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Aim:

Identify statistically significant (FDR < 0.05) differentially expressed genes.

Requirements

Run your samples (FASTQ) using the nextflow nf-core/RNA-seq pipeline using ‘star_salmon’ (Task 3 session) or an alternative pipeline that generates feature counts.

Installing R and Rstudio

You will need to have both R and Studio installed in your laptop/desktop for today’s exercises.

Go to the following page https://posit.co/download/rstudio-desktop/ and follow instructions provided to install first R and then Rstudio.

Install R:

https://cran.r-project.org/bin/windows/base/

install Rstudio:

https://posit.co/download/rstudio-desktop/

RNAseq gene expression feature counts

Go to the /sandpit/demo/run3_full_pipeline/ folder that contains the results for running the complete RNAseq pipeline. The output folders from task 3 look like this:

├── results
│   ├── fastqc
│   ├── multiqc
│   ├── pipeline_info
│   ├── star_salmon
│   └── trimgalore

The gene expression feature counts can be found in the /results/star_salmon/ folder. Let’s access the folder (i.e., cd /run3/results/star_salmon). A list of files and folders in the star_salmon folder will look like this:

.
├── bigwig
├── CD49fmNGFRm_rep1
├── CD49fmNGFRm_rep1.markdup.sorted.bam
├── CD49fmNGFRm_rep1.markdup.sorted.bam.bai
├── CD49fmNGFRm_rep2
├── CD49fmNGFRm_rep2.markdup.sorted.bam
├── CD49fmNGFRm_rep2.markdup.sorted.bam.bai
├── CD49fmNGFRm_rep3
├── CD49fmNGFRm_rep3.markdup.sorted.bam
├── CD49fmNGFRm_rep3.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep1
├── CD49fpNGFRp_rep1.markdup.sorted.bam
├── CD49fpNGFRp_rep1.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep2
├── CD49fpNGFRp_rep2.markdup.sorted.bam
├── CD49fpNGFRp_rep2.markdup.sorted.bam.bai
├── CD49fpNGFRp_rep3
├── CD49fpNGFRp_rep3.markdup.sorted.bam
├── CD49fpNGFRp_rep3.markdup.sorted.bam.bai
├── deseq2_qc
├── dupradar
├── featurecounts
├── log
├── MTEC_rep1
├── MTEC_rep1.markdup.sorted.bam
├── MTEC_rep1.markdup.sorted.bam.bai
├── MTEC_rep2
├── MTEC_rep2.markdup.sorted.bam
├── MTEC_rep2.markdup.sorted.bam.bai
├── MTEC_rep3
├── MTEC_rep3.markdup.sorted.bam
├── MTEC_rep3.markdup.sorted.bam.bai
├── picard_metrics
├── qualimap
├── rseqc
├── salmon.merged.gene_counts_length_scaled.rds
├── salmon.merged.gene_counts_length_scaled.tsv
├── salmon.merged.gene_counts.rds
├── salmon.merged.gene_counts_scaled.rds
├── salmon.merged.gene_counts_scaled.tsv
├── salmon.merged.gene_counts.tsv  <---- We will use this feature counts file for DESeq2 expression analysis. 
├── salmon.merged.gene_tpm.tsv
├── salmon.merged.transcript_counts.rds
├── salmon.merged.transcript_counts.tsv
├── salmon.merged.transcript_tpm.tsv
├── salmon_tx2gene.tsv
├── samtools_stats
└── stringtie

