R language vs Python: Which is the most necessary programming language for a bioinformatician?
I am new to bioinformatics and here is some information for me to get insight into the R programing language. I’m happy to share with you. In my mind, I think the R language is the most suitable language for BI analysis. What do you think?
What is R language?
R is an open-source language for statistical analysis and graphics. The language has been used in a mass of scenarios such as data mining, machine learning, and bioinformatics studies. The package contains a wide range of statistical tests which includes parametric and non-parametric tests for hypothesis testing. Like other languages, it has conditional statements, loops and data structures. R also provides a way to visualize the data and analysis by converting them into plots.
Advantages of R language over other analysis languages
R can handle large data with a large number of columns and rows without compromising the data. In one of the news published by BBC due to restriction of columns and rows in Microsoft excel, Covid-19 data of around 16,000 patients were lost. Due to this loss of data, the number of false negatives increases. This may result in the spread of Covid-19 since those false-negative patients or patients with possible Covid-19 infection can come in contact with other people. This issue can be easily avoided by using R instead of excel where the limit of data is very large as compared to MS Excel.
Application scenarios of the R language in bioinformatics
In life sciences especially in bioinformatics R has been used frequently. Many data analysis algorithms or methods are available in R which was developed by scientific researchers all around the globe. Simple hypothesis tests, like t-test can be used to find the difference in sample data or complex field data can be analyzed using ANOVA which will give the p-value along with other statistics. In biological science co-expression networks between genes using their expression can reveal many interactions pathways which can give insight into the function of genes altogether. In such cases, correlation networks or weighted correlation networks are very helpful. These networks and co-expression can easily be drawn using R. Apart from simple analyses R can be used for NGS analyses. Few of examples include analysis of RNA-Seq, ChIP-Seq, Wole Genome Bisulfite Sequencing, small RNA-seq and many more. Using the Bioconductor package of R all these analyses can be done on a local machine.
Since I am in lack of information about Python, if you think the Python language is also useful for BI analysis, welcome to leave different opinions.