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PreProcessingHelpers.R
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library(data.table)
library(foreign)
library(spatstat)
library(shotGroups)
##### GitHub Tools #####
sourceGitHubFile <- function(user, repo, branch, file)
{
require(curl)
destfile <- tempfile()
fileToGet <- paste0("https://raw.githubusercontent.com/", user, "/", repo, "/", branch, "/", file)
curl_download(url=fileToGet, destfile)
source(destfile)
}
##### Visualization #####
browseShinyData <- function()
{
sourceGitHubFile(user='jaywarrick', repo='R-General', branch='master', file='DataClassBrowser/ui.R')
sourceGitHubFile(user='jaywarrick', repo='R-General', branch='master', file='DataClassBrowser/server.R')
shinyApp(ui=myUI, server=myServer)
}
##### General #####
resample <- function(x, ...)
{
x[sample.int(length(x), ...)]
}
getLocsFromRCs <- function(r, c, numRows)
{
r + max(numRows) * c
}
sind <- function(x)
{
return(sin(x*pi/180))
}
cosd <- function(x)
{
return(cos(x*pi/180))
}
tand <- function(x)
{
return(tan(x*pi/180))
}
refactor <- function(x)
{
return(x[,lapply(.SD, function(x){if(is.factor(x)){factor(x)}else{x}})])
}
##### Wide Table Operations #####
divideManyColsByOneCol <- function(x, numeratorCols, denominatorCol='Stats.Sum')
{
FUN <- function(a,b)
{
ret <- a/b
ret[!is.finite(ret)] <- NA
return(ret)
}
x[, c(numeratorCols):=lapply(.SD, FUN=FUN, get(denominatorCol)), .SDcols=numeratorCols][]
}
divideColAByColB <- function(x, colA, colB)
{
return(divideManyColsByOneCol(x, numeratorCols = colA, denominatorCol=colB))
}
#' The function grabs each column one by one. The function
#' is applied to the first two columns to create a result
#' (e.g., fun(c1, c2, ret) = ret). This result is then used for combining with the
#' next column (e.g., fun(c2, c3, ret) = ret). The final vector result is then
#' returned. This is usually used in conjunction with lapply.data.table
#' to test values in each column, creating logical columns.
#'
#' So, if you want to check for each row if ...
#'
#' All cols are T --> summarizeLogicalsForEachRow(x, FUN=function(a,b,ret){ret <- ret & a & b})
#' Any cols are T --> summarizeLogicalsForEachRow(x, FUN=function(a,b,ret){!a & !b)})
#' Num cols that are T --> summarizeLogicalsForEachRow(x, FUN=function(a,b,ret){a | b})
#'
#' @param col.fun a function that is first applied to all the specified columns
#' @param row.fun a function that is used after applying the col.fun, it is sequentially applied to each column and takes two vector parameters the first representing a column from the table, the second being the return vector of row.fun that is recycled to accumulate results across columns
#' @param ret.init the initial value provided for the ret parameter to row.fun
#' @param mCols the columns to analyze
#' @param mColsContaining if a col name contains this string, that column will be analyzed, otherwise it is left out
#' @param mColFilter a function that when applied using lapply, returns a vector of logicals indicating which cols to analyze
#' @export
#'
summarizeRows <- function(x, col.fun=NULL, row.fun, ret.init=NULL, mCols=NULL, mColsContaining=NULL, mColFilter=NULL)
{
if(is.null(mCols))
{
if(!is.null(mColsContaining))
{
mCols <- getColNamesContaining(x, mColsContaining)
}
else if(!is.null(mColFilter))
{
mCols <- as.logical(lapply(x, mColFilter))
mCols <- names(x)[mCols]
}
else
{
mCols <- names(x)
}
}
# Apply the col.fun
y <- lapply.data.table(x, FUN=col.fun, cols=mCols, in.place=F)
n <- seq_along(mCols)
for(i in n)
{
ret.init <- row.fun(y[[mCols[i]]], ret.init)
}
return(ret.init)
}
allColsTrue <- function(x, test, mCols=NULL, mColsContaining=NULL, mColFilter=NULL)
{
ret <- summarizeRows(x, col.fun=test, row.fun=function(a, ret){a & ret}, ret.init=rep(T, nrow(x)), mCols=mCols, mColsContaining=mColsContaining, mColFilter=mColFilter)
return(ret)
}
anyColsTrue <- function(x, test, mCols=NULL, mColsContaining=NULL, mColFilter=NULL)
{
ret <- summarizeRows(x, col.fun=test, row.fun=function(a, ret){a | ret}, ret.init=rep(F, nrow(x)), mCols=mCols, mColsContaining=mColsContaining, mColFilter=mColFilter)
return(ret)
}
numColsTrue <- function(x, test, mCols=NULL, mColsContaining=NULL, mColFilter=NULL)
{
ret <- summarizeRows(x, col.fun=test, row.fun=function(a, ret){ret + a}, ret.init=rep(0, nrow(x)), mCols=mCols, mColsContaining=mColsContaining, mColFilter=mColFilter)
return(ret)
