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devel.R
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devel.R
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guiHandler_ScatterPlotly_Data <- function(h,...){
if(is.null(htm@settings@columns$experiment)) {
gmessage("You need to specify the experiment and treatment columns [Main..Configure..Assay columns]")
return(NULL)
}
w <- gwindow("Scatter Plotly", visible = F)
print("Scatter Plot")
if (htmGetListSetting(htm,"visualisation","jitterPlot_datatype",gui=T)=="None selected") {
htmSetListSetting(htm,"visualisation","jitterPlot_datatype","images",gui=T)
}
columns <- c("None selected", sort(colnames(htm@data)))
experiments <- sort(unique(htm@data[[htm@settings@columns$experiment]]))
treatments <- sort(unique(htm@data[[htm@settings@columns$treatment]]))
gp <- ggroup(horizontal = T, container=w)
glabel("x axis: ", container=gp)
cx <- gcombobox(columns,
selected = htmGetListSetting(htm,"visualisation","scatterPlotX",gui=T), container=gp,
handler = function(h,...){
htmSetListSetting(htm, "visualisation","scatterPlotX",svalue(h$obj),gui=T)
})
gp <- ggroup(horizontal = T, container=w)
glabel("y axis: ", container=gp)
cy <- gcombobox(columns,
selected = htmGetListSetting(htm,"visualisation","scatterPlotY",gui=T), container=gp,
handler = function(h,...){
htmSetListSetting(htm,"visualisation","scatterPlotY",svalue(h$obj),gui=T)
})
gp <- ggroup(horizontal = T, container=w)
glabel("Experiment selection: ", container=gp)
guiExpSubset <- gcombobox(c("None selected", experiments),
selected = htmGetListSetting(htm,"visualisation","scatterPlotExpSubset",gui=T),
container = gp,
handler = function(h,...){
htmSetListSetting(htm,"visualisation","scatterPlotExpSubset",svalue(h$obj),gui=T)
})
glabel(" ", container=w)
gui_AddRemoveVectorSetting(setting="visualisation$treatmentSelectionForPlotting",
name=" Treatment selection: ",
choices = c("None selected", treatments),
container = w)
gp <- ggroup(horizontal = T, container=w)
glabel("color by: ", container=gp)
gcombobox(columns,
selected = htmGetListSetting(htm,"visualisation","scatterPlotColor",gui=T), container=gp,
handler = function(h,...){
htmSetListSetting(htm,"visualisation","scatterPlotColor",svalue(h$obj),gui=T)
})
gp <- ggroup(horizontal = T, container=w)
glabel("aggregate by: ", container=gp)
gcombobox(columns,
selected = htmGetListSetting(htm,"visualisation","scatterPlotAggregate",gui=T), container=gp,
handler = function(h,...){
htmSetListSetting(htm,"visualisation","scatterPlotAggregate",svalue(h$obj),gui=T)
})
glabel(" ", container=w)
gp <- ggroup(horizontal = T, container=w)
gbutton("Show plot", container = gp, handler = function(h,...) {
print("sourcing")
htm <- get("htm", envir = globalenv())
htm@settings@visualisation$cx = svalue(cx)
htm@settings@visualisation$cy = svalue(cy)
assign("htm", htm, envir = globalenv())
print(shinyApp(ui = ui_scatter_plot, server = server_scatter_plot))
#htmScatterPlotly_Data(get("htm", envir = globalenv()),
# svalue(cx), svalue(cy),
# experimentSubset = svalue(guiExpSubset),
# treatmentSubset = htmGetVectorSettings("visualisation$treatmentSelectionForPlotting"),
# aggregate = htmGetVectorSettings("visualisation$scatterPlotAggregate"),
# colorize = htmGetVectorSettings("visualisation$scatterPlotColor"),
# newdev = T)
})
gbutton("Zoom", container = gp, handler = function(h, ...){
loc = locator(n=2)
htmScatterPlot_Data(get("htm", envir = globalenv()),
svalue(cx), svalue(cy), .xlim=sort(loc$x),.ylim=sort(loc$y),
treatmentSubset = htmGetVectorSettings("visualisation$treatmentSelectionForPlotting"),
aggregate = htmGetVectorSettings("visualisation$scatterPlotAggregate"),
colorize = htmGetVectorSettings("visualisation$scatterPlotColor"),
newdev = F)
})
gbutton("Click & View", container = gp, handler = function(h, ...){
if(dev.cur()==1) {
print("No plot open.")
