https://github.com/vjcitn/brcaproteo
A package that illustrates use of CPTAC data on breast cancer in a multiomic context
https://github.com/vjcitn/brcaproteo
Last synced: 3 months ago
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A package that illustrates use of CPTAC data on breast cancer in a multiomic context
- Host: GitHub
- URL: https://github.com/vjcitn/brcaproteo
- Owner: vjcitn
- Created: 2019-04-19T20:10:28.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-05-01T15:35:54.000Z (about 6 years ago)
- Last Synced: 2025-01-09T13:47:25.326Z (5 months ago)
- Language: R
- Size: 10.3 MB
- Stars: 2
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
title: "brcaProteo -- proteogenomics data archive for breast cancer"
author: "Vincent J. Carey, stvjc at channing.harvard.edu"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{}
%\VignetteEncoding{UTF-8}
output:
BiocStyle::html_document:
highlight: pygments
number_sections: yes
theme: united
toc: yes
bibliography: 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.
```{r getpack}
library(brcaProteo)
data(brProteoMAE)
brProteoMAE
```
The proteomic results are available for a subset of the TCGA
contributions.
```{r lku}
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.
```{r lkint}
library(restfulSE)
rna = experiments(brProteoMAE)[["rnaseq"]]
rna
assay(rna[1:3,1:4])
```
Now we use the orgDb interface to obtain gene symbols.
```{r lkorg}
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.
```{r lkdup}
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:
```{r produceIntersection}
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:
```{r getm, cache=TRUE}
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.
```{r lkcomp}
mt = sapply(experiments(brint["BCL2",]), assay)
pairs(mt)
```
For all the selected, coincident features,
interassay correlation is computed as follows:
```{r getcorrs}
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
```{r lkc2}
mt = sapply(experiments(brint["BRCA2",]), assay)
pairs(mt)
```
# References