Differential expression analysis
Differential expression analysis
Project: GSE107401
Project: GSE107401
The aim of this document is to present the data and differential expression analysis between samples from the project GSE107401.
The main steps are:
- Data preparation
- Quality control
- Normalisation
- Differential expression analysis
For a quick access to the figures, go to:
- Normalisation > Visualisation for
normalised counts
- Results
subsections for the pairwise comparisons
All figures are saved in PNG and PDF format and accessible in the ./project_GSE107401/deseq2_figures/ folder. You can download the code behind this analysis through the “Code” button in the top right corner of the document.
1 Settings
The aim of this section is to:
- load the tools of interest
- define custom functions
- load the input parameters
1.2 Custom functions
We further define custom functions to avoid repetitions in the code.
# -----------------------------------------------------------------------------
# buildCountMatrix
# Create a matrix of reads count
#
# Input:
# files : a vector of files names
# sampleLabel : a vector of sample names
# projectPath: path to the project directory
#
# Output:
# countMatrix : a reads count matrix
#
# Original author : Vivien DESHAIES
# -----------------------------------------------------------------------------
buildCountMatrix = function(files, sampleLabel, expHeader) {
# read first file and create countMatrix
countMatrix = read.table(files[1],
header = expHeader,
stringsAsFactors = F,
quote = "")
colnames(countMatrix) = c("id", "count1")
# read and merge all remaining files with the first
for (i in 2:length(files)) {
# read files
exp = read.table(files[i],
header = expHeader,
stringsAsFactors = F,
quote = "")
# lowercase exp columns names
colnames(exp) = c("id", paste0("count", i))
# merge file data to count matrix by id
countMatrix = merge(countMatrix, exp, by = "id", suffixes = "_")
}
# name rows
rownames(countMatrix) = countMatrix[,1]
# delete first row containing row names
countMatrix = countMatrix[,-1]
# name columns
colnames(countMatrix) = sampleLabel
return(countMatrix)
}
###############################################################################
# -----------------------------------------------------------------------------
# saveRawCountMatrix
#
# A specific treatment is needed for the raw count matrix because the object
# DESeq2::counts(dds) does not include the names of the samples as column names
#
# input: dds -> DESeq object
# fileName -> character (name of the file to output)
# output: rawCountMatrix -> file
#
# -----------------------------------------------------------------------------
saveRawCountMatrix = function(dds,fileName) {
countMatrix = DESeq2::counts(dds)
countMatrix = cbind(countMatrix, row.names(countMatrix))
# Add the samples names as column names + add column Id
colData = SummarizedExperiment::colData(dds)
colnames(countMatrix) = c(colData$Name, "Id")
# Put Ids on the first column
countMatrix = countMatrix[, c("Id", colData$Name)]
# Write the count matrix in a file
write.table(countMatrix,
file = paste0(fileName),
sep = "\t",
row.names = FALSE,
quote = FALSE)
}
###############################################################################
# -----------------------------------------------------------------------------
# saveTable
#
# input: dataframe -> data frame (count matrix)
# fileName -> character (name of the file to output)
# output: countMatrix -> file
# -----------------------------------------------------------------------------
saveTable = function(dataframe, fileName) {
# Add column Id
column_names = colnames(dataframe)
dataframe = cbind(row.names(dataframe), dataframe)
colnames(dataframe) = c("Id", column_names)
# Write the table in a file
write.table(dataframe,
file = paste0(fileName),
sep = "\t",
row.names = FALSE,
quote = FALSE)
}
# -----------------------------------------------------------------------------
# plotDendrogram
# Create a ggplot object corresponding to a dendrogram
#
# Input:
# hc_data : object obtained with the ggdendro::dendro_data function
# fig_title : figure title
# col_vector: named vector of colors
#
# Output:
# p : ggplot object
#
# Original author : Audrey ONFROY
# -----------------------------------------------------------------------------
plotDendrogram = function(hc_data,
fig_title = "",
col_vector = colors_condition) {
p = ggplot2::ggplot() +
# Draw dendrogram
ggplot2::geom_segment(data = ggdendro::segment(hcdata),
aes(x = x, y = y, xend = xend, yend = yend)) +
# Leaf label
ggplot2::geom_label(data = ggdendro::label(hcdata),
aes(x = x, y = y, label = label),
linewidth = 0, fill = NA,
size = 3, angle = 90, hjust = 1,
nudge_y = -0.02*max(ggdendro::segment(hcdata)$yend)) +
# Leaf colors
ggplot2::geom_point(data = ggdendro::label(hcdata),
ggplot2::aes(x = x, y = y, col = Condition),
size = 3, pch = 19) +
ggplot2::scale_color_manual(values = col_vector,
breaks = names(col_vector)) +
# Style
ggplot2::labs(y = "Distance",
title = fig_title) +
ggplot2::coord_cartesian(clip = "off") +
ggplot2::theme_minimal() +
ggplot2::theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.y = element_line(),
axis.ticks.y = element_line(),
panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = ggplot2::unit(c(0.5, 0.5, max(nchar(hcdata$labels$label))/6, 0.5), "cm"),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
return(p)
}
# -----------------------------------------------------------------------------
# plotPCA
# Create a ggplot object corresponding to the PCA plot of individuals
#
# Input:
# pca_df : dataframe
# fig_title : figure title
# col_vector: named vector of colors
#
# Output:
# p : ggplot object
# -----------------------------------------------------------------------------
plotPCA = function(pca_df,
fig_title = "",
col_vector = colors_condition) {
p = ggplot2::ggplot(pca_df, aes(x = Dim.1, y = Dim.2, col = Condition, label = sample)) +
ggplot2::geom_hline(yintercept = 0, lty = 2) +
ggplot2::geom_vline(xintercept = 0, lty = 2) +
# Samples
ggplot2::geom_point(size = 3, pch = 19) +
ggrepel::geom_label_repel(fill = NA, label.size = 0) +
ggplot2::scale_color_manual(values = col_vector,
breaks = names(col_vector)) +
# Style
ggplot2::labs(x = paste0("Dim1 (", round(pcaCount$eig[1, 2], 2), "%)"),
y = paste0("Dim2 (", round(pcaCount$eig[2, 2], 2), "%)"),
title = fig_title) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
return(p)
}
# -----------------------------------------------------------------------------
# plotBarplot
# Create a ggplot object corresponding to a barplot
#
# Input:
# df : dataframe
# y_column : column in df for the Y-axis
# y_title : Y-axis title
# fig_title : figure title
# col_vector: named vector of colors
#
# Output:
# p : ggplot object
# -----------------------------------------------------------------------------
plotBarplot = function(df,
y_column,
y_title = "",
fig_title = "",
col_vector = colors_condition) {
df$y_value = df[, y_column]
p = ggplot2::ggplot(df, aes(x = sample, y = y_value, fill = Condition)) +
ggplot2::geom_bar(stat = "identity", col = "black") +
ggplot2::scale_fill_manual(values = col_vector,
breaks = names(col_vector)) +
# Style
ggplot2::labs(x = "",
y = y_title,
title = fig_title) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, hjust = 1),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
return(p)
}
# -----------------------------------------------------------------------------
# plotBoxplot
# Create a ggplot object corresponding to a boxplot
#
# Input:
# df : dataframe
# y_column : column in df for the Y-axis
# y_title : Y-axis title
# fig_title : figure title
# colors_condition: named vector of colors
#
# Output:
# p : ggplot object
# -----------------------------------------------------------------------------
plotBoxplot = function(df,
y_column,
y_title = "",
fig_title = "",
col_vector = colors_condition) {
df$y_value = df[, y_column]
p = ggplot2::ggplot(df, aes(x = sample, y = y_value, fill = Condition)) +
ggplot2::geom_boxplot(col = "black") +
ggplot2::scale_fill_manual(values = col_vector,
breaks = names(col_vector)) +
# Style
ggplot2::labs(x = "",
y = y_title,
title = fig_title) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 90, hjust = 1),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
return(p)
}
# -----------------------------------------------------------------------------
# expressionColors
# Define a color panel for the heatmap
#
# Input:
# range_values : a numerical vector with two values (min and max)
#
# Output:
# expression_colors : a color or a function built with circlize::colorRamp2
# -----------------------------------------------------------------------------
expressionColors = function(range_values) {
min_value = range_values[1]
max_value = range_values[2]
# min_value < 0 < max_value
if (min_value < 0 & 0 < max_value) {
expression_colors = circlize::colorRamp2(
breaks = c(min_value, 0, max_value),
colors = c("#224897", "#F7F7F7", "#D32A24"))
} else
# min_value < max_value < 0
if (min_value < max_value & max_value < 0) {
expression_colors = circlize::colorRamp2(
breaks = c(min_value, 0),
colors = c("#224897", "#F7F7F7"))
} else
# 0 < min_value < max_value
if (0 < min_value & min_value < max_value) {
expression_colors = circlize::colorRamp2(
breaks = c(0, max_value),
colors = c("#F7F7F7", "#D32A24"))
} else {
# min_value == max_value
expression_colors = "#F7F7F7"
}
return(expression_colors)
}
# -----------------------------------------------------------------------------
# buildContrast
# Define the contrast vector to extract DE results from the dds object
#
# Input:
# dds : DESeqDataSet
# group1 : list of character vectors, for instance:
# list(c("sexe", "F"), c("genotype", "HETERO")
# group2 : list of character vectors, for instance:
# list(c("sexe", "F"), c("genotype", "HOMO")
# weighted : boolean whether to weight the contrast matrix by the
# number of replicates per condition or not
#
# Output: a list of 3 elements
# contrast : a numerical vector
# samples_group1 : a character vector containing sample names in group 1
# samples_group2 : a character vector containing sample names in group 2
#
# Source:
# Modified from: https://www.r-bloggers.com/2024/05/a-guide-to-designs-and-contrasts-in-deseq2/
# -----------------------------------------------------------------------------
buildContrast = function(dds,
