Exploring R

  Introduction In the vast realm of bioinformatics, understanding gene expression is crucial for unraveling the mysteries of biology and disease. One powerful tool in this pursuit is the R programming language, enriched by packages like DESeq2 for conducting differential gene expression analysis. However, navigating these tools can be complex for beginners. That's where our custom package, MyDataAnalytics, steps in to streamline the process. In this blog post, we'll walk you through the steps of conducting differential gene expression analysis using MyDataAnalytics, simplifying a task that can often seem daunting. Part 1: Creating Your Custom R Package Creating a custom R package involves several steps, from defining functions to structuring directories and documentation. Let's outline the process: Define Functions : Begin by defining functions that encapsulate specific tasks related to RNA-Seq analysis, such as creating DESeqDataSet objects, running DESeq analysis, filteri...

  R Markdown  is a file format for making dynamic documents with  R . An  R Markdown  document is written in  markdown  (an easy-to-write plain text format) and contains chunks of embedded  R  code.  Markdown  is a simple formatting syntax for authoring  HTML, PRESENTATIONS, PDF, and MS Word  documents It has  three types of content  which include text, code chunks and  YAML metadata which build  R Markdown.  ✔ YAML metadata consists of information about the title, author, date, and output. It starts and ends with three dashes  (---) .  ✔ Code chunks can be run without interference from the text. Multiple code chunks of different programming languages like  Python and SQL  can also be included in the same document.  A single code can also be run and output can be viewed for the corresponding code in the R console. A code should start with  ```{r}  and end with...