title	author	date	vignette	output	bibliography
brcaProteo -- proteogenomics data archive for breast cancer	Vincent J. Carey, stvjc at channing.harvard.edu	`r format(Sys.time(), '%B %d, %Y')`	%\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{} %\VignetteEncoding{UTF-8}	BiocStyle::html_document highlight number_sections theme toc pygments true united true	brcaProteo.bib

Introduction

@Mertins2016 unites information developed in the NCI CPTAC with TCGA data. The brcaProteo package reviews approaches to interrogating this data with Bioconductor.

Multiassay experiment and a sanity check

The MultiAssayExperiment instance brProteoMAE has two components that use DelayedArray assay representation. The assay data can be retrieved using assay when the environment variable CGC_BILLING is set to a valid Google Cloud Platform project id.

library(brcaProteo)
data(brProteoMAE)
brProteoMAE

The proteomic results are available for a subset of the TCGA contributions.

upsetSamples(brProteoMAE)

We can see that there are 76 samples contributing to three assays.

Intersection of samples and features across assays

We would like to line up samples and features that are common across assay types, in the simplest possible way.

Harmonizing feature annotation

We will relabel the rows of the RNA-seq data, which are given as Entrez identifiers. The other two assays use gene symbols.

We'll start out by isolating the relevant SummarizedExperiment and forcing an authentication, by querying for assay values.

library(restfulSE)
rna = experiments(brProteoMAE)[["rnaseq"]]
rna
assay(rna[1:3,1:4])

Now we use the orgDb interface to obtain gene symbols.

library(org.Hs.eg.db)
id = mapIds(org.Hs.eg.db, keys=rownames(rna), 
       column="SYMBOL", keytype="ENTREZID") 
id[is.na(id)] = rownames(rna)[is.na(id)]
rownames(rna) = id  # if no symbol, just retain entrez id

Reducing multiple instances of features

The proteomic data can have multiple feature values per gene; for this initial view we'll omit all but the first when there are multiplicities.

rd = rowData(experiments(brProteoMAE)[["itraq"]])
ok = which(!is.na(rd$geneName)&!duplicated(rd$geneName))
ph = experiments(brProteoMAE)[["itraq"]][ok,]
rownames(ph) = rowData(ph)$geneName

Using intersect*

The MultiAssayExperiment with common samples and features is then produced as follows:

slot(brProteoMAE, "ExperimentList")[["itraq"]] = ph
slot(brProteoMAE, "ExperimentList")[["rnaseq"]] = rna
brint = intersectColumns(intersectRows(brProteoMAE))

Retrieval from BigQuery

We'll materialize the assay components for RPPA and RNA-seq, taking logs of RNA-seq values:

assay(experiments(brint)[["rnaseq"]]) = 
   log(as.matrix(assay(experiments(brint)[["rnaseq"]]))+1)
assay(experiments(brint)[["rppa"]]) = 
   as.matrix(assay(experiments(brint)[["rppa"]]))
brint

Feature-specific correlation between assays

We extract a matrix for comparison of assay values over samples, focusing here on gene BCL2.

mt = sapply(experiments(brint["BCL2",]), assay) 
pairs(mt)

For all the selected, coincident features, interassay correlation is computed as follows:

fixup = function(x) data.matrix(na.omit(data.frame(x)))
corm = function(x) cor(fixup(sapply(experiments(brint[x,]), assay)))
allc = sapply(rownames(brint)[[1]], corm)
dim(allc)
summary(allc[2,]) # itraq vs rppa
summary(allc[3,]) # itraq vs rnaseq
summary(allc[8,]) # rppa vs rnaseq

The distributions of correlations can be searched to find assays that are not highly concordant across samples. The pairs plot for BRCA2 is

mt = sapply(experiments(brint["BRCA2",]), assay) 
pairs(mt)

References