drdata <- function(substances, experimentator = "%", db = "cytotox",
celltype="IPC-81",whereClause="1",
ok="'ok'")
{
library(RODBC)
channel <- odbcConnect("cytotox",uid="cytotox",pwd="cytotox",case="tolower")
slist <- paste(substances,collapse="','")
query <- paste("SELECT conc,viability,unit,experimentator,substance,celltype,",
"plate,ok FROM cytotox WHERE substance IN ('",
slist,"') AND experimentator LIKE '",
experimentator,"' AND celltype LIKE '",
celltype,"' AND ",
whereClause," AND ok in (",
ok,")",sep="")
data <- sqlQuery(channel,query)
odbcClose(channel)
names(data)[[1]] <- "dose"
names(data)[[2]] <- "response"
data$dosefactor <- factor(data$dose)
data$substance <- factor(data$substance,levels=substances)
return(data)
}
drfit <- function(data, startlogEC50 = NA, lognorm = TRUE, logis = FALSE,
linearlogis = FALSE, b0 = 2, f0 = 0)
{
substances <- levels(data$substance)
unit <- levels(as.factor(data$unit))
logisf <- function(x,x0,b,k=1)
{
k / (1 + (x/x0)^b)
}
linearlogisf <- function(x,k,f,mu,b)
{
k*(1 + f*x) / (1 + ((2*f*(10^mu) + 1) * ((x/(10^mu))^b)))
}
ri <- 0 # an index over the result rows
rsubstance <- array() # the substance names in the results
rn <- vector() # number of dose-response curves
rlhd <- rlld <- vector() # highest and lowest doses tested
mtype <- array() # the modeltypes
logEC50 <- vector()
stderrlogEC50 <- vector()
slope <- vector()
b <- vector()
f <- vector()
splitted <- split(data,data$substance)
for (i in substances)
{
tmp <- splitted[[i]]
n <- round(length(tmp$response)/9)
if (is.na(startlogEC50[i])){
w <- 1/abs(tmp$response - 0.3)
startlogEC50[[i]] <- sum(w * log10(tmp$dose))/sum(w)
}
highestdose <- max(tmp$dose)
lowestdose <- min(tmp$dose)
lhd <- log10(highestdose)
lld <- log10(lowestdose)
responseathighestdose <- mean(subset(tmp,dose==highestdose)$response)
rix <- ri # rix is used late to check if any
# model result was appended
if (responseathighestdose < 0.5) {
if (lognorm)
{
m <- try(nls(response ~ pnorm(-log10(dose),-logEC50,slope),
data=tmp,
start=list(logEC50=startlogEC50[[i]],slope=1)))
if (!inherits(m, "try-error"))
{
ri <- ri + 1
rsubstance[[ri]] <- i
rn[[ri]] <- n
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
mtype[[ri]] <- "lognorm"
s <- summary(m)
logEC50[[ri]] <- coef(m)[["logEC50"]]
if (logEC50[[ri]] > rlhd[[ri]])
{
logEC50[[ri]] <- NA
slope[[ri]] <- NA
stderrlogEC50[[ri]] <- NA
} else
{
slope[[ri]] <- coef(m)[["slope"]]
stderrlogEC50[[ri]] <- s$parameters["logEC50","Std. Error"]
}
}
}
if (logis)
{
# Instead of plogis(), the function logisf() defined above
# could be used for regression against dose, not log10(dose)
m <- try(nls(response ~ plogis(-log10(dose),-logEC50,slope),
data=tmp,
start=list(logEC50=startlogEC50[[i]],slope=1)))
if (!inherits(m, "try-error"))
{
ri <- ri + 1
rsubstance[[ri]] <- i
rn[[ri]] <- n
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
mtype[[ri]] <- "logis"
s <- summary(m)
logEC50[[ri]] <- coef(m)[["logEC50"]]
