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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:GSCA.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings GSCA_2.18.0.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.11-bioc/meat/GSCA.Rcheck'
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'GSCA/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'GSCA' version '2.18.0'
* checking package namespace information ... OK
* checking package dependencies ... NOTE
Depends: includes the non-default packages:
'shiny', 'sp', 'gplots', 'ggplot2', 'reshape2', 'RColorBrewer',
'rhdf5'
Adding so many packages to the search path is excessive and importing
selectively is preferable.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'GSCA' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... NOTE
'library' or 'require' calls in package code:
'Affyhgu133A2Expr' 'Affyhgu133Plus2Expr' 'Affyhgu133aExpr'
'Affymoe4302Expr'
Please use :: or requireNamespace() instead.
See section 'Suggested packages' in the 'Writing R Extensions' manual.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
GSCA: no visible global function definition for 'data'
GSCA: no visible binding for global variable 'Affyhgu133aExprtab'
GSCA: no visible binding for global variable 'Affymoe4302Exprtab'
GSCA: no visible binding for global variable 'Affyhgu133A2Exprtab'
GSCA: no visible binding for global variable 'Affyhgu133Plus2Exprtab'
GSCA: no visible binding for global variable 'geneid'
GSCA: no visible global function definition for 'qnorm'
GSCA: no visible global function definition for 'sd'
GSCA: no visible global function definition for 'quantile'
GSCA: no visible global function definition for 'fisher.test'
GSCAeda: no visible global function definition for 'data'
GSCAeda: no visible binding for global variable 'Affyhgu133aExprtab'
GSCAeda: no visible binding for global variable 'Affymoe4302Exprtab'
GSCAeda: no visible binding for global variable 'Affyhgu133A2Exprtab'
GSCAeda: no visible binding for global variable
'Affyhgu133Plus2Exprtab'
GSCAeda: no visible binding for global variable 'geneid'
GSCAeda: no visible global function definition for 'qnorm'
GSCAeda: no visible global function definition for 'sd'
GSCAeda: no visible global function definition for 'quantile'
GSCAeda: no visible global function definition for 'pdf'
GSCAeda: no visible global function definition for 'str'
GSCAeda: no visible binding for global variable 'variable'
GSCAeda: no visible binding for global variable 'value'
GSCAeda: no visible binding for global variable 'SampleType'
GSCAeda: no visible global function definition for 'par'
GSCAeda: no visible global function definition for 'colorRampPalette'
GSCAeda: no visible global function definition for 't.test'
GSCAeda: no visible binding for global variable 'Var1'
GSCAeda: no visible binding for global variable 'Var2'
GSCAeda: no visible binding for global variable 't.stat'
GSCAeda: no visible binding for global variable 'P.value'
GSCAeda: no visible global function definition for 'fisher.test'
GSCAeda: no visible global function definition for 'dev.off'
GSCAeda: no visible global function definition for 'write.csv'
GSCAeda: no visible global function definition for 'write.table'
GSCAplot: no visible global function definition for 'data'
GSCAplot: no visible binding for global variable 'Affyhgu133aExprtab'
GSCAplot: no visible binding for global variable 'Affymoe4302Exprtab'
GSCAplot: no visible binding for global variable 'Affyhgu133A2Exprtab'
GSCAplot: no visible binding for global variable
'Affyhgu133Plus2Exprtab'
GSCAplot: no visible global function definition for 'pdf'
GSCAplot: no visible global function definition for 'par'
GSCAplot: no visible global function definition for 'hist'
GSCAplot: no visible global function definition for 'title'
GSCAplot: no visible global function definition for 'dev.off'
annotatePeaks: no visible binding for global variable 'allreffile'
tabSearch: no visible global function definition for 'data'
tabSearch: no visible binding for global variable 'Affyhgu133aExprtab'
tabSearch: no visible binding for global variable 'Affymoe4302Exprtab'
tabSearch: no visible binding for global variable 'Affyhgu133A2Exprtab'
tabSearch: no visible binding for global variable
'Affyhgu133Plus2Exprtab'
Undefined global functions or variables:
Affyhgu133A2Exprtab Affyhgu133Plus2Exprtab Affyhgu133aExprtab
Affymoe4302Exprtab P.value SampleType Var1 Var2 allreffile
colorRampPalette data dev.off fisher.test geneid hist par pdf qnorm
quantile sd str t.stat t.test title value variable write.csv
write.table
Consider adding
importFrom("grDevices", "colorRampPalette", "dev.off", "pdf")
importFrom("graphics", "hist", "par", "title")
importFrom("stats", "fisher.test", "qnorm", "quantile", "sd", "t.test")
importFrom("utils", "data", "str", "write.csv", "write.table")
to your NAMESPACE file.
