...
Either bring your own dataset or use the following guide to Download public small RNA-see data
Public human small RNAseq data:
https://www.ebi.ac.uk/ena/browser/view/PRJEB5212 RNA-seq of micro RNAs (miRNAs) in Human prefrontal cortex to identify differentially expressed miRNAs between Huntington's Disease and control brain samples
Download Reference microRNA sequences from miRBase
...
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wget https://www.mirbase.org/download_file/hairpin.fa |
Alternatively, submit the following PBS Pro script to the cluster. Before running the Fetch the genomic coordinated for precursors and mature sequences:
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--mirna_gtf /work/trtp/data/mirbase/hsa.gff3 |
Alternatively, submit the following PBS Pro script to the cluster. Before running the script, create a ‘reference’ folder (i.e., /myteam/data/reference/ ).
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#!/bin/bash -l
#PBS -N nfsmrnaseq
#PBS -l select=1:ncpus=2:mem=4gb
#PBS -l walltime=24:00:00
cd $PBS_O_WORKDIR
wget https://www.mirbase.org/download_file/hairpin.fa
wget https://www.mirbase.org/download_file/mature.fa
wget https://www.mirbase.org/download_file/hsa.gff3 |
Run a test
Before running the pipeline with real data, run the following test:
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#!/bin/bash -l #PBS -N nfsmrnaseq #PBS -l select=1:ncpus=2:mem=4gb #PBS -l walltime=24:00:00 cd $PBS_O_WORKDIR module load java NXF_OPTS='-Xms1g -Xmx4g' nextflow run nf-core/smrnaseq -r 2.1.0 \ -profile singularity \ --outdir outdir \ --input samplesheet.csv \ --genome GRCh38 \ --three_prime_adapter 'AACTGTAGGCACCATCAAT'\ --fastp_min_length 18 \ --fastp_max_length 30 \ --hairpin /work/trtp/data/mirbase/hairpin.fa \ --mature /work/trtp/data/mirbase/mature.fa |
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qsub launch_phase3.pbs |
...
\
--mirna_gtf /work/trtp/data/mirbase/hsa.gff3 |
Submit the job to the HPC cluster:
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qjobs |
...
qsub launch.pbs |
Monitor the progress:
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qjobs |
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#### 3. Loading required packages ####
# This section needs to be run every time
# Load packages
bioconductor_packages <- c("DESeq2", "EnhancedVolcano")
cran_packages <- c("ggrepel", "ggplot2", "plyr", "reshape2", "FactoMineR", "factoextra", "pheatmap")
lapply(cran_packages, require, character.only = TRUE)
lapply(bioconductor_packages, require, character.only = TRUE)
#### 4. Import your count data ####
# Set working directory.
# Change this to your working directory
# Your home wd
setwd("C:/Users/whatmorp/OneDrive - Queensland University of Technology/Desktop/Teaching/HPC_training_workshops/smRNA_seq")
# HPC wd
#setwd("/work/training/smallRNAseq/")
# Import your count data. make sure you've created a 'data' subdirectory and put the count table file there.
metacounts <- read.table("./data/mature_counts.txt", header = TRUE, row.names = 1)
# Import metadata. Again, need a metadata_microRNA.txt file in the data subdirectory.
meta <- read.table("./data/metadata_microRNA.txt", header = TRUE)
# Rename sample names to new sample IDs
counts <- metacounts[as.character(meta$sample_name)]
colnames(counts) <- meta$sample_ID
#### 5. Outliers and batch effects ####
# This section normalises and transforms the count data so that it can be plotted on a PCA plot and a heatmap
## USER INPUT
# Choose the groups you want to plot in a PCA/Heatmap. You can select any 2 or more of the groups (or all of the groups) you have in your 'groups' column of your metadata table
# To see what groups are present, run the following:
unique(meta$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("normal", "Huntingtons_disease")
# Pull out only the counts from the above groups
groupcounts <- counts[meta$group %in% plotgroups]
# Normalise counts by library size, using DeSeq2's estimateSizeFactors() function. Note that DeSeq2 does this internally during DEG calling. The normalisation below is done separately for PCA and density plotting.
