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