} else {
htmScatterPlot_Data(get("htm", envir = globalenv()),
svalue(cx), svalue(cy), .xlim=sort(loc$x),.ylim=sort(loc$y),
treatmentSubset = htmGetVectorSettings("visualisation$treatmentSelectionForPlotting"),
aggregate = htmGetVectorSettings("visualisation$scatterPlotAggregate"),
colorize = htmGetVectorSettings("visualisation$scatterPlotColor"),
newdev=F , action="click")
}
})
gbutton(" Options ", container = gp, handler = guiHandler_ScatterPlot_Options)
#obj <- gbutton("Zoom (not working yet)", editable=FALSE, container = gp, handler = handler_zoomJitterPlot )
#obj <- gbutton("Click and view image", editable=TRUE, container = gp, handler = handler_showImageJitterPlot )
visible(w) <- T
}
scatterLabelPlot_treatFeat <- function(d, cX, cY, subset="None selected", exclude="None selected", xlab="", ylab="") {
#channels <- unique(unlist(lapply(strsplit(rownames(d),"_"), function(x) {x[1]})))
#pdf(file = "/Users/tischi/Desktop/multiScatter.pdf") #, width = 500, height = 500)
#for (channel in channels) {
ds <- d
# subset = channel
# print(channel)
if(subset != "None selected") {
ids = which(grepl(subset,rownames(d)))
ds <- ds[ids,]
}
if(exclude != "None selected") {
print("excluding..")
ids = which(grepl(exclude,rownames(d)))
print(ids)
ds <- ds[-ids,]
}
lables = rownames(ds)
x = ds[[cX]]
y = ds[[cY]]
plot(x, y, #xlab=cX, ylab=cY,
main = "", type = "n",
xlim = c(0,max(x)*1.2),
xlab = xlab,
ylab = ylab
) #,
text(x, y, labels = lables, cex=.7)
#}
#dev.off()
}
plotHeatmap_treatFeat <- function(d,LUT.min,LUT.max,subset="None selected",rotate = F, rowSubset="None selected", readout="") {
if(subset != "None selected") {
ids = which(grepl(subset,colnames(d)))
d <- d[,ids]
}
d$treatment <- NULL
d$date <- NULL
if(rowSubset[1] != "None selected") {
ids = vector(length=0)
for(rs in rowSubset) {
#print(rs)
idsNew = which(grepl(rs,rownames(d)))
print(idsNew)
ids = c(ids,idsNew)
}
#print(ids)
d <- d[ids,]
}
#plot(x = p$col, y = p$row, col=colpal[p$val],
# xaxt = "n", yaxt = "n", # xaxs = "i",
# pch = 15, cex = 5, xlab="", ylab="")
featureNames = unlist(lapply(strsplit(colnames(d),readout), function(x) {x[2]}))
#featureNames = unlist(lapply(strsplit(featureNames,"xx"), function(x) {paste0(x[1],x[3])}))
featureNames <- gsub("_NA","",featureNames)
featureNames <- gsub("NA","",featureNames)
#featureNames <- gsub("Children","_Object",featureNames)
featureNames <- gsub("Sum__AreaShape_Area","_Object__TotalArea",featureNames)
#featureNames <- gsub("_Intensity","",featureNames)
featureNames <- gsub("StdIntensity","IntensityVariation",featureNames)
featureNames <- gsub("MeanIntensity_LocalVar","LocalTexture",featureNames)
featureNames <- gsub("NormDistance__Nucleus","NormDistToNuclearPeriphery",featureNames)
#featureNames <- gsub("Mean_Cells_Mean_MaxGolgi_AreaShape_FormFactor","Compactness",featureNames)
#featureNames <- gsub("Mean_Cells_Children_Golgi_Count","Fragmentation",featureNames)
#featureNames <- gsub("Mean_Cells_Intensity_IntegratedIntensity_GolgiDotsMasked","Vesicularity",featureNames)
#featureNames <- gsub("Mean_Cells_Intensity_IntegratedIntensity_GolgiLinesMasked","Tubularity",featureNames)
