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qualitymetrics_script.R
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qualitymetrics_script.R
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################################################################################################
# ANALYSES FOR QUALITY CONTROL #
# #
# Author: Melanie PETERA #
# User: Galaxy #
# Starting date: 04-09-2014 #
# V-1.0: Restriction of old filter script to CV filter #
# V-1.1: Addition of data check #
# V-1.2: Substitution of deletion by addition of indicator variable #
# V-1.3: Handling special characters #
# #
# #
# Input files: dataMatrix ; sampleMetadata ; variableMetadata #
# Output files: dataMatrix ; sampleMetadata ; variableMetadata #
# #
################################################################################################
# Parameters (for dev)
if(FALSE){
ion.file.in <- "test/ressources/inputs/ex_data_IONS.txt" #tab file
meta.samp.file.in <- "test/ressources/inputs/ex_data_PROTOCOLE1.txt" #tab file
meta.ion.file.in <- "test/ressources/inputs/ex_data_METAION.txt" #tab file
## ion.file.out <- "test/ressources/outputs/QCtest_ex_data_IONS.txt" #tab file
meta.samp.file.out <- "test/ressources/outputs/QCtest_ex_data_PROTOCOLE1.txt" #tab file
meta.ion.file.out <- "test/ressources/outputs/QCtest_ex_data_METAION.txt" #tab file
CV <- TRUE ; if(CV){Compa<-TRUE;seuil<-1.25}else{Compa<-NULL;seuil<-NULL}
poolAsPool1L <- FALSE
if(FALSE) { ## Sacuri dataset
## 'example' input dir
exaDirInpC <- "example/input"
ion.file.in <- file.path(exaDirInpC, "dataMatrix.tsv")
meta.samp.file.in <- file.path(exaDirInpC, "sampleMetadata.tsv")
meta.ion.file.in <- file.path(exaDirInpC, "variableMetadata.tsv")
poolAsPool1L <- FALSE
## 'example' output dir
exaDirOutC <- gsub("input", "output", exaDirInpC)
mata.samp.file.out <- file.path(exaDirOutC, "sampleMetadata.tsv")
meta.ion.file_out <- file.path(exaDirOutC, "variableMetadata.tsv")
fig.out <- file.path(exaDirOutC, "figure.pdf")
log.out <- file.path(exaDirOutC, "information.txt")
stopifnot(file.exists(exaDirOutC))
}
}
QualityControl <- function(ion.file.in, meta.samp.file.in, meta.ion.file.in,
CV, Compa, seuil, poolAsPool1L,
ion.file.out, meta.samp.file.out, meta.ion.file.out, fig.out, log.out){
# This function allows to analyse data to check its quality
# It needs 3 datasets: the data matrix, the variables' metadata, the samples' metadata.
# It generates 3 new datasets corresponding to the 3 inputs with additional columns.
#
# Parameters:
# - xxx.in: input files' names
# - xxx.out: output files' names
# - CV: CV calculation yes/no
# | > Compa: comparing pool and sample CVs (TRUE) or simple pool CV calculation (FALSE)
# | > seuil: maximum ratio tolerated between pool and sample CVs or maximum pool CV
# Input -----------------------------------------------------------------------------------
ion.data <- read.table(ion.file.in,sep="\t",header=TRUE,check.names=FALSE, stringsAsFactors = FALSE)
meta.samp.data <- read.table(meta.samp.file.in,sep="\t",header=TRUE,check.names=FALSE, stringsAsFactors = FALSE)
meta.ion.data <- read.table(meta.ion.file.in,sep="\t",header=TRUE,check.names=FALSE, stringsAsFactors = FALSE)
# Error vector
err.stock <- "\n"
# Table match check
table.check <- match3(ion.data,meta.samp.data,meta.ion.data)
check.err(table.check)
# StockID
samp.id <- stockID(ion.data,meta.samp.data,"sample")