}
# Function for calculating point stats in data.table call.
getPointStats <- function(x, y, weights)
{
require(spatstat)
if(length(x) > 1)
{
pts <- matrix(c(x, y), ncol=2)
circ <- getMinCircle(pts)
}
else if(length(x) == 1)
{
circ <- list(rad=1L, ctr=c(x,y))
}
else
{
return(lapply(list(Intensity=NA, WeightedIntensity=NA, ConvexArea=NA, ConvexPerimeter=NA), as.double))
}
pp <- ppp(x, y, window=disc(radius=circ$rad*1.01, centre=circ$ctr))
hull <- convexhull(pp)
ret <- lapply(list(Intensity=intensity(pp), WeightedIntensity=intensity(pp, weights=weights), ConvexArea=area.owin(hull), ConvexPerimeter=perimeter(hull), Diameter=circ$rad), as.double)
if(ret$ConvexArea == 0 || ret$ConvexPerimeter == 0)
{
ret$Circularity <- 0
}
else
{
ret$Circularity <- as.double(4*pi*ret$ConvexArea/(ret$ConvexPerimeter*ret$ConvexPerimeter))
}
return(ret)
}
#' Takes a table with cId, ImageChannel, and MaskChannels (+ feature columns)
#' Geometry.<X> features have values for sub-regions of the masks so each
#' MaskChannel value has encoded the subregion number as well.
#'
#' Note: The names must be 'fixed' as well using either 'fixColNames' or 'fixLongTableStringsInCol'
#'
#' This function collapses the subregion data to just single values for each cell.
#'
summarizeGeometry <- function(x, cellIdCols='cId', removeXY=T)
{
idCols <- cellIdCols
# If we haven't run this function before
if(!('Geometric.SizeIterable' %in% names(x)))
{
stop("It looks like you already ran this function on the table given it is missing columns that are deleted by this function. Aborting.")
}
# Gather important columns for segregating the data for calculations
colsToSummarize <- getColNamesContaining(x, 'Geometric.')
colsToSummarize <- colsToSummarize[!(colsToSummarize %in% c('Geometric.COMX','Geometric.COMY','Geometric.MaximaX','Geometric.MaximaY'))]
colsToKeep <- getAllColNamesExcept(x, colsToSummarize)
# Table to keep
tableToKeep <- x[, mget(colsToKeep)]
rowsToDiscard <- allColsTrue(tableToKeep, test=is.na, mCols=getAllColNamesExcept(tableToKeep, c(idCols,'ImageChannel','MaskChannel')))
tableToKeep <- tableToKeep[!rowsToDiscard]
if(any(grepl('.p', tableToKeep$MaskChannel, fixed=T)))
{
stop("There shouldn't be any .p nomenclature left over in the MaskChannel column at this step within the function. Check to see if all of the id columns were provided. Aborting.")
}
# Table to summarize
x <- x[, mget(colsToSummarize), by=c(idCols,'MaskChannel','ImageChannel')] # Use by statement to keep idcols
rowsToDiscard <- allColsTrue(x, test=is.na, mCols=getAllColNamesExcept(x, c(idCols,'ImageChannel','MaskChannel')))
x <- x[!rowsToDiscard]
# If there isn't one already, create a column that just has the MaskChannel information split from the subregion ids (i.e., p1, p2, ..., pn)
if(!('MaskChannel2' %in% names(x)))
{
splitColumnAtString(x, colToSplit='MaskChannel', sep='.p', newColNames=c('MaskChannel2'), keep=c(1L))
}
# # Check to make sure that eventual remerging of the tables will be legitimate.
# uniqueKeep <- tableToKeep[, list(v=T), by=c('ImageChannel','MaskChannel2')]
# uniqueSummary <- x[, list(v=T), by=c('ImageChannel','MaskChannel2')]
# testTable <- rbindlist(list(uniqueKeep,uniqueSummary), use.names=T)
# testTable <- testTable[, list(v=T), by=c('ImageChannel','MaskChannel')]
# if((nrow(uniqueKeep) + nrow(uniqueSummary)) != nrow(testTable))
# {
# stop("We use rbindlist to merge the tables later which assumes unique cId,
# ImageChannel, MaskChannel combinations for geometric data compared to
# other data. This doesn't appear to be true so throwing error and aborting.
# Need to use merge function for portions of the table that share cId,
# ImageChannel, MaskChannel combinations and rbindlist for unique ones.")
# }
# Make columns with weights for each subregion
x[, ':='(weights=Geometric.SizeIterable/(sum(Geometric.SizeIterable, na.rm=T)), countWeights=Geometric.SizeIterable/(max(Geometric.SizeIterable, na.rm=T))), by=c(idCols, 'ImageChannel', 'MaskChannel2')]
# Calculate ratios and remove the corresponding parent metrics
if(all(c('Geometric.MaximumFeretsDiameter', 'Geometric.MinimumFeretsDiameter') %in% names(x)))
{
x[, ':='(Geometric.FeretsAspectRatio = Geometric.MaximumFeretsDiameter/Geometric.MinimumFeretsDiameter)]
}
if(all(c('Geometric.MajorAxis', 'Geometric.MinorAxis') %in% names(x)))
{
x[, ':='(Geometric.EllipseAspectRatio = Geometric.MajorAxis/Geometric.MinorAxis)]
}
removeCols(x, c('Geometric.MaximumFeretsDiameter', 'Geometric.MinimumFeretsDiameter', 'Geometric.MajorAxis', 'Geometric.MinorAxis', 'Geometric.MaximumFeretsAngle', 'Geometric.MinimumFeretsAngle'))
# Remove subregion data where any Geometric feature measure is NA (typically small regions with no area etc.)
rowsToDiscard <- anyColsTrue(x, test=is.na, mColsContaining='Geometric.')