#featureNames <- gsub("Mean_Cells_Intensity_IntegratedIntensity_GolgiDiffuse","Diffuse",featureNames)
# general replacements
featureNames <- gsub("Masked","",featureNames)
featureNames <- gsub("Mean_Cells_","",featureNames)
colnames(d) <- featureNames
d <- d[,order(colnames(d), decreasing = TRUE)]
rownames(d) <- gsub("8h","_08h",rownames(d))
rownames(d) <- gsub("24h","_24h",rownames(d))
rownames(d) <- gsub("Control","AAAA",rownames(d))
d <- d[order(rownames(d)),]
rownames(d) <- gsub("AAAA","Control",rownames(d))
#print(d)
p <- convertMatrixToVectorList(d)
#print(length(p$val))
# rescale values to 8-bit LUT
p$val = scaleForDisplay(p$val,LUT.min,LUT.max)
#print(length(p$val))
colpal <- colorRampPalette(c("blue","white","red"))(256)
#print(length(colpal))
#dev.new()
#print(p$val)
#print(length(colpal[p$val]))
if(rotate) {
par(mar=c(15,15,1,1), bg="grey") # bg="white"
x = p$row
y = p$col
axis1labels = rownames(d)
axis2labels = colnames(d)
cex1 = 1
cex2 = 1
} else {
par(mar=c(15,15,1,1), bg="grey") # bg="white"
x = p$col
y = p$row
axis1labels = colnames(d)
axis2labels = rownames(d)
cex2 = 1
cex1 = 1
}
plot(x = x, y = y,
xaxt = "n", yaxt = "n",
pch = 15, xlab="", ylab="", cex = 3, xlim=c(0,length(axis1labels)+1), col=colpal[p$val])
#print(length(axis1labels))
#print(length(axis2labels))
#points(x = x, y = y, col=colpal[p$val],
# pch = 15, cex = 2.6)
axis(1, at=1:length(axis1labels), labels=axis1labels, las=2, cex.axis = cex1)
axis(2, at=1:length(axis2labels), labels=axis2labels, las=1, cex.axis = cex2)
abline(v = seq(1:84), col="lightgray", lty = "dotted")
# Now plot the x axis, but without labels
# axis(1, at=seq(1, 10, by=2), labels = FALSE)
# Now draw the textual axis labels
#text(seq(1, ncol(d), by = 1), par("usr")[3] - 0.0,
# labels = featureNames,
# srt = 45, pos = 1, xpd = TRUE)
}
htmScatterPlotly_Data <- function(htm, cx, cy, .xlim=NA, .ylim=NA, colorize="None selected", aggregate = "None selected", experimentSubset="None selected", treatmentSubset="None selected", newdev=T, action="plot") {
print("")
print("")
print("Scatter Plotly")
data <- htm@data
if(experimentSubset[1] != "None selected") data <- subset(data, data[[htm@settings@columns$experiment]] %in% experimentSubset)
if(treatmentSubset[1] != "None selected") data <- subset(data, data[[htm@settings@columns$treatment]] %in% treatmentSubset)
qc <- data$HTM_qc
treatments <- data[[htm@settings@columns$treatment]]
experiments <- data[[htm@settings@columns$experiment]]
if(aggregate != "None selected") {
vx <- tapply(htm@data[[cx]], htm@data[[aggregate]], mean, na.rm=TRUE)
vy <- tapply(htm@data[[cy]], htm@data[[aggregate]], mean, na.rm=TRUE)
} else {
vx <- data[[cx]]
vy <- data[[cy]]
}
if( colorize != "None selected") {
if(aggregate != "None selected") {
.colors <- tapply(htm@data[[colorize]], htm@data[[aggregate]], function(z) {z[1]})
} else {
.colors = data[[colorize]]
}
n = length(unique(data[[colorize]]))
if(n<=8) {
col_pal <- brewer.pal(8,"Set2")
} else {
col_pal <- rainbow(n)
}
.colors = col_pal[factor(.colors)]
} else {
.colors = rep(1, length(vx))
}
if(!is.null(qc)) {
print(" ..qc column exists!")