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... ERROR
Running examples in 'GSCA-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: GSCA
> ### Title: GSCA
> ### Aliases: GSCA
> ### Keywords: GSCA
>
> ### ** Examples
>
> ## First load the TF target genes derived from Oct4 ChIPx data
> ## in embryonic stem cells. The data is in the form of a list
> ## where the first item contains the activated (+) target genes in
> ## Entrez GeneID format and the second item contains the repressed (-)
> ## target genes in Entrez GeneID format.
> data(Oct4ESC_TG)
>
> ## We want to analyze Oct4, so we need to specify the EntrezGeneID for Oct4
> ## and input the activated (+) and repressed (-) target genes of Oct4.
> ## Constucting the input genedata required by GSCA. There are two genesets
> ## one is the TF and another is the TF target genes. Note that constructing genedata
> ## with many genesets could be laborious, so using the interactive UI is recommended to
> ## easily start up the analysis.
> activenum <- length(Oct4ESC_TG[[1]])
> repressnum <- length(Oct4ESC_TG[[2]])
> Octgenedata <- data.frame(gsname=c("GS1",rep("GS2",activenum+repressnum)),gene=c(18999,Oct4ESC_TG[[1]],Oct4ESC_TG[[2]]),weight=c(rep(1,1+activenum),rep(-1,repressnum)),stringsAsFactors=FALSE)
>
> ## We are interested in the pattern that TF and its target genes are all highly expressed.
> ## We also need to define how high the cutoffs should be such
> ## that each cutoff corresponds to the p-value of 0.1
> ## based on fitted normal distributions.
> ## Constructing pattern required by GSCA, all geneset names in genedata should appear
> ## exactly once in the first column
> Octpattern <- data.frame(gsname=c("GS1","GS2"),acttype="High",cotype="Norm",cutoff=0.1,stringsAsFactors=FALSE)
>
> ## Lastly, we specify the chipdata to be "moe4302" and the significance of enriched
> ## biological contexts must be at least 0.05 to be reported.
> Octoutput <- GSCA(Octgenedata,Octpattern,"moe4302",Pval.co=0.05)
Loading required package: Affymoe4302Expr
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
GSCA
--- call from context ---
GSCA(Octgenedata, Octpattern, "moe4302", Pval.co = 0.05)
--- call from argument ---
genedata[, 1] == singlegeneset && genedata[, 2] %in% geneid
--- R stacktrace ---
where 1: GSCA(Octgenedata, Octpattern, "moe4302", Pval.co = 0.05)
--- value of length: 857 type: logical ---
[1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE
--- function from context ---
function (genedata, pattern, chipdata, scaledata = F, Pval.co = 0.05,
directory = NULL)
{
genedata[, 1] <- as.character(genedata[, 1])
pattern[, 1] <- as.character(pattern[, 1])
path <- system.file("extdata", package = paste0("Affy", chipdata,
"Expr"))
load(paste0(path, "/geneid.rda"))
if (chipdata == "hgu133a") {
if (!require(Affyhgu133aExpr)) {
stop("Affyhgu133aExpr Package is not found")
}
else {
data(Affyhgu133aExprtab)
tab <- Affyhgu133aExprtab
}
}
else if (chipdata == "moe4302") {
if (!require(Affymoe4302Expr)) {
stop("Affymoe4302Expr Package is not found")
}