x <- x[!rowsToDiscard]
# Now we have only subregion data where the geometric information is fully defined
# Decide how to combine different geometric features
#geomFeatures <- c('Convexity', 'Solidity', 'SizeIterable', 'BoundarySize', 'MainElongation', 'Circularity',
#'Boxivity', 'Eccentricity', 'MajorAxis', 'MaximumFeretsDiameter', 'MinimumFeretsDiameter',
#'MinorAxis', 'Roundness', 'X', 'Y')
geomFeatures_Total <- c('Geometric.SizeIterable', 'Geometric.BoundarySize')
geomFeatures_Total <- geomFeatures_Total[geomFeatures_Total %in% names(x)]
geomFeatures_SizeWeightedMean <- c('Geometric.SizeIterable', 'Geometric.Convexity', 'Geometric.Solidity', 'Geometric.MainElongation', 'Geometric.Circularity', 'Geometric.Boxivity', 'Geometric.Eccentricity', 'Geometric.BoundarySize','Geometric.FeretsAspectRatio','Geometric.EllipseAspectRatio','Geometric.Roundness')
geomFeatures_SizeWeightedMean <- geomFeatures_SizeWeightedMean[geomFeatures_SizeWeightedMean %in% names(x)]
# Aggregate geometry data from the different subregions (this created duplicate row information)
# all na.rm's are removed to cause errors if any NA's are found.
for(feature in geomFeatures_Total)
{
x[, (paste0(feature, '.Total')):=sum(get(feature)), by=c(idCols, 'ImageChannel', 'MaskChannel2')]
}
for(feature in geomFeatures_SizeWeightedMean)
{
x[, (feature):=sum(get(feature)*weights), by=c(idCols, 'ImageChannel', 'MaskChannel2')]
}
# Create new count columns (this also results in duplicate row information)
x[, ':='(N=as.double(.N), weightedN=sum(countWeights)), by=c(idCols, 'ImageChannel', 'MaskChannel2')]
if(all(c('Geometric.X','Geometric.Y') %in% names(x)))
{
# Calculate point stats
x[, c('Geometric.SubRegionIntensity', 'Geometric.SubRegionWeightedIntensity', 'Geometric.SubRegionConvexArea', 'Geometric.SubRegionConvexPerimeter', 'Geometric.SubRegionRadius', 'Geometric.SubRegionCircularity'):=getPointStats(Geometric.X, Geometric.Y, weights), by=c(idCols, 'ImageChannel', 'MaskChannel2')]
}
# Overwrite Maskchannel that currently encodes subregion as well and replace with just MaskChannel information.
# Also remove helper columns
if(removeXY)
{
x[, ':='(MaskChannel=MaskChannel2, MaskChannel2=NULL, weights=NULL, countWeights=NULL, Geometric.X=NULL, Geometric.Y=NULL)]
}
else
{
x[, ':='(MaskChannel=MaskChannel2, MaskChannel2=NULL, weights=NULL, countWeights=NULL)]
}
# Remove all the duplicate information that was created during calculations
x <- unique(x)
# Now merge tableToKeep and x
x <- merge(x=tableToKeep, y=x, by=c(idCols, 'MaskChannel', 'ImageChannel'), all=T)
# Return the result
return(x)
}
summarizeSymmetryData <- function(x, sim.trans=T, logit.trans=T)
{
# Summarize Symmetry Data
toDelete <- getColNamesContaining(x,'SymmetryCorrelation')
setnames(x, 'SymmetryCorrelation.1.1', 'SymmetryCorrelation.1.Avg')
sym2 <- getColNamesContaining(x,'SymmetryCorrelation.2')
sym3 <- getColNamesContaining(x,'SymmetryCorrelation.3')
sym4 <- getColNamesContaining(x,'SymmetryCorrelation.4')
x[, SymmetryCorrelation.2.Avg:=apply(.SD,1,mean,na.rm=T), .SDcols=sym2]
x[, SymmetryCorrelation.3.Avg:=apply(.SD,1,mean,na.rm=T), .SDcols=sym3]
x[, SymmetryCorrelation.4.Avg:=apply(.SD,1,mean,na.rm=T), .SDcols=sym4]
x[, c(sym2,sym3,sym4):=NULL]
x[, SymmetryLobe.1:=SymmetryAmplitude.1*SymmetryCorrelation.1.Avg]
x[, SymmetryLobe.2:=SymmetryAmplitude.2*SymmetryCorrelation.2.Avg]
x[, SymmetryLobe.3:=SymmetryAmplitude.3*SymmetryCorrelation.3.Avg]
x[, SymmetryLobe.4:=SymmetryAmplitude.4*SymmetryCorrelation.4.Avg]
correlationNames <- c(getColNamesContaining(x,'SymmetryCorrelation'), getColNamesContaining(x,'SymmetryLobe'))
correlationNames <- correlationNames[!grepl('Similarity',correlationNames)]
if(length(correlationNames) > 0 & sim.trans)
{
lapply.data.table(x, FUN=sim.transform, cols=correlationNames, in.place=T)
setnames(x, correlationNames, paste0(correlationNames, '.Similarity'))