if(htmGetListSetting(htm,"visualisation","plotting_showQCfailData_TF")==T) { # label data points that did not pass QC
pchQC = ifelse(qc==1, 16, 4)
} else { # remove data points that did not pass QC
print(" not showing data points that failed QC")
vx = vx[which(qc==1)]
vy = vy[which(qc==1)]
data = data[which(qc==1),]
.colors = .colors[which(qc==1)]
treatments = treatments[which(qc==1)]
qc <- rep(1,length(vx))
pchQC = rep(16, length(vx))
}
} else {
print(" ..no qc column => show all data")
pchQC = rep(16,length(vx))
}
if(action=="plot") {
#factors <- factor(treatments, levels=unique(treatments))
#palette(rainbow(length(unique(treatments))))
#if(newdev){
# dev.new()
#}
dotsize = 0.75
#op <- par(mar = c(10,10,4,2) + 0.1)
if(htmGetListSetting(htm,"visualisation","scatterPlot_scaleFromZero_TF")==T) {
.ylim = c(0, max(vy))
.xlim = c(0, max(vx))
}
# actual plotting
plotTitle = ""
if(experimentSubset[1] != "None selected") {
plotTitle = experimentSubset[1]
} else {
plotTitle = "All experiments"
}
if(treatmentSubset[1] != "None selected") {
plotTitle = paste(plotTitle,paste(treatmentSubset,collapse=" "),sep="\n")
} else {
plotTitle = paste(plotTitle,"All treatments",sep="\n")
}
if( colorize != "None selected") {
p <- plot_ly(x=vx, y=vy, mode = "markers", text=treatments, color=.colors)
} else {
p <- plot_ly(x=vx, y=vy, mode = "markers", text=treatments)
}
p <- layout(p, title = plotTitle, xaxis = list(title = cx), yaxis = list(title = cy))
print(p)
if( htmGetListSetting(htm,"visualisation","scatterPlot_showTreatmentMean_TF")==T) {
factors <- factor(treatments, levels=unique(treatments))
# compute stats for plotting
vxMean = tapply(vx[which(qc==1)], factors[which(qc==1)], function(z) {mean(z,na.rm=T)})
vyMean = tapply(vy[which(qc==1)], factors[which(qc==1)], function(z) {mean(z,na.rm=T)})
colorsMean = tapply(.colors[which(qc==1)], factors[which(qc==1)], function(z) {z[1]})
points(vxMean, vyMean, pch = 16, type = type, cex = 5*dotsize, main="", col=colorsMean)
cat("\nMean values:\n")
print(vxMean)
print(vyMean)
}
return(0)
linefit = FALSE
if(linefit) {
fit <- lm(vy~vx)
print(fit)
yfit = predict(fit, list(vx=vx))
lines(vx,yfit,col="red",lwd=2)
#fit <- lm(vx~vy)
#print(fit)
#xfit = predict(fit, list(vy=vy))
#lines(xfit,vy,col="blue",lwd=2)
#pcaFit(vx,vy)
}
}
if(action=="click") {
print("")
i <- identify(vx, vy, n = 1, plot = FALSE)
print(paste(cx,"=",data[i,cx]))
print(paste(cy,"=",data[i,cy]))
print(paste("treatment =",treatments[i]))
htmShowDataFromRow(htm, data, i)
}
}
convertMatrixToVectorList <- function(m) {
p = list()
i = 1
for (ir in seq(1,nrow(m))) {
for (ic in seq(1,ncol(m))) {
p$col[i] = ic
p$row[i] = ir
p$val[i] = m[ir,ic]
i=i+1
}
}
return(p)
}
server_scatter_plot <- function(input, output, session) {
output$plot <- renderPlotly({
vx <- htm@data[[htm@settings@visualisation$cx]]
vy <- htm@data[[htm@settings@visualisation$cy]]
plot_ly(x = vx, y = vy, mode = "markers")
layout(p, xaxis = list(title = ""),
yaxis = list(title = ""))
})
output$info <- renderPrint({
print(htm@settings@visualisation$cx)
print(htm@settings@visualisation$cy)
})
output$selection <- renderPrint({
s <- event_data("plotly_click")
if (length(s) == 0) {
"Click on a data point!"