ion.data <- samp.id$dataMatrix
meta.samp.data <- samp.id$Metadata
samp.id <- samp.id$id.match
# Function 1: CV calculation --------------------------------------------------------------
# Allows to class ions according to the Coefficient of Variation (CV):
# Compa=TRUE:
# CV of pools and CV of samples are compared (ration between pools' one and samples' one)
# and confronted to a given ration.
# Compa=FALSE:
# only CV of pools are considered ; compared to a given threshold
if(CV){
# Checking the sampleType variable
if(is.null(meta.samp.data$sampleType)){
err.stock <- c(err.stock,"\n-------",
"\nWarning : no 'sampleType' variable detected in sample meta-data !",
"\nCV can not be calculated.\n-------\n")
}else{
if(!("pool"%in%levels(factor(meta.samp.data$sampleType)))){
err.stock <- c(err.stock,"\n-------",
"\nWarning : no 'pool' detected in 'sampleType' variable (sample meta-data) !",
"\nCV can not be calculated.\n-------\n")
}else{
if((!("sample"%in%levels(factor(meta.samp.data$sampleType))))&(Compa)){
err.stock <- c(err.stock,"\n-------",
"\nWarning : no 'sample' detected in 'sampleType' variable (sample meta-data) !",
"\nCV can not be calculated.\n-------\n")
}else{
# Statement
tmp.ion <- data.frame(CV.ind=rep(NA,nrow(ion.data)),CV.samp=rep(NA,nrow(ion.data)),
CV.pool=rep(NA,nrow(ion.data)),ion.data,stringsAsFactors=FALSE)
# CV samples
tmp.samp <- which(colnames(tmp.ion)%in%meta.samp.data[which(meta.samp.data$sampleType=="sample"),1])
tmp.ion$CV.samp <- apply(tmp.ion[,tmp.samp],1,function(x)sd(x, na.rm = TRUE)) / rowMeans(tmp.ion[,tmp.samp], na.rm = TRUE)
tmp.ion$CV.samp[which(apply(tmp.ion[,tmp.samp],1,function(x)sd(x, na.rm = TRUE))==0)] <- 0
# CV pools
tmp.samp <- which(colnames(tmp.ion)%in%meta.samp.data[which(meta.samp.data$sampleType=="pool"),1])
tmp.ion$CV.pool <- apply(tmp.ion[,tmp.samp],1,function(x)sd(x, na.rm = TRUE)) / rowMeans(tmp.ion[,tmp.samp], na.rm = TRUE)
tmp.ion$CV.pool[which(apply(tmp.ion[,tmp.samp],1,function(x)sd(x, na.rm = TRUE))==0)] <- 0
# CV indicator
if(Compa){tmp.ion$CV.ind <- ifelse((tmp.ion$CV.pool)/(tmp.ion$CV.samp)>seuil,0,1)
}else{tmp.ion$CV.ind <- ifelse((tmp.ion$CV.pool)>seuil,0,1)}
# Addition of new columns in meta.ion.data
if(Compa){tmp.ion<-tmp.ion[,c(4,2,3,1,1)]}else{tmp.ion<-tmp.ion[,c(4,3,1,1)]}
tmp.ion[,ncol(tmp.ion)] <- 1:nrow(tmp.ion)
meta.ion.data <- merge(x=meta.ion.data,y=tmp.ion,by.x=1,by.y=1)
meta.ion.data <- meta.ion.data[order(meta.ion.data[,ncol(meta.ion.data)]),][,-ncol(meta.ion.data)]
rownames(meta.ion.data) <- NULL
rm(tmp.ion,tmp.samp)
}}}
} # end if(CV)
## complementary metrics (ET)
datMN <- t(as.matrix(ion.data[, -1]))
colnames(datMN) <- ion.data[, 1]
datMN <- datMN[, meta.ion.data[, 1]] ## in case meta.ion.data has been re-ordered during the CV = TRUE computations
quaLs <- qualityMetricsF(datMN,
meta.samp.data,
meta.ion.data,
poolAsPool1L,
fig.out,
log.out)
meta.samp.data <- quaLs[["samDF"]]
meta.ion.data <- quaLs[["varDF"]]
