library(ccPaper)

Muscle Skin Data processing

How do we process the data for muscle and skin to something we can start running through categoryCompare?

options(stringsAsFactors = FALSE)
library(affy)

Skin

Get the raw data in (this should actually be on GEO, but doesn't seem to be there yet)

currLoc <- getwd()
dataLoc <- "/mlab/data/rmflight/Documents/projects/work/Petruska/drg_sc_skin_denervation/Skin/"
setwd(dataLoc)

attFile <- "Sample information file.txt"
dataAttributes <- read.table(attFile, sep = "\t", header = T)
dataAttributes$timePoint <- 0
dataAttributes$timePoint[(dataAttributes$Time == "7 day")] <- 7
dataAttributes$timePoint[(dataAttributes$Time == "14 day")] <- 14

celData <- ReadAffy(phenoData = dataAttributes)
skinData <- rma(celData)

setwd(currLoc)
.sessionInfo <- sessionInfo()
.timeDate <- Sys.time()
save(skinData, .sessionInfo, .timeDate, file = "inst/data/skin_rma_data.RData")

Muscle

library(GEOquery)
muscleData <- getGEO(GEO = "GSE4411")[[1]]

controlSamples <- grep("Control", pData(muscleData)$title)
muscleData <- muscleData[, controlSamples]

tmpP <- pData(muscleData)
tmpP$innervation <- "innervated"
denLoc <- grep("Denervated", tmpP$title)
tmpP$innervation[denLoc] <- "denervated"
pData(muscleData) <- tmpP
.sessionInfo <- sessionInfo()
.timeDate <- Sys.time()
save(muscleData, .sessionInfo, .timeDate, file = "inst/data/muscle_data.RData")

Gene Lists

For both the skin and muscle data sets, we will calculate the p-values using limma. We will probably attempt to do both hypergeometric enrichment and GSEA.

Do not forget that these are from different organisms. Skin: rat, muscle: mouse.

Skin Gene Lists and Enrichment

library(limma)
library(rat2302.db)

## Loading required package: org.Rn.eg.db

data(skin_rma_data)

skinComps <- c("T7 - T0", "T14 - T0")
geneID <- unlist(mget(featureNames(skinData), rat2302ENTREZID))

skinExpr <- exprs(skinData)
skinCollapse <- collapseProbes(skinExpr, geneID)  # collapse to single genes using median of expression
skinCharacter <- pData(skinData)
skinCharacter$timePoint2 <- paste("T", skinCharacter$timePoint, sep = "")

skinFC <- rankGenes(skinCollapse, skinCharacter$timePoint2, skinComps, doAggregation = FALSE, 
    aggregateIndex = NA)
names(skinFC) <- c("T7", "T14")

skinDiff <- lapply(skinFC, getDiffGenes, id = "id")

library(GO.db)
library(org.Rn.eg.db)
library(limma)
rnGO <- as.list(org.Rn.egGO2ALLEGS)
rnGO <- rnGO[(Ontology(names(rnGO)) == "BP")]
rnGO <- lapply(rnGO, unique)

skinSet <- symbols2indices(rnGO, rownames(skinCollapse))

skinStatus <- skinCharacter$timePoint2
f <- factor(skinStatus)
skinDesign <- model.matrix(~0 + f)
colnames(skinDesign) <- levels(f)

skinContrast <- makeContrasts(contrasts = skinComps, levels = skinDesign)

save(list = ls(), file = "runSkinRomer.RData")

load("runSkinRomer.RData")
options(mc.cores = 12)  # at least if we are on hera
t1 <- Sys.time()
skinRomer <- multicontrastRomer(skinSet, skinCollapse, skinDesign, skinContrast, 
    nrot = 10000)
names(skinRomer) <- c("T7", "T14")
save(skinRomer, file = "inst/data/skinRomer.RData")
t2 <- Sys.time()
difftime(t2, t1)