group1,
group2,
weighted = FALSE){
#-- Model matrix for each sample
mod_mat = model.matrix(DESeq2::design(dds),
SummarizedExperiment::colData(dds))
#-- Samples in group1
# A list of TRUE / FALSE
# Each element of the list is of length the number of samples
# The i-th element of the list corresponds to the i-th condition in group1
grp1_rows = lapply(c(1:length(group1)), FUN = function(group1_i) {
group1_i_name = group1[[group1_i]][1]
group1_i_values = group1[[group1_i]][2:length(group1_i)]
samples_in_group1_i = (SummarizedExperiment::colData(dds)[[group1_i_name]] %in% group1_i_values) # TRUE / FALSE
return(samples_in_group1_i)
})
grp1_rows = Reduce(function(x, y) x & y, grp1_rows)
grp2_rows = lapply(c(1:length(group2)), FUN = function(group2_i) {
group2_i_name = group2[[group2_i]][1]
group2_i_values = group2[[group2_i]][2:length(group2_i)]
samples_in_group2_i = (SummarizedExperiment::colData(dds)[[group2_i_name]] %in% group2_i_values) # TRUE / FALSE
return(samples_in_group2_i)
})
grp2_rows = Reduce(function(x, y) x & y, grp2_rows)
#-- Model matrix with only samples of interest
mod_mat1 = mod_mat[grp1_rows, , drop = FALSE]
mod_mat2 = mod_mat[grp2_rows, , drop = FALSE]
#-- Consider the weight or not
if(!weighted){
mod_mat1 = mod_mat1[!duplicated(mod_mat1), , drop = FALSE]
mod_mat2 = mod_mat2[!duplicated(mod_mat2), , drop = FALSE]
}
#-- Output
contrast = colMeans(mod_mat1)-colMeans(mod_mat2)
samples_group1 = colnames(dds)[grp1_rows]
samples_group2 = colnames(dds)[grp2_rows]
output = list(contrast = contrast,
samples_group1 = samples_group1,
samples_group2 = samples_group2)
return(output)
}These functions are listed below.
- buildContrast
- buildCountMatrix
- current_version
- expressionColors
- plotBarplot
- plotBoxplot
- plotDendrogram
- plotPCA
- saveRawCountMatrix
- saveTable
- version_tested
1.3 Parameters
The table below summarizes all the input parameters.
projectName = params$projectName
designPath = params$designPath
comparisonPath = params$comparisonPath
diffanaTest = as.logical(toupper(params$diffanaTest))
expHeader = as.logical(toupper(params$expHeader))
deseqModel = params$deseqModel
sizeFactorType = params$sizeFactorType
fitType = params$fitType
statisticTest = params$statisticTest
weightContrast = params$weightContrast
prefix = params$prefix
plotInteractive = params$plotInteractive
leaveOnError = params$leaveOnError
# Display them as a table
c("Project name (projectName)" = projectName,
"Path to the design file (designPath)" = designPath,
"Path to the comparison file (comparisonPath)" = comparisonPath,
"Whether to perform the differential expression analysis or not (diffanaTest)" = diffanaTest,
"Whether the design table has a header or not (expHeader)" = expHeader,
"DESeq2 model (deseqModel)" = deseqModel,
"Size factor type (sizeFactorType)" = sizeFactorType,
"Fit type (fitType)" = fitType,
"Statistic test (statisticTest)" = statisticTest,
"Whether to weight the contrast vector by the number of samples or not (weightContrast)" = weightContrast,
"Prefix to save files (prefix)" = prefix,
"Whether to make interactive plots or not (plotInteractive)" = plotInteractive,
"Whether to stop the rendering in case of error (leaveOnError)" = leaveOnError) %>%
data.frame(Parameter = names(.),
Value = .,
row.names = NULL) %>%
knitr::kable("html", escape = FALSE) %>%
kableExtra::kable_styling("striped", full_width = FALSE) %>%
kableExtra::column_spec(1, bold = TRUE)| Parameter | Value |
|---|---|
| Project name (projectName) | GSE107401 |
| Path to the design file (designPath) | ./project_GSE107401/deseq2_GSE107401-deseq2Design.txt |
| Path to the comparison file (comparisonPath) | ./project_GSE107401/deseq2_GSE107401-comparisonFile.txt |
| Whether to perform the differential expression analysis or not (diffanaTest) | TRUE |
| Whether the design table has a header or not (expHeader) | TRUE |
| DESeq2 model (deseqModel) | ~Condition |
| Size factor type (sizeFactorType) | ratio |
| Fit type (fitType) | parametric |
| Statistic test (statisticTest) | Wald |
| Whether to weight the contrast vector by the number of samples or not (weightContrast) | TRUE |
| Prefix to save files (prefix) | ./project_GSE107401/deseq2_ |
| Whether to make interactive plots or not (plotInteractive) | TRUE |
| Whether to stop the rendering in case of error (leaveOnError) | FALSE |
2 Data preparation
The purpose of this section is to:
- load the design file, containing metadata about each sample ;
- load the individual sample count data, assemble them as a gene-by-sample count matrix, and save it as a TSV file ; and,
- build a dataset in a format compatible with the differential expression analysis (DESeq2-related format).
2.1 Load metadata
We load the design file and display the 6 first rows.
design = read.table(designPath,
sep = "\t",
header = TRUE,
dec = ".",
stringsAsFactors = FALSE)
# About a warning in DESeq2
design$Condition = stringr::str_replace_all(
design$Condition,
pattern = "-",
replacement = "_")
# Add Reference if does not exist
if (!("Reference" %in% colnames(design))) {
design$Reference = Inf
complexMode = TRUE
} else {
complexMode = FALSE
}
# Reorder samples based on Condition
design = design %>%
dplyr::arrange(Condition, RepTechGroup, Name) %>%
dplyr::mutate(Condition = factor(Condition, levels = unique(Condition))) %>%
dplyr::mutate(RepTechGroup = factor(RepTechGroup, levels = unique(RepTechGroup))) %>%
dplyr::mutate(Name = factor(Name, levels = unique(Name)))
# Remove columns out of interest
design = design[, !(colnames(design) %in% c("SampleId", "Description", "Date", "FastqFormat"))]
# Display
head(design) %>%
knitr::kable("html") | Name | expressionFile | RepTechGroup | Condition | Reference |
|---|---|---|---|---|
| GSM2866306 | ./project_GSE107401/GSM2866306_expression_output_expression_20130267.tsv | KO1 | KO | 1 |
| GSM2866307 | ./project_GSE107401/GSM2866307_expression_output_expression_20130268.tsv | KO2 | KO | 1 |
| GSM2866308 | ./project_GSE107401/GSM2866308_expression_output_expression_20130269.tsv | KO3 | KO | 1 |
| GSM2866303 | ./project_GSE107401/GSM2866303_expression_output_expression_20130270.tsv | WT1 | WT | 0 |
| GSM2866304 | ./project_GSE107401/GSM2866304_expression_output_expression_20130271.tsv | WT2 | WT | 0 |
| GSM2866305 | ./project_GSE107401/GSM2866305_expression_output_expression_20130272.tsv | WT3 | WT | 0 |
Description of the columns:
Name: Unique identifier of each technical replicate.expressionFile: File path to the count expression data. The TSV file contains two columns: one for the gene identifiers and one for the number of reads mapping to their genomic location. For compatibility with Excel, please use the XLSX file instead of the TSV file (read more). Furthermore, the XLSX file contains additional columns, such as a description of each gene.RepTechGroup: Samples with the same value in theRepTechGroupcolumn are technical replicates. This occurs when the sequencing library is split over sequencing lanes. Each lane generates a technical replicate from the same biological library.Condition: Samples with the same value in theConditioncolumn are biological replicates. They are produced by the experimenter.Reference: Numerical values used to define the pairwise comparisons between conditions in the differential expression analysis.
We associate a color to each condition.
colors_condition = unique(design$Condition) %>%
setNames(nm = .,
grDevices::hcl.colors(length(.), "Spectral"))
data.frame(Color = unname(colors_condition),
Condition = names(colors_condition)) %>%
knitr::kable("html",
col.names = c("Color (hex code)", "Condition")) %>%
kableExtra::column_spec(1, color = colors_condition) %>%
kableExtra::kable_styling(full_width = FALSE)| Color (hex code) | Condition |
|---|---|
| #A71B4B | KO |
| #584B9F | WT |
2.2 Build count matrix
From the individual count data, we build a gene-by-sample count matrix. The count value for a given gene corresponds to the number (integer value) of sequencing reads that map to its genomic region. We display the top left corner of this matrix, meaning 5 genes and 5 samples. The complete matrix is saved as a TSV file (quick access).
count_mat = buildCountMatrix(files = design$expressionFile,
sampleLabel = design$Name,
expHeader = expHeader)
# Reorder columns
count_mat = count_mat[, levels(design$Name)]
# Display
count_mat[c(1:5), c(1:5)] %>%
knitr::kable("html") %>%
kableExtra::kable_styling(full_width = FALSE)| GSM2866306 | GSM2866307 | GSM2866308 | GSM2866303 | GSM2866304 | |
|---|---|---|---|---|---|
| ENSMUSG00000000001 | 10177 | 7880 | 10372 | 7353 | 8767 |
| ENSMUSG00000000003 | 0 | 0 | 1 | 0 | 0 |
| ENSMUSG00000000028 | 933 | 915 | 1184 | 204 | 317 |
| ENSMUSG00000000031 | 66488 | 44221 | 88514 | 72959 | 70285 |
| ENSMUSG00000000037 | 89 | 65 | 120 | 119 | 85 |
The count matrix contains 46500 genes, 6 samples and 41.94% of null values.
2.3 Create DESeqDataSet object
We create the DESeqDataSet object that includes the count matrix and the design file.
3 Quality control
The aim of this section is to better apprehend the variability between the technical replicates. For information on the correspondence between the identifiers of technical replicates and sample names, please refer to the design file.
3.1 Raw counts dendrogram
We compute euclidean distances between each pair of samples, considering the raw count values.
dist.mat = dist(t(count_mat))
hc = stats::hclust(dist.mat)
hcdata = ggdendro::dendro_data(hc, type = "rectangle")
# Add grouping for colors
hcdata$labels = dplyr::left_join(x = hcdata$labels,
y = design[, c("Name", "Condition")],
by = c("label" = "Name"))We visualise these distances through a dendrogram. It informs whether technical replicates or closely related conditions are closer together than with other samples.
3.2 Raw counts PCA