# Output ----------------------------------------------------------------------------------
# Getting back original identifiers
id.ori <- reproduceID(ion.data,meta.samp.data,"sample",samp.id)
ion.data <- id.ori$dataMatrix
meta.samp.data <- id.ori$Metadata
# Error checking
if(length(err.stock)>1){
stop(err.stock)
}else{
## write.table(ion.data, ion.file.out, sep="\t", row.names=FALSE, quote=FALSE)
write.table(meta.samp.data, meta.samp.file.out, sep="\t", row.names=FALSE, quote=FALSE)
write.table(meta.ion.data, meta.ion.file.out, sep="\t", row.names=FALSE, quote=FALSE)
}
} # end of QualityControl function
# Typical function call
# QualityControl(ion.file.in, meta.samp.file.in, meta.ion.file.in,
# CV, Compa, seuil,
# ion.file.out, meta.samp.file.out, meta.ion.file.out)
qualityMetricsF <- function(datMN,
samDF,
varDF,
pooAsPo1L = TRUE,
fig.pdfC = NULL,
log.txtC = NULL) {
optWrnN <- options()$warn
options(warn = -1)
##------------------------------
## Functions
##------------------------------
allDigF <- function (string) { ## from the Hmisc package (all.digits)
k <- length(string)
result <- logical(k)
for (i in 1:k) {
st <- string[i]
ls <- nchar(st)
ex <- substring(st, 1:ls, 1:ls)
result[i] <- all(match(ex, c("0", "1", "2", "3", "4",
"5", "6", "7", "8", "9", "."), nomatch = 0) > 0)
}
result
}
datPloF <- function() { ## ploting data matrix
thrVn <- c(pvalue=0.001,
poolCv=0.3)
## Constants
marLs <- list(dri = c(2.1, 2.6, 1.1, 1.1),
ima = c(1.1, 2.6, 4.1, 1.1),
msd = c(2.1, 2.6, 1.1, 0.6),
sam = c(3.1, 3.6, 1.1, 0.6),
pca = c(2.6, 3.6, 1.1, 0.6),
sca = c(1.1, 4.1, 4.1, 0.6),
tit = c(0.1, 0.6, 1.1, 0.6))
palHeaVc <- rev(rainbow(ceiling(256 * 1.5))[1:256])
## Functions
axiPreF <- function(valVn,
lenN) {
if(NA %in% valVn) {
warning("NA in valVn")
valVn <- as.vector(na.omit(valVn))
}
if(lenN < length(valVn))
stop("The length of in vector must be inferior to the length of the length parameter.")
if(length(valVn) < lenN)
valVn <- seq(from = min(valVn), to = max(valVn), length.out = lenN)
preValVn <- pretty(valVn)
preLabVn <- preAtVn <- c()
for(n in 1:length(preValVn))
if(min(valVn) < preValVn[n] && preValVn[n] < max(valVn)) {
preLabVn <- c(preLabVn, preValVn[n])
preAtVn <- c(preAtVn, which(abs(valVn - preValVn[n]) == min(abs(valVn - preValVn[n])))[1])
}
return(list(atVn = preAtVn,
labVn = preLabVn))
}
colF <- function(vecVn)
sapply(vecVn,
function(outN) {
if(outN < ploRgeVn[1])
return(palHeaVc[1])
else if(outN > ploRgeVn[2])
return(palHeaVc[256])
else return(palHeaVc[round((outN - ploRgeVn[1]) / diff(ploRgeVn) * 256 + 1)])})
obsColF <- function(typVc) {
## available color palette
palVc <- palette()
## colors for common types are set aside
palVc <- palVc[!(palVc %in% c("black", "red", "green3"))]
## filling in the types with dedicated colors
samTypVc <- sort(unique(samDF[, "sampleType"]))
samColVc <- character(length(samTypVc))
if("blank" %in% samTypVc)
samColVc[grepl("blank", samTypVc)] <- "black"