else {
data(Affymoe4302Exprtab)
tab <- Affymoe4302Exprtab
}
}
else if (chipdata == "hgu133A2") {
if (!require(Affyhgu133A2Expr)) {
stop("Affyhgu133A2Expr Package is not found")
}
else {
data(Affyhgu133A2Exprtab)
tab <- Affyhgu133A2Exprtab
}
}
else if (chipdata == "hgu133Plus2") {
if (!require(Affyhgu133Plus2Expr)) {
stop("Affyhgu133Plus2Expr Package is not found")
}
else {
data(Affyhgu133Plus2Exprtab)
tab <- Affyhgu133Plus2Exprtab
}
}
else {
stop("Please enter valid name for chipdata. Current Supported chipdata: 'hgu133a', 'moe4302', 'hgu133Plus2', 'hgu133A2'")
}
tabsamplename <- tab$SampleName
genesetname <- NULL
for (tmpgenesetname in unique(genedata[, 1])) {
if (sum(geneid %in% genedata[genedata[, 1] == tmpgenesetname,
2]) == 0) {
warning(paste("No matching target genes found on the compendium for gene set",
tmpgenesetname))
}
else {
genesetname <- c(genesetname, tmpgenesetname)
}
}
if (length(genesetname) == 0)
stop("No matching target genes found on the compendium for all gene sets")
selectsample <- 1:nrow(tab)
activity <- matrix(0, nrow = length(genesetname), ncol = nrow(tab))
rownames(activity) <- genesetname
genesetcutoff <- genesettotalgenenum <- genesetmissinggene <- rep(0,
length(genesetname))
names(genesetcutoff) <- names(genesettotalgenenum) <- names(genesetmissinggene) <- genesetname
for (genesetid in 1:length(genesetname)) {
singlegeneset <- genesetname[genesetid]
currentgeneset <- genedata[genedata[, 1] == singlegeneset &
genedata[, 2] %in% geneid, ]
tmpgeneexpr <- t(h5read(paste0(path, "/data.h5"), "expr",
index = list(NULL, match(currentgeneset[, 2], geneid))))/1000
if (scaledata)
tmpgeneexpr <- t(apply(tmpgeneexpr, 1, scale))
tmpgeneexpr <- sweep(tmpgeneexpr, 1, currentgeneset[,
3], "*")
score <- colMeans(tmpgeneexpr)
activity[genesetid, ] <- score
}
for (genesetid in 1:length(genesetname)) {
score <- activity[genesetid, ]
singlegeneset <- genesetname[genesetid]
missinggene <- setdiff(genedata[singlegeneset == genedata[,
1], 2], geneid)
genesetmissinggene[genesetid] <- length(missinggene)
genesettotalgenenum[genesetid] <- length(genedata[, 1] ==
singlegeneset && genedata[, 2] %in% geneid)
singlepattern <- pattern[pattern[, 1] == singlegeneset,
]
if (singlepattern[, 3] == "Norm") {
if (singlepattern[, 2] == "High") {
cutoff <- qnorm(1 - singlepattern[, 4], mean(score),
sd(score))
selectsample <- intersect(selectsample, which(score >=
cutoff))
}
else if (singlepattern[, 2] == "Low") {
cutoff <- qnorm(singlepattern[, 4], mean(score),
sd(score))
selectsample <- intersect(selectsample, which(score <
cutoff))
}
else {
stop(paste("Second Column of pattern in", singlegeneset,
"is not correctly given"))
}
}
else if (singlepattern[, 3] == "Quantile") {
if (singlepattern[, 2] == "High") {
cutoff <- quantile(score, 1 - singlepattern[,
4])
selectsample <- intersect(selectsample, which(score >=
cutoff))
}
else if (singlepattern[, 2] == "Low") {
cutoff <- quantile(score, singlepattern[, 4])
selectsample <- intersect(selectsample, which(score <
cutoff))
}
else {
stop(paste("Second Column of pattern in", singlegeneset,