The 6 samples are represented in a space of 46500 dimensions, corresponding to genes. We perform a Principal Component Analysis (PCA) to visualise samples on a two-dimension space such that it represents the highest variability between samples.
We visualise the samples on the two-dimensional projection. The coordinates of each sample depend on those of the other samples. This projection captures 69.93% of the total variability between samples. Therefore, it lacks 30.07% of the initial information. In this representation, two points (samples) are close together if they have similar global transcriptomic profiles. We expect the technical and biological replicates to be closer to each other than to the other samples and conditions.
pcaCount$ind$coord %>%
as.data.frame() %>%
dplyr::mutate(sample = rownames(.)) %>%
dplyr::left_join(x = .,
y = unique(design[, c("Name", "Condition")]),
by = c("sample" = "Name")) %>%
plotPCA(.,
fig_title = paste0("PCA of the raw count matrix - ", projectName))
3.3 Null counts barplot
This figure displays the proportion of null counts per sample, meaning, the genes that are not being expressed or not captured by the protocol, within the biological samples. We expect samples from the same condition to be similar.
colMeans(count_mat == 0) %>%
data.frame(sample = factor(names(.), levels = levels(design$Name)),
prop_null_counts = .) %>%
dplyr::mutate(prop_null_counts = 100*prop_null_counts) %>%
dplyr::left_join(x = .,
y = design[, c("Name", "Condition")],
by = c("sample" = "Name")) %>%
# Plot
plotBarplot(.,
y_column = "prop_null_counts",
y_title = "Proportion of null counts (%)",
fig_title = paste0("Proportion of null counts per sample - ",
projectName))
3.4 Raw counts barplot
This figure displays the total number of reads per sample, also known as library size. We can assess if some samples have a low library size compared to others, and whether technical replicates share a similar number of reads or not.
colSums(DESeq2::counts(dds)) %>%
data.frame(sample = factor(names(.), levels = levels(design$Name)),
read_counts = .) %>%
dplyr::left_join(x = .,
y = design[, c("Name", "Condition")],
by = c("sample" = "Name")) %>%
# Plot
plotBarplot(.,
y_column = "read_counts",
y_title = "Total read counts",
fig_title = paste0("Number of reads per sample - ", projectName))
3.5 Raw counts boxplot
This figure displays, as a boxplot, the number of raw counts for each gene, per sample. It informs whether the genes show a similar count distribution between samples and conditions.
log2(DESeq2::counts(dds)+1) %>%
reshape2::melt() %>%
`colnames<-`(c("gene_ID", "sample", "log2_counts")) %>%
dplyr::mutate(sample = factor(sample, levels = levels(design$Name))) %>%
dplyr::left_join(x = .,
y = design[, c("Name", "Condition")],
by = c("sample" = "Name")) %>%
# Plot
plotBoxplot(.,
y_column = "log2_counts",
y_title = "log_2 (counts+1)",
fig_title = paste0("Raw counts distribution per sample - ", projectName))
In this context, there is no technical replicate.
4 Normalisation
The aim of this section is to normalise the count matrix, before performing the differential expression analysis. The normalisation method considers the sequencing depth and RNA composition of each sample to compute normalised count values.
For more details, please visit:
https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html
4.1 Data handling
We normalise the count matrix. We display the top left corner of this matrix, meaning 5 genes and 5 samples. Unlike count data, normalised count values are not necessarily integers.
dds = DESeq2::estimateSizeFactors(dds,
type = sizeFactorType)
DESeq2::counts(dds, normalized = TRUE)[c(1:5), c(1:5)] %>%
knitr::kable("html") %>%
kableExtra::kable_styling(full_width = FALSE)| KO1 | KO2 | KO3 | WT1 | WT2 | |
|---|---|---|---|---|---|
| ENSMUSG00000000001 | 8545.49706 | 8663.24632 | 8.367172e+03 | 8697.1743 | 7718.40931 |
| ENSMUSG00000000003 | 0.00000 | 0.00000 | 8.067077e-01 | 0.0000 | 0.00000 |
| ENSMUSG00000000028 | 783.42820 | 1005.94802 | 9.551419e+02 | 241.2925 | 279.08472 |
| ENSMUSG00000000031 | 55829.12536 | 48616.42328 | 7.140492e+04 | 86296.3607 | 61878.45308 |
| ENSMUSG00000000037 | 74.73216 | 71.46079 | 9.680492e+01 | 140.7539 | 74.83344 |
We save the normalised count matrix as a TSV file.
filename = paste0(prefix, projectName,
"-normalisation_normalisedCountMatrix.tsv")
saveTable(DESeq2::counts(dds, normalized = TRUE),
fileName = filename)Output file
name:
./project_GSE107401/deseq2_GSE107401-normalisation_normalisedCountMatrix.tsv
4.2 Visualisation
4.2.1 Normalised counts dendrogram
We compute euclidean distances between each pair of samples, considering the normalised count values.
ddsStabilized = dds %>%
DESeq2::varianceStabilizingTransformation() %>%
SummarizedExperiment::assay()
dist.mat = dist(t(ddsStabilized))
hc = stats::hclust(dist.mat)
hcdata = ggdendro::dendro_data(hc, type = "rectangle")
# Add grouping for colors
hcdata$labels = dplyr::left_join(
x = hcdata$labels,
y = unique(design[, c("RepTechGroup", "Condition")]),
by = c("label" = "RepTechGroup"))We visualise these distances through a dendrogram. It informs whether the samples share a similar transcriptomic profile per condition or not.
plotDendrogram(
hc_data,
fig_title = paste0("Dendrogram on the normalised count matrix - ", projectName))
4.2.2 Normalised counts PCA
We perform a PCA for all samples using the normalised count matrix.
pcaCount = dds %>%
DESeq2::counts(., normalized = TRUE) %>%
t() %>%
FactoMineR::PCA(., graph = FALSE)We visualise the samples on the two-dimensional projection. The coordinates of each sample depend on those of the other samples. This projection captures 65.42% of the total variability between samples. Therefore, it lacks 34.58% of the initial information. In this representation, two points (samples) are close together if they have similar global transcriptomic profiles. We expect samples from the same biological condition to be closer to each other than to samples from other conditions.
pcaCount$ind$coord %>%
as.data.frame() %>%
dplyr::mutate(sample = rownames(.)) %>%
dplyr::left_join(x = .,
y = unique(design[, c("RepTechGroup", "Condition")]),
by = c("sample" = "RepTechGroup")) %>%
plotPCA(.,
fig_title = paste0("PCA of the normalised count matrix - ", projectName))
4.2.3 Normalised counts boxplot
This figure displays, as a boxplot, the number of normalised counts for each gene, per sample.
log2(DESeq2::counts(dds, normalized = TRUE)+1) %>%
reshape2::melt() %>%
`colnames<-`(c("gene_ID", "sample", "log2_counts")) %>%
dplyr::mutate(sample = factor(sample, levels = levels(design$RepTechGroup))) %>%
dplyr::left_join(x = .,
y = unique(design[, c("RepTechGroup", "Condition")]),
by = c("sample" = "RepTechGroup")) %>%
# Plot
plotBoxplot(
.,
y_column = "log2_counts",
y_title = "log_2 (counts+1)",
fig_title = paste0("Normalised counts distribution per sample - ", projectName))
4.2.4 Most expressed genes plot
We identified the most expressed gene in each sample.
# preparation of 2 data frame with the same number of column than
# the dds count matrix
maxCounts = DESeq2::counts(dds)[1,]
transcriptNames = DESeq2::counts(dds)[1,]
# for each sample (column)
for (i in 1:ncol(DESeq2::counts(dds))) {
# selection of the maximum number of count
maxCounts[i] = (max(DESeq2::counts(dds, normalized = TRUE)[,i])/
sum(DESeq2::counts(dds, normalized = TRUE)[,i]))*100
# selection of the name of the genes this the maximum of count
transcriptNames[i] = row.names(
subset(DESeq2::counts(dds, normalized = TRUE),
DESeq2::counts(dds, normalized = TRUE)[,i] ==
max(DESeq2::counts(dds, normalized = TRUE)[,i])))
}We represent the information as a barplot.
dplyr::left_join(x = data.frame(sample = names(maxCounts),
max_counts = maxCounts),
y = data.frame(sample = names(transcriptNames),
gene_id = transcriptNames),
by = "sample") %>%
dplyr::mutate(sample = factor(sample, levels = levels(design$RepTechGroup))) %>%
dplyr::left_join(x = .,
y = unique(design[, c("RepTechGroup", "Condition")]),
by = c("sample" = "RepTechGroup")) %>%
# Plot
ggplot2::ggplot(., aes(x = sample, y = max_counts,
fill = Condition, label = gene_id)) +
ggplot2::geom_bar(stat = "identity", col = "black") +
ggplot2::geom_label(mapping = aes(x = sample, y = 0),
linewidth = 0, fill = NA, hjust = 0) +
ggplot2::scale_fill_manual(values = colors_condition,
breaks = names(colors_condition)) +
# Style
ggplot2::labs(y = "Proportion of reads (%)",
title = paste0("Most expressed genes - ", projectName)) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
ggplot2::coord_flip() +
ggplot2::theme_classic() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
5 Differential analysis
The aim of this section is to perform the differential expression analysis.
The code behind this section can be downloaded at:
5.1 Dispersion estimates
After the normalisation, the first step of the differential expression analysis is the estimation of gene-wise dispersion. The dispersion estimates reflect the variability in the expression of a given gene across samples.
5.2 Dispersion plot
We visualise the dispersion estimates of each gene (black dots) as a function of the mean of normalised counts across samples. The red curve corresponds to the expected dispersion value for genes, based on their mean normalised counts. Then, a shrinkage of the gene-wise dispersion estimates consists in computed a final dispersion value for each gene (blue dots). This is important to reduce the false positives in the differential expression analysis.
For more details, please visit:
https://hbctraining.github.io/DGE_workshop/lessons/04_DGE_DESeq2_analysis.html
5.3 Generalized linear model
The last step of the differential expression analysis pipeline is to fit a model to each gene to compute their log2 fold change. A Wald statistical test is then used to evaluate if the values are distinct from 0, meaning if the genes are differentially expressed or not.
6 Results
The aim of this section is to extract the results for each paired comparison and build relevant figures.
List of comparisons
The table below is a summary of the conditions of interest.
| Condition | Reference |