Run using geneSetTest

load("runSkinRomer.RData")
options(mc.cores = 12)  # at least if we are on hera
t1 <- Sys.time()
skinGeneSetTest <- lapply(skinFC, function(x) {
    mmGeneSetTest(skinSet, x$t)
})

t2 <- Sys.time()
difftime(t2, t1)
save(skinGeneSetTest, t1, t2, file = "inst/data/skinGeneSetTest.RData")

Muscle Gene Lists and Enrichment

library(limma)
library(mouse4302.db)

## Loading required package: org.Mm.eg.db

data(muscle_data)

muscleComps <- c("denervated - innervated")
geneID <- unlist(mget(featureNames(muscleData), mouse4302ENTREZID))

muscleExpr <- exprs(muscleData)
muscleCollapse <- collapseProbes(muscleExpr, geneID)  # collapse to single genes using median of expression
muscleCharacter <- pData(muscleData)

muscleFC <- rankGenes(muscleCollapse, muscleCharacter$innervation, muscleComps, 
    doAggregation = FALSE, aggregateIndex = NA)
names(muscleFC) <- "denervation"

muscleDiff <- lapply(muscleFC, getDiffGenes, id = "id")

library(GO.db)
library(org.Mm.eg.db)
library(limma)
mmGO <- as.list(org.Mm.egGO2ALLEGS)
mmGO <- mmGO[(Ontology(names(mmGO)) == "BP")]
mmGO <- lapply(mmGO, unique)

muscleSet <- symbols2indices(mmGO, rownames(muscleCollapse))

muscleStatus <- muscleCharacter$innervation
f <- factor(muscleStatus)
muscleDesign <- model.matrix(~0 + f)
colnames(muscleDesign) <- levels(f)

muscleContrast <- makeContrasts(contrasts = muscleComps, levels = muscleDesign)

save(muscleSet, muscleCollapse, muscleDesign, muscleContrast, mmGO, muscleFC, 
    file = "runMuscleRomer.RData")

load("runMuscleRomer.RData")
options(mc.cores = 12)  # at least if we are on hera
t1 <- Sys.time()
muscleRomer <- multicontrastRomer(muscleSet, muscleCollapse, muscleDesign, muscleContrast, 
    nrot = 10000)
names(muscleRomer) <- "denervation"
save(muscleRomer, file = "inst/data/muscleRomer.RData")
t2 <- Sys.time()
difftime(t2, t1)

Compare Skin and Muscle

data(muscleRomer)
data(skinRomer)

bothGO <- union(names(rnGO), names(mmGO))
combMapping <- lapply(bothGO, function(inName) {
    isMM <- inName %in% names(mmGO)
    isRN <- inName %in% names(rnGO)

    tmpGene <- character(0)

    if (isMM) {
        tmpGene <- c(tmpGene, mmGO[[inName]])
    }
    if (isRN) {
        tmpGene <- c(tmpGene, rnGO[[inName]])
    }
    tmpGene <- unique(tmpGene)
    return(tmpGene)
})

names(combMapping) <- bothGO

geneAnnMapping <- new("namedList", .Data = combMapping, names = names(combMapping))


getSigID <- function(inRomer, pCut = 0.05, whichCol = c("Up", "Down")) {
    sigID <- lapply(whichCol, function(inCol) {
        rownames(inRomer[(inRomer[, inCol] <= pCut), ])
    })
    names(sigID) <- whichCol
    return(sigID)
}

muscleSigRomer <- lapply(muscleRomer, getSigID, pCut = 0.01)
muscleSigRomer <- unlist(muscleSigRomer, recursive = FALSE)
names(muscleSigRomer) <- c("muscle.Up", "muscle.Down")

skinSigRomer <- lapply(skinRomer, getSigID, pCut = 0.01)
skinSigRomer <- unlist(skinSigRomer, recursive = FALSE)

allSigRomer <- c(skinSigRomer, muscleSigRomer)

genCCSigList <- function(inSig) {
    tmp <- new("ccSigList", sigID = inSig)
}

allCCSig <- lapply(allSigRomer, genCCSigList)

allCCRomer <- new("GENccEnrichResult", allCCSig, categoryName = "GSEAGO", geneAnnMapping = geneAnnMapping, 
    overlapType = "overlap", annDescription = Term(names(geneAnnMapping)))

allRomerOpts <- new("ccOptions", listNames = names(allCCRomer), colorType = "pie")
compareSMRomer <- ccCompare(allCCRomer, allRomerOpts)

