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Update Tuesday/methylGSA_Functional_analysis/data/NonAgedVSAged_Manova_DMRs.txt, Tuesday/methylGSA_Functional_analysis/FuncAnalysis_methylGSA.html, Tuesday/methylGSA_Functional_analysis/FuncAnalysis_methylGSA.Rmd, Tuesday/methylGSA_Functional_analysis/results/methyGSA_GSEA_GO_hypohypermethylated_table.txt

Tuesday/methylGSA_Functional_analysis/FuncAnalysis_methylGSA.Rmd

+---
+title: 'Functional Analysis of differentially methylated regions - NonAged vs Aged'
+subtitle: 'methylGSA - FCS approach' 
+author: "Claudia Chica"
+date: "`r Sys.Date()`"
+output: 
+  html_document: 
+    keep_md: yes
+    number_sections: yes
+    smart: no
+    toc: yes
+    toc_float: yes
+editor_options: 
+  chunk_output_type: console
+---
+
+
+
+```{r setup, include=FALSE, message=FALSE, warning=FALSE, echo=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+
+# working directory
+workdir="/Volumes/bioit-1/PHINDAcces_HandsOn/Tuesday/methylGSA_Functional_analysis"
+
+library(methylGSA)
+library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
+
+```
+
+# Methylated regions analysis
+
+Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant concordant differences between two biological states. In this case aged (CSI_A & CSI_B) and non aged (WT + UVSS).
+
+MethylGSA (https://doi.org/10.1093/bioinformatics/bty892) proposes and implementation of GSEA specific for methylation datasets. MethylGSA adapts the robust rank aggregation (RRA) approach to adjust for number of CpGs in DNA methylation gene set testing. 
+
+```{r uploadData_MR, message=FALSE, warning=FALSE, echo=FALSE}
+
+dataset='allMRs' ; method="GSEA"
+# Data and result folders 
+outdir=paste0(workdir,"/results/")
+datadir=paste0(workdir,"/data/")
+
+data_file='NonAgedVSAged_Manova_DMRs.txt'
+count.data <- read.table(paste0(datadir,data_file),header = T)
+```
+
+## methylGSA GSEA on gene sets
+
+```{r methylGSA_GO, message=FALSE, warning=FALSE, echo=FALSE}
+# Perform GSEA with GO
+cpg.pval=count.data$NonAgedVSAged_Manova_minAdj_BH
+names(cpg.pval)=count.data$ID_REF
+hist(cpg.pval)
+mGSAres_GO = methylRRA(cpg.pval = cpg.pval, method = method, 
+  GS.type = "GO", GS.idtype = "SYMBOL", minsize = 100, maxsize = 500)
+
+# Print results
+## Print first 15 enriched terms
+print(head(mGSAres_GO,15)[,c(1,2,3,5,7)])
+## Print table with enriched terms
+fileOut=paste0(outdir,"/methyGSA_",method,"_GO_hypohypermethylated_table.txt")
+write.table(mGSAres_GO[,-8],fileOut,row.names = F, sep="\t",quote = F)
+
+```
+
+
+<details>
+  <summary>**Now try:**</summary>
+  
+How would you try to do the same analysis but using pathways, instead of GO terms? 
+
+```{r methylGSA_PW, message=FALSE, warning=FALSE, echo=FALSE, include=FALSE, eval=FALSE}
+# Perform GSEA with GO
+mGSAres_PW = methylRRA(...)
+
+# Print results
+## Print first 15 enriched terms
+print(head(mGSAres_PW,15)[,c(1,2,3,5,7)])
+## Print table with enriched term
+fileOut=paste0(outdir,"/methyGSA_",method,"_PW_hypohypermethylated_table.txt")
+write.table(mGSAres_PW[,-8],fileOut,row.names = F, sep="\t",quote = F)
+
+
+```
+
+
+What's the meaning of the following parameters? How do they change the results?
+`minsize = 100` and  `maxsize = 500`
+
+```{r, include=FALSE, eval=FALSE}
+mGSAres = methylRRA(cpg.pval = cpg.pval, method = method, 
+  GS.type = "GO", GS.idtype = "SYMBOL", minsize = 10, maxsize = 100)
+
+print(head(mGSAres,15)[,c(1,2,3,5,7)])
+
+```
+ 
+
+</details>
+
+# Visualization with REVIGO
+
+From the GO enriched terms, select the most significant and try to summarize them using the REVIGO tool.
+http://revigo.irb.hr
+
+
+R session information
+---------------------
+
+In the reproducible research framework, an important step is to save all the versions of the softwares used to perform the statistical analysis. They must be provided when submitting a paper.
+
+```{r}
+sessionInfo()
+```