https://github.com/jcaperella29/processing-rna_seq-data-with-r-

An R script that reads in RNA_Seq data , preprocesses it , then performs differential expression analysis and feature selection.
https://github.com/jcaperella29/processing-rna_seq-data-with-r-

Last synced: 3 months ago
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An R script that reads in RNA_Seq data , preprocesses it , then performs differential expression analysis and feature selection.

• Host: GitHub
• URL: https://github.com/jcaperella29/processing-rna_seq-data-with-r-
• Owner: jcaperella29
• Created: 2024-03-26T00:07:56.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-18T20:07:24.000Z (about 1 year ago)
• Last Synced: 2024-04-18T21:25:59.079Z (about 1 year ago)
• Language: R
• Size: 10.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Processing-RNA_Seq-data-with-R-

An R script that reads in RNA_Seq data  , preprocesses it , then performs differential expression analysis and feature selection.

#First needed libraries are imported.

#Sample data from the "airways" package is read in.

#the counts and phenotype data are isolated.

#data is then filtered based on Group size and undergo VST transformation.

#then the distance between samples is measured and a visualized via a heatmap.

#Next PCA analysis is performed .

#Then diffenrential expression  analysis is performed.

#results of differential expression are then sorted by adjusted p-value and p-values less 5e-8 are kept.

# After a bit of rearrangize the genes that produced the low p-values are isolated from the  counts matrix

# A new dataframe is prepared with columns of the counts and the phenotype.

# Using a Random Forest model , hits are narrowed down via feature selection

# Finally the top 20 genes are output as a text file.

# Note when using real data, enter your counts matrix at line 62 and other information(like phenotypes) at line 64