"is not correctly given"))
}
}
else if (singlepattern[, 3] == "Exprs") {
if (singlepattern[, 2] == "High") {
cutoff <- singlepattern[, 4]
selectsample <- intersect(selectsample, which(score >=
cutoff))
}
else if (singlepattern[, 2] == "Low") {
cutoff <- singlepattern[, 4]
selectsample <- intersect(selectsample, which(score <
cutoff))
}
else {
stop(paste("Second Column of pattern in", singlegeneset,
"is not correctly given"))
}
}
else {
stop(paste("Cutoff type pattern of geneset", singlegeneset,
"is not correctly given"))
}
genesetcutoff[genesetid] <- cutoff
}
ExpID <- tab[selectsample, "ExperimentID"]
tmpTypes <- tab[selectsample, "SampleType"]
tabTWO <- table(tab$SampleType)
tabTWO <- names(tabTWO)[tabTWO > 2]
tab <- tab[tab$SampleType %in% tabTWO, ]
ExpID <- ExpID[tmpTypes %in% tab$SampleType]
tmpTypes <- tmpTypes[tmpTypes %in% tab$SampleType]
if (length(tmpTypes) > 0) {
ttT <- table(tmpTypes)
sT <- sum(ttT)
bgT <- table(tab$SampleType)
bgT <- bgT[names(ttT)]
ContextN <- sum(table(tab$SampleType) > 2)
SCORE <- matrix(0, nrow = length(ttT), ncol = 4)
ExperimentID <- rep("0", length(bgT))
for (i in 1:length(bgT)) {
r1c1 <- ttT[i]
r1c2 <- sT - ttT[i]
r2c1 <- bgT[i] - ttT[i]
r2c2 <- length(tab$SampleType) - r1c1 - r1c2 - r2c1
tmpmat <- matrix(c(r1c1, r2c1, r1c2, r2c2), ncol = 2)
SCORE[i, 1] <- fisher.test(tmpmat, alternative = "greater")$p.value
SCORE[i, 2] <- round(((as.numeric(ttT)[i] + sT/length(tab$SampleType))/(as.numeric(bgT)[i] +
1))/(sT/length(tab$SampleType)), 3)
SCORE[i, 3] <- min(SCORE[i, 1] * ContextN, 1)
ExperimentID[i] <- paste(unique(unlist(strsplit(paste(ExpID[tmpTypes ==
names(ttT)[i]], collapse = ";"), ";"))), collapse = ";")
}
FIN <- data.frame(as.numeric(ttT), as.numeric(bgT), SCORE[,
2], SCORE[, 3], rownames(ttT), ExperimentID, stringsAsFactors = F)
colnames(FIN) <- c("Active", "Total", "FoldChange", "Adj.Pvalue",
"SampleType", "ExperimentID")
FIN <- FIN[order(as.numeric(FIN[, "Adj.Pvalue"], -1 *
as.numeric(FIN[, "Active"]), decreasing = FALSE)),
]
FIN <- FIN[as.numeric(FIN[, "Adj.Pvalue"]) <= Pval.co,
]
FIN[, 4] <- signif(FIN[, 4], 3)
if (!is.null(directory)) {
expiddex <- unique(unlist(strsplit(as.character(FIN$ExperimentID),
";")))
for (k in 1:length(expiddex)) {
filepath <- paste0(directory, "/", expiddex[k])
Temp <- tabSearch(expiddex[k], chipdata)
if (nrow(Temp) > 1) {
dir.create(filepath)
GSCAeda(genedata, pattern, chipdata = chipdata,
SearchOutput = Temp, Pval.co = Pval.co, Ordering = "Average",
Title = expiddex[k], outputdir = filepath)
}
}
}
if (is.null(dim(FIN)) | nrow(FIN) == 0) {
message("No significant biological contexts found.")
}
else {
FIN <- cbind(1:nrow(FIN), FIN)
colnames(FIN)[1] <- "Rank"
rownames(FIN) <- 1:nrow(FIN)
}
colnames(activity) <- tabsamplename
return(list(Ranking = FIN, Score = activity, Pattern = pattern,
Cutoff = genesetcutoff, SelectedSample = selectsample,
Totalgene = genesettotalgenenum, Missinggene = genesetmissinggene,
Chipdata = chipdata))
}
else {
stop("No samples show the pattern of interest.\n Try relaxing cutoffs.")