} else {
print("You selected:")
print(s)
i = s[['pointNumber']]
print(data[i,])
htmShowDataFromRow(htm,data,i)
#print("Launching Fiji now....")
#system(fiji_binary,wait=F)
}
})
}
ui_scatter_plot <- fluidPage(
mainPanel(
plotlyOutput("plot")
),
verbatimTextOutput("selection"),
verbatimTextOutput("info")
)
htmMaxDeviationPerChannel <- function(treatFeat) {
treatFeat = htm@other$treatFeat
treatFeat[apply(treatFeat, 2, Negate(is.finite))] <- NA
treatFeat<-treatFeat[,-which(grepl("Max",colnames(treatFeat)))]
#treatFeat<-treatFeat[,which(grepl("IntegratedIntensity_",colnames(treatFeat)))]
treatFeat<-treatFeat[,which(grepl("Texture_",colnames(treatFeat)))]
idsChannels = split(1:ncol(treatFeat), unlist(lapply(strsplit(colnames(treatFeat),"_"), function(x) {x[3]})))
print(idsChannels)
for (ichannel in 1:nrow(treatFeat)) {
print(rownames(treatFeat)[ichannel])
for (ids in idsChannels) {
iMax = ids[which.max(abs(treatFeat[ichannel,ids]))]
print(paste(colnames(treatFeat)[iMax], treatFeat[ichannel,iMax]))
}
}
}
htmAnalyseMahalanobis <- function(htm, d, selectSubset = "") {
# input:
# rows: treat_cells
# cols: features
treatments <- unique(unlist(lapply(strsplit(rownames(d),"_"), function(x) x[1])))
channels <- unique(unlist(lapply(strsplit(colnames(d),"xx"), function(x) {x[2]})))
N = ncol(d)
#for (channel in channels) {
# selectSubset = ""
# print(channel)
d$SelfMahalanobis <- NA
for (treatment in treatments) {
idsTreatment = which(grepl(treatment,rownames(d)))
x <- d[idsTreatment,]
if(selectSubset != "") {
ids = which(grepl(selectSubset, colnames(x)))
x <- x[,ids]
}
ids = which( !grepl("SelfMahalanobis", colnames(x)))
x <- x[,ids]
N = ncol(x)
Sx <- cov(x)
D2 <- mahalanobis(x, colMeans(x), Sx)
d[names(D2),"SelfMahalanobis"] = D2 / N
# minimum:
i = which.min(d[idsTreatment,"SelfMahalanobis"])
iObject = as.numeric(strsplit(rownames(d)[idsTreatment[i]],"_")[[1]][2])
print(paste(treatment,"i",idsTreatment[i],"iObjectdata",iObject))
#htmShowObjectsFromRow(htm,iObject)
# maximum:
#i = which.max(d[idsTreatment,"SelfMahalanobis"])
#iObject = as.numeric(strsplit(rownames(d)[idsTreatment[i]],"_")[[1]][2])
#print(paste(treatment,"i",idsTreatment[i],"iObjectdata",iObject))
#htmShowObjectsFromRow(htm,iObject)
}
#}# channel loop
return(d)
}
htmObjectMultiFeatureAnalysis <- function(htm, centerChannel, stats="classic", perBatchNorm = F, divideByCellArea = F) {
print("")
print("Image-based multi-feature analysis:")
print("***********************************")
print("")
data <- htm@objectdata
# get all necessary information
treatments <- sort(unique(htm@objectdata[[htm@settings@columns$treatment]]))
features_channels <- htmGetVectorSettings("statistics$multipleFeatureSelection")
negative_ctrl <- c(htmGetListSetting(htm,"statistics","negativeControl"))
experiments <- sort(unique(htm@objectdata[[htm@settings@columns$experiment]]))
experiments_to_exclude <- htmGetVectorSettings("statistics$experiments_to_exclude")
# output
print("");print("Experiments:")
print(experiments)
print("");print(paste("Number of Treatments:",length(treatments)))
print("");print(paste("Negative Control:",negative_ctrl))
print(""); print("")
numEntries = length(treatments)
#channels <- unique(unlist(lapply(strsplit(features_channels,"xx"), function(x) {x[2]})))
#features <- unique(unlist(lapply(strsplit(features_channels,"xx"), function(x) {paste(x[1],x[3],sep=".*")})))
channels_to_exclude = c("Vimentin","Cla")