## Warning:
## '/mlab/data/rmflight/Documents/projects/work/ccPaper/vignettes/pieGraphs'
## already exists

compareSMRomer

## Annotation category:  GSEAGO    
## Main graph: A graphNEL graph with directed edges
## Number of Nodes = 563 
## Number of Edges = 22155

save(compareSMRomer, allRomerOpts, file = "skinMuscleCompare.RData")

compareSMRomer <- breakEdges(compareSMRomer, 0.8)
cwSM <- ccOutCyt(compareSMRomer, allRomerOpts, postText = "GSEAGO", rpcPort = 9001)

.sessionInfo <- sessionInfo()
.timeDate <- Sys.time()
save(cwSM, compareSMRomer, allRomerOpts, .sessionInfo, .timeDate, file = "inst/data/skinMuscleCompareOutput.RData")

Get out the groups of related GO terms:

smNodes <- cytOutNodes("typeII hypersensitivity - T7.Up", cwSM)
smNodes <- cytOutNodes("pH and lysosome regulation - Skin.T7.Down,Skin.T14.Up,Muscle.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("Neurotransmitter secretion and transport - Skin.T7.Down,Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("entry into host and movement - Skin.T7.Up", cwSM, smNodes)
smNodes <- cytOutNodes("steriod biosynthesis - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("microtubule organization - Skin.T7.Up,Skin.T14.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("cellular respiration - Skin.T14.Down,Muscle.Down", cwSM, 
    smNodes)
smNodes <- cytOutNodes("epithelial tube branching - Skin.T7.Down,Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("rRNA processing - Muscle.Up", cwSM, smNodes)
smNodes <- cytOutNodes("digestion - Skin.T14.Up", cwWM, smNodes)
smNodes <- cytOutNodes("digestion - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("response to virus - Skin.T7.Up", cwSM, smNodes)
smNodes <- cytOutNodes("purine metabolism - Muscle.Down", cwSM, smNodes)
smNodes <- cytOutNodes("protein localization in mitochondrion - Skin.T14.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("synapse assembly and nervous system development - Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("folic acid compound metabolism - Skin.T14.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("retinoic acid biosynthesis - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("mitotic spindle assembly - Skin.T7.Up", cwSM, smNodes)
smNodes <- cytOutNodes("regulation of phospholipase C - Muscle.Down", cwSM, 
    smNodes)
smNodes <- cytOutNodes("acetyl CoA biosynthesis - Skin.T14.Down,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("response to glucose - Skin.T7.Down, Muscle.Down", cwSM, 
    smNodes)
smNodes <- cytOutNodes("response to VEGF - Muscle.Down", cwSM, smNodes)
smNodes <- cytOutNodes("mitotic cell cycle checkpoint - Skin.T7.Down,Skin.T14.Down,Muscle.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("urea cycle - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("response to prostaglandin - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("keratinocyte migration - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("atp biosynthesis - Skin.T7.Down,Skin.T14.Down,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("membrane repolarization - Muscle.Down", cwSM, smNodes)
smNOdes <- cytOutNodes("muscle contraction and development - Skin.T7.Down,Skin.T14.Down,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("muscle contraction and development - Skin.T7.Down,Skin.T14.Down,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("muscle contraction and development - Skin.T7.Down,Skin.T14.Down,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("blood vessel endothelial cell differentiation - Skin.T7.Up,Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("endocrine regulation of blood pressure - Skin.T7.Down,Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("tube development - Skin.T7.Down,Muscle.Up", cwSM, smNodes)
smNodes <- cytOutNodes("amino acid transport - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("cation channel activity - Skin.T14.Down", cwSM, smNodes)
smNodes <- cytOutNodes("interferon gamma response - Skin.T14.Down", cwSM, smNodes)
smNodes <- cytOutNodes("lung cell differentiation - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("neg regulation of astrocyte diff. - Skin.T7.Down", cwSM, 
    smNodes)