}
}
<bytecode: 0x09a5dac8>
<environment: namespace:GSCA>
--- function search by body ---
Function GSCA in namespace GSCA has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
** running examples for arch 'x64' ... ERROR
Running examples in 'GSCA-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: GSCA
> ### Title: GSCA
> ### Aliases: GSCA
> ### Keywords: GSCA
>
> ### ** Examples
>
> ## First load the TF target genes derived from Oct4 ChIPx data
> ## in embryonic stem cells. The data is in the form of a list
> ## where the first item contains the activated (+) target genes in
> ## Entrez GeneID format and the second item contains the repressed (-)
> ## target genes in Entrez GeneID format.
> data(Oct4ESC_TG)
>
> ## We want to analyze Oct4, so we need to specify the EntrezGeneID for Oct4
> ## and input the activated (+) and repressed (-) target genes of Oct4.
> ## Constucting the input genedata required by GSCA. There are two genesets
> ## one is the TF and another is the TF target genes. Note that constructing genedata
> ## with many genesets could be laborious, so using the interactive UI is recommended to
> ## easily start up the analysis.
> activenum <- length(Oct4ESC_TG[[1]])
> repressnum <- length(Oct4ESC_TG[[2]])
> Octgenedata <- data.frame(gsname=c("GS1",rep("GS2",activenum+repressnum)),gene=c(18999,Oct4ESC_TG[[1]],Oct4ESC_TG[[2]]),weight=c(rep(1,1+activenum),rep(-1,repressnum)),stringsAsFactors=FALSE)
>
> ## We are interested in the pattern that TF and its target genes are all highly expressed.
> ## We also need to define how high the cutoffs should be such
> ## that each cutoff corresponds to the p-value of 0.1
> ## based on fitted normal distributions.
> ## Constructing pattern required by GSCA, all geneset names in genedata should appear
> ## exactly once in the first column
> Octpattern <- data.frame(gsname=c("GS1","GS2"),acttype="High",cotype="Norm",cutoff=0.1,stringsAsFactors=FALSE)
>
> ## Lastly, we specify the chipdata to be "moe4302" and the significance of enriched
> ## biological contexts must be at least 0.05 to be reported.
> Octoutput <- GSCA(Octgenedata,Octpattern,"moe4302",Pval.co=0.05)
Loading required package: Affymoe4302Expr
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
GSCA
--- call from context ---
GSCA(Octgenedata, Octpattern, "moe4302", Pval.co = 0.05)
--- call from argument ---
genedata[, 1] == singlegeneset && genedata[, 2] %in% geneid
--- R stacktrace ---
where 1: GSCA(Octgenedata, Octpattern, "moe4302", Pval.co = 0.05)
--- value of length: 857 type: logical ---
[1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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--- function from context ---
function (genedata, pattern, chipdata, scaledata = F, Pval.co = 0.05,
directory = NULL)
{
genedata[, 1] <- as.character(genedata[, 1])
pattern[, 1] <- as.character(pattern[, 1])
path <- system.file("extdata", package = paste0("Affy", chipdata,
"Expr"))
load(paste0(path, "/geneid.rda"))
if (chipdata == "hgu133a") {
if (!require(Affyhgu133aExpr)) {
stop("Affyhgu133aExpr Package is not found")
}
else {
data(Affyhgu133aExprtab)
tab <- Affyhgu133aExprtab
}
}
else if (chipdata == "moe4302") {
if (!require(Affymoe4302Expr)) {