}
ampNames <- getColNamesContaining(x, 'SymmetryAmplitude')
if(length(ampNames) > 0 & logit.trans)
{
# Use logit transform for amplitudes because from 0 to 1
lapply.data.table(x, FUN=logit.transform, cols=ampNames, in.place=T)
setnames(x, ampNames, paste0(ampNames, '.Logit'))
}
return(x)
}
standardizeWideData <- function(x, row.normalize=F, row.use.median=F, col.use.median=F, col.use.mad=F, col.use.percentiles=F, percentiles=c(0.05,0.95), data.cols=NULL, data.cols.contains=NULL, by=NULL, trySDIfNeeded=T, na.rm.no.variance.cols=F, suffix=NULL)
{
# x <- data.table(a=1.1:3.1, b=4.1:6.1, c=c(100.1,110.1,120.1)); duh <- copy(x); duh2 <- as.data.table(lapply(x, log))
# data.cols <- c('b','c')
# row.normalize=F
# row.use.median=F
# col.use.median=F
# col.use.mad=F
# data.cols.contains = NULL
# by=NULL
# Get the names of the columns of interest
cols <- getNumericColsOfInterest(x, data.cols=data.cols, data.cols.contains=data.cols.contains)
# Remove cols with now variance
removeNoVarianceCols(x, use.mad=col.use.mad, cols=cols, by=by, trySDIfNeeded=trySDIfNeeded, na.rm=na.rm.no.variance.cols)
# Redefine cols just in case
cols <- getNumericColsOfInterest(x, data.cols=data.cols, data.cols.contains=data.cols.contains)
# New cols
if(!is.null(suffix))
{
newCols <- paste(cols, suffix, sep='.')
}
if(row.normalize)
{
# Take each row and divide it by the row mean/median (i.e., row normalize)
x[, rowNum:=as.double(1:nrow(x))] # create a rowNum column to do operations by row
if(row.use.median)
{
x[, c(newCols) := lapply(.SD, "-", median(unlist(.SD))), .SDcols=cols, by=.(rowNum)]
}else
{
x[, c(newCols) := lapply(.SD, "-", mean(unlist(.SD))), .SDcols=cols, by=.(rowNum)]
}
x[, rowNum:=NULL] # Remove the rowNum column
}
if(row.normalize)
{
x[,c(newCols):=lapply(.SD, FUN=robustScale, use.median=col.use.median, use.mad=col.use.mad, use.percentiles=col.use.percentiles, percentiles=percentiles, trySDIfNeeded=trySDIfNeeded), .SDcols=newCols, by=by]
}
else
{
x[,c(newCols):=lapply(.SD, FUN=robustScale, use.median=col.use.median, use.mad=col.use.mad, use.percentiles=col.use.percentiles, percentiles=percentiles, trySDIfNeeded=trySDIfNeeded), .SDcols=cols, by=by]
}
return(x)
}
# Now perform regular column standardization (grouping as appropriate using 'by')
robustScale <- function(x, use.median, use.mad, use.percentiles=F, percentiles=c(0.05,0.95), trySDIfNeeded=T)
{
if(use.median)
{
m <- median(x[is.finite(x)], na.rm=TRUE)
}
else if(use.percentiles)
{
l(lo, hi) %=% getPercentileValues(x[is.finite(x)], c(percentiles[1],percentiles[2]))
l(daMin, daMax) %=% qnorm(percentiles)
if(any(!is.finite(c(lo, hi, daMin, daMax))))
{
stop(c("Couldn't calculate the percentile limits... ", c(lo, hi, daMin, daMax)))
}
return(adjustIntensity(x, lo, hi, daMin, daMax))
}
else
{
m <- mean(x[is.finite(x)], na.rm=TRUE)
}
if(use.mad)
{
sig <- mad(x[is.finite(x)], center=m, na.rm=TRUE)
if(!is.na(sig) & sig == 0 & trySDIfNeeded)
{
sig <- sd(x[is.finite(x)], na.rm=TRUE)
}
}
else
{
sig <- sd(x[is.finite(x)], na.rm=TRUE)
}
return((x-m)/sig)
}
removeNoVarianceCols <- function(x, use.mad=F, cols=NULL, by=NULL, trySDIfNeeded=T, na.rm=F)
{
namesToRemove <- getNoVarianceCols(x, use.mad=use.mad, cols=cols, by=by, trySDIfNeeded=trySDIfNeeded, na.rm=na.rm)
if(length(namesToRemove) > 0)
{
if(use.mad)
{
print("Removing cols with a MAD of zero...")
}
else
{
print("Removing cols with a variance of zero...")
}
for(name in namesToRemove)
{
print(name)
x[,(name):=NULL]
}
}
}
tempSD1 <- function(y, trySD)
{
temp <- mad(y[is.finite(y)], na.rm = TRUE)
if(!is.na(temp) & temp == 0 & trySD)
{
temp <- sd(y[is.finite(y)], na.rm = TRUE)
}
return(temp)
}
tempSD2 <- function(y, trySD)
{
return(sd(y[is.finite(y)], na.rm = TRUE))
}
#'@param na.rm is whether or not to keep cols with groups for which an SD or MAD cannot be calculated (e.g., only 1 value or no values).