smNodes <- cytOutNodes("fear response - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("N acetylglucosamine metabolism - Skin.T7.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("proteoglycan biosynthesis - Skin.T7.Up", cwSM, smNodes)
smNodes <- cytOutNodes("carbohydrate catabolism - Muscle.Down", cwSM, smNodes)
smNodes <- cytOutNodes("response to muscle activity - Skin.T14.Down,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("response to indole 3 methanol - Muscle.Up", cwSM, smNodes)
smNodes <- cytOutNodes("negative regulation of response to granulocyte / myeloid cell diff. - Skin.T7.Up,Muscle.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("vitamin metabolism - Skin.T14.Up,Muscle.Up", cwSM, smNodes)
smNodes <- cytOutNodes("DNA break repair - Muscle.Up", cwSM, smNodes)
smNodes <- cytOutNodes("calcineurin NFAT signaling cascade - Skin.T14.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("histone demethylation - Skin.T14.Down", cwSM, smNodes)
smNodes <- cytOutNodes("negative regulation of Ras signal transduction - Skin.T14.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("positive regulation of metalloenzyme activity - Skin.T14.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("glucocorticoid receptor signaling pathway - Skin.T14.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("mesenchymal cell diff in kidney / renal system dev. - Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("axon extension - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("lung lobe dev. and morphogenesis - Skin.T14.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("embryonic digestive tract dev. and morphogenesis - Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("negative regulation of protein transport - Skin.T7.Up,Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("phosphate ion transport - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("fluid transport - Skin.T7.Down,Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("fluid secretion - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("interleukin 13 production - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("glia guided migration - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("central nervous system maturation - Skin.T14.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("bile acid transport - Skin.T14.Up", cwSM, smNodes)
smNodes <- cytOutNodes("positive regulation of muscle cell apoptosis - Skin.T14.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("atrioventricular valve dev. and morphogenesis - Skin.T14.Up,Muscle.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("dopamine transport - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("collateral sprouting - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("response to osmotic stress - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("positive regulation of epidermal growth factor signaling - Skin.T7.Down", 
    cwSM, smNodes)
smNodes <- cytOutNodes("response to leptin - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("catecholamin biosynthesis - Skin.T7.Down", cwSM, smNodes)
smNodes <- cytOutNodes("negative regulation of cell junction assembly - Skin.T7.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("endocardial cell differentiation - Skin.T7.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("sequestering of actin monomers - Skin.T7.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("beta amyloid formation - Skin.T7.Up", cwSM, smNodes)
smNodes <- cytOutNodes("response to platelet derived growth factor stimulus - Skin.T7.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("lipopolysaccharid biosynthesis - Skin.T7.Up", cwSM, 
    smNodes)
smNodes <- cytOutNodes("aminoglycan metabolism - Skin.T7.Up", cwSM, smNodes)
smNodes <- cytOutNodes("epithelial to mesenchymal transition, endocardial cushion formation - Skin.T7.Up", 
    cwSM, smNodes)
smNodes <- cytOutNodes("pulmonary valve dev. and morphogenesis - Skin.T7.Up", 
    cwSM, smNodes)
.sessionInfo <- sessionInfo()
.timeDate <- Sys.time()
save(smNodes, cwSM, allCCRomer, allRomerOpts, compareSMRomer, file = "inst/data/skinMuscleCCOutput.RData")

Lets generate a couple of different tables for output.

