Empowering RNA-Seq Analysis: Building and Utilizing Your Custom R Package

 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:

  1. Define Functions: Begin by defining functions that encapsulate specific tasks related to RNA-Seq analysis, such as creating DESeqDataSet objects, running DESeq analysis, filtering data, and visualizing results.
  2. Structure Directories: Organize your package's files into directories following the standard R package structure, including directories for R code ('R/'), documentation ('man/'), tests ('tests/'), and data ('data/').
  3. Documentation: Document your functions using roxygen2-style comments to generate documentation files automatically.
  4. Tests: Write tests for your functions to ensure they work as expected and maintain functionality across updates.
  5. Build Package: Use 'devtools' or 'pkgbuild' to build your package into a distributable format.

Part 2:

Step 1: Reading Sample Information

Before diving into analysis, it's essential to have a clear understanding of the samples under study. Our colData function within MyDataAnalytics facilitates the seamless reading of sample information, ensuring that your analysis starts on a solid foundation.

Step 2: Preparing Count Data

The core of any differential gene expression analysis lies in the count data. Our counts_data script, integrated into MyDataAnalytics, takes care of this crucial step. By preparing the count data, we pave the way for meaningful insights into gene expression patterns.

Step 3: Constructing a DESeqDataset Object

With count data in hand, the next step is to construct a DESeqDataset object. Our 'dds' function simplifies this process, allowing users to focus on the analysis itself rather than getting bogged down in technical details.

Step 4: Prefiltering Low-Count Rows

Not all genes are created equal, and prefiltering helps in focusing on the most relevant ones. Our 'keep' function aids in prefiltering by removing rows with low gene counts, ensuring that only genes with sufficient data are included in the analysis.

Conclusion

By integrating these functionalities into MyDataAnalytics, we aim to democratize the field of bioinformatics and empower researchers of all levels to delve into the intricate world of gene expression analysis. Whether you're a seasoned bioinformatician or just starting your journey, MyDataAnalytics is designed to make your analysis smoother, more efficient, and ultimately more insightful.

Please check the link for my Githib for detailed files, descriptions, Code and visualization.

https://github.com/Divya090597/DESeq-package/blob/1f78560c64fc019092103f35e8b7384795680d77/DGEA.R

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