## Introduction

We looked at the code for dfHclust in some detail. It makes significant use of universal aspects of data.frame instances

• rows can represent the objects to be clustered
• columns represent the features of the objects used to compare and group them
• rownames can be used to label the objects
• colnames can be used to name the features

The name concepts make it easy for us to think substantively about what is being done in the app.

When we move to the context of genome-scale data, things are a bit different. Let’s focus on expression array applications

• the volume of data is often very substantial
• assuming the objects of interest are the individual biological samples that have been arrayed, the labeling of objects can be complex
• assuming we use an ExpressionSet to manage the data, both rownames and colnames will tend to use unilluminating vocabularies such as probe set and sample identifiers

This last concern is hard to defeat in general but for the tissuesGeneExpression data there is a simple solution. The first concern, about data volume, can be managed nicely with ExpressionSet instances through bracket-based subsetting.

## A simple app

Let’s write a function that transforms any ExpressionSet into a data.frame and then runs dfHclust. This will be our “app” for interactively clustering data in expression experiments!

library(Biobase)
library(hgu133a.db)

##

library(ph525x)

esHclust = function(es) {
emat = t(exprs(es))
rownames(emat) = sampleNames(es)
dd = data.frame(emat)
dfHclust(dd)
}


## Preparing tissue expression data for use with the app

We’ll transform the matrix/data.frame data in tissuesGeneExpression package into a conveniently annotated ExpressionSet.

library(tissuesGeneExpression)
data(tissuesGeneExpression)
tgeES = ExpressionSet(e)
annotation(tgeES) = "hgu133a.db"
pData(tgeES) = tab
featureNames(tgeES) =
make.names(mapIds(hgu133a.db, keys=featureNames(tgeES),
keytype="PROBEID", column="SYMBOL"), unique=TRUE)

## 'select()' returned 1:many mapping between keys and columns

sampleNames(tgeES) = make.names(tgeES$Tissue, unique=TRUE)  Here’s code that runs the app on a small selection of genes and samples: set.seed(1234) esHclust( tgeES[1:50, sample(1:ncol(tgeES), size=40) ] )  ## Exploring cluster analysis for tissue differentiation Which genes are important for distinguishing the various tissues available in this dataset? Can the analytical tools identified to this point help us to identify and understand them? Neither of these questions is particularly clear, and the experimental designs underlying the tissue expression sets would need to be fully understood. However, the following code can help identify genes whose expression is statistically unlikely to be constant over all the tissues present. We’ll collect moderated F tests for the gene-specific null hypotheses that mean expression is constant over all tissues. library(limma) mm = model.matrix(~Tissue, data=pData(tgeES)) f1 = lmFit(tgeES, mm) ef1 = eBayes(f1)  A heatmap of the 50 genes identified as most discriminating by this simple F testing approach is: heatmap(ef1$coef[order(ef1$F,decreasing=TRUE)[1:50],-1], cexCol=.8)  This is a visualization of mean expression relative to a reference category (cerebellum). The same genes visualized on the raw data: sig50 = rownames(ef1$coef[order(ef1\$F,decreasing=TRUE)[1:50],])
heatmap(exprs(tgeES[sig50,]))


This is all very informal, using only default values for heatmap presentations. We’ll continue in this vein. In my inspection of the heatmap of means, I considered the following five genes to be a potential signature for tissue differentiation:

sig5 = c("IL26", "ZNF674", "UBC.1", "C7orf25.1", "RPS13")


In the exercises we’ll see whether this is at all satisfactory.

## A machine-learning approach to assessing discriminatory capacity

This is not directly addressing visualization but it is brief and useful for thinking about what we can do with visualization. The MLInterfaces package makes it easy to use various R-based statistical learning tools with ExpressionSet instances. We’ll use random forests to get a measure of effectiveness of the sig50 defined above.

library(MLInterfaces)
library(randomForest)
set.seed(1234)
rf1 = MLearn(Tissue~., tgeES[sig50,], randomForestI, xvalSpec("NOTEST"))
RObject(rf1)

##
## Call:
##  randomForest(formula = formula, data = trdata)
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 7
##
##         OOB estimate of  error rate: 9.52%
## Confusion matrix:
##             cerebellum colon endometrium hippocampus kidney liver placenta
## cerebellum          37     0           0           0      1     0        0
## colon                0    31           1           0      1     1        0
## endometrium          0     4           9           0      2     0        0
## hippocampus          0     0           0          30      0     1        0
## kidney               0     1           0           0     36     2        0
## liver                0     0           0           0      0    26        0
## placenta             0     2           0           0      1     1        2
##             class.error
## cerebellum   0.02631579
## colon        0.08823529
## endometrium  0.40000000
## hippocampus  0.03225806
## kidney       0.07692308
## liver        0.00000000
## placenta     0.66666667


Here using the defaults we see that the 50 gene signature does a reasonable job of sorting out the tissues. With simple modifications to the single line of code using MLearn above you can assess effects of changing the signature or the learning procedure.