data(skinMuscleCCOutput)

allDescStrings <- sapply(smNodes, function(x) {
    x$descStr
})
string2List <- strsplit(allDescStrings, " - ", fixed = TRUE)
justDesc <- sapply(string2List, function(x) {
    x[1]
})
listMem <- sapply(string2List, function(x) {
    x[2]
})
listMem[1] <- "Skin.T7.Up"  # fix an error in the initial setup
listMemSplit <- strsplit(listMem, ",", fixed = TRUE)

descMembershipTable <- matrix("", nrow = length(allDescStrings), ncol = 7)
colnames(descMembershipTable) <- c("Description", "Skin.T7.Down", "Skin.T7.Up", 
    "Skin.T14.Down", "Skin.T14.Up", "Muscle.Down", "Muscle.Up")

descMembershipTable[, "Description"] <- justDesc

for (inRow in seq(1, nrow(descMembershipTable))) {
    useSplit <- listMemSplit[[inRow]]
    trimSplit <- gsub(" ", "", useSplit)
    descMembershipTable[inRow, trimSplit] <- "X"
}

orderBy <- c("Skin.T7.Down,Skin.T14.Down,Muscle.Down", "Skin.T7.Down,Skin.T14.Down,Muscle.Up", 
    "Skin.T7.Down,Skin.T14.Down", "Skin.T7.Down,Skin.T14.Up,Muscle.Up", "Skin.T7.Down,Skin.T14.Up", 
    "Skin.T7.Down,Muscle.Up", "Skin.T7.Down,Muscle.Down", "Skin.T7.Down", "Skin.T7.Up,Skin.T14.Down", 
    "Skin.T7.Up,Skin.T14.Up", "Skin.T7.Up,Muscle.Up", "Skin.T7.Up", "Skin.T14.Down,Muscle.Down", 
    "Skin.T14.Down", "Skin.T14.Up,Muscle.Down", "Skin.T14.Up,Muscle.Up", "Skin.T14.Up", 
    "Muscle.Down", "Muscle.Up")

listMem <- gsub(" ", "", listMem)
newOrder <- unlist(lapply(orderBy, function(x) {
    which(listMem %in% x)
}))

descMembershipTable <- descMembershipTable[newOrder, ]
require(xtable)

## Loading required package: xtable

# add an html link to each entry in the table
useLink <- paste("<a href=\"#loc", seq(1, nrow(descMembershipTable)), "\">", 
    descMembershipTable[, "Description"], "</a>", sep = "")
descMembershipTable[, "Description"] <- useLink

Print out the table.

We also want the set of GO terms, their descriptions, and list memberships so that the inquisitive mind can see how we generated the groups.

atp biosynthesis - Skin.T7.Down,Skin.T14.Down,Muscle.Down

back

muscle contraction and development - Skin.T7.Down,Skin.T14.Down,Muscle.Down

back

mitotic cell cycle checkpoint - Skin.T7.Down,Skin.T14.Down,Muscle.Up

back

pH and lysosome regulation - Skin.T7.Down,Skin.T14.Up,Muscle.Up

back

Neurotransmitter secretion and

transport - Skin.T7.Down,Skin.T14.Up

back

epithelial tube branching - Skin.T7.Down,Skin.T14.Up

back

endocrine regulation of blood pressure - Skin.T7.Down,Skin.T14.Up

back

fluid transport - Skin.T7.Down,Skin.T14.Up

back

tube development - Skin.T7.Down,Muscle.Up

back

response to glucose - Skin.T7.Down, Muscle.Down

back

steriod biosynthesis - Skin.T7.Down

back

lung cell differentiation - Skin.T7.Down

back

neg regulation of astrocyte diff. - Skin.T7.Down

back

fear response - Skin.T7.Down

back

dopamine transport - Skin.T7.Down

back

collateral sprouting - Skin.T7.Down

back

response to osmotic stress - Skin.T7.Down

back

positive regulation of epidermal growth factor signaling - Skin.T7.Down

back

response to leptin - Skin.T7.Down

back

catecholamin biosynthesis - Skin.T7.Down

back

microtubule organization - Skin.T7.Up,Skin.T14.Down

back

blood vessel endothelial cell differentiation - Skin.T7.Up,Skin.T14.Up

back

negative regulation of protein transport - Skin.T7.Up,Skin.T14.Up

back

negative regulation of response to granulocyte / myeloid cell diff. - Skin.T7.Up,Muscle.Up