if("pool" %in% samTypVc)
samColVc[grepl("pool", samTypVc)] <- "red"
if("sample" %in% samTypVc)
samColVc[grepl("sample", samTypVc)] <- "green4"
## filling in the other types
palColI <- 1
palColMaxI <- length(palVc)
while(any(samColVc == "")) {
typToColI <- which(samColVc == "")[1]
if(palColI <= palColMaxI)
samColVc[typToColI] <- palVc[palColI]
else
samColVc[typToColI] <- "gray"
palColI <- palColI + 1
}
names(samColVc) <- samTypVc
samColVc[typVc]
}
par(font = 2,
font.axis = 2,
font.lab = 2,
pch=18)
layout(matrix(c(1, 3, 4, 5, 5,
1, 7, 7, 7, 6,
2, 7, 7, 7, 6),
byrow = TRUE,
nrow = 3),
heights = c(1.8, 1.2, 2.5),
widths = c(3.5, 1.8, 2.8, 1, 0.8))
## Colors
##-------
if("sampleType" %in% colnames(samDF)) {
obsColVc <- obsColF(samDF[, "sampleType"])
} else
obsColVc <- rep("black", nrow(samDF))
## PCA and Hotelling ellipse
##--------------------------
vVn <- getPcaVarVn(ropLs)
vRelVn <- vVn / ncol(datMN)
par(mar = marLs[["pca"]])
plot(ropScoreMN,
type = "n",
xlab = "",
ylab = "",
xlim = range(ropScoreMN[, 1]) * 1.1)
mtext(paste("t1 (", round(vRelVn[1] * 100), "%)", sep = ""),
cex = 0.7,
line = 2,
side = 1)
mtext(paste("t2 (", round(vRelVn[2] * 100), "%)", sep = ""),
cex = 0.7,
las = 0,
line = 2,
side = 2)
abline(h = 0, lty = "dashed")
abline(v = 0, lty = "dashed")
radVn <- seq(0, 2 * pi, length.out = 100)
hotFisN <- hotN * qf(1 - thrVn["pvalue"], 2, n - 2)
lines(sqrt(var(ropScoreMN[, 1]) * hotFisN) * cos(radVn),
sqrt(var(ropScoreMN[, 2]) * hotFisN) * sin(radVn))
text(ropScoreMN[, 1],
ropScoreMN[, 2],
cex = 0.7,
col = obsColVc,
labels = rownames(datMN))
if("sampleType" %in% colnames(samDF)) {
obsColVuc <- obsColVc[sort(unique(names(obsColVc)))]
legOrdVc <- c("blank", paste0("pool", 8:1), "pool", "other", "sample")
obsColVuc <- obsColVuc[legOrdVc[legOrdVc %in% names(obsColVuc)]]
text(rep(par("usr")[1], times = length(obsColVuc)),
par("usr")[3] + (0.97 - length(obsColVuc) * 0.03 + 1:length(obsColVuc) * 0.03) * diff(par("usr")[3:4]),
col = obsColVuc,
font = 2,
labels = names(obsColVuc),
pos = 4)
}
## Missing/low intensities and decile values
##------------------------------------------
par(mar = marLs[["sam"]])
plot(missZscoVn,
deciZscoMaxVn,
type = "n",
xlab = "",
ylab = "",
xlim = c(min(missZscoVn),
max(missZscoVn) + 0.5))
mtext("amount of missing values (z-score)",
cex = 0.7,
line = 2,
side = 1)
mtext("deciles (zscore)",
cex = 0.7,
las = 0,
line = 2,
side = 2)
abline(h = qnorm(1 - thrVn["pvalue"] / 2) * c(-1, 1), lty = "dashed")
abline(v = qnorm(1 - thrVn["pvalue"] / 2) * c(-1, 1), lty = "dashed")
text(missZscoVn,
deciZscoMaxVn,
cex = 0.7,
col = obsColVc,
labels = rownames(datMN))
## tit: Title
##-----------
par(mar = marLs[["tit"]])
plot(0:1, bty = "n", type = "n", xaxt = "n", yaxt = "n", xlab = "", ylab = "")
text(1.5, 1, cex = 1.3, labels = "Quality Metrics")
text(1, 0.85, adj=0, cex = 1.1, labels = paste0("NAs: ",
round(length(which(is.na(c(datMN)))) / cumprod(dim(datMN))[2] * 100), "%"))