if(divideByCellArea) {
htm@objectdata[[htm@settings@columns$treatment]]
cNorm <- htm@objectdata[["Shape_xxCELLxx_Area"]]
for (feature_channel in features_channels) {
htm@objectdata[[feature_channel]] = htm@objectdata[[feature_channel]] / cNorm
}
# htm@objectdata[["Shape_xxCELLxx_Area"]] <- cNorm
}
for (feature_channel in features_channels) {
#feature_channels = colnames(htm@objectdata)[which(grepl(feature,colnames(htm@objectdata)))]
#varTotal = 0
#vars = vector()
#for(feature_channel in feature_channels) {
# vars <- append(vars, sd(htm@objectdata[[feature_channel]])^2)
# print(paste(feature_channel, sqrt(sd(htm@objectdata[[feature_channel]])^2)))
for(experiment in experiments) {
# normalise to control
# find control cells
if(perBatchNorm) {
idsCtrl = which( (htm@objectdata[[htm@settings@columns$treatment]]==negative_ctrl)
& (htm@objectdata[[htm@settings@columns$experiment]]==experiment) )
} else {
idsCtrl = which( (htm@objectdata[[htm@settings@columns$treatment]]==negative_ctrl))
}
meanCtrlLog2 = mean(log2(htm@objectdata[idsCtrl, feature_channel]))
meanCtrl = mean(htm@objectdata[idsCtrl, feature_channel])
sdCtrl = sd(htm@objectdata[idsCtrl, feature_channel])
medianCtrl = median(htm@objectdata[idsCtrl, feature_channel])
madCtrl = mad(htm@objectdata[idsCtrl, feature_channel])
ids = which( (htm@objectdata[[htm@settings@columns$experiment]]==experiment) )
channel = strsplit(feature_channel,"xx")[[1]][2]
feature_channel_Norm = paste0(channel,"_",feature_channel,"__Norm")
feature_channel_raw = paste0(channel,"_",feature_channel,"__raw")
htm@objectdata[ids, feature_channel_raw] = htm@objectdata[ids, feature_channel]
if(stats=="classic") {
htm@objectdata[ids, feature_channel_Norm] = (htm@objectdata[ids, feature_channel] - meanCtrl) / sdCtrl
}
if(stats=="robust") {
htm@objectdata[ids, feature_channel_Norm] = (htm@objectdata[ids, feature_channel] - medianCtrl) / madCtrl
}
if(stats=="log2") {
htm@objectdata[ids, feature_channel_Norm] = log2(htm@objectdata[ids, feature_channel]) - meanCtrlLog2
}
if(stats=="none") {
htm@objectdata[ids, feature_channel_Norm] = htm@objectdata[ids, feature_channel]
}
}
#}
# variance normalisation
#iCenter = which(grepl(centerChannel,feature_channels))
#meanCenter = mean(htm@objectdata[[feature_channels[iCenter]]])
#print(paste("Centering around",feature_channels[iCenter]))
#print(paste("*** Standard deviation",feature,sqrt(mean(vars)),length(vars)))
#print("")
#for(feature_channel in feature_channels) {
# # channel = strsplit(feature_channel,"xx")[[1]][2]
# feature_channel_varNorm = paste0(feature_channel,"__VarNorm")
# htm@objectdata[[feature_channel_varNorm]] = (htm@objectdata[[feature_channel]] - meanCenter) / sqrt(mean(vars))
#}
}
l = list()
# varNorm
#ids = which(grepl("__VarNorm",colnames(htm@objectdata)) )
#d = htm@objectdata[ids]
#print(paste(htm@objectdata[[htm@settings@columns$treatment]],ids,sep="_"))
#rownames(d) <- paste(htm@objectdata[[htm@settings@columns$treatment]],seq(1:nrow(htm@objectdata)),sep="_")
#d <- d[,order(colnames(d))]
#d <- d[order(rownames(d)),]
#l$cells_varNorm <- d
# zScoreNorm
ids = which( grepl("__Norm",colnames(htm@objectdata)) )
d = htm@objectdata[ids]
d$treatment = htm@objectdata[[htm@settings@columns$treatment]]
d$date = htm@objectdata[[htm@settings@columns$experiment]]
rownames(d) <- paste(htm@objectdata[[htm@settings@columns$treatment]], htm@objectdata[[htm@settings@columns$experiment]], formatC(seq(1:nrow(htm@objectdata)), width = 3, format = "d", flag = "0"),sep="_")