if (logEC50[[ri]] > rlhd[[ri]])
{
logEC50[[ri]] <- NA
slope[[ri]] <- NA
stderrlogEC50[[ri]] <- NA
} else
{
slope[[ri]] <- coef(m)[["slope"]]
stderrlogEC50[[ri]] <- s$parameters["logEC50","Std. Error"]
}
}
}
if (linearlogis)
{
m <- try(nls(response ~ linearlogisf(dose,1,f,logEC50,b),
data=tmp,
start=list(f=f0,logEC50=startlogEC50[[i]],b=b0)))
if (!inherits(m, "try-error"))
{
ri <- ri + 1
rsubstance[[ri]] <- i
rn[[ri]] <- n
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
mtype[[ri]] <- "linearlogis"
s <- summary(m)
#print(s)
logEC50[[ri]] <- coef(m)[["logEC50"]]
if (logEC50[[ri]] > rlhd[[ri]])
{
logEC50[[ri]] <- NA
stderrlogEC50[[ri]] <- NA
b[[ri]] <- NA
f[[ri]] <- NA
} else
{
stderrlogEC50[[ri]] <- s$parameters["logEC50","Std. Error"]
b[[ri]] <- coef(m)[["b"]]
f[[ri]] <- coef(m)[["f"]]
}
}
}
}
if (ri == rix) # if no entry was appended for this substance
{
ri <- ri + 1
rsubstance[[ri]] <- i
rn[[ri]] <- n
rlld[[ri]] <- log10(lowestdose)
rlhd[[i]] <- log10(highestdose)
mtype[[ri]] <- "none"
logEC50[[ri]] <- NA
stderrlogEC50[[ri]] <- NA
slope[[ri]] <- NA
b[[ri]] <- NA
f[[ri]] <- NA
}
}
results <- data.frame(rsubstance,rn, rlld, rlhd, mtype, logEC50, stderrlogEC50, unit)
names(results) <- c("Substance","n", "lld","lhd","mtype","logEC50","std","unit")
if (lognorm || logis) {
results$slope <- slope
}
if (linearlogis) {
results$b <- b
results$f <- f
}
return(results)
}
drplot <- function(drresults, data = FALSE, dtype = "std", alpha = 0.95,
path = "./", fileprefix = "drplot", overlay = FALSE,
postscript = FALSE,
color = TRUE,
datacolors = 1:8, fitcolors = "default")
{
# Prepare plots
devices <- 1
if (postscript && !overlay) psdevices <- vector()
if (!postscript && !overlay) x11devices <- vector()
unit <- levels(as.factor(drresults$unit))
# Get the plot limits on the x-axis (log of the dose)
if(is.data.frame(data))
{
lld <- log10(min(data$dose))
lhd <- log10(max(data$dose))
hr <- max(data$response)
dsubstances <- levels(data$substance)
} else {
lld <- min(drresults[["logEC50"]],na.rm=TRUE) - 2
lhd <- max(drresults[["logEC50"]],na.rm=TRUE) + 2
if (length(subset(drresults,mtype=="linearlogis")$substance) != 0) {
hr <- 1.8
} else {
hr <- 1.0
}
}
# Prepare overlay plot if requested
if (overlay)
{
devices <- devices + 1
if (postscript) {
filename = paste(path,fileprefix,".eps",sep="")
postscript(file=filename,
paper="special",width=7,height=7,horizontal=FALSE,pointsize=12)
cat("Created File: ",filename,"\n")
} else {
x11(width=7,height=7,pointsize=12)
}
plot(0,type="n",
xlim=c(lld - 0.5, lhd + 2),
ylim= c(-0.1, hr + 0.2),
xlab=paste("Decadic Logarithm of the dose in ", unit),
ylab="Normalized response")
}
# Plot the data either as raw data or as error bars
if(is.data.frame(data))
{
splitted <- split(data,data$substance)
n <- 0
for (i in dsubstances)
{
# Prepare the single graphs if an overlay is not requested
if (!overlay)
{
devices <- devices + 1
if (postscript) {
filename = paste(path,fileprefix,sub(" ","_",gsub("([\(\) ])", "", i)),".eps",sep="")