text(1, 0.75, adj=0, cex = 1.1, labels = paste0("0 values: ",
round(sum(abs(datMN) < epsN, na.rm=TRUE) / cumprod(dim(datMN))[2] * 100, 2), "%"))
text(1, 0.65, adj=0, cex = 1.1, labels = paste0("min: ", signif(min(datMN, na.rm=TRUE), 2)))
text(1, 0.55, adj=0, cex = 1.1, labels = paste0("median: ", signif(median(datMN, na.rm=TRUE), 2)))
text(1, 0.45, adj=0, cex = 1.1, labels = paste0("mean: ", signif(mean(datMN, na.rm=TRUE), 2)))
text(1, 0.35, adj=0, cex = 1.1, labels = paste0("max: ", signif(max(datMN, na.rm=TRUE), 2)))
if("sampleType" %in% colnames(samDF) &&
"pool" %in% samDF[, "sampleType"]) {
poolCvNanVl <- is.nan(varDF[, "pool_CV"])
text(1,
0.25,
adj=0, cex = 1.1,
labels = paste0("pool CV < ",
round(thrVn["poolCv"] * 100), "%: ",
round(sum(varDF[!poolCvNanVl, "pool_CV", drop = FALSE] < thrVn["poolCv"]) / nrow(varDF) * 100),
"%"))
}
text(1, 0.1, adj=0, labels = paste0("Thresholds used in plots:"))
text(1, 0, adj=0, labels = paste0(" p-value = ", thrVn["pvalue"]))
## dri: Analytical drift
##----------------------
par(mar = marLs[["dri"]])
## ordering
driDatMN <- datMN
driSamDF <- samDF
driSamDF[, "ordIniVi"] <- 1:nrow(driDatMN)
if("injectionOrder" %in% colnames(driSamDF)) {
if("batch" %in% colnames(driSamDF))
ordVi <- order(driSamDF[, "batch"],
driSamDF[, "injectionOrder"])
else
ordVi <- order(driSamDF[, "injectionOrder"])
} else
ordVi <- 1:nrow(driDatMN)
driDatMN <- driDatMN[ordVi, ]
driSamDF <- driSamDF[ordVi, ]
driColVc <- rep("black", nrow(driDatMN))
if("sampleType" %in% colnames(driSamDF))
driColVc <- obsColF(driSamDF[, "sampleType"])
plot(rowSums(driDatMN, na.rm=TRUE),
col = driColVc,
pch = 18,
xlab = "",
ylab = "")
mtext("injection order",
cex = 0.7,
line = 2,
side = 1)
mtext("Sum of intens. for all variables",
cex = 0.7,
line = 2,
side = 2)
## msd: Sd vs Mean plot
##---------------------
par(mar = marLs[["msd"]])
plot(apply(datMN, 2, function(y) mean(y, na.rm = TRUE)),
apply(datMN, 2, function(y) sd(y, na.rm = TRUE)),
pch = 18,
xlab = "",
ylab = "")
mtext("mean",
cex = 0.7,
line = 2,
side = 1)
mtext("sd",
cex = 0.7,
line = 2,
side = 2)
## sca-6: Color scale
##-------------------
par(mar = marLs[["sca"]])
ylimVn <- c(0, 256)
ybottomVn <- 0:255
ytopVn <- 1:256
plot(x = 0,
y = 0,
font.axis = 2,
font.lab = 2,
type = "n",
xlim = c(0, 1),
ylim = ylimVn,
xlab = "",
ylab = "",
xaxs = "i",
yaxs = "i",
xaxt = "n",
yaxt = "n")
rect(xleft = 0,
ybottom = ybottomVn,
xright = 1,
ytop = ytopVn,
col = palHeaVc,
border = NA)
eval(parse(text = paste("axis(at = axiPreF(c(ifelse(min(datMN, na.rm = TRUE) == -Inf, yes = 0, no = min(datMN, na.rm = TRUE)) , max(datMN, na.rm = TRUE)), 256)$atVn,
font = 2,
font.axis = 2,
labels = axiPreF(c(ifelse(min(datMN, na.rm = TRUE) == -Inf, yes = 0, no = min(datMN, na.rm = TRUE)), max(datMN, na.rm = TRUE)), 256)$labVn,
las = 1,
lwd = 2,
lwd.ticks = 2,
side = 2,
xpd = TRUE)", sep = "")))
arrows(par("usr")[1],
par("usr")[4],
par("usr")[1],
par("usr")[3],
code = 0,
lwd = 2,
xpd = TRUE)
