Comparative Analysis of R and Mathematica Package for Differential Gene Expression Analysis Using Microarray Dataset on Pancreatic Cancer

Microarrays produces enormous amounts of information requiring a series repeated analyses to condense data. To analyze this data several computational software is used. Objective: compare the analysis R and Mathematica package for differential gene expression using microarray dataset. Methods: Microarray Data were collected from an online database GEO (gene omnibus). was used comparative analysis. In software, Robust Multi-Array Average (RMA), normalization. While Limma DGE AffyDGED normalization Results: 3,426 non-differentially expressed genes 14936 with separated R. The thresholds identifying "up" "down" estimated be 0.98 -0.19, respectively, RMA method detected 1,832 as differentially expressed; them, 1,591 overlap real 1,944 differently genes, giving true positive rate (1591/1944) =0.818. This indicates that 18% genuine list could not reliably identified by AffyDGED. Conclusions: programming one most popular recommendable tools microarrays perform different analysis, along Bioconductor it makes best algorithms On other hand, brings contemporary algorithm useful in world user.