d <- d[,order(colnames(d))]
d <- d[order(rownames(d)),]
l$cells_Norm <- d
# raw
ids = which( grepl("__raw",colnames(htm@objectdata)) )
d = htm@objectdata[ids]
d$treatment = htm@objectdata[[htm@settings@columns$treatment]]
d$date = htm@objectdata[[htm@settings@columns$experiment]]
rownames(d) <- paste(htm@objectdata[[htm@settings@columns$treatment]],seq(1:nrow(htm@objectdata)),sep="_")
d <- d[,order(colnames(d))]
d <- d[order(rownames(d)),]
l$cells_raw <- d
return(l)
#print(colnames(htm@data))
if(0) {
# initialisation
d <- data.frame(treatment = rep(NA,numEntries))
for (feature in features) {
d[[feature]] <- NA
htm@data[[paste0(feature,"__zScore")]] <- NA
}
htm@data$MDD_vsCtrl = 0
htm@data$MDD_vsTreat = 0
# computation
for(i in 1:length(treatments)) {
treatment = treatments[i]
#print(treatment)
for (measurement in measurements) {
indices_ok_treat <- which( ( htm@data[[htm@settings@columns$treatment]] == treatment )
& (htm@data$HTM_qcImages==1)
& !is.nan(htm@data[[measurement]]) & !(htm@data[[htm@settings@columns$experiment]] %in% htmGetVectorSettings("statistics$experiments_to_exclude")))
indices_ok_ctrl <- which( ( htm@data[[htm@settings@columns$treatment]] == negative_ctrl)
& (htm@data$HTM_qcImages==1)
& !is.nan(htm@data[[measurement]]) & !(htm@data[[htm@settings@columns$experiment]] %in% htmGetVectorSettings("statistics$experiments_to_exclude")))
#print(paste(" Treatment", treatment))
#print(paste(" Images Valid Treatment", length(indices_ok_treat)))
#print(paste(" Images Valid Control", length(indices_ok_ctrl)))
mean_treat = mean(htm@data[indices_ok_treat,measurement])
mean_ctrl = mean(htm@data[indices_ok_ctrl,measurement])
sd_ctrl = sd(htm@data[indices_ok_ctrl,measurement])
d[i,measurement] = ( mean_treat - mean_ctrl ) / sd_ctrl
htm@data[indices_ok_treat,"MDD_vsCtrl"] = htm@data[indices_ok_treat,"MDD_vsCtrl"] + ((htm@data[indices_ok_treat,measurement] - mean_ctrl) / sd_ctrl)^2
htm@data[indices_ok_treat,"MDD_vsTreat"] = htm@data[indices_ok_treat,"MDD_vsTreat"] + ((htm@data[indices_ok_treat,measurement] - mean_treat) / sd_ctrl)^2
htm@data[indices_ok_treat,paste0(measurement,"__zScore")] = (htm@data[indices_ok_treat,measurement] - mean_ctrl) / sd_ctrl
}
d[i,"treatment"] = treatment
}
htm@data$MDD_vsCtrl = sqrt(htm@data$MDD_vsCtrl / length(measurements))
htm@data$MDD_vsTreat = sqrt(htm@data$MDD_vsTreat / length(measurements))
rownames(d) <- d$treatment
d$treatment <- NULL
l = list()
l$treatFeat <- d
l$images <- htm@data
}
# return(l)
}
htmMakeFeatureFrame <- function(d) {
channels <- unique(unlist(lapply(strsplit(colnames(d),"xx"), function(x) {x[2]})))
i = 0
for (channel in channels) {
print(channel)
ids = which(grepl(channel,colnames(d)))
ds = d[,ids]
rownames(ds) <- paste(rep(channel,nrow(ds)),rownames(ds),sep="_")
colnames(ds) <- unlist(lapply(colnames(ds), function(x) {gsub(channel,"",x)}))
#print( rownames(ds))
#print( colnames(ds))
if(i==0) {
dc = ds
} else {
dc = rbind(dc, ds)
}
i = i + 1
}
dc2 <- dc
dc2$channeltreat = unlist(lapply(strsplit(rownames(dc),"_"), function(x) {paste(x[1],x[2],sep="_")}))
dc2mean <- ddply(dc2, .(channeltreat), numcolwise(mean))
rownames(dc2mean) <- dc2mean$channeltreat
dc2mean$channeltreat <- NULL
l = list()
l$df <- dc
l$dfmean <- dc2mean
return(l)
}
htmMDStreatFeat <- function(dTreatFeat, negCtrl) {
print("htmMDStreatFeat...")
