The NIH-funded LINCS program has been initiated to generate a library of 
integrated, network-based, cellular signatures (LINCS). A novel high-throughput 
gene-expression profiling assay known as L1000 was the main technology used to 
generate more than a million transcriptional profiles. The profiles are based on 
the treatment of 14 cell lines with one of many perturbation agents of interest 
at a single concentration for 6 and 24 hours duration. In this study, we focus 
on the chemical compound treatments within the LINCS data set. The experimental 
variables available include number of replicates, cell lines, and time points. 
Our study reveals that compound characterization based on three cell lines at 
two time points results in more genes being affected than six cell lines at a 
single time point. Based on the available LINCS data, we conclude that the most 
optimal experimental design to characterize a large set of compounds is to test 
them in duplicate in three different cell lines. Our conclusions are constrained 
by the fact that the compounds were profiled at a single, relative high 
concentration, and the longer time point is likely to result in phenotypic 
rather than mechanistic effects being recorded.