Standardized high-throughput evaluation of cell-based compound screens

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Description

Simultaneous evaluation of a large number of compound screens on 96- and 384-well plates.

Usage

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ic50()
hts.96(indir=".",plates=2,measure=NULL,control=NULL,dilution=NULL,inhib=NULL,
       normalize="mean",graphics="mean",outdir="./results")
hts.384(indir=".",plates=2,measure=NULL,control=NULL,dilution=NULL,inhib=NULL,
        normalize="single",graphics="mean",outdir="./results")

Arguments

indir

A character specifying the directory which contains the raw data files.

plates

Number of plates used for each experiment.

measure

Configuration file for the locations of the measurement wells.

control

Configuration file for the locations of the control wells.

dilution

Configuration for the concentrations in each measurement.

inhib

Vector of real numbers between 0 and 1 specifying the percentage of inhibition to compute concentrations for. Defaults to 0.5 for all compounds.

normalize

Method to normalize the measurement by the controls. If "mean", the mean of the controls specified by control is used; "single" requires one individual control well per measurement well.

graphics

A character specifying the plotting method. For "mean", a dose-response curve of the mean values of the measurement series is given, whereas one curve is plotted for each if "single" is specified. For "fitted", a sigmoid-shaped derivation of the logistic model is fitted to the data.

outdir

The directory where the results will be written.

Details

In cytotoxicity screens of chemical compounds, biological activity is typically quantified by the concentration for which a particular fraction (typically 0.5) of cell growth is inhibited after a predefined treatment period. For this purpose, all concentrations are plotted against the percentages of cells still being alive under this treatment, forming a dose-response curve under which the preimage of the 0.5 point is defined as the half-maximum inhibitory concentration (IC50). For high-throughput screens (HTS), in particular, the evaluation of the data needs to be performed in an automatic fashion.

The hts.96 and hts.384 functions provide a powerful tool to simultaneously evaluate all data in the specified input directory indir. The data files are handled in groups of the size specified by plates and the file names should be arranged in a way that two plates with replicates for the same measurements are displayed one below the other in a file browser. The data are expected to be arranged in tab-delimited text files which is the typical output of appropriate microplate readers. Just as for the evaluation of a single measurement, the design must be specified by tab-delimited files for measure, control and dilution. Details on these are given in the manual of the default384_measure and default384_control files. In addition, a tutorial document describing how to prepare the data and configuration is included in the ic50 package.

For each compound in the screen and each group of data files, a graphics output is given in the file "dose_response_curves.pdf" in the current workspace directory. In addition, the text file "ic50.txt" contains a tab-delimted table with the same evaluation as for the ic50.96 and ic50.384 functions but for all experiments one below the other.

ic50() starts a GUI-based version of the hts.96 and hts.384 functions. Preliminary change of the workspace directory to the folder containing the data will remarkably reduce the number of mouse clicks.

Please make use of the tutorial document in the doc folder which helps users to get started with the software.

Value

A data frame with the following columns:

first_file

Filename of the respective first input file.

compound

Compound names.

ic50

The inhibitory concentrations for the respective compounds.

clow

Lower 0.95 confidence limits for the IC values.

cup

Upper 0.95 confidence limits for the IC values.

maxsd

Maximum of the standard deviations at the measured concentrations as determined from the single replicates.

cv

Coefficient of variation of the IC values as determined from the single replicates.

Author(s)

Peter Frommolt, University of Cologne peter.frommolt@uni-koeln.de
http://portal.ccg.uni-koeln.de/

References

Frommolt P, Thomas RK (2008): Standardized high-throughput evaluation of cell-based compound screens. BMC Bioinformatics, 9(1): 475

Sos ML, Michel K, Zander T, Weiss J, Frommolt P, et al. (2009): Predicting drug susceptibility in non-small cell lung cancers based on genetic lesions. J Clin Invest, 119(6): 1727-40

Examples

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#Example from a non-small cell lung cancer (NSCLC) cell line screen. In
#total, 84 samples were screened. The evaluation is exemplarily shown for
#the cell lines A549, Calu1, H322 and HCC2429.

data(A549_1,A549_2,Calu1_1,Calu1_2,H322_1,H322_2,HCC2429_1,HCC2429_2)
dir.create("NSCLC_screen")
write.table(A549_1,file="NSCLC_screen/A549_1.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(A549_2,file="NSCLC_screen/A549_2.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(Calu1_1,file="NSCLC_screen/Calu1_1.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(Calu1_2,file="NSCLC_screen/Calu1_2.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(H322_1,file="NSCLC_screen/H322_1.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(H322_2,file="NSCLC_screen/H322_2.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(HCC2429_1,file="NSCLC_screen/HCC2429_1.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(HCC2429_2,file="NSCLC_screen/HCC2429_2.txt",row.names=FALSE,col.names=FALSE,sep="\t")

data(mpi384_measure,mpi384_control,mpi384_dilution)
write.table(mpi384_measure,file="mpi384_measure.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(mpi384_control,file="mpi384_control.txt",row.names=FALSE,col.names=FALSE,sep="\t")
write.table(mpi384_dilution,file="mpi384_dilution.txt",row.names=FALSE,col.names=FALSE,sep="\t")

print(hts.384(indir="NSCLC_screen",
              measure="mpi384_measure.txt",control="mpi384_control.txt",dilution="mpi384_dilution.txt",
              inhib=rep(0.5,7),outdir="NSCLC_results",normalize="mean"))