Gene expression profiling using transcriptional drug perturbations are useful 
for many biomedical discovery studies including drug repurposing and elucidation 
of drug mechanisms (MoA) and many other pharmacogenomic applications. However, 
limited data availability across cell types has severely hindered our capacity 
to progress in these areas. To fill this gap, recently, the LINCS program 
generated almost 1.3 million profiles for over 40,000 drug and genetic 
perturbations for over 70 different human cell types, including meta information 
about the experimental conditions and cell lines. Unfortunately, Big Data like 
the ones generated from the ongoing LINCS program do not enable easy insights 
from the data but possess considerable challenges toward their analysis. In this 
paper, we address some of these challenges. Specifically, first, we study the 
gene expression signature profiles from all cell lines and their perturbagents 
in order to obtain insights in the distributional characteristics of available 
conditions. Second, we investigate the differential expression of genes for all 
cell lines obtaining an understanding of condition dependent differential 
expression manifesting the biological complexity of perturbagents. As a result, 
our analysis helps the experimental design of follow-up studies, e.g., by 
selecting appropriate cell lines.