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a. In windows explorer, go to: H:\workshop\RNAseq
b. In this folder, create a new folder called ‘functional_annotation’ (case-sensitive)
c. Open RStudio and create a new R script (‘File’ → “New File” → “R script”). Now hit ‘File’ → ‘Save’ and save the script in the H:\workshop\RNAseq\functional_annotation folder you created. Save the script file as ‘functional_annotation.R’
In the following sections you will be copying and running the R code into your functional_annotation.R script.
5. Installing packages
IMPORTANT: don’t run this code in this rVDI session, the packages are already installed. This code is only here if you need to run the analysis on your computer.
#### 5. Installing required packages ####
bioconductor_packages <- c("clusterProfiler", "pathview", "AnnotationHub", "org.Mm.eg.db") cran_packages <- c("tidyverse", "ggplot2", "plyr", "readxl", "scales") # Compares installed packages to above packages and returns a vector of missing packages new_packages <- bioconductor_packages[!(bioconductor_packages %in% installed.packages()[,"Package"])] new_cran_packages <- cran_packages[!(cran_packages %in% installed.packages()[,"Package"])] # Install missing bioconductor packages if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(new_packages) # Install missing cran packages if (length(new_cran_packages)) install.packages(new_cran_packages, repos = http://cran.us.r-project.org) # Update all installed packages to the latest version update.packages(bioconductor_packages, ask = FALSE) update.packages(cran_packages, ask = FALSE, repos = http://cran.us.r-project.org) |
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NOTE: You need to have the correct path for your DE_genes_Basal_cells_Vs_Differentiated_cells.csv file, that you generated in session 3. It should be in H:/workshop/RNAseq/DE_analysis_workshop/Tables for most people. If not, change this to the correct path containing your DE genes table.
If you’ve deleted your DE_genes_Basal_cells_Vs_Differentiated_cells.csv file or didn’t attend section 3, you can download the file here
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H:/workshop/RNAseq/DE_analysis_workshop/Tables folders and put the file in the Tables folder.| Code Block |
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#### 7. Convert to Entrez gene IDs ####
# Set your working directory
setwd("H:/workshop/RNAseq/functional_annotation")
# Import your DE genes data:
# NOTE: THE DIRECTORY THAT CONTAINS YOUR RESULTS DATA MAY VARY. YOU NEED TO LOOK IN 'H:/workshop/RNAseq' TO SEE WHERE YOUR 'Tables' SUBDIRECTORY IS, AND CHANGE THE BELOW PATH TO REFLECT THAT.
dat <- read.csv("H:/workshop/RNAseq/DE_analysis_workshop/Tables/DE_genes_Basal_cells_Vs_Differentiated_cells.csv", row.names = 1)
# Convert the gene symbol column to Entrez IDs
# NOTE: 'OrgDb=' needs to be your organism database. We're using mouse here, so we're using 'OrgDb=org.Mm.eg.db' if your species was human you'd use 'OrgDb=org.Hs.eg.db', etc
gene_list <- bitr(gene = dat$SYMBOL, fromType="SYMBOL", toType="ENTREZID", OrgDb=org.Mm.eg.db, drop=TRUE)
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#### 8. KEGG pathway enrichment ####### # Use clusterProfiler's enrichKEGG() function to match your genes list to KEGG pathways kk <- enrichKEGG(gene = gene_list$ENTREZID, organism = "mmu", pvalueCutoff = 0.05, qvalueCutoff = 0.2) # Now you can save this as a table of enriched KEGG pathways # Create a 'results' subdirectory where all figures will be output dir.create("results", showWarnings = FALSE) write.csv(as.data.frame(kk), "./results/Enriched_KEGG_pathways.csv") |
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NOTE: this only plots one of your enriched pathways at a time. You’ll need to enter one of these KEGG pathways IDs into into the keggpath <- .. line. You can see your set of enriched pathways in the H:\workshop\RNAseq\functional_annotation\results\Enriched_KEGG_pathways.csv or type as.data.frame(kk)$ID into the RStudio console.
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