|---|---|
| WT | 0 |
| KO | 1 |
The values in the Reference column are used to define
the pairwise comparisons:
- Reference < 0: the samples were considered by the normalisation step but not by the differential expression analysis
- Reference = 0: the condition associated with these samples is considered as a reference in the differential expression analysis
- Reference > 0: the condition associated with these samples is compared to each condition having a lower reference value
The table below summarised the paired comparisons (condition1 vs condition2) of interest.
| condition1 | condition2 | |
|---|---|---|
| Comparison 1 | KO | WT |
6.1 KO vs_WT
Details:
- number of samples in the KO condition: 3
- number of samples in the WT condition: 3
6.1.1 Results table
We extract the dataframe containing the differential analysis results between condition KO and condition WT. We display the 6 first rows of this table. The complete table is saved as a TSV file (quick access).
res = DESeq2::results(dds,
contrast = comparison_contrast) %>%
as.data.frame() %>%
dplyr::mutate(ID = rownames(.)) %>%
dplyr::relocate(ID)
head(res[, -1]) %>%
knitr::kable("html") %>%
kableExtra::kable_styling(full_width = FALSE)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSMUSG00000000001 | 8.545201e+03 | -0.0065238 | 0.1155344 | -0.0564664 | 0.9549703 | 0.9707938 |
| ENSMUSG00000000003 | 1.344513e-01 | 0.6813419 | 4.0804729 | 0.1669762 | 0.8673888 | NA |
| ENSMUSG00000000028 | 5.881430e+02 | 1.8048132 | 0.1745103 | 10.3421607 | 0.0000000 | 0.0000000 |
| ENSMUSG00000000031 | 6.747721e+04 | -0.3810503 | 0.1980244 | -1.9242593 | 0.0543221 | 0.1064735 |
| ENSMUSG00000000037 | 9.885491e+01 | -0.5189149 | 0.3569505 | -1.4537448 | 0.1460171 | 0.2411026 |
| ENSMUSG00000000049 | 1.637230e+02 | -2.6679215 | 0.3835931 | -6.9550819 | 0.0000000 | 0.0000000 |
Description of the columns:
- The rownames correspond to the gene identifiers.
baseMean: Average normalised expression of the gene across all samples, after correcting for sequencing depth (size factors). It informs about how much the gene is expressed overall.log2FoldChange: log2 of the ratio of the average expression of samples from the condition KO to samples from the condition WT. Positive values correspond to a higher expression in the condition KO, and conversely. A value of 1 means that the gene is twice more expressed in the condition KO compared to the condition WT.lfcSE: Standard error of the log2 fold change. It reflects the uncertainty in the estimated fold change.stat: Test statistic, corresponding to the ratio betweenlog2FoldChangeandlfcSE.pvalue: Raw p-value assessing if the log2 fold change is different from 0 or not, meaning if the gene is differentially expressed or not.padj: Adjusted p-value, corrected for multiple testing using the Benjamini-Hochberg method. It controls the false discovery rate and must be used to decide significance.
6.1.2 p-value distribution
Among 46500 genes, there are 31405 (68%) genes with an available p-value and 15095 (32%) with a non-available (NA) p-value. Below is the summary for the available values and a figure depicting the distribution of the raw p-values.
| Value | |
|---|---|
| Min. | 0.0000000 |
| 1st Qu. | 0.0011700 |
| Median | 0.1772734 |
| Mean | 0.3184053 |
| 3rd Qu. | 0.6320702 |
| Max. | 0.9999217 |
ggplot2::ggplot(res, aes(x = pvalue)) +
ggplot2::geom_histogram(col = "black", fill = "#A20F58", bins = 50) +
# Style
ggplot2::labs(x = "Adjusted p-value",
y = "Number of genes",
title = "Raw p-value distribution",
subtitle = comparison_subtitle) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
6.1.3 Adjusted p-value distribution
Among 46500 genes, there are 24771 (53%) genes with an available adjusted p-value and 21729 (47%) with a non-available (NA) adjusted p-value. Below is the summary for the available values and a figure depicting the distribution of the adjusted p-values.
| Value | |
|---|---|
| Min. | 0.0000000 |
| 1st Qu. | 0.0003975 |
| Median | 0.0948678 |
| Mean | 0.2706592 |
| 3rd Qu. | 0.5215269 |
| Max. | 0.9999217 |
ggplot2::ggplot(res, aes(x = padj)) +
ggplot2::geom_histogram(col = "black", fill = "#77B241", bins = 50) +
# Style
ggplot2::labs(x = "Adjusted p-value",
y = "Number of genes",
title = "Adjusted p-value distribution",
subtitle = comparison_subtitle) +
ggplot2::scale_y_continuous(expand = c(0,0)) +
ggplot2::theme_classic() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
6.1.4 MA plot
The MA plot represents the log2 fold change of each gene
(dot) as a function of their mean normalised expression value across all
samples. Dots are colored based on the significance of the statistical
test, using a significant threshold of 0.05 for the adjusted p-value
(padj).
ggplot2::ggplot(res, aes(x = baseMean, y = log2FoldChange,
col = padj < 0.05)) +
ggplot2::geom_point(size = 1) +
ggplot2::geom_hline(yintercept = 0, lty = 1, col = "lightgray") +
ggplot2::scale_color_manual(name = "padj < 0.05",
breaks = c(FALSE, TRUE, NA),
values = c("black", "#D32A24", "lightgray"),
na.value = "lightgray") +
# Style
ggplot2::scale_x_log10() +
ggplot2::labs(x = "Mean of normalised counts",
y = "Log_2 fold change",
title = "MA plot",
subtitle = comparison_subtitle) +
ggplot2::theme_minimal() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
For more details, please visit:
https://hbctraining.github.io/DGE_workshop/lessons/05_DGE_DESeq2_analysis2.html
6.1.5 Number of differentially expressed genes
We compute the number of significantly differentially expressed genes for varying significant adjusted p-value thresholds.
signif_limit_vec = c(1e-6, 1e-5, 1e-4, 1e-3, 0.01, 0.05)
nb_DE_genes = lapply(signif_limit_vec, FUN = function(signif_limit) {
nb_genes = res %>%
# Differentially expressed genes
dplyr::filter(abs(log2FoldChange) > 1) %>%
# Significantly, based on the threshold
dplyr::filter(padj < signif_limit) %>%
# Number of genes
nrow()
return(nb_genes)
}) %>% unlist() %>%
data.frame(signif_limit = signif_limit_vec,
nb_genes = .)The barplot indicates the number of significantly differentially expressed genes according to the significant adjusted p-value threshold. Only genes with a log2 fold change greater than 1 or smaller than -1 are considered.
Considering an adjusted p-value threshold of 1e-6, 2822 genes are significantly differentially expressed. When considering an adjusted p-value threshold of 0.05, 5525 genes are significantly differentially expressed, meaning, 2703 additional genes.
p = ggplot2::ggplot(nb_DE_genes, aes(x = signif_limit, y = nb_genes)) +
ggplot2::geom_bar(stat = "identity", col = "black", fill = "#A3ADB8") +
ggplot2::geom_label(aes(x = signif_limit,
y = nb_genes + 0.03*nb_DE_genes[6, 2],
label = nb_genes),
linewidth = 0, size = 5, fill = NA) +
# Style
ggplot2::scale_x_log10(breaks = signif_limit_vec,
labels = signif_limit_vec) +
ggplot2::labs(x = "Adjusted p-value threshold",
y = "Number of differentially expressed genes",
title = "Impact of the adjusted p-value threshold on the number of differentially expressed genes",
subtitle = comparison_subtitle) +
ggplot2::theme_classic() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
if (nb_DE_genes[6, 2] != 0) {
p = p +
ggplot2::scale_y_continuous(expand = c(0,0),
limits = c(0, 1.1*nb_DE_genes[6, 2]))
}
p
6.1.6 Volcano plot
The volcano plot represents the adjusted p-value as a function of the log2 fold change between condition KO and condition WT. We label a maximum of 10 genes in both directions. We identify such genes.
padj_threshold = 0.05
log2FC_threshold = 2
genes_up = res %>%
dplyr::filter(padj < padj_threshold) %>%
dplyr::filter(log2FoldChange > 0) %>%
dplyr::top_n(n = 10, wt = log2FoldChange) %>%
dplyr::pull(ID)
genes_dn = res %>%
dplyr::filter(padj < padj_threshold) %>%
dplyr::filter(log2FoldChange < 0) %>%
dplyr::top_n(n = 10, wt = -log2FoldChange) %>%
dplyr::pull(ID)We label 10 significantly over-regulated and 10 significantly down-regulated genes, considering an adjusted p-value of 0.05.
non_zero_min = min(res$padj[res$padj != 0], na.rm = TRUE)
p = ggplot2::ggplot(res %>% dplyr::filter(!is.na(padj)),
aes(x = log2FoldChange,
y = ifelse(padj == 0, # -log10(0) = Inf
yes = -log10(non_zero_min),
no = -log10(padj)),
label = ID,
text = paste0(ID, "<br>",
"log_2 (fold change): ", round(log2FoldChange, 2), "<br>",
"adjusted p-value: ", round(padj, 4)))) +
# Lines
ggplot2::geom_hline(yintercept = 0, col = "lightgray") +
ggplot2::geom_hline(yintercept = -log10(padj_threshold), lty = 2, col = "#A3ADB8") +
ggplot2::geom_vline(xintercept = log2FC_threshold, lty = 2, col = "#A3ADB8") +
ggplot2::geom_vline(xintercept = -log2FC_threshold, lty = 2, col = "#A3ADB8") +
# Points
ggplot2::geom_point(data = res %>%
dplyr::filter(padj >= padj_threshold),
mapping = aes(col = "Others")) +
ggplot2::geom_point(data = res %>%
dplyr::filter(log2FoldChange > -log2FC_threshold &
log2FoldChange < log2FC_threshold),
mapping = aes(col = "Others")) +
ggplot2::geom_point(data = res %>%
dplyr::filter(padj < padj_threshold) %>%
dplyr::filter(log2FoldChange >= log2FC_threshold),
mapping = aes(col = "Significantly up-regulated")) +
ggplot2::geom_point(data = res %>%
dplyr::filter(padj < padj_threshold) %>%
dplyr::filter(log2FoldChange <= -log2FC_threshold),
mapping = aes(col = "Significantly down-regulated")) +
ggplot2::scale_color_manual(name = "genes",
values = c("#D32A24", "#224897", "#A3ADB8"),
breaks = c("Significantly up-regulated",
"Significantly down-regulated",
"Others")) +
# Labels
ggplot2::labs(x = "log_2 (fold change)",
y = "-log10 (adjusted p-value)",
title = "Volcano plot",
subtitle = comparison_subtitle) +
# Style
ggplot2::theme_minimal() +
ggplot2::theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(margin = margin(l = 0.5)),
# Transparent background
panel.background = ggplot2::element_blank(),
plot.background = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
6.1.7 Heatmap
We draw a heatmap for the 20 gene(s) presented above. We compute Z-scores from the normalised count matrix and visualise the top left corner of the resulting table, meaning the 20 gene(s) and their Z-scores in two samples. The Z-scores are only used for visualisation. The Z-score matrix is not saved.