back

typeII hypersensitivity - T7.Up

back

entry into host and movement - Skin.T7.Up

back

response to virus - Skin.T7.Up

back

mitotic spindle assembly - Skin.T7.Up

back

N acetylglucosamine metabolism - Skin.T7.Up

back

proteoglycan biosynthesis - Skin.T7.Up

back

negative regulation of cell junction assembly - Skin.T7.Up

back

endocardial cell differentiation - Skin.T7.Up

back

sequestering of actin monomers - Skin.T7.Up

back

beta amyloid formation - Skin.T7.Up

back

response to platelet derived growth factor stimulus - Skin.T7.Up

back

lipopolysaccharid biosynthesis - Skin.T7.Up

back

aminoglycan metabolism - Skin.T7.Up

back

epithelial to mesenchymal transition, endocardial cushion formation - Skin.T7.Up

back

pulmonary valve dev. and morphogenesis - Skin.T7.Up

back

cellular respiration - Skin.T14.Down,Muscle.Down

back

acetyl CoA biosynthesis - Skin.T14.Down,Muscle.Down

back

response to muscle activity - Skin.T14.Down,Muscle.Down

back

protein localization in mitochondrion - Skin.T14.Down

back

cation channel activity - Skin.T14.Down

back

interferon gamma response - Skin.T14.Down

back

calcineurin NFAT signaling cascade - Skin.T14.Down

back

histone demethylation - Skin.T14.Down

back

negative regulation of Ras signal transduction - Skin.T14.Down

back

positive regulation of metalloenzyme activity - Skin.T14.Down

back

glucocorticoid receptor signaling pathway - Skin.T14.Down

back

atrioventricular valve dev. and morphogenesis - Skin.T14.Up,Muscle.Down

back

vitamin metabolism - Skin.T14.Up,Muscle.Up

back

digestion - Skin.T14.Up

back

synapse assembly and nervous system development - Skin.T14.Up

back

folic acid compound metabolism - Skin.T14.Up

back

retinoic acid biosynthesis - Skin.T14.Up

back

urea cycle - Skin.T14.Up

back

response to prostaglandin - Skin.T14.Up

back

keratinocyte migration - Skin.T14.Up

back

amino acid transport - Skin.T14.Up

back

mesenchymal cell diff in kidney / renal system dev. - Skin.T14.Up

back

axon extension - Skin.T14.Up

back

lung lobe dev. and morphogenesis - Skin.T14.Up

back

embryonic digestive tract dev. and morphogenesis - Skin.T14.Up

back

phosphate ion transport - Skin.T14.Up

back

fluid secretion - Skin.T14.Up

back

interleukin 13 production - Skin.T14.Up

back

glia guided migration - Skin.T14.Up

back

central nervous system maturation - Skin.T14.Up

back

bile acid transport - Skin.T14.Up

back

positive regulation of muscle cell apoptosis - Skin.T14.Up

back

purine metabolism - Muscle.Down

back

regulation of phospholipase C - Muscle.Down

back

response to VEGF - Muscle.Down

back

membrane repolarization - Muscle.Down

back

carbohydrate catabolism - Muscle.Down

back

rRNA processing - Muscle.Up

back

response to indole 3 methanol - Muscle.Up

back

DNA break repair - Muscle.Up

back

Collateral Sprouting

One of our groups above seems to be related to collateral sprouting, which is the process the skin data was actually seeking to measure, but it is in an unexpected direction. We should look at the genes involved and see what is actually going on.

allDescStrings <- sapply(smNodes, function(x) {
    x$descStr
})
isCollateral <- grep("collateral sprouting", allDescStrings, ignore.case = TRUE)

collateralGO <- smNodes[[isCollateral]]$nodes

skinRomer$T7[collateralGO, ]

##            NGenes     Up      Down  Mixed
## GO:0048668     14 0.9992 0.0008999 0.1133
## GO:0048670     11 0.9979 0.0021998 0.5132

skinRomer$T14[collateralGO, ]