The expression count file that we are interested is salmon.merged.gene_counts.tsv Let's see the content of the file by printing the top lines using the following command:

head salmon.merged.gene_counts.tsv

the above command will print:

gene_id gene_name       CD49fmNGFRm_rep1        CD49fmNGFRm_rep2        CD49fmNGFRm_rep3        CD49fpNGFRp_rep1        CD49fpNGFRp_rep2        CD49fpNGFRp_rep3     MTEC_rep1       MTEC_rep2       MTEC_rep3
ENSMUSG00000000001      Gnai3   2460    2395    2749    2686    3972    4419    7095    4484    6414
ENSMUSG00000000003      Pbsn    0       0       0       0       0       0       0       0       0
ENSMUSG00000000028      Cdc45   43      57      55      79      87.999  89      1241    830     1041.999
ENSMUSG00000000031      H19     2       0       1       17.082  24      16.077  200     139     145.604
ENSMUSG00000000037      Scml2   8       8       16      23      29.001  29      69      57      67
ENSMUSG00000000049      Apoh    1       0       2       1       2       0       0       0       2
ENSMUSG00000000056      Narf    522     496     539     368     457     538     1939    1483    1734
ENSMUSG00000000058      Cav2    1352.999        1349    1371.999        2684.001        4370    4386    6018.999        3429    5501
ENSMUSG00000000078      Klf6    4411    3492    4500    3221    3989    4637    3812    2741    3558

Now let’s copy the ‘salmon.merged.gene_counts.tsv’ file to your laptop/desktop using the file finder.

  • Windows - in the file finder type: \\hpc-fs\work\

  • Mac - open ‘Finder’ → press ‘command + k’ → type: ‘smb://hpc-fs/work/

Now let’s find the full path to the ‘salmon.merged.gene_counts.tsv’ file:

  • Windows:

  • Mac:

    • cd /folder/that/contains/feature_counts/

    • pwd

  • Rstudio:

    • Open Rstudio, go to the top bar a click on “Session” → “Select working directory: → “Choose directory

    • The path to the directory will be printed in Rstudio console, copy and paste in line 1 of the script ‘RNAseq_DESeq2_analysis.R’ (see below)

Differential Expression Analysis using DESeq2

We will now perform the following tasks using Rstudio:

  • install R package manager

  • install R packages (only needed once) - After installation we only need to load the packages

  • set working directory (directory where the gene counts file is located)

  • run DESeq2 analysis, export results and generate plots

Open R Studio and if not prompted automatically, create a R script file as follows:

Open Rstudio → click on ‘File’ in the top bar → select “New File” → select “R script

Install R package manager

We will use Bioconductor to install the “BiocManager”. Copy and paste the following code into the R studio script. Select all three lines in the R script and click on “Run”. Note: version 3.17 requires a R version 4.3 (which.

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install(version = "3.17")

Install / Load packages:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("DESeq2")

#load packages
library("DESeq2")
library(ggplot2)
library("ggrepel")
library("EnhancedVolcano")

#install.packages("dendsort")
library(dendsort)
library("reshape2")
library("plyr")
library(Cairo)
library(pheatmap)
library("dendextend")
require(reshape2)

Prepare INPUT files

  • INPUT 1: Create a folder called ‘data’ and copy the gene counts file to a folder. For example, the gene counts table looks like this:

gene_id gene_name       CD49fmNGFRm_rep1        CD49fmNGFRm_rep2        CD49fmNGFRm_rep3        CD49fpNGFRp_rep1        CD49fpNGFRp_rep2        CD49fpNGFRp_rep3     MTEC_rep1       MTEC_rep2       MTEC_rep3
ENSMUSG00000000001      Gnai3   2460    2395    2749    2686    3972    4419    7095    4484    6414
ENSMUSG00000000003      Pbsn    0       0       0       0       0       0       0       0       0
ENSMUSG00000000028      Cdc45   43      57      55      79      87.999  89      1241    830     1041.999
ENSMUSG00000000031      H19     2       0       1       17.082  24      16.077  200     139     145.604
ENSMUSG00000000037      Scml2   8       8       16      23      29.001  29      69      57      67
ENSMUSG00000000049      Apoh    1       0       2       1       2       0       0       0       2
ENSMUSG00000000056      Narf    522     496     539     368     457     538     1939    1483    1734
ENSMUSG00000000058      Cav2    1352.999        1349    1371.999        2684.001        4370    4386    6018.999        3429    5501
ENSMUSG00000000078      Klf6    4411    3492    4500    3221    3989    4637    3812    2741    3558
ENSMUSG00000000085      Scmh1   1561.002        1549.002        1694.001        2276.001        2663    3290.999        2437.001        1759.001    2325.002
ENSMUSG00000000088      Cox5a   1136    1425    1691    947     1410    1613.998        3173.999        1873.992        2865.998
ENSMUSG00000000093      Tbx2    74      40      66      354     380     460     444     295     436
ENSMUSG00000000094      Tbx4    1       0       0       0       0       0       1       1       1
ENSMUSG00000000103      Zfy2    0       0       0       0       0       0       0       0       0
ENSMUSG00000000120      Ngfr    379     313     363     6577    7083    8670    4050    3336    3534
ENSMUSG00000000125      Wnt3    11      19      21      1       0       0       3       0       3
ENSMUSG00000000126      Wnt9a   69      104     89      405     480     554     1663    1012    1730
ENSMUSG00000000127      Fer     696     776     898     558     777     958     911     700     892
ENSMUSG00000000131      Xpo6    2206    2172    2408    2402    2866    3327.002        3919    3204    3763
  • INPUT 2: Sample metadata:

sample_name	sample_ID	group
CD49fmNGFRm_rep1	DC1	Differentiated_cells
CD49fmNGFRm_rep2	DC2	Differentiated_cells
CD49fmNGFRm_rep3	DC3	Differentiated_cells
CD49fpNGFRp_rep1	BC1	Basal_cells
CD49fpNGFRp_rep2	BC2	Basal_cells
CD49fpNGFRp_rep3	BC3	Basal_cells
MTEC_rep1	mTEC1	Murine_tracheal_epithelial_cell
MTEC_rep2	mTEC2	Murine_tracheal_epithelial_cell
MTEC_rep3	mTEC3	Murine_tracheal_epithelial_cell

R script - run the script using Rstudio

A copy of the script is at /demo/run3_full_pipeline/

RNAseq_DESeq2.R script:


# Differential expression analysis

# When you see '## USER INPUT', this means you have to modify the code for your computer or dataset. All other code can be run as-is (i.e. you don't need to understand the code, just run it)

#### 1. Installing required packages ####

# **NOTE: this section only needs to be run once (or occasionally to update the packages)
# Install devtools
install.packages("devtools", repos = "http://cran.us.r-project.org")
# Install R packages. This only needs to be run once.
bioconductor_packages <- c("DESeq2", "EnhancedVolcano", "org.Hs.eg.db", "org.Mm.eg.db", "org.Rn.eg.db", "org.EcK12.eg.db", "org.EcSakai.eg.db", "org.Dr.eg.db", "org.Dm.eg.db")
cran_packages <- c("ggrepel", "ggplot2", "plyr", "reshape2", "readxl", "FactoMineR", "factoextra", "pheatmap")
# 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 are the latest version
update.packages(bioconductor_packages, ask = FALSE)
update.packages(cran_packages, ask = FALSE, repos = "http://cran.us.r-project.org")


#### 2. Loading required packages ####

# This section needs to be run very time
# Load packages
bioconductor_packages <- c("DESeq2", "EnhancedVolcano", "org.Hs.eg.db", "org.Mm.eg.db", "org.Rn.eg.db", "org.EcK12.eg.db", "org.EcSakai.eg.db", "org.Dr.eg.db", "org.Dm.eg.db")
cran_packages <- c("ggrepel", "ggplot2", "plyr", "reshape2", "readxl", "FactoMineR", "factoextra", "pheatmap")
lapply(cran_packages, require, character.only = TRUE)
lapply(bioconductor_packages, require, character.only = TRUE)


#### 3. Import your count data ####

# Make sure you have: a) your count table (salmon.merged.gene_counts.tsv file, if you used Nextflow nfcore/rnaseq to analyse your data). Copy this to a subdirectory called 'data'. b) your metadata file. This should be either an Excel file called 'metadata.xlsx' or a tab-separated text file called 'metadata.txt'. It needs 3 columns called 'sample_name', 'sample_ID' and 'group'. The sample names should be EXACTLY the same as the names in the count table. These names are often uninformative and long, so the 'sample_ID' is the sample labels you want to put on your plots. E.g. if you have a 'high fat' group, you might want to rename the samples HF1, HF2, HF3, etc)