samples_of_interest = c(comparison_samples_group1,
comparison_samples_group2)
# if-else in case on 0 differential expression gene
nb_genes = length(c(genes_up, genes_dn))
if (nb_genes < 1) {
zscore_mat = matrix(data = 0,
nrow = 1,
ncol = length(samples_of_interest),
dimnames = list(c(""),
samples_of_interest))
} else {
zscore_mat = DESeq2::counts(dds, normalized = TRUE)[c(genes_up, genes_dn), samples_of_interest]
# Special case if there is only one gene: vector instead of matrix
if (nb_genes == 1) {
zscore_mat = matrix(zscore_mat,
nrow = nb_genes,
dimnames = list(row_names = c(genes_up, genes_dn),
col_names = samples_of_interest))
}
# Z-score
zscore_mat = zscore_mat %>%
t() %>% scale() %>% t()
}
# The code below is compatible with 1 gene
# Otherwise, zscore_mat[, c(1:2)] would have sufficed and been much simpler
matrix(zscore_mat[, c(1:2)],
nrow = max(1, nb_genes),
dimnames = list(row_names = c(genes_up, genes_dn),
col_names = samples_of_interest[c(1:2)])) %>%
knitr::kable("html") %>%
kableExtra::kable_styling(full_width = FALSE)| KO1 | KO2 | |
|---|---|---|
| ENSMUSG00000005220 | 0.1308313 | 1.6997768 |
| ENSMUSG00000005994 | 1.0693969 | 1.2403233 |
| ENSMUSG00000015437 | -0.4848211 | 1.9206515 |
| ENSMUSG00000042514 | -0.5728235 | 0.3946229 |
| ENSMUSG00000043168 | -0.6032827 | 0.6301209 |
| ENSMUSG00000043549 | 1.2225999 | -0.0009235 |
| ENSMUSG00000046160 | 0.4559807 | 1.6655504 |
| ENSMUSG00000067860 | 0.0741459 | 1.6942920 |
| ENSMUSG00000075217 | 0.0315710 | 1.9744592 |
| ENSMUSG00000093400 | 0.0829010 | 1.7700690 |
| ENSMUSG00000001672 | -0.9057268 | -0.9057268 |
| ENSMUSG00000026535 | -0.8900501 | -0.8931796 |
| ENSMUSG00000026824 | -0.5341863 | -0.5341863 |
| ENSMUSG00000028148 | -0.8708964 | -0.8627911 |
| ENSMUSG00000032517 | -0.7686147 | -0.7701786 |
| ENSMUSG00000046317 | -0.9120461 | -0.9120461 |
| ENSMUSG00000048377 | -0.9095362 | -0.9095362 |
| ENSMUSG00000050917 | -0.9002409 | -0.9012911 |
| ENSMUSG00000059213 | -0.9022808 | -0.9011421 |
| ENSMUSG00000100444 | -0.7429518 | -0.7429518 |
On the heatmap, each gene is one row and each sample is one column. Samples are annotated for their condition. The sample dendrogram is calculated based on the distances between samples considering only the genes represented on the heatmap. The condition associated to each sample is not considered by the method (unsupervised method). The genes dendrogram is calculated based on the Z-score of that gene among all samples.
# Heatmap top annotation
sub_design = design %>%
dplyr::select(RepTechGroup, Condition) %>%
dplyr::filter(RepTechGroup %in% samples_of_interest) %>%
unique() %>%
`rownames<-`(.$RepTechGroup)
if (complexMode) {
sub_design = sub_design %>%
# Add the complex condition
`colnames<-`(c("RepTechGroup", "SubCondition")) %>%
dplyr::mutate(Condition = ifelse(RepTechGroup %in% comparison_samples_group1,
yes = condition1,
no = condition2)) %>%
dplyr::select(Condition, SubCondition)
sub_design = sub_design[samples_of_interest, ] # re-order
# Annotation
ha_top = ComplexHeatmap::HeatmapAnnotation(
df = sub_design,
col = list(Condition = setNames(nm = c(condition1, condition2),
c("#3AA5C1", "#FF6800")),
SubCondition = colors_condition[unique(sub_design$SubCondition)]),
na_col = "#F7F7F7")
} else {
# Re-order
sub_design = sub_design %>%
dplyr::select(Condition)
sub_design = sub_design[samples_of_interest, ] # re-order
# Annotation
ha_top = ComplexHeatmap::HeatmapAnnotation(
df = sub_design,
col = list(Condition = colors_condition[c(condition1, condition2)]),
na_col = "#F7F7F7")
}
# Heatmap title
if (nb_genes > 1) {
ht_title = paste0("Heatmap of ", nb_genes,
" differentially expressed genes\n",
comparison_subtitle)
} else {
ht_title = paste0("Heatmap of ", nb_genes,
" differentially expressed gene\n",
comparison_subtitle)
}
# Heatmap main
ht = ComplexHeatmap::Heatmap(
as.matrix(zscore_mat),
# Expression colors
col = expressionColors(range(zscore_mat)),
heatmap_legend_param = list(title = "Z-score"),
# Annotation
top_annotation = ha_top,
# Sample order
cluster_columns = TRUE,
# gene order
cluster_rows = TRUE,
# Visual aspect
show_heatmap_legend = TRUE,
border = TRUE,
use_raster = FALSE,
# Title
column_title = ht_title)
# Draw
ComplexHeatmap::draw(ht,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left",
padding = unit(c(samples_padding, 2, 2, 5), "mm"),
background = "transparent")
Note: The clustering of samples may change, depending on the genes that are considered.
6.1.8 Save
We re-order the genes based on the adjusted p-value and edit the column names for enhanced readability. We display the 6 first rows of this table.
res = res %>%
dplyr::mutate(dispersions.dds. = DESeq2::dispersions(dds)) %>%
dplyr::arrange(padj) %>%
`colnames<-`( c("ID",
"baseMean",
paste0("log2foldchange ", condition1, " vs ", condition2),
"standard error",
"Wald statistic",
"Wald test p-value",
"BH adjusted p-values",
"dispersions.dds.") )
head(res[, -1]) %>%
knitr::kable("html") %>%
kableExtra::kable_styling(full_width = FALSE)| baseMean | log2foldchange KO vs WT | standard error | Wald statistic | Wald test p-value | BH adjusted p-values | dispersions.dds. | |
|---|---|---|---|---|---|---|---|
| ENSMUSG00000000223 | 69095.112 | -8.825746 | 0.2039640 | -43.27110 | 0 | 0 | 0.0284741 |
| ENSMUSG00000021061 | 11643.299 | -7.241691 | 0.1319283 | -54.89112 | 0 | 0 | 0.0095483 |
| ENSMUSG00000059213 | 10761.636 | -9.379653 | 0.2173631 | -43.15200 | 0 | 0 | 0.0200004 |
| ENSMUSG00000056569 | 2192423.490 | -4.989391 | 0.1308831 | -38.12096 | 0 | 0 | 0.0123420 |
| ENSMUSG00000033361 | 24278.624 | -6.175509 | 0.1627914 | -37.93510 | 0 | 0 | 0.0183975 |
| ENSMUSG00000022456 | 5115.165 | -6.712565 | 0.1814406 | -36.99593 | 0 | 0 | 0.0189653 |
We save the table as a TSV file.
filename = paste0(prefix, projectName, "-diffana_",
condition1, "_vs_", condition2,".tsv")
saveTable(dataframe = res,
fileName = filename)Output file
name:
./project_GSE107401/deseq2_GSE107401-diffana_KO_vs_WT.tsv
7 Versions
▼ show
## [1] "Packages location:"
## [1] "/usr/local/lib/R/library"
## [1] ""
## [1] "Session Information:"
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so; LAPACK version 3.12.1
##
## locale:
## [1] C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_4.0.0 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] rlang_1.1.6 magrittr_2.0.3
## [3] clue_0.3-66 GetoptLong_1.0.5
## [5] matrixStats_1.5.0 compiler_4.5.1
## [7] png_0.1-8 systemfonts_1.3.1
## [9] vctrs_0.6.5 reshape2_1.4.4
## [11] stringr_1.5.2 shape_1.4.6.1
## [13] pkgconfig_2.0.3 crayon_1.5.3
## [15] fastmap_1.2.0 XVector_0.48.0
## [17] rmdformats_1.0.4 labeling_0.4.3
## [19] rmarkdown_2.29 UCSC.utils_1.4.0
## [21] purrr_1.0.4 xfun_0.52
## [23] cachem_1.1.0 GenomeInfoDb_1.44.0
## [25] jsonlite_2.0.0 flashClust_1.01-2
## [27] DelayedArray_0.34.1 BiocParallel_1.42.0
## [29] parallel_4.5.1 cluster_2.1.8.1
## [31] R6_2.6.1 bslib_0.9.0
## [33] stringi_1.8.7 RColorBrewer_1.1-3
## [35] GenomicRanges_1.60.0 jquerylib_0.1.4
## [37] estimability_1.5.1 Rcpp_1.0.14
## [39] bookdown_0.46 SummarizedExperiment_1.38.0
## [41] iterators_1.0.14 knitr_1.50
## [43] base64enc_0.1-3 IRanges_2.42.0
## [45] Matrix_1.7-3 tidyselect_1.2.1
## [47] rstudioapi_0.17.1 abind_1.4-8
## [49] yaml_2.3.10 doParallel_1.0.17
## [51] codetools_0.2-20 lattice_0.22-7
## [53] tibble_3.2.1 plyr_1.8.9
## [55] Biobase_2.68.0 withr_3.0.2
## [57] S7_0.2.0 evaluate_1.0.3
## [59] xml2_1.3.8 circlize_0.4.16
## [61] pillar_1.10.2 MatrixGenerics_1.20.0
## [63] DT_0.34.0 foreach_1.5.2
## [65] stats4_4.5.1 plotly_4.11.0
## [67] generics_0.1.3 S4Vectors_0.46.0
## [69] scales_1.4.0 leaps_3.2
## [71] glue_1.8.0 emmeans_2.0.0
## [73] scatterplot3d_0.3-44 lazyeval_0.2.2
## [75] tools_4.5.1 data.table_1.17.0
## [77] locfit_1.5-9.12 mvtnorm_1.3-3
## [79] grid_4.5.1 tidyr_1.3.1
## [81] crosstalk_1.2.1 colorspace_2.1-1
## [83] GenomeInfoDbData_1.2.14 cli_3.6.5
## [85] kableExtra_1.4.0 textshaping_1.0.0
## [87] S4Arrays_1.8.0 viridisLite_0.4.2
## [89] svglite_2.2.2 ComplexHeatmap_2.24.0
## [91] ggdendro_0.2.0 gtable_0.3.6
## [93] DESeq2_1.48.0 sass_0.4.10
## [95] digest_0.6.37 BiocGenerics_0.54.0
## [97] SparseArray_1.8.0 ggrepel_0.9.6
## [99] rjson_0.2.23 FactoMineR_2.12
## [101] htmlwidgets_1.6.4 farver_2.1.2
## [103] htmltools_0.5.8.1 lifecycle_1.0.4
## [105] httr_1.4.7 multcompView_0.1-10
## [107] GlobalOptions_0.1.2 MASS_7.3-658 Reference
The differential expression analysis uses the DESeq2 R
package (vignette).
Love, M.I., Huber, W. & Anders, S., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). DOI: 10.1186/s13059-014-0550-8
---
params:
  projectName: "GSE107401"
  designPath: "./project_GSE107401/deseq2_GSE107401-deseq2Design.txt"
  comparisonPath: "./project_GSE107401/deseq2_GSE107401-comparisonFile.txt"
  diffanaTest: TRUE
  expHeader: TRUE
  deseqModel: "~Condition"
  sizeFactorType: "ratio"
  fitType: "parametric"
  statisticTest: "Wald"
  weightContrast: FALSE
  prefix: "./project_GSE107401/deseq2_"
  plotInteractive: TRUE
  logoUrl: "https://www.outils.genomique.biologie.ens.fr/eoulsan/images/eoulsan.png"
  authorName: "Eoulsan"
  authorEmail: "eoulsan@bio.ens.psl.eu"
  leaveOnError: TRUE
title: "<center>Differential expression analysis</center>"
subtitle: "<center>`r paste0('Project: ', params$projectName)`</center>"
date: "`r invisible(Sys.setlocale('LC_TIME', 'C')) ; paste0('Analysis run on: ', format(Sys.time(), '%d %B %Y'))`
<br><span class='glyphicon glyphicon-pushpin'></span>`r paste0(' Project: ', params$projectName)`
<br><span class='glyphicon glyphicon-menu-right'></span> Version 2.1
<br><span class='glyphicon glyphicon-link'></span> Generated by <a href='https://www.outils.genomique.biologie.ens.fr/eoulsan/'>Eoulsan</a>"
# above is a small hack to add new lines in the postample without editing the template
author:
  - name: "`r params$authorName`"
    email: "`r params$authorEmail`"
output:
  rmdformats::readthedown:
    code_folding: show
    code_download: true
    number_sections: true
    toc_depth: 3
---

