Exercises

  1. Install the “ALL” Bioconductor package.
BiocManager::install(c("ALL"))
  1. Use library(ALL) and data(ALL) to load the ALL ExpresssionSet.
library(ALL)
## Loading required package: Biobase
## Loading required package: BiocGenerics
## Loading required package: parallel
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB
## 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,
##     colMeans, colnames, colSums, dirname, do.call, duplicated,
##     eval, evalq, Filter, Find, get, grep, grepl, intersect,
##     is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
##     paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
##     Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
##     table, tapply, union, unique, unsplit, which, which.max,
##     which.min
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
data(ALL)
  1. Use pData to examine the phenotype data.
str(pData(ALL))
## 'data.frame':    128 obs. of  21 variables:
##  $ cod           : chr  "1005" "1010" "3002" "4006" ...
##  $ diagnosis     : chr  "5/21/1997" "3/29/2000" "6/24/1998" "7/17/1997" ...
##  $ sex           : Factor w/ 2 levels "F","M": 2 2 1 2 2 2 1 2 2 2 ...
##  $ age           : int  53 19 52 38 57 17 18 16 15 40 ...
##  $ BT            : Factor w/ 10 levels "B","B1","B2",..: 3 3 5 2 3 2 2 2 3 3 ...
##  $ remission     : Factor w/ 2 levels "CR","REF": 1 1 1 1 1 1 1 1 1 1 ...
##  $ CR            : chr  "CR" "CR" "CR" "CR" ...
##  $ date.cr       : chr  "8/6/1997" "6/27/2000" "8/17/1998" "9/8/1997" ...
##  $ t(4;11)       : logi  FALSE FALSE NA TRUE FALSE FALSE ...
##  $ t(9;22)       : logi  TRUE FALSE NA FALSE FALSE FALSE ...
##  $ cyto.normal   : logi  FALSE FALSE NA FALSE FALSE FALSE ...
##  $ citog         : chr  "t(9;22)" "simple alt." NA "t(4;11)" ...
##  $ mol.biol      : Factor w/ 6 levels "ALL1/AF4","BCR/ABL",..: 2 4 2 1 4 4 4 4 4 2 ...
##  $ fusion protein: Factor w/ 3 levels "p190","p190/p210",..: 3 NA 1 NA NA NA NA NA NA 1 ...
##  $ mdr           : Factor w/ 2 levels "NEG","POS": 1 2 1 1 1 1 2 1 1 1 ...
##  $ kinet         : Factor w/ 2 levels "dyploid","hyperd.": 1 1 1 1 1 2 2 1 1 NA ...
##  $ ccr           : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ relapse       : logi  FALSE TRUE TRUE TRUE TRUE TRUE ...
##  $ transplant    : logi  TRUE FALSE FALSE FALSE FALSE FALSE ...
##  $ f.u           : chr  "BMT / DEATH IN CR" "REL" "REL" "REL" ...
##  $ date last seen: chr  NA "8/28/2000" "10/15/1999" "1/23/1998" ...
  1. Load and experiment with the Heatplus library to make heatmaps.
# find the 128 most variant genes
top128 <- order(apply(exprs(ALL),1,var, na.rm=TRUE), decreasing = TRUE)[1:128]
ALL.top <- ALL[top128,]
library(Heatplus)
myMap <- regHeatmap(exprs(ALL.top))
plot(myMap)