Abstract
Background: R is one of the renowned programming language which is an open source software developed by the scientific community to compute, analyze and visualize big data of any field including biomedical research for bioinformatics applications.
Methods: Here, we outlined R allied packages and affiliated bioinformatics infrastructures e.g. Bioconductor and CRAN. Moreover, basic concepts of factor, vector, data matrix and whole transcriptome RNA-Seq data was analyzed and discussed. Particularly, differential expression workflow on simulated prostate cancer RNA-Seq data was performed through experimental design, data normalization, hypothesis testing and downstream investigations using EdgeR package. A few genes with ectopic expression were retrieved and knowhow to gene enrichment pathway analysis is highlighted using available online tools.
Results: Data matrix of (4×3) was constructed, and a complex data matrix of Golub et al., was analyzed through χ2 statistics by generating a frequency table of 15 true positive, 4 false positive, 15 true negative and 4 false negative on gene expression cut-off values, and a test statistics value of 10.52 with 1 df and p= 0.001 was obtained, which reject the null hypothesis and supported the alternative hypothesis of “predicted state of a person by gene expression cut-off values is dependent on the disease state of patient” in our data. Similarly, sequence data of human Zyxin gene was selected and a null hypothesis of equal frequencies was rejected.
Conclusion: Machine-learning approaches using R statistical package is a supportive tool which can provide systematic prediction of putative causes, present state, future consequences and possible remedies of any problem of modern biology.
Rashid Saif, Kinza Qazi, Saeeda Zia, Tania Mahmood, Aniqa Ejaz, Talha Tamseel, Suliman Mohammad Alghanem, Adnan Khaliq. (2018) First Step with R for Life Sciences: Learning Basics of this Tool for NGS Data Analysis, Advancements in Life Sciences, Volume 6, Issue 4.
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