stop("Affymoe4302Expr Package is not found")
}
else {
data(Affymoe4302Exprtab)
tab <- Affymoe4302Exprtab
}
}
else if (chipdata == "hgu133A2") {
if (!require(Affyhgu133A2Expr)) {
stop("Affyhgu133A2Expr Package is not found")
}
else {
data(Affyhgu133A2Exprtab)
tab <- Affyhgu133A2Exprtab
}
}
else if (chipdata == "hgu133Plus2") {
if (!require(Affyhgu133Plus2Expr)) {
stop("Affyhgu133Plus2Expr Package is not found")
}
else {
data(Affyhgu133Plus2Exprtab)
tab <- Affyhgu133Plus2Exprtab
}
}
else {
stop("Please enter valid name for chipdata. Current Supported chipdata: 'hgu133a', 'moe4302', 'hgu133Plus2', 'hgu133A2'")
}
tabsamplename <- tab$SampleName
genesetname <- NULL
for (tmpgenesetname in unique(genedata[, 1])) {
if (sum(geneid %in% genedata[genedata[, 1] == tmpgenesetname,
2]) == 0) {
warning(paste("No matching target genes found on the compendium for gene set",
tmpgenesetname))
}
else {
genesetname <- c(genesetname, tmpgenesetname)
}
}
if (length(genesetname) == 0)
stop("No matching target genes found on the compendium for all gene sets")
selectsample <- 1:nrow(tab)
activity <- matrix(0, nrow = length(genesetname), ncol = nrow(tab))
rownames(activity) <- genesetname
genesetcutoff <- genesettotalgenenum <- genesetmissinggene <- rep(0,
length(genesetname))
names(genesetcutoff) <- names(genesettotalgenenum) <- names(genesetmissinggene) <- genesetname
for (genesetid in 1:length(genesetname)) {
singlegeneset <- genesetname[genesetid]
currentgeneset <- genedata[genedata[, 1] == singlegeneset &
genedata[, 2] %in% geneid, ]
tmpgeneexpr <- t(h5read(paste0(path, "/data.h5"), "expr",
index = list(NULL, match(currentgeneset[, 2], geneid))))/1000
if (scaledata)
tmpgeneexpr <- t(apply(tmpgeneexpr, 1, scale))
tmpgeneexpr <- sweep(tmpgeneexpr, 1, currentgeneset[,
3], "*")
score <- colMeans(tmpgeneexpr)
activity[genesetid, ] <- score
}
for (genesetid in 1:length(genesetname)) {
score <- activity[genesetid, ]
singlegeneset <- genesetname[genesetid]
missinggene <- setdiff(genedata[singlegeneset == genedata[,
1], 2], geneid)
genesetmissinggene[genesetid] <- length(missinggene)
genesettotalgenenum[genesetid] <- length(genedata[, 1] ==
singlegeneset && genedata[, 2] %in% geneid)
singlepattern <- pattern[pattern[, 1] == singlegeneset,
]
if (singlepattern[, 3] == "Norm") {
if (singlepattern[, 2] == "High") {
cutoff <- qnorm(1 - singlepattern[, 4], mean(score),
sd(score))
selectsample <- intersect(selectsample, which(score >=
cutoff))
}
else if (singlepattern[, 2] == "Low") {
cutoff <- qnorm(singlepattern[, 4], mean(score),
sd(score))
selectsample <- intersect(selectsample, which(score <
cutoff))
}
else {
stop(paste("Second Column of pattern in", singlegeneset,
"is not correctly given"))
}
}
else if (singlepattern[, 3] == "Quantile") {
if (singlepattern[, 2] == "High") {
cutoff <- quantile(score, 1 - singlepattern[,
4])
selectsample <- intersect(selectsample, which(score >=
cutoff))
}
else if (singlepattern[, 2] == "Low") {
cutoff <- quantile(score, singlepattern[, 4])
selectsample <- intersect(selectsample, which(score <
cutoff))
}
else {
stop(paste("Second Column of pattern in", singlegeneset,
"is not correctly given"))
}
}
else if (singlepattern[, 3] == "Exprs") {
if (singlepattern[, 2] == "High") {