## USER INPUT
# Set working directory. 
# Change this to your working directory (In the RStudio menu: Session -> Set working directory -> Choose working directory)
setwd("C:/Users/whatmorp/OneDrive - Queensland University of Technology/Desktop/Projects/RNA-Seq downstream analysis")

# Import your count data. make sure you've created a 'data' subdirectory and put the count table file there.
metacountdata <- read.table("./data/salmon.merged.gene_counts.tsv", header = TRUE, row.names = 1)

# Import metadata. Again, need a metadata.txt file in the data subdirectory.
meta <- read_excel("./data/metadata.xlsx")

# Remove 1st columns of metadata (gene_name)
counts <- metacountdata[ ,2:ncol(metacountdata)]

# Rename sample names to new sample IDs
counts <- counts[as.character(meta$sample_name)]
colnames(counts) <- meta$sample_ID

# Counts need to be rounded to integers
counts <- ceiling(counts)


#### 4. Outliers and batch effects ####

# This section normalises and transforms the count data so that it can be plotted on a PCA plot and a heatmap

## USER INPUT
# Choose the groups you want to plot in a PCA/Heatmap. You can select any 2 or more of the groups (or all of the groups) you have in your 'groups' column of your metadata table
# To see what groups are present, run the following:
unique(meta$group)
# Now add which groups you want to plot (i.e. replace the groupnames below, and add more, separated by a comma and in "quotes", as needed). NOTE: R is case-sensitive, so these group names must be named EXACTLY the same as in the metadata table.
plotgroups <- c("Differentiated_cells", "Basal_cells")

# Pull out only the counts from the above groups
groupcounts <- counts[meta$group %in% plotgroups]

# Normalise counts by library size, using DeSeq2's estimateSizeFactors() function. Note that DeSeq2 does this internally during DEG calling. The normalisation below is done separately for PCA and density plotting.
# Set up the initial DeSeq2 experimental parameters.
condition <- factor(1:length(groupcounts))
# Set up the column data. A data frame of sample ID's and conditions
coldata <- data.frame(row.names=colnames(groupcounts), condition)
# Set up the DeSeq2 data set structure
f <- DESeqDataSetFromMatrix(countData = groupcounts, colData = coldata, design= ~ condition)
# Estimate the size factors. See DeSeq2 manual for details
f <- estimateSizeFactors(f)
# Size factors can be viewed by: sizeFactors(f)

# Multiply each row (sample) by the corresponding size factor
subcount_norm <- as.matrix(groupcounts) %*% diag(sizeFactors(f))
# Re-add column names
colnames(subcount_norm) <- colnames(groupcounts)

## Remove low coverage transcripts (mean count < 10) ##

# Find the mean of each row (and output as a data frame object)
means <- as.data.frame(rowMeans(subcount_norm))
# Then join the means data with the counts
means <- cbind(means, subcount_norm)
# Then subset out only genes with mean > 10
data <- subset(means, means[ , 1] > 10)
# Remove the means column
data <- data[,-1]

# Transform data
data_log <- vst(round(as.matrix(data)))
# Transformation can create some infinite values. Can't generate PCA data on these. Can see how many by: sum(sapply(data_log, is.infinite))
# To remove infinite rows, use 'is.finte' or '!is.infinite'
data_log <- data_log[is.finite(rowSums(data_log)),]
colnames(data_log) <- colnames(groupcounts)

### Set up the PCA plot base data ###

# We're using the FactoMineR package to generate PCA plots (http://factominer.free.fr/index.html)

# Need to transpose the data first
data_log_t <- t(data_log)
# Add the group data
data_log_t_vars <- data.frame(meta$group[meta$group %in% plotgroups], data_log_t)
# Generate the PCA data using FactoMineR package
res.pca <- PCA(data_log_t_vars, quali.sup = 1, graph=FALSE)