<!-- CSS to customize the colors -->
```{css style_section1, echo = FALSE}
body {
text-align: justify
}

/* Width of the main div */
#content {
max-width: 80%;
}
```

```{r style_section2, echo = FALSE, results = "asis"}
# NOTE: A CSS chunk cannot interpret the R input parameter. In order to parse the 'params$logoUrl' parameter, we require an R chunk. Therefore, the CSS content is displayed through an R chunk.

# Header in the sidebar
cat(paste0("
<style>
#sidebar h2 {
background-color:#A3ADB8;
background-size: 32%;
background-repeat: no-repeat;
background-position: center bottom;
background-image:url('", params$logoUrl, "');
background-origin: content-box;
height: 13%;
}
</style>
"))
```

```{css style_section3, echo = FALSE}
/* Sidebar body */
#sidebar {
background:#535F6A;
}

/* Small space about the authoring section */
#postamble {
border-top:solid 10px #A3ADB8;
}

/* Titles */
h1, h2, h3, h4, h5, h6, legend {
color: #000038;
}

/* Two-column mode */
.cols {
display: flex;
align-items: center;
}

.col-center {
justify-content: center;
}

.col-right {
margin-left: auto;
}

/* Button */
.btn-custom {
font-family: inherit;
font-size: inherit;
color: inherit;
background-color: #E3E3E3;
border-color: #E3E3E3;
padding: 1px 2px;
border-radius: 5px;
border-style: solid;
color: #000038;
}

.btn-custom:hover {
background-color: #535F6A;
border-color: #535F6A;
color: #FCFCFC;
}
```