## ima: Image
##-----------
par(mar = marLs[["ima"]])
ploRgeVn <- range(datMN, na.rm = TRUE)
imaMN <- t(datMN)[, rev(1:nrow(datMN)), drop = FALSE]
image(x = 1:nrow(imaMN),
y = 1:ncol(imaMN),
z = imaMN,
col = palHeaVc,
font.axis = 2,
font.lab = 2,
xaxt = "n",
yaxt = "n",
xlab = "",
ylab = "")
if(length(rownames(datMN)) == 0) {
rowNamVc <- rep("", times = nrow(datMN))
} else
rowNamVc <- rownames(datMN)
if(length(colnames(datMN)) == 0) {
colNamVc <- rep("", times = ncol(datMN))
} else
colNamVc <- colnames(datMN)
xlaVc <- paste(paste(rep("[", 2),
c(1, nrow(imaMN)),
rep("] ", 2),
sep = ""),
rep("\n", times = 2),
c(colNamVc[1], tail(colNamVc, 1)),
sep = "")
for(k in 1:2)
axis(side = 3,
hadj = c(0, 1)[k],
at = c(1, nrow(imaMN))[k],
cex = 0.8,
font = 2,
labels = xlaVc[k],
line = -0.5,
tick = FALSE)
ylaVc <- paste(paste(rep("[", times = 2),
c(ncol(imaMN), 1),
rep("]", times = 2),
sep = ""),
rep("\n", times = 2),
c(tail(rowNamVc, 1), rowNamVc[1]),
sep = "")
for(k in 1:2)
axis(side = 2,
at = c(1, ncol(imaMN))[k],
cex = 0.8,
font = 2,
hadj = c(0, 1)[k],
labels = ylaVc[k],
las = 0,
line = -0.5,
lty = "blank",
tick = FALSE)
box(lwd = 2)
}
zScoreF <- function(x) {
sdxN <- sd(x, na.rm = TRUE)
if(sdxN < epsN)
return(rep(0, length(x)))
else
return((x - mean(x, na.rm = TRUE)) / sdxN)
}
## Option
##-------
strAsFacL <- options()$stringsAsFactors
options(stingsAsFactors = FALSE)
## Constants
##----------
modNamC <- "Quality Metrics" ## module name
epsN <- .Machine[["double.eps"]] ## [1] 2.22e-16
##------------------------------
## Start
##------------------------------
if(!is.null(log.txtC)) {
sink(log.txtC)
cat("\nStart of the '", modNamC, "' Galaxy module call: ",
format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="")
}
## Checking the numerical type of the dataMatrix
##----------------------------------------------
if(mode(datMN) != "numeric") {
sink()
stop("dataMatrix is not of numeric type;\ncheck your tables with the Check Format module\n",
call. = FALSE)
}
## Re-ordering dataMatrix samples if need (internally only)
##---------------------------------------------------------
if(!identical(rownames(datMN), samDF[, 1])) {
cat("\n\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! WARNING !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\nThe sample order is not identical in dataMatrix and sampleMetadata;\nRe-ordering of the dataMatrix samples will be performed internally in this module\nfor the computation of the metrics,\nwithout changing the orders in the sampleMetadata output;\n\nTo get a re-ordered dataMatrix as output, please use the Check Format module\n\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
datMN <- datMN[samDF[, 1], , drop = FALSE]
}
## Description
##------------
cat("\n\nData description:\n\n", sep = "")
cat("observations:", nrow(datMN), "\n")
cat("variables:", ncol(datMN), "\n")
cat("missing:", sum(is.na(datMN)), "\n")
cat("0 values (%):",