#'This is different than if the SD or MAD calculates to 0. If a 0 is encountered for a group, the col will be removed.
#'
getNoVarianceCols <- function(x, use.mad, cols=NULL, by=NULL, trySDIfNeeded=T, na.rm=F)
{
cols <- getNumericColsOfInterest(x, data.cols=cols)
tempMin <- function(x, na.rm)
{
ret <- min(x, na.rm=na.rm)
if(is.na(ret))
{
return(0)
}
return(ret)
}
if(use.mad)
{
tempSD <- tempSD1
if(is.null(by))
{
tempNames <- x[,lapply(.SD, tempSD, trySD=trySDIfNeeded), .SDcols=cols]
}
else
{
tempNames <- x[,lapply(.SD, tempSD, trySD=trySDIfNeeded), .SDcols=cols, by=by]
tempNames <- tempNames[, lapply(.SD, tempMin, na.rm=na.rm), .SDcols=cols]
}
}
else
{
tempSD <- tempSD2
if(is.null(by))
{
tempNames <- x[,lapply(.SD, tempSD, trySD=trySDIfNeeded), .SDcols=cols]
}
else
{
tempNames <- x[,lapply(.SD, tempSD, trySD=trySDIfNeeded), .SDcols=cols, by=by]
tempNames <- tempNames[, lapply(.SD, tempMin, na.rm=na.rm), .SDcols=cols]
}
}
return(names(tempNames)[as.numeric(as.vector(tempNames))==0])
}
##### Long Table Operations #####
#' This is verified to work with randomly ordered tables.
divideMAbyMBbyRef <- function(x, mA, mB, idCols='cId')
{
tempDivide <- function(values, Measurement, mA, mB)
{
a <- values[Measurement==mA]
b <- values[Measurement==mB]
if(any(!is.finite(c(a,b))))
{
return(NA)
}
if(length(a) != 1 || length(b) != 1)
{
stop("Id cols were specific enough to produce one value for each id")
}
else
{
if(Measurement[1]==mA)
{
return(c(a/b, b))
}
else
{
return(c(b, a/b))
}
}
}
x[Measurement %in% c(mA,mB), Value:=tempDivide(Value, Measurement, mA, mB), by=idCols]
# mATable <- x[Measurement==mA]
# mBTable <- x[Measurement==mB]
# if(nrow(mATable) != nrow(mBTable))
# {
# # Try to perform the operation on the subset of the mB column (can't do reverse because we are editing the mA column)
# mBTable <- mBTable[MaskChannel %in% unique(mATable$MaskChannel)]
# if(nrow(mATable) != nrow(mBTable))
# {
# stop('Number of rows for these measurements do not match! Aborting operation.')
# }
# }
# ret <- mATable$Value / mBTable$Value
# x[Measurement==mA]$Value <- ret
# return(x)
}
#' JEX outputs the normalized (size scale invariant) central moments already
#' To make them intensity invariant, we just need to divide by the mean
#' Not sure why we were normalizing by the total.
#' Plus, the zernike's already give us a rotationally invariant measure and can be
#' made to be contrast invariant by dividing by the mean
#' so, tending to avoid use of these basic moments now since they are directional.
#' If we want rotationally invariant, then we have Hu's.
# intIntensityNormalizeCentralMoments <- function(x, idCols='cId')
# {
# mNames <- getMeasurementNamesContaining(x, 'ImageMoments.CentralMoment')
# for(mName in mNames)
# {
# x <- divideMAbyMBbyRef(x, mName, 'Stats.Sum', idCols=idCols)
# }
# return(x)
# }
getRowsMatching <- function(x, col, baseName)
{
return(x[grepl(baseName, x[[col]])])
}
getLongTable <- function(x, idCols, measurementName='Measurement', valueName='Value')
{
return(melt(x, getAllColNamesExcept(x, idCols), variable.name=measurementName, value.name=valueName, na.rm=TRUE))
}
getLongTableFromTemplate <- function(x, longTemplate)
{
return(getLongTable(x, idCols=getAllColNamesExcept(x, getAllColNamesExcept(longTemplate, c('Measurement','Value')))))
}
getMeasurementNamesContaining <- function(x, names)
{
ms <- unique(x$Measurement)
ret <- character(0)
for(name in names)
{
ret <- c(ret, ms[grepl(name,ms,fixed=T)])
}
ret <- unique(ret)
return(ret)
}
removeMeasurementNamesContaining <- function(x, names)
{
namesToRemove <- getMeasurementNamesContaining(x, names)
return(removeMeasurementNames(x, namesToRemove))
}
removeMeasurementNames <- function(x, names)
{
print("Removing the following Measurements...")
