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#set the working directory |
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setwd("C:/Users/Magda/OneDrive - Queensland Cyber Infrastructure Foundation Ltd/Projects/10_00551 BYOD/eResearch/Public RNA-seq dataset/at_counts") |
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#install the following packages if not yet installed |
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library("ggrepel") |
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library("DESeq2") |
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library("ggplot2") |
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library("EnhancedVolcano") |
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library("reshape2") |
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library("plyr") |
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require(reshape2) |
IMPORT AND PREPROCESS GENE COUNTS DATA
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geneCountData <- read.csv('salmon.merged.gene_counts_length_scaled.tsv', header = TRUE, sep = "\t") |
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print(head(geneCountData)) |
get rid of the gene_name column
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geneCountData = geneCountData[, colnames(geneCountData)[colnames(geneCountData) != 'gene_name']] |
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print(head(geneCountData)) |
create a new table with only numbers
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onlyCountsData = geneCountData[, colnames(geneCountData)[colnames(geneCountData) != 'gene_id']] |
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print(head(onlyCountsData)) |
counts need to be rounded to integers
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onlyCountsData <- ceiling(onlyCountsData) |
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print(head(onlyCountsData)) |
prepare to re-add the 'gene_id' column to the numbers table
create names of the new columns
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new_colnames = cbind("gene_id", t(colnames(onlyCountsData))) |
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print(new_colnames) |
merge the "gene_id" column with the numbers table
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countsDataReady <- cbind(geneCountData$gene_id, onlyCountsData) |
rename the columns
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colnames(countsDataReady) = new_colnames |
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print(head(countsDataReady)) |
IMPORT AND PREPROCESS META DATA
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metaData <- read.csv('metadata.txt', header = TRUE, sep = "\t") |
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print(metaData) |
make sure that the condition in the metadata is factorised to keep order for DESeq2
first group (Ax) is the baseline, DESeq2 will compare the second group against the first group
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growth_levels = c("Ax", "S.Fr1") |
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metaData$growth_condition = factor(metaData$growth_condition, levels = growth_levels) |
CREATE DESEQ2 OBJECT AND RUN THE ANALYSIS
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dds <- DESeqDataSetFromMatrix(countData=countsDataReady, colData=metaData, design=~growth_condition, tidy = TRUE) |
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dds <- DESeq(dds) |
Take a look at the results table
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res <- results(dds) |
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head(results(dds, tidy=TRUE)) #let's look at the results table |
Summary of differential gene expression
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summary(res) |
Sort summary list by p-value
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res <- res[order(res$padj),] |
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print(head(res)) |
Save DEGs to file
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write.table(res, file = "DEGs.txt") |
Print the results for the genes of interest
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genes_of_interest = c("AT2G19190", "AT1G51890", "AT2G14610", "AT3G57260", |
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"AT3G04720", "AT1G75040", "AT5G44420", "AT5G26920", |
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"AT3G56400", "AT3G45140", "AT5G24780") |
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print(res[genes_of_interest,]) |