sum(abs(datMN) < epsN, na.rm=TRUE) / cumprod(dim(datMN))[2] * 100, "\n")
cat("min:", min(datMN, na.rm=TRUE), "\n")
cat("mean:", signif(mean(datMN, na.rm=TRUE), 2), "\n")
cat("median:", signif(median(datMN, na.rm=TRUE), 2), "\n")
cat("max:", signif(max(datMN, na.rm=TRUE), 2), "\n")
if("sampleType" %in% colnames(samDF)) {
cat("\nSample types:\n", sep = "")
print(table(samDF[, "sampleType"]))
cat("\n", sep="")
}
##------------------------------
## Variable metrics
##------------------------------
## 'blank' observations
if("sampleType" %in% colnames(samDF) && "blank" %in% samDF[, "sampleType"]) {
cat("\nVariables: Blank mean, sd, and CV\n", sep="")
blkVl <- samDF[, "sampleType"] == "blank"
if(sum(blkVl) == 1)
varDF[, "blank_mean"] <- datMN[blkVl, ]
else
varDF[, "blank_mean"] <- apply(datMN[blkVl, , drop=FALSE], 2, function(varVn) mean(varVn, na.rm=TRUE))
if(sum(blkVl) == 1)
varDF[, "blank_sd"] <- rep(0, nrow(varDF))
else
varDF[, "blank_sd"] <- apply(datMN[blkVl, , drop=FALSE], 2, function(varVn) sd(varVn, na.rm=TRUE))
varDF[, "blank_CV"] <- varDF[, "blank_sd"] / varDF[, "blank_mean"]
}
## 'sample' observations
if("sampleType" %in% colnames(samDF) && "sample" %in% samDF[, "sampleType"]) {
cat("\nVariables: Sample mean, sd, and CV\n", sep="")
samVl <- samDF[, "sampleType"] == "sample"
if(sum(samVl) == 1)
varDF[, "sample_mean"] <- datMN[samVl, ]
else
varDF[, "sample_mean"] <- apply(datMN[samVl, , drop=FALSE], 2, function(varVn) mean(varVn, na.rm=TRUE))
if(sum(samVl) == 1)
varDF[, "sample_sd"] <- rep(0, nrow(varDF))
else
varDF[, "sample_sd"] <- apply(datMN[samVl, , drop=FALSE], 2, function(varVn) sd(varVn, na.rm=TRUE))
varDF[, "sample_CV"] <- varDF[, "sample_sd"] / varDF[, "sample_mean"]
}
## 'blank' mean / 'sample' mean ratio
if(all(c("blank_mean", "sample_mean") %in% colnames(varDF))) {
cat("\nVariables: Blank mean over sample mean\n", sep="")
varDF[, "blankMean_over_sampleMean"] <- varDF[, "blank_mean"] / varDF[, "sample_mean"]
}
## 'pool' observations
if("sampleType" %in% colnames(samDF) && "pool" %in% samDF[, "sampleType"]) {
cat("\nVariables: Pool mean, sd, and CV\n", sep="")
pooVl <- samDF[, "sampleType"] == "pool"
if(sum(pooVl) == 1)
varDF[, "pool_mean"] <- datMN[pooVl, ]
else
varDF[, "pool_mean"] <- apply(datMN[pooVl, , drop=FALSE], 2, function(varVn) mean(varVn, na.rm=TRUE))
if(sum(pooVl) == 1)
varDF[, "pool_sd"] <- rep(0, nrow(varDF))
else
varDF[, "pool_sd"] <- apply(datMN[pooVl, , drop=FALSE], 2, function(varVn) sd(varVn, na.rm=TRUE))
varDF[, "pool_CV"] <- varDF[, "pool_sd"] / varDF[, "pool_mean"]
}
## 'pool' CV / 'sample' CV ratio
if(all(c("pool_CV", "sample_CV") %in% colnames(varDF))) {
cat("\nVariables: Pool CV over sample CV\n", sep="")
varDF[, "poolCV_over_sampleCV"] <- varDF[, "pool_CV"] / varDF[, "sample_CV"]
}
## 'pool' dilutions
if("sampleType" %in% colnames(samDF) && any(grepl("pool.+", samDF[, "sampleType"]))) {
pooVi <- grep("pool.*", samDF[, "sampleType"]) ## pool, pool2, pool4, poolInter, ...