# Set up the initial DeSeq2 experimental parameters.
condition <- factor(1:length(groupcounts))
# Set up the column data. A data frame of sample ID's and conditions
coldata <- data.frame(row.names=colnames(groupcounts), condition)
# Set up the DeSeq2 data set structure
f <- DESeqDataSetFromMatrix(countData = groupcounts, colData = coldata, design= ~ condition)
# Estimate the size factors. See DeSeq2 manual for details
f <- estimateSizeFactors(f)
# Size factors can be viewed by: sizeFactors(f)
# Multiply each row (sample) by the corresponding size factor
subcount_norm <- as.matrix(groupcounts) %*% diag(sizeFactors(f))
# Re-add column names
colnames(subcount_norm) <- colnames(groupcounts)
## Remove low coverage transcripts (mean count < 10) ##
# Find the mean of each row (and output as a data frame object)
means <- as.data.frame(rowMeans(subcount_norm))
# Then join the means data with the counts
means <- cbind(means, subcount_norm)
# Then subset out only genes with mean > 10
data <- subset(means, means[ , 1] > 10)
# Remove the means column
data <- data[,-1]
# Transform data
data_log <- vst(round(as.matrix(data)), nsub = nrow(data)-20)
# Transformation can create some infinite values. Can't generate PCA data on these. Can see how many by: sum(sapply(data_log, is.infinite))
# To remove infinite rows, use 'is.finte' or '!is.infinite'
data_log <- data_log[is.finite(rowSums(data_log)),]
colnames(data_log) <- colnames(groupcounts)
### Set up the PCA plot base data ###
# We're using the FactoMineR package to generate PCA plots (http://factominer.free.fr/index.html)
# Need to transpose the data first
data_log_t <- t(data_log)
# Add the group data
data_log_t_vars <- data.frame(meta$group[meta$group %in% plotgroups], data_log_t)
# Generate the PCA data using FactoMineR package
res.pca <- PCA(data_log_t_vars, quali.sup = 1, graph=FALSE)
## Set up the dendogram/heatmaps base data ##
# Calculate the distance matrix:
distance_matrix <- as.matrix(dist(t(data_log)))
#### 5a. PCA plot ####
# Generate the PCA plot. Groups are shaded with ellipses at 95% confidence level. NOTE: at least 4 replicates need to be in a group for an ellipses to be drawn.
# NOTE: change the group point colours by changing 'palette = ' below. Use the 'RColourBrewer' colour names (https://r-graph-gallery.com/38-rcolorbrewers-palettes.html). For example, if you are plotting 3 groups and choose palette = "Set1", this will use the first 3 colours from the Set1 colour palette.
p <- fviz_pca_ind(res.pca,
geom.ind = c("point", "text"), # show points only (but not "text")
col.ind = meta$group[meta$group %in% plotgroups], # color by groups
pointsize = 5, label = "all", title = "", legend.title = "Treatment groups", palette = "Dark2",
addEllipses = TRUE, ellipse.type = "t", ellipse.level = 0.95) + theme(legend.text = element_text(size = 12), legend.title = element_text(size = 14), axis.title=element_text(size=16), axis.text=element_text(size=14))
p
# Output as publication quality (300dpi) tiff and pdf.
# This will name your output files with the treatment groups you selected.
# Create a 'results_outliers_included' subdirectory where all results_outliers_included will be output
dir.create("results_outliers_included", showWarnings = FALSE)
# Create a (300dpi) tiff
ggsave(file = paste0("./results_outliers_included/PCA_", paste(plotgroups, collapse = "_Vs_"), ".tiff"), dpi = 300, compression = "lzw", device = "tiff", width = 10, height = 8, plot = p)
# Create a pdf
ggsave(file = paste0("./results_outliers_included/PCA_", paste(plotgroups, collapse = "_Vs_"), ".pdf"), device = "pdf", width = 10, height = 8, plot = p)
#### 5b. Samples heatmap and dendrogram ####
# This section plots a heatmap and dendrogram of pairwise relationships between samples. In this way you can see if samples cluster by treatment group.