##            NGenes     Up   Down    Mixed
## GO:0048668     14 0.9867 0.0134 0.006299
## GO:0048670     11 0.9800 0.0201 0.008599

rnGenes <- unique(unlist(rnGO[collateralGO]))

rnFCGenes <- rownames(skinFC$T7)
rnGenes <- rnGenes[(rnGenes %in% rnFCGenes)]
rnExtra <- cbind(unlist(mget(rnGenes, org.Rn.egSYMBOL)), unlist(mget(rnGenes, 
    org.Rn.egGENENAME)))

rnData <- lapply(skinFC, function(inFC) {
    tmp <- cbind(inFC[rnGenes, ], rnExtra)
    names(tmp) <- c(names(tmp)[1:7], "Symbol", "Name")
    return(tmp)
})
lapply(rnData, dim)

## $T7
## [1] 14  9
## 
## $T14
## [1] 14  9

Lets actually generate tables for T7 and T14.

T7

T14

Run using geneSetTest

load("runMuscleRomer.RData")
options(mc.cores = 12)  # at least if we are on hera
t1 <- Sys.time()
muscleGeneSetTest <- lapply(muscleFC, function(x) {
    mmGeneSetTest(muscleSet, x$t)
})

t2 <- Sys.time()
difftime(t2, t1)
save(muscleGeneSetTest, t1, t2, file = "inst/data/muscleGeneSetTest.RData")

Date and System information

Sys.time()

## [1] "2014-02-19 10:34:49 EST"

sessionInfo()

## R version 3.0.1 (2013-05-16)
## Platform: x86_64-unknown-linux-gnu (64-bit)
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=C                 LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] xtable_1.7-1                mouse4302.db_2.10.1        
##  [3] org.Mm.eg.db_2.10.1         rat2302.db_2.10.1          
##  [5] org.Rn.eg.db_2.10.1         affy_1.40.0                
##  [7] colorspace_1.2-4            mgcv_1.7-27                
##  [9] nlme_3.1-113                hgu133plus2.db_2.10.1      
## [11] hgu95av2.db_2.10.1          ALL_1.4.16                 
## [13] GO.db_2.10.1                org.Hs.eg.db_2.10.1        
## [15] devtools_1.4.1              ccPaper_0.0.19             
## [17] ggplot2_0.9.3.1             limma_3.18.7               
## [19] categoryComparePaper_1.6.15 AnnotationDbi_1.24.0       
## [21] RSQLite_0.11.4              DBI_0.2-7                  
## [23] Biobase_2.22.0              BiocGenerics_0.8.0         
## [25] roxygen2_2.2.2              digest_0.6.4               
## 
## loaded via a namespace (and not attached):
##  [1] affyio_1.30.0         annotate_1.40.0       AnnotationForge_1.4.4
##  [4] BiocInstaller_1.12.0  brew_1.0-6            Category_2.28.0      
##  [7] dichromat_2.0-0       evaluate_0.5.1        formatR_0.10         
## [10] genefilter_1.44.0     GOstats_2.28.0        graph_1.40.1         
## [13] grid_3.0.1            GSEABase_1.24.0       gtable_0.1.2         
## [16] httr_0.2              hwriter_1.3           IRanges_1.20.6       
## [19] knitr_1.5             labeling_0.2          lattice_0.20-24      
## [22] markdown_0.6.4        MASS_7.3-29           Matrix_1.1-1.1       
## [25] memoise_0.1           munsell_0.4.2         plyr_1.8             
## [28] preprocessCore_1.24.0 proto_0.3-10          RBGL_1.38.0          
## [31] RColorBrewer_1.0-5    RCurl_1.95-4.1        RCytoscape_1.12.0    
## [34] reshape2_1.2.2        scales_0.2.3          splines_3.0.1        
## [37] stats4_3.0.1          stringr_0.6.2         survival_2.37-4      
## [40] tools_3.0.1           whisker_0.3-2         XML_3.98-1.1         
## [43] XMLRPC_0.3-0          zlibbioc_1.8.0