Comparative Analysis of R and Mathematica Package for Differential Gene Expression Analysis Using Microarray Dataset on Pancreatic Cancer
Comparative Analysis of R and Mathematica Package
Keywords:R Language, Mathematica, Differential Gene Expression, Microarray
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.
Michaud D. Epidemiology of pancreatic cancer. Minerva Chirurgica. 2004 Apr; 59(2): 99-111.
Kaur S, Baine MJ, Jain M, Sasson AR, Batra SK. Early diagnosis of pancreatic cancer: challenges and new developments. Biomarkers in Medicine. 2012 Oct; 6(5): 597-612. doi: 10.2217/bmm.12.69.
Yang S, Wang X, Contino G, Liesa M, Sahin E, Ying H, et al. Pancreatic cancers require autophagy for tumor growth. Genes & Development. 2011 Apr; 25(7): 717-29. doi: 10.1101/gad.2016111.
Kuhn K, Baker SC, Chudin E, Lieu MH, Oeser S, Bennett H, et al. A novel, high-performance random array platform for quantitative gene expression profiling. Genome Research. 2004 Nov; 14(11): 2347-56. doi: 10.1101/gr.2739104.
Mark D, Haeberle S, Roth G, Von Stetten F, Zengerle R. Microfluidic lab-on-a-chip platforms: requirements, characteristics and applications. Chemical Society Reviews. 2010 Jan; 39: 1153-82. doi: 10.1039/b820557b.
Heller MJ. DNA microarray technology: devices, systems, and applications. Annual Review of Biomedical Engineering. 2002 Aug; 4(1): 129-53. doi: 10.1146/annurev.bioeng.4.020702.153438.
Higgins JP, Shinghal R, Gill H, Reese JH, Terris M, Cohen RJ, et al. Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray. The American Journal of Pathology. 2003 Mar; 162(3): 925-32. doi: 10.1016/S0002-9440(10)63887-4.
Lockhart DJ and Winzeler EA. Genomics, gene expression and DNA arrays. Nature. 2000 Jun; 405(6788): 827-36. doi: 10.1038/35015701.
Costa-Silva J, Domingues D, Lopes FM. RNA-Seq differential expression analysis: An extended review and a software tool. PloS One. 2017 Dec; 12(12): e0190152. doi: 10.1371/journal.pone.0190152.
Márquez-Zacarías P, Pineau RM, Gomez M, Veliz-Cuba A, Murrugarra D, Ratcliff WC, et al. Evolution of cellular differentiation: from hypotheses to models. Trends in Ecology & Evolution. 2021 Jan; 36(1): 49-60. doi: 10.1016/j.tree.2020.07.013.
Can T. Introduction to Bioinformatics. Methods in Molecular Biology. 2013 Nov; 1107: 51-71. doi: 10.1007/978-1-62703-748-8_4.
Harrington CA, Rosenow C, Retief J. Monitoring gene expression using DNA microarrays. Current Opinion in Microbiology. 2000 Jun; 3(3): 285-91. doi: 10.1016/S1369-5274(00)00091-6.
Kerr MK and Churchill GA. Statistical design and the analysis of gene expression microarray data. Genetics Research. 2001 Feb; 77(2): 123-8. doi: 10.1017/S0016672301005055.
Xu J, Shu Y, Xu T, Zhu W, Qiu T, Li J, et al. Microarray expression profiling and bioinformatics analysis of circular RNA expression in lung squamous cell carcinoma. American Journal of Translational Research. 2018 Mar; 10(3): 771-83.
Chambers JM. Software for data analysis: programming with R. New York: Springer; 2008. doi: 10.1007/978-0-387-75936-4.
Maeder R. Programming in mathematica. Addison-Wesley Longman Publishing Co., Inc.; 1991.
Afshari CA, Nuwaysir EF, Barrett JC. Application of complementary DNA microarray technology to carcinogen identification, toxicology, and drug safety evaluation. Cancer Research. 1999 Oct; 59(19): 4759-60.
Irizarry RA, Hobbs B, Collin F, Beazer‐Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003 Apr; 4(2): 249-64. doi: 10.1093/biostatistics/4.2.249.
Gregory Alvord W, Roayaei JA, Quiñones OA, Schneider KT. A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R. Briefings in Bioinformatics. 2007 Nov; 8(6): 415-31. doi: 10.1093/bib/bbm043.
Allen T. Detecting differential gene expression using affymetrix microarrays. The Mathematica Journal. 2013; 15: 1-26. doi: 10.3888/tmj.15-11.
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