cutoff <- singlepattern[, 4]
selectsample <- intersect(selectsample, which(score >=
cutoff))
}
else if (singlepattern[, 2] == "Low") {
cutoff <- singlepattern[, 4]
selectsample <- intersect(selectsample, which(score <
cutoff))
}
else {
stop(paste("Second Column of pattern in", singlegeneset,
"is not correctly given"))
}
}
else {
stop(paste("Cutoff type pattern of geneset", singlegeneset,
"is not correctly given"))
}
genesetcutoff[genesetid] <- cutoff
}
ExpID <- tab[selectsample, "ExperimentID"]
tmpTypes <- tab[selectsample, "SampleType"]
tabTWO <- table(tab$SampleType)
tabTWO <- names(tabTWO)[tabTWO > 2]
tab <- tab[tab$SampleType %in% tabTWO, ]
ExpID <- ExpID[tmpTypes %in% tab$SampleType]
tmpTypes <- tmpTypes[tmpTypes %in% tab$SampleType]
if (length(tmpTypes) > 0) {
ttT <- table(tmpTypes)
sT <- sum(ttT)
bgT <- table(tab$SampleType)
bgT <- bgT[names(ttT)]
ContextN <- sum(table(tab$SampleType) > 2)
SCORE <- matrix(0, nrow = length(ttT), ncol = 4)
ExperimentID <- rep("0", length(bgT))
for (i in 1:length(bgT)) {
r1c1 <- ttT[i]
r1c2 <- sT - ttT[i]
r2c1 <- bgT[i] - ttT[i]
r2c2 <- length(tab$SampleType) - r1c1 - r1c2 - r2c1
tmpmat <- matrix(c(r1c1, r2c1, r1c2, r2c2), ncol = 2)
SCORE[i, 1] <- fisher.test(tmpmat, alternative = "greater")$p.value
SCORE[i, 2] <- round(((as.numeric(ttT)[i] + sT/length(tab$SampleType))/(as.numeric(bgT)[i] +
1))/(sT/length(tab$SampleType)), 3)
SCORE[i, 3] <- min(SCORE[i, 1] * ContextN, 1)
ExperimentID[i] <- paste(unique(unlist(strsplit(paste(ExpID[tmpTypes ==
names(ttT)[i]], collapse = ";"), ";"))), collapse = ";")
}
FIN <- data.frame(as.numeric(ttT), as.numeric(bgT), SCORE[,
2], SCORE[, 3], rownames(ttT), ExperimentID, stringsAsFactors = F)
colnames(FIN) <- c("Active", "Total", "FoldChange", "Adj.Pvalue",
"SampleType", "ExperimentID")
FIN <- FIN[order(as.numeric(FIN[, "Adj.Pvalue"], -1 *
as.numeric(FIN[, "Active"]), decreasing = FALSE)),
]
FIN <- FIN[as.numeric(FIN[, "Adj.Pvalue"]) <= Pval.co,
]
FIN[, 4] <- signif(FIN[, 4], 3)
if (!is.null(directory)) {
expiddex <- unique(unlist(strsplit(as.character(FIN$ExperimentID),
";")))
for (k in 1:length(expiddex)) {
filepath <- paste0(directory, "/", expiddex[k])
Temp <- tabSearch(expiddex[k], chipdata)
if (nrow(Temp) > 1) {
dir.create(filepath)
GSCAeda(genedata, pattern, chipdata = chipdata,
SearchOutput = Temp, Pval.co = Pval.co, Ordering = "Average",
Title = expiddex[k], outputdir = filepath)
}
}
}
if (is.null(dim(FIN)) | nrow(FIN) == 0) {
message("No significant biological contexts found.")
}
else {
FIN <- cbind(1:nrow(FIN), FIN)
colnames(FIN)[1] <- "Rank"
rownames(FIN) <- 1:nrow(FIN)
}
colnames(activity) <- tabsamplename
return(list(Ranking = FIN, Score = activity, Pattern = pattern,
Cutoff = genesetcutoff, SelectedSample = selectsample,
Totalgene = genesettotalgenenum, Missinggene = genesetmissinggene,
Chipdata = chipdata))
}
else {
stop("No samples show the pattern of interest.\n Try relaxing cutoffs.")
}
}
<bytecode: 0x000000000f7cfbe8>
<environment: namespace:GSCA>
--- function search by body ---
Function GSCA in namespace GSCA has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 ERRORs, 3 NOTEs
See
'C:/Users/biocbuild/bbs-3.11-bioc/meat/GSCA.Rcheck/00check.log'
for details.