<!-- Automatically computes and prints in the output the running time for any code chunk -->
```{r, echo = FALSE}
hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)
    
    if (isFALSE(options[[paste0("fold_", type)]])) return(res)
    
    paste0(
      "<details><summary>", "\U25BC show", "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}
knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot")
)
```

<!-- knit setup -->
```{r knit_setup, echo = FALSE}
fig_save_folder = paste0(params$prefix, "figures/")

knitr::opts_chunk$set(
  echo = TRUE,
  eval = TRUE,
  cache.lazy = FALSE,
  message = FALSE,
  warning = FALSE,
  fold_output = FALSE,
  fold_plot = FALSE,
  fig.align = "center",
  class.source = "fold-hide",
  # R base plots on transparent background
  dev.args = list(bg = 'transparent'),
  # Export figures
  dev = c('png', 'pdf'),
  fig.path = fig_save_folder,
  pdf.options(encoding = "ISOLatin9.enc"))
```

------------------------------------------------------------------------

The aim of this document is to present the data and differential expression analysis between samples from the project **`r params$projectName`**.

The main steps are:

- Data preparation
- Quality control
- Normalisation
- Differential expression analysis

```{r echo = FALSE, results = "asis"}
# NOTE: This section is very ugly. Due to the conditional section, we need to evaluate in advance whether the section will be executed or not, in order to create a short link to the conditionally-executed section. This requires the design file to be loaded. This section is hidden in the final rendered notebook. We only make calculations here to print a message. But, calculations are performed again in a visible way in the next sections. Not elegant and (low) resource-consuming.

text_to_print = "For a quick access to the figures, go to"

design_tmp = read.table(params$designPath,
                        sep = "\t",
                        header = TRUE,
                        dec = ".",
                        stringsAsFactors = FALSE)

if (!("Reference" %in% colnames(design_tmp))) {
  # Complex design, no reference column
  design_tmp = c(0,1)
} else {
  # Simple design, check if there are diverse Reference values above 0
  design_tmp = design_tmp |>
    dplyr::filter(Reference >= 0) |>
    dplyr::pull(Reference) |>
    unique()
}

if (params$diffanaTest & length(design_tmp) > 1) {
  text_to_print = paste0(
    text_to_print, ":<br>",
    "- [Normalisation > Visualisation](#normalisation_visu) for normalised counts<br>",
    "- [Results subsections](#results_subsection) for the pairwise comparisons")
} else {
  text_to_print = paste0(
    text_to_print, " the section [Normalisation > Visualisation](#normalisation_visu).")
}

cat(paste0(text_to_print, "\n"))
```

All figures are saved in PNG and PDF format and accessible in the *`r fig_save_folder`* folder. You can download the code behind this analysis through the "Code" button in the top right corner of the document.