for(name in names)
{
print(name)
}
x <- x[!(Measurement %in% names)]
return(x)
}
fixLongTableStringsInCol <- function(x, col)
{
replaceSubStringInAllRowsOfCol(x,'_Order_','',col)
replaceSubStringInAllRowsOfCol(x,'_Rep_','',col)
replaceSubStringInAllRowsOfCol(x,'$','.',col)
replaceSubStringInAllRowsOfCol(x,'net.imagej.ops.Ops.','',col)
replaceSubStringInAllRowsOfCol(x,'function.ops.JEXOps.','',col)
replaceSubStringInAllRowsOfCol(x,' ','',col)
replaceSubStringInAllRowsOfCol(x,':','_',col)
}
##### Feature Calculations #####
filterLBPCodes <- function(x, nSigma=-2)
{
LBPCols <- getColNamesContaining(x, 'LBP')
LBPTots <- as.numeric(as.vector(lapply.data.table(x, FUN=function(x){log(sum(x, na.rm=T)+1)}, cols=LBPCols)))
LBPColsToDelete <- LBPCols[LBPTots < (median(LBPTots) + nSigma*mad(LBPTots))]
removeCols(x, LBPColsToDelete)
}
#' Intended to be used with a wide table that still has ImageChannel and MaskChannel information.
calculateChannelProducts <- function(x, comboCol, valsToPermute, idCols, mCols=NULL, mColContains=NULL, FUN=getComboProducts, sep='_')
{
return(performChannelComboCalcs(x, comboCol=comboCol, valsToPermute=valsToPermute, idCols=idCols, mCols=mCols, mColContains=mColContains, FUN=FUN, sep=sep))
}
#' Intended to be used with a wide table that still has ImageChannel and MaskChannel information.
calculateChannelDifferences <- function(x, comboCol, valsToPermute, idCols, mCols=NULL, mColContains=NULL, FUN=getComboDifferences, sep='_')
{
return(performChannelComboCalcs(x, comboCol=comboCol, valsToPermute=valsToPermute, idCols=idCols, mCols=mCols, mColContains=mColContains, FUN=FUN, sep=sep))
}
#' Intended to be used with a wide table that still has ImageChannel and MaskChannel information.
calculateChannelRatios <- function(x, comboCol, valsToPermute, idCols, mCols=NULL, mColContains=NULL, FUN=getComboRatios, sep='_')
{
return(performChannelComboCalcs(x, comboCol=comboCol, valsToPermute=valsToPermute, idCols=idCols, mCols=mCols, mColContains=mColContains, FUN=FUN, sep=sep))
}
#' Intended to be used with a wide table that still has ImageChannel and MaskChannel information.
calculateChannelLogRatios <- function(x, comboCol, valsToPermute, idCols, mCols=NULL, mColContains=NULL, FUN=getComboLogRatios, sep='_')
{
return(performChannelComboCalcs(x, comboCol=comboCol, valsToPermute=valsToPermute, idCols=idCols, mCols=mCols, mColContains=mColContains, FUN=FUN, sep=sep))
}
#' Intended to be used with a wide table that still has ImageChannel and MaskChannel information.
performChannelComboCalcs <- function(x, comboCol, valsToPermute, idCols, mCols=NULL, mColContains=NULL, FUN, sep='_', ...)
{
uniqueChannels <- valsToPermute
if(is.null(mCols))
{
mCols <- getColNamesContaining(x, mColContains)
}
if(length(uniqueChannels) > 1)
{
validRows <- x[[comboCol]] %in% uniqueChannels
for(mCol in mCols)
{
validRows <- validRows & !is.na(x[[mCol]])
}
x2 <- x[validRows, lapply(.SD, FUN=FUN, ...), .SDcols=mCols, by=idCols]
x2[[comboCol]] <- x[validRows, lapply(.SD, FUN=getComboNames, sep=sep), .SDcols=comboCol, by=idCols][[comboCol]]
return(x2)
}else
{
# return an empty table with the same columns as provided
return(x[FALSE])
}
}
#' Example:
#'
#' # Calculate Geometric Size Ratios
#' ratioData <- calculateChannelLogRatios(x2, comboCol='MaskChannel', valsToPermute=c("1_Lower","1_Upper","3","4","5","6","Cyt","WholeCell"), idCols=c('cId','ImageChannel'), mCols='Geometric.SizeIterable.Total')
#' x2 <- mergeComboData(x2, ratioData, mCol.old='Geometric.SizeIterable.Total', mCol.new='Geometric.SizeIterable.Total.Ratio')
#' rm(ratioData)
#'
#' # also
#' diffs <- calculateChannelDifferences(x2, comboCol='MaskChannel', idCols=c('cId','ImageChannel'), mCols='Geometric.SizeIterable')
#' x3 <- mergeComboData(x3, diffs, mCol.old='Geometric.SizeIterable', mCol.new='Geometric.SizeIterableDiff')
#' rm(diffs)
#'
#' The setnames is performed on the combodata just prior to merging with x
mergeComboData <- function(x, comboData, mCol.old, mCol.new)
{
setnames(comboData, old=mCol.old, new=mCol.new)
x <- merge(x, comboData, by=c('cId','ImageChannel','MaskChannel'), all=T)
return(x)
}
# Meant to be called on a subset of the main table
getComboNames <- function(x, sep='_')
{
if(length(x) < 2)
{
return(NULL)
}
temp <- combn(x, 2)
#print(temp)
temp <- paste0(temp[1,],sep,temp[2,])
return(temp)
}
# Meant to be called on a subset of the main table
getComboDifferences <- function(x)
{
if(length(x) < 2)
{
return(NULL)
}
temp <- combn(x, 2)
temp <- temp[1,]-temp[2,]
return(temp)
}
# Meant to be called on a subset of the main table
getComboProducts <- function(x)
{
if(length(x) < 2)
{
return(NULL)
}
temp <- combn(x, 2)
temp <- temp[1,]*temp[2,]
return(temp)
}
# Meant to be called on a subset of the main table
getComboRatios <- function(x)
{
if(length(x) < 2)
{
return(NULL)
}
temp <- combn(x, 2)
temp <- temp[1,]/temp[2,]
return(temp)
}
# Meant to be called on a subset of the main table
getComboLogRatios <- function(x)
{
if(length(x) < 2)
{
return(NULL)
}
temp <- combn(x, 2)
temp <- log(temp[1,]/temp[2,])
return(temp)
}
calculateRMSofHaralick <- function(x, removeOriginalHaralickMeasures=FALSE)