## Set up the dendogram/heatmaps base data ##

# Calculate the distance matrix:
distance_matrix <- as.matrix(dist(t(data_log)))


#### 4a. PCA plot ####

# Generate the PCA plot. Groups are shaded with ellipses at 95% confidence level. NOTE: at least 4 replicates need to be in a group for an ellipses to be drawn.
# NOTE: change the group point colours by changing 'palette = ' below. Use the 'RColourBrewer colour names (https://r-graph-gallery.com/38-rcolorbrewers-palettes.html). For example, if you are plotting 3 groups and choose palette = "Set1", this will use the first 3 colours from the Set1 colour palette.
p <- fviz_pca_ind(res.pca,
                  geom.ind = "point", # show points only (but not "text")
                  col.ind = meta$group[meta$group %in% plotgroups], # color by groups
                  pointsize = 5, title = "", legend.title = "Treatment groups", palette = "Dark2",
                  addEllipses = TRUE, ellipse.type = "t", ellipse.level = 0.95) + theme(legend.text = element_text(size = 12), legend.title = element_text(size = 14), axis.title=element_text(size=16), axis.text=element_text(size=14))

p

# Output as publication quality (300dpi) tiff and pdf.
# This will name your output files with the treatment groups you selected.

# Create a 'figures' subdirectory where all figures will be output
dir.create("figures", showWarnings = FALSE)
# Create a (300dpi) tiff
ggsave(file = paste0("./figures/PCA_", paste(plotgroups, collapse = "_Vs_"), ".tiff"), dpi = 300, compression = "lzw", device = "tiff", width = 10, height = 8, plot = p)
# Create a pdf
ggsave(file = paste0("./figures/PCA_", paste(plotgroups, collapse = "_Vs_"), ".pdf"), device = "pdf", width = 10, height = 8, plot = p)




#### 4b. Samples heatmap and dendrogram ####

# This section plots a heatmap and dendrogram of pairwise relationships between samples. In this way you can see if samples cluster by treatment group.

# See here: https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/

# Define annotation column
annot_columns <- data.frame(meta$group[meta$group %in% plotgroups])
# Make the row names the sample IDs
row.names(annot_columns) <- meta$sample_ID[meta$group %in% plotgroups]
colnames(annot_columns) <- "Treatment groups"
# Need to factorise it
annot_columns[[1]] <- factor(annot_columns[[1]])

# Generate dendrogram and heatmap
pheatmap(distance_matrix, color=colorRampPalette(c("white", "#9999FF", "#990000"))(50), cluster_rows = TRUE, show_rownames = TRUE, treeheight_row = 0, treeheight_col = 70, fontsize_col = 12, annotation_names_col = F, annotation_col = annot_columns, filename = paste0("./figures/Pairwise_sample_heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff"))

# Notes about heatmap colours.
# You can change the colours used in the heatmap itself by changing the colour names (color=colorRampPalette....)
# If you want to change the annotation colours, see here: https://zhiganglu.com/post/pheatmap_change_annotation_colors/



#### 5. Differential expression analysis ####

# In this section we use the Deseq2 package to identify differentially expressed genes.

## USER INPUT
# Choose the treatment groups you want to compare. 
# To see what groups are present, run the following:
unique(meta$group)
# Enter which groups you want to compare (two groups only). BASELINE OR CONTROL GROUP SHOULD BE LISTED FIRST.
degroups <- c("Basal_cells", "Differentiated_cells")

# From the count table, pull out only the counts from the above groups
expdata <- as.matrix(counts[,meta$group %in% degroups])

# Set up the experimental condition
# 'factor' sets up the reference level, i.e. which is the baseline group (otherwise the default baseline level is in alphabetic order)
condition <- factor(meta$group[meta$group %in% degroups], levels = degroups)
# Type 'condition' in the console to see is the levels are set correctly