# See here: https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/
# Define annotation column
annot_columns <- data.frame(meta$group[meta$group %in% plotgroups])
# Make the row names the sample IDs
row.names(annot_columns) <- meta$sample_ID[meta$group %in% plotgroups]
colnames(annot_columns) <- "Treatment groups"
# Need to factorise it
annot_columns[[1]] <- factor(annot_columns[[1]])
# Generate dendrogram and heatmap
pheatmap(distance_matrix, color=colorRampPalette(c("white", "#9999FF", "#990000"))(50), cluster_rows = TRUE, show_rownames = TRUE, treeheight_row = 0, treeheight_col = 70, fontsize_col = 12, annotation_names_col = F, annotation_col = annot_columns, filename = paste0("./results_outliers_included/Pairwise_sample_heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff"))
# Notes about heatmap colours.
# You can change the colours used in the heatmap itself by changing the colour names (color=colorRampPalette....)
# If you want to change the annotation colours, see here: https://zhiganglu.com/post/pheatmap_change_annotation_colors/
#### 6. Differential expression analysis ####
# In this section we use the Deseq2 package to identify differentially expressed genes.
## USER INPUT
# Choose the treatment groups you want to compare.
# To see what groups are present, run the following:
unique(meta$group)
# Enter which groups you want to compare (two groups only). BASELINE OR CONTROL GROUP SHOULD BE LISTED FIRST.
degroups <- c("normal", "Huntingtons_disease")
# 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)
# 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$sizeFactor)
colnames(expdata_norm) <- colnames(expdata)
annot_counts <- merge(x = res, y = expdata_norm, by = 0, all = TRUE)
# Pull out just significant genes (change from 0.05 to 0.01 if needed)
DE_genes <- subset(annot_counts, padj < 0.05, select=colnames(annot_counts))
# Export as a csv table
write.csv(DE_genes, file=paste0("./results_outliers_included/DE_genes_", paste(degroups, collapse = "_Vs_"), ".csv"), row.names = FALSE)
#### 6b. Volcano plot ####
p <- EnhancedVolcano(res, lab = row.names(res), selectLab = row.names(res)[1:20], drawConnectors = TRUE, title = NULL, subtitle = NULL, x = 'log2FoldChange', y = 'pvalue')
p <- EnhancedVolcano(res, lab = rownames(res), pointSize = 3, drawConnectors = TRUE, title = NULL, subtitle = NULL, x = 'log2FoldChange', y = 'pvalue')
p
# NOTE: the above plot shows labels for the top significantly DE (i.e. by lowest adjusted p value) genes.
# Output as publication quality (300dpi) tiff and pdf.
# Create a (300dpi) tiff
ggsave(file = paste0("./results_outliers_included/volcano_", paste(degroups, collapse = "_Vs_"), ".tiff"), dpi = 300, compression = "lzw", device = "tiff", width = 10, height = 8, plot = p)
# Create a pdf
ggsave(file = paste0("./results_outliers_included/volcano_", paste(degroups, collapse = "_Vs_"), ".pdf"), device = "pdf", width = 10, height = 8, plot = p)
#### 6c. DE genes heatmaps and dendrograms ####
# sort by p-value
DE_genes <- DE_genes[order(DE_genes$padj), ]
row.names(DE_genes) <- DE_genes$Row.names
# Pull out normalised counts only
siggc <- DE_genes[colnames(DE_genes) %in% colnames(expdata)]
# Scale and center each row. This is important to visualise relative differences between groups and not have row-wise colouration dominated by high or low gene expression.
xts <- scale(t(siggc))
xtst <- t(xts)
# Define annotation column
annot_columns <- data.frame(meta$group[meta$group %in% degroups])
# Make the row names the sample IDs
row.names(annot_columns) <- meta$sample_ID[meta$group %in% degroups]
colnames(annot_columns) <- "Treatment groups"
# Need to factorise it
annot_columns[[1]] <- factor(annot_columns[[1]])
# Generate dendrogram and heatmap for ALL DE genes
pheatmap(xtst, color=colorRampPalette(c("#D55E00", "white", "#0072B2"))(100), annotation_col=annot_columns, annotation_names_col = F, fontsize_col = 12, fontsize_row = 7, labels_row = row.names(siggc), show_rownames = T, filename = paste0("./results_outliers_included/All_DEG_Heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff"))
#### OUTLIER REMOVAL ####
# This section repeats the above, but removes outliers first
# REMOVE OUTLIERS FROM METADATA TABLE AN COUNT TABLE.
meta <- meta[- grep(c("WT4"), meta$sample_ID),]
counts <- counts[- grep(c("WT4"), colnames(counts))]
#### 5. 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$group)
# Now add which groups you want to plot (i.e. replace the groupnames below, and add more, separated by a comma and in "quotes", as needed). NOTE: R is case-sensitive, so these group names must be named EXACTLY the same as in the metadata table.
