This repository includes code for processing data of kshv_deg. kshv_deg.Rmd script has code for the results.
This pipeline is designed to be used in R environment.
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Install the R statistical package. We used version 4.0.4.
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Install the following R packages, which can be obtained using either the install.packages function in R or via the Bioconductor framework:
- limma
- DESeq2
- calibrate
- ggplot
- fgsea
- tidyverse
- data.table
Using uci.txt, the average_if value of each genes can be got. Then, using delta11_vs_mock.txt, delta_vs_wt.txt and mock_vs_wt.txt, Deseq2_uci.R, the differentially expressed genes (degs) can be got.
Using c2.cp.v7.2.symbols.gtm and c2.cp.kegg.v7.2.symbols.gmt, gene name and the t-test value to generate the kegg and canonical pathway. The input files are delta11_vs_mock_fgsea.txt, delta11_vs_we_fgsea.txt and mock_vs_wt_fgsea.txt, by using fgsea_uci.R, can get the kegg and the whole canonical pathways.
Input the differentially expresed protein into david functional annotation bioinformatics, generate the GO analysis. Only picked the Description, Gene_Count, P_Value and Enrichment_Ratio value to generate the txt fle. The input files are delta11_vs_mock_bp01.txt, delta11_vs_mock_cc01.txt, delta11_vs_mock_mf01.txt, delta11_vs_wt_bp01.txt files,by using david_uci.R, the plots of the cellular component and molecular function can be drawn.
- Moom R. Roosam. [email protected]
- Yue Li. [email protected]