ivygapSE -- Bioconductor container for Ivy-GAP expression and metadata

In ivygapSE: A SummarizedExperiment for Ivy-GAP data

Introduction

CSV files from the Ivy-GAP project have been assembled into a SummarizedExperiment instance.

suppressPackageStartupMessages({
library(SummarizedExperiment)
library(ivygapSE)
library(DT)
library(grid)
library(png)
library(ggplot2)
library(limma)
library(randomForest)
})

library(ivygapSE)
data(ivySE)
ivySE

There are several types of metadata collected with the object, including the README.txt (use cat(metadata(ivySE)$README, sep="\n") to see this in R), the URL where data were retrieved, a character vector (builder) with the R code for creating (much of) the SummarizedExperiment, and two tables of tumor-specific and block-specific information.

Background on the ivyGlimpse app

The ivyGlimpse app is a rapid prototype of a browser-based interface to salient features of the data. The most current code is maintained in the Bioconductor ivygapSE package, but a public version of the app may be visited at shinyapps.io.

The ivygapSE package will evolve, based in part on associations observed through the use of this app. Briefly, the main visualization of the app is a scatterplot of user-selected tumor image features. All contributions, based on tumor sub-blocks (that have varying multiplicities per tumor block and donor) are assembled together without regard for source; interactive aspects of the display allow the user to see which donor contributes each point.

Strata can be formed interactively by brushing over the scatterplot; after the brushing event, the survival times of donors contributing selected points are compared to donors all of whose contributions lie outside the selection. Expression data are also stratified in this way and gene-specific boxplot sets (for user-specified gene sets) are produced for each stratum.

Summary information on the underlying data

The number of RNA-seq samples is r ncol(ivySE). The FPKM matrix has dimensions

dim(ivySE)

There are 42 different tumor donors.

length(unique(metadata(ivySE)$tumorDetails$donor_id))

However, only 37 donors contributed tumor RNA that was sequenced:

sum(metadata(ivySE)$tumorDetails$tumor_name %in% ivySE$tumor_name)

subd = metadata(ivySE)$subBlockDetails
dsub = dim(subd)

Features of images from sub-blocks were quantified according to the following terminology for anatomical characteristics. Not all images provided information on all attributes.

nomenclat()

Additional details

We have used information in the IvyGAP technical white paper to spell out additional background on the data underlying the app and SummarizedExperiment.

Basic experimental design layout

There are six substudies contributing data in a partly sequential design.

designOverview()

Tumor-level details

The following table has one record per tumor (N=r nrow(tumorDetails(ivySE))).

datatable(tumorDetails(ivySE), options=list(lengthMenu=c(3,5,10,50,100)))

Sub-block-level details

The following table has one record per sub-block (N=r nrow(subBlockDetails(ivySE))).

datatable(subBlockDetails(ivySE), options=list(lengthMenu=c(3,5,10,50,    100)))

Details on RNA-seq samples

The complete annotation on RNA-seq samples is provided in colData(ivySE). The table follows here:

datatable(as.data.frame(colData(ivySE)), options=list(lengthMenu=c(3,5,10,50,100)))

Key RNA-seq subsets

Subsets of design origin

The sub-blocks arose from a number of measurement objectives.

sb = subBlockDetails(ivySE)
table(sb$study_name)

Subsets based on structure

We use the structure_acronym variable to assess the composition of sources in the RNA-seq collection.

struc = as.character(colData(ivySE)$structure_acronym)
spls = strsplit(struc, "-")
basis = vapply(spls, function(x) x[1], character(1))
spec = vapply(spls, function(x) x[2], character(1))
table(basis, exclude=NULL)
barplot(table(basis))

Each of the major structural types contributes multiple samples from specific objectives.

lapply(split(spec,basis), function(x)sort(table(x),decreasing=TRUE))

A simple differential expression study

We have used r Biocpkg("limma") to test for differential expression among samples identified as reference histology in classes CT, CT-mvp, CT-pan, IT, and LE. The resulting mean expression estimates (FPKM scale) and moderated test statistics are obtained as follows:

library(limma)
ebout = getRefLimma()

The ten genes that are most significantly differentially expressed between conditions CT and CT-mvp are found as follows:

odig = options()$digits
options(digits=3)
limma::topTable(ebout, 2)
options(digits=odig) # revert

Differential expression by molecular subtype

We can bind the molecular subtype information from the tumor details to the expression sample annotation as follows:

moltype = tumorDetails(ivySE)$molecular_subtype
names(moltype) = tumorDetails(ivySE)$tumor_name
moltype[nchar(moltype)==0] = "missing"
ivySE$moltype = factor(moltype[ivySE$tumor_name])

We will confine attention to samples annotated as "reference histology" and compute the duplicate correlation for modeling the effect of molecular subtype in the available samples.

library(limma)
refex = ivySE[, grep("reference", ivySE$structure_acronym)]
refmat = assay(refex)
tydes = model.matrix(~moltype, data=as.data.frame(colData(refex)))
ok = which(apply(tydes,2,sum)>0)  # some subtypes don't have ref histo samples
tydes = tydes[,ok]
block = factor(refex$tumor_id)
dd = duplicateCorrelation(refmat, tydes, block=block)
f2 = lmFit(refmat, tydes, correlation=dd$consensus)
ef2 = eBayes(f2)
colnames(tydes)
topTable(ef2,2)

Classification of structural character

We assess the capacity of the expression measures to discriminate the structural type (CT, CT-mvp, CT-pan, LE, IT) using the random forests algorithm. Features used have interquartile range (IQR) over all relevant samples exceeding the median IQR over all genes.

refex = ivySE[, grep("reference", ivySE$structure_acronym)]
refex$struc = factor(refex$structure_acronym)
iqrs = rowIQRs(assay(refex))
inds = which(iqrs>quantile(iqrs,.5))
set.seed(1234)
rf1 = randomForest(x=t(assay(refex[inds,])), 
        y=refex$struc, mtry=30, importance=TRUE) 
rf1
varImpPlot(rf1)

Next steps

Patel et al. Science 2014 (344(6190): 1396–1401) present single cell RNA-seq for 430 cells from 5 tumors of different molecular subtypes. It would be interesting to use signature of structural origin to see whether intra-tumor variation can be resolved into components coherent with the five-element typology. It would also be of interest to assess whether structural type signatures are associated with any signatures of drug sensitivity in relevant cell lines.

ivygapSE documentation built on Nov. 8, 2020, 8:08 p.m.