postscript(file=filename,
paper="special",width=7,height=7,horizontal=FALSE,pointsize=12)
psdevices[[i]] <- devices
cat("Created File: ",filename,"\n")
} else {
x11(width=7,height=7,pointsize=12)
x11devices[[i]] <- devices
}
plot(0,type="n",
xlim=c(lld - 0.5, lhd + 2),
ylim= c(-0.1, hr + 0.2),
xlab=paste("Decadic Logarithm of the dose in ", unit),
ylab="Normalized response")
}
if (color == FALSE) datacolors <- rep("black",length(dsubstances))
n <- n + 1
if (!overlay) legend(lhd - 1, hr + 0.1, i,lty = 1, col = datacolors[[n]])
tmp <- splitted[[i]]
tmp$dosefactor <- factor(tmp$dose) # necessary because the old
# factor has all levels, not
# only the ones tested with
# this substance
if (dtype == "raw") {
points(log10(tmp$dose),tmp$response,col=datacolors[[n]])
} else {
splitresponses <- split(tmp$response,tmp$dosefactor)
means <- sapply(splitresponses,mean)
lengths <- sapply(splitresponses,length)
vars <- sapply(splitresponses,var)
standarddeviations <- sqrt(vars)
}
if (dtype == "std")
{
tops <- means + standarddeviations
bottoms <- means - standarddeviations
}
if (dtype == "conf")
{
confidencedeltas <- qt((1 + alpha)/2, lengths - 1) * sqrt(vars)
tops <- means + confidencedeltas
bottoms <- means - confidencedeltas
}
if (dtype != "raw")
{
x <- log10(as.numeric(levels(tmp$dosefactor)))
segments(x,bottoms,x,tops,col=datacolors[[n]])
points(x,means,col=datacolors[[n]])
smidge <- 0.05
segments(x - smidge,bottoms,x + smidge,bottoms,col=datacolors[[n]])
segments(x - smidge,tops,x + smidge,tops,col=datacolors[[n]])
}
}
}
# Plot the fitted dose response models from drresults
fits <- subset(drresults,!is.na(logEC50))
nf <- length(fits$Substance) # number of fits to plot
if (fitcolors[[1]] == "default")
{
if (nf <= 8)
{
defaultfitcolors <- palette()
} else
{
defaultfitcolors <- rainbow(nf)
}
}
legendcolors <- vector()
for (i in 1:nf)
{
s <- as.character(fits[i,"Substance"]) # The substance name to display
if (!overlay && !is.data.frame(data))
{
devices <- devices + 1
if (postscript) {
filename = paste(path,fileprefix,sub(" ","_",gsub("([\(\) ])", "", s)),".eps",sep="")
postscript(file=filename,
paper="special",width=7,height=7,horizontal=FALSE,pointsize=12)
psdevices[[s]] <- devices
cat("Created File: ",filename,"\n")
} else {
x11(width=7,height=7,pointsize=12)
x11devices[[s]] <- devices
}
plot(0,type="n",
xlim=c(lld - 0.5, lhd + 2),
ylim= c(-0.1, hr + 0.2),
xlab=paste("Decadic Logarithm of the dose in ", unit),
ylab="Normalized response")
}
if (postscript && !overlay) {
dev.set(psdevices[[s]]) }
if (!postscript && !overlay) {
dev.set(x11devices[[s]]) }
if (color == FALSE) {
fitcolor <- "black"
} else {
if (fitcolors[[1]] == "default")
{
fitcolor <- defaultfitcolors[[i]]
} else {
fitcolor <- fitcolors[[i]]
}
}
if (!overlay) legend(lhd - 1, hr + 0.1, s,lty = 1, col = fitcolor)
legendcolors[[i]] <- fitcolor
logEC50 <- fits[i,"logEC50"]
mtype <- as.character(fits[i, "mtype"])
