Differential Expression Analysis on Array Data from GEO using Limma R

Want to do a Bioinformatics Project from already available datasets online?
If you're a beginner, one of your best options is to perform a Differential Expression Analysis.
This video is about conducting a Differential Expression Analysis on Array data from GEO (Gene Express Omnibus) using the Limma Package in R.
Here, you can see how to search and select a dataset from GEO based on your research question.
Copy-Paste the R script into your RStudio script and run. Adjust the script where necessary according to your dataset and research question.
Refer to the Basic_Instructions_DEAon ArrayDatafromGEO document on how to customize the given workflow and R script for your analysis.
Refer to the RScript_Explanation_DEAonArrayData document to learn more about the R script and Differential Expression Analysis workflow and statistical models.

and/or

In the example shown in this video, I tried to find a dataset of gene expression profiles from Parkinson's patients. It can be mRNA, miRNA, or circRNA but should be profiled using array (i.e. microarray), not by high throughput sequencing (RNA-seq).
This is because the workflow to perform differential expression analysis on RNA-seq data is different from Array data.

there, you can find relevant R scripts and workflows.

Follow TLR4 BioScripts for more Bioinformatics workflows.

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