plotgroups <- c("normal", "Huntingtons_disease")
# Pull out only the counts from the above groups
groupcounts <- counts[meta$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)), nsub = nrow(data)-20)
# 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)))
#### 5a. 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 = c("point", "text"), # show points only (but not "text")
col.ind = meta$group[meta$group %in% plotgroups], # color by groups
pointsize = 5, label = "all", 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 'results_outliers_removed' subdirectory where all results_outliers_removed will be output
dir.create("results_outliers_removed", showWarnings = FALSE)
# Create a (300dpi) tiff
ggsave(file = paste0("./results_outliers_removed/PCA_", paste(plotgroups, collapse = "_Vs_"), ".tiff"), dpi = 300, compression = "lzw", device = "tiff", width = 10, height = 8, plot = p)
# Create a pdf
ggsave(file = paste0("./results_outliers_removed/PCA_", paste(plotgroups, collapse = "_Vs_"), ".pdf"), device = "pdf", width = 10, height = 8, plot = p)
#### 5b. 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("./results_outliers_removed/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/
#### 6. 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$group)
# Enter which groups you want to compare (two groups only). BASELINE OR CONTROL GROUP SHOULD BE LISTED FIRST.
degroups <- c("normal", "Huntingtons_disease")
# From the count table, pull out only the counts from the above groups
expdata <- as.matrix(counts[,meta$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$padj), ]
# You can do a summary of the results to see how many significantly (alpha=0.05, adjust to 0.01 if needed) upregulated and downregulated DE genes were found
summary(res, alpha=0.05)
# Convert from DESeq object to a data frame.
res <- data.frame(res)
# Look at the top 6 DE genes
head(res)
# Add normalised counts to the output table. This is so you can later plot expression trends for individual genes in R, Excel, etc.
# Need to normalise the counts first, using the size factors calculated by DESeq2 (in the 'deseq' object)
expdata_norm <- as.matrix(expdata) %*% diag(deseq$sizeFactor)
colnames(expdata_norm) <- colnames(expdata)
annot_counts <- merge(x = res, y = expdata_norm, by = 0, all = TRUE)
# 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))
# Export as a csv table
write.csv(DE_genes, file=paste0("./results_outliers_removed/DE_genes_", paste(degroups, collapse = "_Vs_"), ".csv"), row.names = FALSE)
#### 6b. Volcano plot ####
p <- EnhancedVolcano(res, lab = row.names(res), selectLab = row.names(res)[1:20], drawConnectors = TRUE, title = NULL, subtitle = NULL, x = 'log2FoldChange', y = 'pvalue')
p <- EnhancedVolcano(res, lab = rownames(res), pointSize = 3, 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.
# Output as publication quality (300dpi) tiff and pdf.
# Create a (300dpi) tiff
ggsave(file = paste0("./results_outliers_removed/volcano_", paste(degroups, collapse = "_Vs_"), ".tiff"), dpi = 300, compression = "lzw", device = "tiff", width = 10, height = 8, plot = p)
# Create a pdf
ggsave(file = paste0("./results_outliers_removed/volcano_", paste(degroups, collapse = "_Vs_"), ".pdf"), device = "pdf", width = 10, height = 8, plot = p)
#### 6c. DE genes heatmaps and dendrograms ####
# sort by p-value
DE_genes <- DE_genes[order(DE_genes$padj), ]
row.names(DE_genes) <- DE_genes$Row.names
# 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 = T, filename = paste0("./results_outliers_removed/All_DEG_Heatmap_", paste(plotgroups, collapse = "_Vs_"), ".tiff"))
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