{
# If keeping Haralick features, combine measures for each direction by averaging to make "rotationally invariant".
# Find all names with Horizontal in them
hNames <- getColNamesContaining(x, 'Horizontal')
vNames <- gsub("Horizontal", "Vertical", hNames)
dNames <- gsub("Horizontal", "Diagonal", hNames)
adNames <- gsub("Horizontal", "AntiDiagonal", hNames)
avgNames <- gsub("Horizontal", "Avg", hNames)
haralickNames <- data.frame(H=hNames, V=vNames, D=dNames, AD=adNames, avg=avgNames, stringsAsFactors=FALSE)
myfunc <- function(row, theNames)
{
return(mean(row[,theNames$H] + row[,theNames$V] + row[,theNames$D] + row[,theNames$AD]))
}
x <- data.frame(x)
for(i in 1:nrow(haralickNames))
{
x[,haralickNames[i,5]] <- (x[,haralickNames[i,1]] + x[,haralickNames[i,2]] + x[,haralickNames[i,3]] + x[,haralickNames[i,4]])/4
if(removeOriginalHaralickMeasures)
{
x <- x[,!(names(x) %in% as.character(haralickNames[i,1:4]))]
}
}
return(data.table(x))
}
calculateGeometricFeaturesFromDNZernikeCircles <- function(x)
{
if(sum(grepl('DNZernike',names(x),fixed=T)) == 0)
{
warning("Didn't find any DNZernike circles to perform calculations. Aborting.")
}
else
{
# the outercircle is 1.5 times the equivalent radius of the whole cell, so adjust appropriately to get actual cell radius
x[, ':='(Geometric.Area.Nuc=pi*DNZernikeInnerCircleR^2, Geometric.Area.WholeCell=pi*((2.0/3.0)*DNZernikeOuterCircleR)^2)]
x[, ':='(Geometric.NucAreaFraction=Geometric.Area.Nuc/Geometric.Area.WholeCell)]
x[, ':='(Geometric.NucNormalizedOffset=sqrt((DNZernikeInnerCircleX-DNZernikeOuterCircleX)^2 + (DNZernikeInnerCircleY-DNZernikeOuterCircleY)^2)/DNZernikeInnerCircleR)]
x[, ':='(Geometric.WholeCellNormalizedOffset=sqrt((DNZernikeInnerCircleX-DNZernikeOuterCircleX)^2 + (DNZernikeInnerCircleY-DNZernikeOuterCircleY)^2)/DNZernikeOuterCircleR)]
x[, ':='(Geometric.NormalizedOffset=sqrt((DNZernikeInnerCircleX-DNZernikeOuterCircleX)^2 + (DNZernikeInnerCircleY-DNZernikeOuterCircleY)^2)/(DNZernikeOuterCircleR-DNZernikeInnerCircleR))]
