-04-26
## assertthat 0.1 2013-12-06
## BiasedUrn * 1.06.1 2013-12-29
## Biobase * 2.28.0 2015-04-17
## BiocGenerics * 0.14.0 2015-04-17
## BiocInstaller * 1.18.4 2015-07-22
## BiocParallel 1.2.20 2015-08-07
## biomaRt 2.24.0 2015-04-17
## Biostrings 2.36.3 2015-08-12
## bitops 1.0-6 2013-08-17
## bladderbatch * 1.6.0 2015-08-26
## broom * 0.3.7 2015-05-06
## caTools 1.17.1 2014-09-10
## cluster 2.0.3 2015-07-21
## colorspace 1.2-6 2015-03-11
## corpcor 1.6.8 2015-07-08
## curl 0.9.2 2015-08-08
## DBI * 0.3.1 2014-09-24
## dendextend * 1.1.0 2015-07-31
## DESeq2 * 1.8.1 2015-05-02
## devtools * 1.8.0 2015-05-09
## digest 0.6.8 2014-12-31
## dplyr * 0.4.3 2015-09-01
## edge * 2.1.0 2015-09-06
## evaluate 0.7.2 2015-08-13
## foreign 0.8-66 2015-08-19
## formatR 1.2 2015-04-21
## Formula * 1.2-1 2015-04-07
## futile.logger 1.4.1 2015-04-20
## futile.options 1.0.0 2010-04-06
## gdata 2.17.0 2015-07-04
## genefilter * 1.50.0 2015-04-17
## geneLenDataBase * 1.4.0 2015-09-06
## geneplotter 1.46.0 2015-04-17
## GenomeInfoDb * 1.4.2 2015-08-15
## GenomicAlignments 1.4.1 2015-04-24
## GenomicFeatures 1.20.2 2015-08-14
## GenomicRanges * 1.20.5 2015-06-09
## genstats * 0.1.02 2015-09-05
## ggplot2 * 1.0.1 2015-03-17
## git2r 0.11.0 2015-08-12
## GO.db 3.1.2 2015-09-06
## goseq * 1.20.0 2015-04-17
## gplots * 2.17.0 2015-05-02
## gridExtra 2.0.0 2015-07-14
## gtable 0.1.2 2012-12-05
## gtools 3.5.0 2015-05-29
## highr 0.5 2015-04-21
## HistData * 0.7-5 2014-04-26
## Hmisc * 3.16-0 2015-04-30
## htmltools 0.2.6 2014-09-08
## httr 1.0.0 2015-06-25
## IRanges * 2.2.7 2015-08-09
## KernSmooth 2.23-15 2015-06-29
## knitr * 1.11 2015-08-14
## lambda.r 1.1.7 2015-03-20
## lattice * 0.20-33 2015-07-14
## latticeExtra 0.6-26 2013-08-15
## lazyeval 0.1.10 2015-01-02
## limma * 3.24.15 2015-08-06
## lme4 1.1-9 2015-08-20
## locfit 1.5-9.1 2013-04-20
## magrittr 1.5 2014-11-22
## MASS * 7.3-43 2015-07-16
## Matrix * 1.2-2 2015-07-08
## MatrixEQTL * 2.1.1 2015-02-03
## memoise 0.2.1 2014-04-22
## mgcv * 1.8-7 2015-07-23
## minqa 1.2.4 2014-10-09
## mnormt 1.5-3 2015-05-25
## munsell 0.4.2 2013-07-11
## nlme * 3.1-122 2015-08-19
## nloptr 1.0.4 2014-08-04
## nnet 7.3-10 2015-06-29
## org.Hs.eg.db * 3.1.2 2015-07-17
## plyr 1.8.3 2015-06-12
## preprocessCore * 1.30.0 2015-04-17
## proto 0.3-10 2012-12-22
## psych 1.5.6 2015-07-08
## qvalue * 2.0.0 2015-04-17
## R6 2.1.1 2015-08-19
## RColorBrewer 1.1-2 2014-12-07
## Rcpp * 0.12.0 2015-07-25
## RcppArmadillo * 0.5.400.2.0 2015-08-17
## RCurl 1.95-4.7 2015-06-30
## reshape2 1.4.1 2014-12-06
## rmarkdown 0.7 2015-06-13
## rpart 4.1-10 2015-06-29
## Rsamtools 1.20.4 2015-06-01
## RSkittleBrewer * 1.1 2015-09-05
## RSQLite * 1.0.0 2014-10-25
## rstudioapi 0.3.1 2015-04-07
## rtracklayer 1.28.9 2015-08-19
## rversions 1.0.2 2015-07-13
## S4Vectors * 0.6.5 2015-09-01
## scales 0.3.0 2015-08-25
## snm 1.16.0 2015-04-17
## snpStats * 1.18.0 2015-04-17
## stringi 0.5-5 2015-06-29
## stringr 1.0.0 2015-04-30
## survival * 2.38-3 2015-07-02
## sva * 3.14.0 2015-04-17
## tidyr 0.2.0 2014-12-05
## UsingR * 2.0-5 2015-08-06
## whisker 0.3-2 2013-04-28
## XML 3.98-1.3 2015-06-30
## xml2 0.1.2 2015-09-01
## xtable 1.7-4 2014-09-12
## XVector 0.8.0 2015-04-17
## yaml 2.1.13 2014-06-12
## zlibbioc 1.14.0 2015-04-17
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## BioconductorIt is also useful to compile the time the document was processed. This document was processed on: 2015-09-06.