R/compareFitnesses.R

In qfa: Tools for Quantitative Fitness Analysis (QFA) of Arrayed Microbial Cultures Growing on Solid Agar Surfaces

# Comparing DT with MDP...
# Modified version of densCols
#dens<-function (x, y = NULL, nbin = 128, bandwidth, colramp = colorRampPalette(blues9[-(1:3)])) 
#{
#    xy <- xy.coords(x, y)
#    select <- is.finite(xy$x) & is.finite(xy$y)
#    x <- cbind(xy$x, xy$y)[select, ]
#    map <- grDevices:::.smoothScatterCalcDensity(x, nbin, bandwidth)
#    mkBreaks <- function(u) u - diff(range(u))/(length(u) - 1)/2
#    xbin <- cut(x[, 1], mkBreaks(map$x1), labels = FALSE)
#    ybin <- cut(x[, 2], mkBreaks(map$x2), labels = FALSE)
#    dens <- map$fhat[cbind(xbin, ybin)]
#    dens[is.na(dens)] <- 0
#    dens
#}

#mkPlt<-function(qf,cutoff=0.03,lab,...){
#	# Discard "dead" cultures before calculating correlation, as it skews result
#	qfc=signif(cor(qf$MDR[(qf$MDR>0)],qf$MDP[(qf$MDR>0)]),3)
#	cramp=colorRampPalette(c("blue", "orange", "red"), space = "Lab")
#	cols=densCols(qf$MDR,qf$MDP,nbin=256,bandwidth=0.1,colramp=cramp)
#	densit=dens(qf$MDR,qf$MDP,nbin=256,bandwidth=0.1,colramp=cramp)
#	# Discard text for high density points
#	txt=qf$Gene
#	txt[densit>cutoff]=""
#	delt=rep(0,length(qf$Gene))
#	getDelt<-function(MDR,MDP){
#		medMDR=median(qf$MDR[(qf$MDP>0.90*MDP)&(qf$MDP<1.1*MDP)])
#		if(MDR<medMDR){return(-1)}else{return(1)}		
#	}
#	delt[densit<=cutoff]=sapply(qf$MDR[densit<=cutoff],getDelt,qf$MDP[densit<=cutoff])
#	
#	# Move text to the left of cloud to left of points, same for right...
#	#reg=lm(qf$MDP~qf$MDR)
#	#A0=as.numeric(reg$coefficients[1])
#	#m=as.numeric(reg$coefficients[2])
#	qf$pos=2
#	#qf$pos[qf$MDR>(qf$MDP-A0)/m]=4
#	qf$pos[delt>0]=4
#	plot(qf$MDR,qf$MDP,pch=16,cex=0.5,main=paste(lab,"Correlation:",qfc),col=cols,xlab="Doubling rate (MDR)",ylab="Doubling potential (MDP)",...)
#	text(qf$MDR,qf$MDP,txt,cex=0.3,pos=qf$pos)
#}

summarise<-function(data,wctest=TRUE){
	###### Get ORF median fitnesses for control & double #######
	print("Calculating median (or mean) fitness for each ORF")
	## LIK ##
	# Get orfs in question
	orfs<-as.character(data$ORF)
	orfs<-unique(orfs)
	getMDR<-function(orf,fitframe){
        orfd<-fitframe[fitframe$ORF==orf,]
        return(orfd$MDR)
	}
	getMDP<-function(orf,fitframe){
        orfd<-fitframe[fitframe$ORF==orf,]
        return(orfd$MDP)
	}
	getGene<-function(orf,fitframe){
        orfd<-fitframe[fitframe$ORF==orf,]
        return(orfd$Gene[1])
	}
	# Get lists with fitnesses for each repeat
	mdrs<-lapply(orfs,getMDR,data)
	names(mdrs)<-orfs
	mdps<-lapply(orfs,getMDP,data)
	names(mdps)<-orfs
	genes<-lapply(orfs,getGene,data)
	names(genes)<-orfs
	# Get means or medians for each ORF
	if(wctest){mdrsumm<-sapply(mdrs,median)}else{mdrsumm<-sapply(mdrs,mean)}
	names(mdrsumm)<-orfs
	if(wctest){mdpsumm<-sapply(mdps,median)}else{mdpsumm<-sapply(mdps,mean)}
	names(mdpsumm)<-orfs
	mdrSE<-sapply(mdrs,sterr)
	mdpSE<-sapply(mdps,sterr)
	res=data.frame(ORF=as.character(orfs),Gene=as.character(genes),MDR=as.numeric(mdrsumm),MDP=as.numeric(mdpsumm),MDRSE=as.numeric(mdrSE),MDPSE=as.numeric(mdpSE))
}