# MDS
# Classical MDS
# N rows (objects) x p columns (variables)
# each row identified by a unique row name
rCtrl = which(rownames(dTreatFeat)==negCtrl)
dTreatFeat[rCtrl,] = 0
d <- (dist(dTreatFeat)) # euclidean distances between the rows
#print(d)
fit <- cmdscale(d, eig=TRUE, k=2) # k is the number of dim
pc = fit$points[negCtrl,1:2]
x <- fit$points[,1]
y <- fit$points[,2]
# center the negative control
x <- x - pc[1]
y <- y - pc[2]
#print(fit)
dMDS = dist(fit$points)
dDiff = dMDS - d
#print(d)
#print(dDiff)
# plot solution
#dev.new()
#plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",
# main="Metric MDS", type="n", asp=1)
#text(x, y, labels = rownames(dTreatFeat), cex=.7)
MDS = data.frame(treatment=rownames(dTreatFeat), x=x, y=y)
return(MDS)
}
htmMDStreatFeat2 <- function(d) {
# MDS
# Classical MDS
# N rows (objects) x p columns (variables)
# each row identified by a unique row name
#ids = which(grepl("Control",rownames(d)))
#d <- d[ids,]
dd <- sqrt(dist(d)) # euclidean distances between the rows
fit <- cmdscale(dd, eig=TRUE, k=2) # k is the number of dim
x <- fit$points[,1]
y <- fit$points[,2]
dMDS = dist(fit$points)
dDiff = dMDS - dd
lables = unlist(lapply(strsplit(rownames(d),"_"), function(x) {paste(x[1],"",sep="_")}))
lables = rownames(d)
dev.new()
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",
main="Metric MDS", type="n", asp=1)
text(x, y, labels = lables, cex=.7)
MDS = data.frame(treatment=rownames(d), x=x, y=y)
return(MDS)
}
htmCellsToTreatments <- function(d, stats = "classic", keepdate = F){
if(keepdate) {
d$treatment = unlist(lapply(strsplit(rownames(d),"_"), function(x) paste(x[1],x[2])))
} else {
d$treatment = unlist(lapply(strsplit(rownames(d),"_"), function(x) x[1]))
}
if(stats =="classic") {
dmean <- ddply(d, .(treatment), numcolwise(mean))
rownames(dmean) <- dmean$treatment
dmean$treatment <- NULL
return(dmean)
}
if(stats =="robust") {
dmean <- ddply(d, .(treatment), numcolwise(median))
rownames(dmean) <- dmean$treatment
dmean$treatment <- NULL
return(dmean)
}
}
#
# Assay overview
#
htmOverview <- function(htm) {
cExp = htm@settings@columns$experiment
cWell = htm@settings@columns$wellnum
cPos = htm@settings@columns$posnum
nExp = length(unique(paste(htm@data[[cExp]])))
nWell = length(unique(paste(htm@data[[cExp]],htm@data[[cWell]],sep="_")))
nPos = length(unique(paste(htm@data[[cExp]],htm@data[[cWell]],htm@data[[cPos]],sep="_")))
print("")
print("Overiew of the contents of the image table:")
print(paste("experiments =",nExp))
print(paste("wells =",nWell))
print(paste("wells/experiment =",nWell/nExp))
print(paste("positions =",nPos))
print(paste("positions/well =",nPos/nWell))
}
htmHisto <- function(cx, experimentSubset = "None selected", treatmentSubset= "None selected", datatype, save2file = F, newdev = T) {
if(datatype=="wells") {
data <- htm@wellSummary