pooNamVc <- samDF[pooVi, "sampleType"]
if(pooAsPo1L) {
pooNamVc[pooNamVc == "pool"] <- "pool1" ## 'pool' -> 'pool1'
} else {
pooVl <- pooNamVc == "pool"
pooVi <- pooVi[!pooVl]
pooNamVc <- pooNamVc[!pooVl]
}
pooDilVc <- gsub("pool", "", pooNamVc)
pooDilVl <- sapply(pooDilVc, allDigF)
if(sum(pooDilVl)) {
cat("\nVariables: Pool dilutions\n", sep="")
pooNamVc <- pooNamVc[pooDilVl] ## for the plot
pooVi <- pooVi[pooDilVl]
dilVn <- 1 / as.numeric(pooDilVc[pooDilVl])
varDF[, "poolDil_correl"] <- apply(datMN[pooVi, , drop=FALSE], 2,
function(varVn) cor(dilVn, varVn))
varDF[, "poolDil_pval"] <- apply(datMN[pooVi, , drop=FALSE], 2,
function(varVn) cor.test(dilVn, varVn)[["p.value"]])
}
}
##------------------------------
## Sample metrics
##------------------------------
## Hotelling: p-value associated to the distance from the center in the first PCA score plane
cat("\nObservations: Hotelling ellipse\n", sep="")
ropLs <- opls(datMN, predI = 2, plotL = FALSE, printL = FALSE)
ropScoreMN <- getScoreMN(ropLs)
invCovScoMN <- solve(cov(ropScoreMN))
n <- nrow(datMN)
hotN <- 2 * (n - 1) * (n^2 - 1) / (n^2 * (n - 2))
hotPvaVn <- apply(ropScoreMN,
1,
function(x)
1 - pf(1 / hotN * t(as.matrix(x)) %*% invCovScoMN %*% as.matrix(x), 2, n - 2))
samDF[, "hotelling_pval"] <- hotPvaVn
## p-value associated to number of missing values
cat("\nObservations: Missing values\n", sep="")
missZscoVn <- zScoreF(apply(datMN,
1,
function(rowVn) {
sum(is.na(rowVn))
}))
samDF[, "missing_pval"] <- sapply(missZscoVn, function(zscoN) 2 * (1 - pnorm(abs(zscoN))))
## p-value associated to the deciles of the profiles
cat("\nObservations: Profile deciles\n", sep="")
deciMN <- t(as.matrix(apply(datMN,
1,
function(x) quantile(x, 0.1 * 1:9, na.rm = TRUE))))
deciZscoMN <- apply(deciMN, 2, zScoreF)
deciZscoMaxVn <- apply(deciZscoMN, 1, function(rowVn) rowVn[which.max(abs(rowVn))])
samDF[, "decile_pval"] <- sapply(deciZscoMaxVn, function(zscoN) 2 * (1 - pnorm(abs(zscoN))))
##------------------------------
## Figure
##------------------------------
cat("\nPlotting\n")
if(!is.null(fig.pdfC)) {
pdf(fig.pdfC, width=11, height=7)
} else
dev.new(width=11, height=7)
datPloF()
if(!is.null(fig.pdfC))
dev.off()
##------------------------------
## End
##------------------------------
if(!is.null(log.txtC)) {
cat("\nEnd of '", modNamC, "' Galaxy module call: ",
as.character(Sys.time()), "\n", sep = "")
cat("\n\n\n============================================================================")
cat("\nAdditional information about the call:\n")
cat("\n1) Parameters:\n")
print(args)
cat("\n2) Session Info:\n")
sessioninfo <- sessionInfo()
cat(sessioninfo$R.version$version.string,"\n")
cat("Main packages:\n")
for (pkg in names(sessioninfo$otherPkgs)) { cat(paste(pkg,packageVersion(pkg)),"\t") }; cat("\n")
cat("Other loaded packages:\n")
for (pkg in names(sessioninfo$loadedOnly)) { cat(paste(pkg,packageVersion(pkg)),"\t") }; cat("\n")
cat("============================================================================\n")
sink()
}
options(stingsAsFactors = strAsFacL)
options(warn = optWrnN)
return(list(samDF=samDF,
varDF=varDF))
} ## qualityMetricsF