# Set up column data (treatment groups and sample ID)
coldata <- data.frame(row.names=colnames(expdata), condition)

# Create the DESeq2 dataset (dds)
dds <- DESeq2::DESeqDataSetFromMatrix(countData=expdata, colData=coldata, design=~condition)
dds$condition <- factor(dds$condition, levels = degroups)

# Run DESeq2 to identify differentially expressed genes
deseq <- DESeq(dds)

# Extract a results table from the DESeq analysis
res <- results(deseq)

# Reorder results by adjusted p vales, so that the most signififcantly DE genes are at the top
res <- res[order(res$padj), ]

# You can do a summary of the results to see how many significantly (alpha=0.05, adjust to 0.01 if needed) upregulated and downregulated DE genes were found
summary(res, alpha=0.05)

# Convert from DESeq object to a data frame.
res <- data.frame(res)

# Look at the top 6 DE genes
head(res)


#### 5a. Annotating your DE genes ####

# The table of DE genes will have gene names according to the reference genome that was used to generate the count table. This is usually an Entrez gene ID if the NCBI genome was used, or an Ensemble gene ID if the Ensemble genome was used.
# These DE gene names can be individually looked to see what genes they are, or we can annotate them all together for their common gene symbol and description, using the code in this section.

# We'll be using bioconductor genome wide annotation packages to provide the annotation data. https://bioconductor.org/packages/3.17/data/annotation/
# Annotation packages for human (org.Hs.eg.db), mouse (org.Mm.eg.db), rat (org.Rn.eg.db), E. coli strain K12 (org.EcK12.eg.db), E. coli strain Sakai (org.EcSakai.eg.db), zebrafish (org.Dr.eg.db) and Drosophila (org.Dm.eg.db). If your species is not in the above list, contact the eResearch team.

## USER INPUT
# Input your species genome below (select from the above list)
my_genome <- org.Mm.eg.db

# Pull out just the Entrez/Ensembl gene IDs
gene_ids <- row.names(res)

# You can see the list of annotations you can apply to your data by:
keytypes(my_genome)

# Annotate gene symbol and description to your DE gene IDs (you can add other keytypes from the above 'keytypes(my_genome)' list, if you choose)
cols <- c("SYMBOL", "GENENAME")

## USER INPUT
# Provide the gene ID type for your DE data
# If you have Ensemble IDs, enter "ENSEMBL", if you have Entrez IDs, enter "ENTREZID", if you have gene symbols, enter "SYMBOL"
idtype <- "ENSEMBL"

# Map the Entrez/Ensembl gene IDs to gene symbol and description
# NOTE: IF YOUR GENE ID IS AN ENTREZ ID, CHANGE keytype="ENSEMBL" TO keytype="ENTREZID"
map <- AnnotationDbi::select(my_genome, keys=gene_ids, columns=cols, keytype=idtype)

# Combine the annotation data with the DE data
# Since we're matching Ensembl -> Entrez there aren't a 1:1 mapping, so need to merge rather than cbind
annot <- merge(x = res, y = map, by.x = 0, by.y = idtype, all = F)
# There isn't always a 1:1 mapping between gene identifiers, so we also need to remove duplicates
annot_nodups <- annot[!duplicated(annot$Row.names), ]
# Reorder by adjusted p
annot_nodups <- annot_nodups[order(annot_nodups$padj), ]

# Add normalised counts to the output table. This is so you can later plot expression trends for individual genes in R, Excel, etc.
# Need to normalise the counts first, using the size factors calculated by DESeq2 (in the 'deseq' object)
expdata_norm <- as.matrix(expdata) %*% diag(deseq$sizeFactor)
colnames(expdata_norm) <- colnames(expdata)
annot_counts <- merge(x = annot_nodups, y = expdata_norm, by.x = "Row.names", by.y = 0, all = TRUE)

# Export the full annotated dataset as a csv (for journal submission)
# Creates a 'Tables' subdirectory where all tables will be output
dir.create("Tables", showWarnings = FALSE)
write.csv(annot_counts, file=paste0("./Tables/all_genes_", paste(degroups, collapse = "_Vs_"), ".csv"), row.names = FALSE)