}
}
##### JUNK YARD #####
# getTableList <- function(dir, fileList, class, expt, sampleSize=NULL, cellIds=NULL)
# {
# if(!is.null(sampleSize))
# {
# subSampleSize <- sampleSize / length(fileList)
# }
# tableList <- list()
# for(f in fileList)
# {
# print(paste0('Reading file: ', file.path(dir, f)))
# temp <- fread(file.path(dir, f))
# temp$Class <- class
# temp$Expt <- expt
# if('Z' %in% names(temp))
# {
# temp[,Z:=NULL]
# }
# if('A' %in% names(temp))
# {
# temp[,A:=NULL]
# }
# if('B' %in% names(temp))
# {
# temp[,B:=NULL]
# }
# if(!('ImRow' %in% names(temp)))
# {
# temp$ImRow <- 1
# }
# if(!('ImCol' %in% names(temp)))
# {
# temp$ImCol <- 1
# }
# if(!('Loc' %in% names(temp)))
# {
# temp[,Loc:=getLocsFromRCs(ImRow, ImCol, max(ImRow) + 1)]
# }
# temp$file <- f
# temp$cId <- paste(temp$Expt, temp$file, temp$Loc, temp$Id, sep='.')
# temp[,ImRow:=NULL]
# temp[,ImCol:=NULL]
# if(!is.null(cellIds))
# {
# rIds <- cellIds
# temp <- temp[cId %in% rIds]
# }
# if(!is.null(sampleSize))
# {
# rIds <- trySample(unique(temp$cId), subSampleSize)
# temp <- temp[cId %in% rIds]
# }
# tableList <- append(tableList, list(temp))
# }
# return(tableList)
# }
#
# getTableList <- function(dir, featureTable, colocTable, class, expt, sampleSize=NULL, cellIds=NULL)
# {
# if(!is.null(sampleSize))
# {
# subSampleSize <- sampleSize / length(fileList)
# }
# tableList <- list()
# for(f in fileList)
# {
# print(paste0('Reading file: ', file.path(dir, f)))
# temp <- fread(file.path(dir, f))
# temp$Class <- class
# temp$Expt <- expt
# if('Z' %in% names(temp))
# {
# temp[,Z:=NULL]
# }
# if('A' %in% names(temp))
# {
# temp[,A:=NULL]
# }
# if('B' %in% names(temp))
# {
# temp[,B:=NULL]
# }
# if(!('ImRow' %in% names(temp)))
# {
# temp$ImRow <- 1
# }
# if(!('ImCol' %in% names(temp)))
# {
# temp$ImCol <- 1
# }
# if(!('Loc' %in% names(temp)))
# {
# temp[,Loc:=getLocsFromRCs(ImRow, ImCol, max(ImRow) + 1)]
# }
# temp$file <- f
# temp$cId <- paste(temp$Expt, temp$file, temp$Loc, temp$Id, sep='.')
# temp[,ImRow:=NULL]
# temp[,ImCol:=NULL]
# if(!is.null(cellIds))
# {
# rIds <- cellIds
# temp <- temp[cId %in% rIds]
# }
# if(!is.null(sampleSize))
# {
# rIds <- trySample(unique(temp$cId), subSampleSize)
# temp <- temp[cId %in% rIds]
# }
# tableList <- append(tableList, list(temp))
# }
# return(tableList)
# }
# getXYCSVsAsTableFromDir <- function(dir, xName='SNR', xExpression='(x+1)', yName='BLUR', yExpression='(y+1)*0.05')
# {
# ret <- list()
# fList <- list.files(path = dir, recursive = TRUE)
# for(f in fList)
# {
# if((grepl('x', f) || grepl('y', f)) & grepl('.csv', f))
# {
# fileName <- strsplit(f, "\\.")[[1]][1]
# ret[[fileName]] <- getXYCSVAsTable(dir, f, xName, xExpression, yName, yExpression)
# }
# }
# retTable <- rbindlist(ret)
# return(retTable)
# }
#
# getXYCSVAsTable <- function(dir, file, xName='SNR', xExpression='(x+1)', yName='BLUR', yExpression='(y+1)*0.05')
# {
# fileName <- strsplit(file, "\\.")[[1]][1]
# xy <- strsplit(fileName, "_")[[1]]
# y <- as.numeric(substr(xy[1],2,nchar(xy[1])))
# x <- as.numeric(substr(xy[2],2,nchar(xy[2])))
# xVal <- eval(parse(text=xExpression))
# yVal <- eval(parse(text=yExpression))
# print(paste0('Reading ', file.path(dir,file), ' as ', xName, '=', xVal, ', ', yName, '=', yVal, '.'))
# theTable <- fread(file.path(dir,file))
# theTable[,(xName),with=FALSE] <- xVal
# theTable[,(yName),with=FALSE] <- yVal
# return(theTable)
# }
#
# getXYArffsAsTableFromDir <- function(dir, xName='SNR', xExpression='(x+1)', yName='BLUR', yExpression='(y+1)*0.05')
# {
# ret <- list()
# fList <- list.files(path = dir, recursive = TRUE)
# for(f in fList)
# {
# if((grepl('x', f) || grepl('y', f)) & grepl('.arff', f))
# {
# fileName <- strsplit(f, "\\.")[[1]][1]
# ret[[fileName]] <- getXYArffAsTable(dir, f, xName, xExpression, yName, yExpression)
# }
# }
# retTable <- rbindlist(ret)
# return(retTable)
# }
#
# getXYArffAsTable <- function(dir, file, xName='SNR', xExpression='(x+1)', yName='BLUR', yExpression='(y+1)*0.05')
# {
# fileName <- strsplit(file, "\\.")[[1]][1]
# xy <- strsplit(fileName, "_")[[1]]
# y <- as.numeric(substr(xy[1],2,nchar(xy[1])))
# x <- as.numeric(substr(xy[2],2,nchar(xy[2])))
# xVal <- eval(parse(text=xExpression))
# yVal <- eval(parse(text=yExpression))
# print(paste0('Reading ', file.path(dir,file), ' as ', xName, '=', xVal, ', ', yName, '=', yVal, '.'))
# theTable <- read.arff(file.path(dir,file))
# theTable[,xName] <- xVal
# theTable[,yName] <- yVal
# return(data.table(theTable))
# }
#' Depricated
#' StandardizeWideData is preferred
dep_standardizeLongData <- function(x, val.col='Value', by, ignoreMeasurementsContaining=c('ZernikePhase'))
{
robustScale <- function(x, measurement, ignore)
{
if(any(sapply(ignore, grepl, measurement, fixed=T)))
{
return(x)
}
else
{
m <- median(x, na.rm=TRUE)