if (mtype == "lognorm")
{
slope <- fits[i,"slope"]
plot(function(x) pnorm(-x,-logEC50,slope),lld - 0.5, lhd + 2, add=TRUE,col=fitcolor)
}
if (mtype == "logis")
{
slope <- fits[i,"slope"]
plot(function(x) plogis(-x,-logEC50,slope),lld - 0.5, lhd + 2, add=TRUE,col=fitcolor)
}
}
if (overlay) {
legend(lhd - 1, hr + 0.1, as.vector(fits$Substance), lty = 1, col = legendcolors)
}
if (devices > 1 && postscript)
{
for (i in 2:devices) {
dev.off(i)
}
}
}
checkplate <- function(plate,db="cytotox") {
library(RODBC)
channel <- odbcConnect(db,uid=db,pwd=db,case="tolower")
platequery <- paste("SELECT experimentator,substance,celltype,conc,unit,viability,performed,ok FROM ",
db," WHERE plate=", plate)
controlquery <- paste("SELECT type,response FROM controls WHERE plate=",plate)
platedata <- sqlQuery(channel,platequery)
controldata <- sqlQuery(channel,controlquery)
odbcClose(channel)
if (length(platedata$experimentator) < 1) {
cat("There is no response data for plate ",plate," in database ",db,"\n")
} else {
platedata$experimentator <- factor(platedata$experimentator)
platedata$celltype <- factor(platedata$celltype)
platedata$substance <- factor(platedata$substance)
platedata$unit <- factor(platedata$unit)
platedata$performed <- factor(platedata$performed)
platedata$ok <- factor(platedata$ok)
blinds <- subset(controldata,type=="blind")
controls <- subset(controldata,type=="control")
numberOfBlinds <- length(blinds$response)
numberOfControls <- length(controls$response)
meanOfBlinds <- mean(blinds$response)
meanOfControls <- mean(controls$response)
stdOfBlinds <- sd(blinds$response)
stdOfControls <- sd(controls$response)
cat("Plate ",plate," from database ",db,"\n",
"\tExperimentator: ",levels(platedata$experimentator),"\n",
"\tCell type(s): ",levels(platedata$celltype),"\n",
"\tPerformed on : ",levels(platedata$performed),"\n",
"\tSubstance(s): ",levels(platedata$substance),"\n",
"\tConcentration unit: ",levels(platedata$unit),"\n",
"\tOK: ",levels(platedata$ok),"\n",
"\t\tNumber \tMean \tStandard Deviation\n",
"blind\t\t",numberOfBlinds,"\t",meanOfBlinds,"\t",stdOfBlinds,"\n",
"control\t",numberOfControls,"\t",meanOfControls,"\t",stdOfControls,"\n")
par(ask=TRUE)
boxplot(blinds$response,controls$response,names=c("blinds","controls"),ylab="Response",main=paste("Plate ",plate))
drdata <- subset(platedata,select=c(substance,conc,viability))
drdata$substance <- factor(drdata$substance)
substances <- levels(drdata$substance)
substances
plot(log10(drdata$conc),drdata$viability,
xlim=c(-2.5, 4.5),
ylim= c(-0.1, 2),
xlab=paste("Decadic Logarithm of the concentration in ",levels(platedata$unit)),
ylab="Viability")
drdatalist <- split(drdata,drdata$substance)
for (i in 1:length(drdatalist)) {
points(log10(drdatalist[[i]]$conc),drdatalist[[i]]$viability,col=i);
}
legend(3.0,1.5,substances, pch=1, col=1:length(substances))
title(main=paste("Plate ",plate," - ",levels(platedata$experimentator)," - ",levels(platedata$celltype)))
}
}