# Pull out just significant genes (change from 0.05 to 0.01 if needed)
DE_genes <- subset(annot_counts, padj < 0.05, select=colnames(annot_counts))
# NOTE: add lfc cutoffs if needed. E.g., log2FoldChange > 1 and < log2FoldChange -1 cutoff
# DE_genes <- subset(DE_genes, log2FoldChange > 1 | log2FoldChange < -1, select=colnames(DE_genes))

# Export the sig DE genes as a table (to be used in downstream analysis)
write.csv(DE_genes, file=paste0("./Tables/DE_genes_", paste(degroups, collapse = "_Vs_"), ".csv"), row.names = FALSE)


#### 5b. Volcano plot ####

# Now that we have our list of DE genes, we can visualise them with a volcano plot and heat map. First, the volcano plot. We'll be using the 'EnhancedVolcano' package.

p <- EnhancedVolcano(annot_nodups, lab = annot_nodups$SYMBOL, selectLab = annot_nodups$SYMBOL[1:20], drawConnectors = TRUE, title = NULL, subtitle = NULL, x = 'log2FoldChange', y = 'pvalue')

p

# NOTE: the above plot shows labels for the top significantly DE (i.e. by lowest adjusted p value) genes. You can label whatever genes you like, change plot colours, change axis labels, change log fold change cutoffs, etc, by following the EnhancedVolcano guide here:https://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html 

# Output as publication quality (300dpi) tiff and pdf.
# Create a (300dpi) tiff
ggsave(file = paste0("./figures/volcano_", paste(degroups, collapse = "_Vs_"), ".tiff"), dpi = 300, compression = "lzw", device = "tiff", width = 10, height = 8, plot = p)
# Create a pdf
ggsave(file = paste0("./figures/volcano_", paste(degroups, collapse = "_Vs_"), ".pdf"), device = "pdf", width = 10, height = 8, plot = p)


#### 5c. DE genes heatmaps and dendrograms ####

# This section plots a heatmap and dendrogram of DE gene expression per sample. Expression counts are scaled and centered so that groupwise relationships can be examined. 

# Make the row names gene symbols.
DE_genes <- na.omit(DE_genes)
row.names(DE_genes) <- make.unique(DE_genes$SYMBOL)

# sort by p-value
DE_genes <- DE_genes[order(DE_genes$padj), ]

# Pull out normalised counts only
siggc <- DE_genes[colnames(DE_genes) %in% colnames(expdata)]

# Scale and center each row. This is important to visualise relative differences between groups and not have row-wise colouration dominated by high or low gene expression.
xts <- scale(t(siggc))
xtst <- t(xts)

# Define annotation column
annot_columns <- data.frame(meta$group[meta$group %in% degroups])
# Make the row names the sample IDs
row.names(annot_columns) <- meta$sample_ID[meta$group %in% degroups]
colnames(annot_columns) <- "Treatment groups"
# Need to factorise it
annot_columns[[1]] <- factor(annot_columns[[1]])

# Generate dendrogram and heatmap for ALL DE genes
pheatmap(xtst, color=colorRampPalette(c("#D55E00", "white", "#0072B2"))(100), annotation_col=annot_columns, annotation_names_col = F, fontsize_col = 12, fontsize_row = 7, labels_row = row.names(siggc), show_rownames = F, filename = paste0("./figures/All_DEG_Heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff"))

# Generate dendrogram and heatmap for top 20 DE genes
pheatmap(xtst[1:20,], color=colorRampPalette(c("#D55E00", "white", "#0072B2"))(100), annotation_col=annot_columns, annotation_names_col = F, fontsize_col = 14, fontsize_row = 12, labels_row = row.names(siggc), show_rownames = T, filename = paste0("./figures/Top_DEG_Heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff"))
# NOTE: you can plot more than 20 top genes by adjusting 'xtst[1:20,]'. If you wanted to plot the top 50 genes you'd change this to 'xtst[1:50,]'



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