Result Analysis results showed drug treatment effect is cell line dependent In the cMap data, each drug treatment profile includes several treated samples from different cell lines. Whether the effects of the same drug treatments differ for different cell lines need to be investigated before a drug MoA net work can be constructed. To this end, samples of cMap data were first www.selleckchem.com/products/Calcitriol-(Rocaltrol).html grouped based on compounds and the compounds with more than 30 samples were retained. Note that since the data have already been normalized and fold changed over the control sample in the same cell line, the cell line dependent bias should be eliminated. any dif ferences in expression levels within the samples of the same compound are manifestation of differences in chemo effectiveness due to differences in cell line, drug concentrations, or a combination of both.

Hierarchical clustering was performed to the samples in each com pound group to reveal Inhibitors,Modulators,Libraries potential differences in expression patterns within the same compound. Correlating the clustering results with cell line types and concentrations revealed that chemo effectiveness depends mainly on cell lines and is independent of concentration when it is effective. This finding is significant because it suggested that network construction and drug predictions should be performed by considering cell lines separately. Knowing the effect of one drug for treating breast cancer does not provide information on its effectiveness in lung cancer. including samples from cells other than breast cancer cells introduce only noise to drug treatment net work construction.

As a result, removing samples from other cells mitigates the interference and consequently improves the accuracy and robustness of the prediction result. Since MCF7 breast cancer cell line cohort contains the largest number of samples, and it contains more drug replicate samples than other cell lines, Inhibitors,Modulators,Libraries we focused Inhibitors,Modulators,Libraries in this work on developing a breast cancer specific MoA network. Drug signature gene set selection The goal of signature gene set selection is to identify a set of genes that have significant differential expression after the drug treatment. However, the Inhibitors,Modulators,Libraries use of the conventional differential analysis methods such as t test is hampered by the lack of the biological replicates in the cMap data set. This limitation becomes even severer after the quality con trol.

For the MCF7 cell line, among all 1251 drugs in cMap, only 32 drugs have more than 5 Inhibitors,Modulators,Libraries samples and 1055 drugs have 2 samples. With such small sample size, any statisti cally based differential analysis becomes infeasible. To this end, we proposed two criteria based on which the signature gene set of drug was selected first, the signature genes should have high fold change expression, and second, the fold change levels of the signature genes should be consis tently high among cisplatin synthesis the replicate samples.