# Settings

The aim of this section is to:

- load the tools of interest
- define custom functions
- load the input parameters

## Environment

We load the tools to handle data and figures.

```{r library, fold_output=TRUE}
library(dplyr)
library(ggplot2)
```

```{r check_deseq2_version, echo = FALSE}
version_tested = c(1, 48, 0) # version 1.48.0

current_version = utils::packageVersion("DESeq2") %>% # A.B.C
  as.character() %>%
  base::strsplit(., split = "[^0-9]+") %>%
  unlist() %>%
  as.numeric()

if (current_version[[1]] < version_tested[[1]] |
    (current_version[[1]] <= version_tested[[1]] &
     current_version[[2]] < version_tested[[2]])) {
  warning("The notebook has been tested using version 1.48.0 of DESeq2",
          " You are using an older version (",
          utils::packageVersion("DESeq2"),
          ") so it cannot be guaranteed that the script will work...")
}
```

## Custom functions

We further define custom functions to avoid repetitions in the code.

```{r custom_functions, class.source = "fold-hide"}
# -----------------------------------------------------------------------------
# buildCountMatrix
# Create a matrix of reads count
#
# Input:
#   files : a vector of files names
#   sampleLabel : a vector of sample names
#   projectPath: path to the project directory
#
# Output:
#   countMatrix : a reads count matrix
#
# Original author : Vivien DESHAIES
# -----------------------------------------------------------------------------
buildCountMatrix = function(files, sampleLabel, expHeader) {
  
  # read first file and create countMatrix
  countMatrix = read.table(files[1],
                           header = expHeader,
                           stringsAsFactors = F,
                           quote = "")
  colnames(countMatrix) = c("id", "count1")
  
  # read and merge all remaining files with the first
  for (i in 2:length(files)) {
    
    # read files
    exp = read.table(files[i],
                     header = expHeader,
                     stringsAsFactors = F,
                     quote = "")
    
    # lowercase exp columns names
    colnames(exp) = c("id", paste0("count", i))
    
    # merge file data to count matrix by id
    countMatrix = merge(countMatrix, exp, by = "id", suffixes = "_")
  }
  
  # name rows
  rownames(countMatrix) = countMatrix[,1]
  
  # delete first row containing row names
  countMatrix = countMatrix[,-1]
  
  # name columns
  colnames(countMatrix) = sampleLabel
  return(countMatrix)
}

###############################################################################
# -----------------------------------------------------------------------------
# saveRawCountMatrix
#
#   A specific treatment is needed for the raw count matrix because the object
#   DESeq2::counts(dds) does not include the names of the samples as column names
#
#   input: dds -> DESeq object
#          fileName -> character (name of the file to output)
#   output: rawCountMatrix -> file
#
# -----------------------------------------------------------------------------

saveRawCountMatrix = function(dds,fileName) {
  
  countMatrix = DESeq2::counts(dds)
  countMatrix = cbind(countMatrix, row.names(countMatrix))
  
  # Add the samples names as column names + add column Id
  colData = SummarizedExperiment::colData(dds)
  colnames(countMatrix) = c(colData$Name, "Id")
  
  # Put Ids on the first column
  countMatrix = countMatrix[, c("Id", colData$Name)]
  
  # Write the count matrix in a file
  write.table(countMatrix,
              file = paste0(fileName),
              sep = "\t",
              row.names = FALSE,
              quote = FALSE)
}

###############################################################################
# -----------------------------------------------------------------------------
# saveTable
#
#   input: dataframe -> data frame (count matrix)
#          fileName -> character (name of the file to output)
#   output: countMatrix -> file
# -----------------------------------------------------------------------------

saveTable = function(dataframe, fileName) {
  # Add column Id
  column_names = colnames(dataframe)
  dataframe = cbind(row.names(dataframe), dataframe)
  colnames(dataframe) = c("Id", column_names)
  
  # Write the table in a file
  write.table(dataframe,
              file = paste0(fileName),
              sep = "\t",
              row.names = FALSE,
              quote = FALSE)
}

# -----------------------------------------------------------------------------
# plotDendrogram
# Create a ggplot object corresponding to a dendrogram
#
# Input:
#   hc_data : object obtained with the ggdendro::dendro_data function
#   fig_title : figure title
#   col_vector: named vector of colors
#
# Output:
#   p : ggplot object
#
# Original author : Audrey ONFROY
# -----------------------------------------------------------------------------
plotDendrogram = function(hc_data,
                          fig_title = "",
                          col_vector = colors_condition) {
  
  p = ggplot2::ggplot() +
    # Draw dendrogram
    ggplot2::geom_segment(data = ggdendro::segment(hcdata), 
                          aes(x = x, y = y, xend = xend, yend = yend)) +
    # Leaf label
    ggplot2::geom_label(data = ggdendro::label(hcdata), 
                        aes(x = x, y = y, label = label),
                        linewidth = 0, fill = NA,
                        size = 3, angle = 90, hjust = 1,
                        nudge_y = -0.02*max(ggdendro::segment(hcdata)$yend)) +
    # Leaf colors
    ggplot2::geom_point(data = ggdendro::label(hcdata),
                        ggplot2::aes(x = x, y = y, col = Condition),
                        size = 3, pch = 19) +
    ggplot2::scale_color_manual(values = col_vector,
                                breaks = names(col_vector)) +
    # Style
    ggplot2::labs(y = "Distance",
                  title = fig_title) +
    ggplot2::coord_cartesian(clip = "off") +
    ggplot2::theme_minimal() +
    ggplot2::theme(axis.text.x = element_blank(),
                   axis.ticks.x = element_blank(),
                   axis.title.x = element_blank(),
                   axis.line.y = element_line(),
                   axis.ticks.y = element_line(),
                   panel.grid = element_blank(),
                   plot.title = element_text(hjust = 0.5),
                   plot.margin = ggplot2::unit(c(0.5, 0.5, max(nchar(hcdata$labels$label))/6, 0.5), "cm"),
                   # Transparent background
                   panel.background = ggplot2::element_blank(),
                   plot.background = ggplot2::element_blank(),
                   legend.background = ggplot2::element_blank())
  
  return(p)
}

# -----------------------------------------------------------------------------
# plotPCA
# Create a ggplot object corresponding to the PCA plot of individuals
#
# Input:
#   pca_df : dataframe
#   fig_title : figure title
#   col_vector: named vector of colors
#
# Output:
#   p : ggplot object
# -----------------------------------------------------------------------------
plotPCA = function(pca_df,
                   fig_title = "",
                   col_vector = colors_condition) {
  
  p = ggplot2::ggplot(pca_df, aes(x = Dim.1, y = Dim.2, col = Condition, label = sample)) +
    ggplot2::geom_hline(yintercept = 0, lty = 2) +
    ggplot2::geom_vline(xintercept = 0, lty = 2) +
    # Samples
    ggplot2::geom_point(size = 3, pch = 19) +
    ggrepel::geom_label_repel(fill = NA, label.size = 0) +
    ggplot2::scale_color_manual(values = col_vector,
                                breaks = names(col_vector)) +
    # Style
    ggplot2::labs(x = paste0("Dim1 (", round(pcaCount$eig[1, 2], 2), "%)"),
                  y = paste0("Dim2 (", round(pcaCount$eig[2, 2], 2), "%)"),
                  title = fig_title) +
    ggplot2::theme_minimal() +
    ggplot2::theme(plot.title = element_text(hjust = 0.5),
                   # Transparent background
                   panel.background = ggplot2::element_blank(),
                   plot.background = ggplot2::element_blank(),
                   legend.background = ggplot2::element_blank())
  
  return(p)
}

# -----------------------------------------------------------------------------
# plotBarplot
# Create a ggplot object corresponding to a barplot
#
# Input:
#   df : dataframe
#   y_column : column in df for the Y-axis
#   y_title : Y-axis title
#   fig_title : figure title
#   col_vector: named vector of colors
#
# Output:
#   p : ggplot object
# -----------------------------------------------------------------------------
plotBarplot = function(df,
                       y_column,
                       y_title = "",
                       fig_title = "",
                       col_vector = colors_condition) {
  df$y_value = df[, y_column]
  
  p = ggplot2::ggplot(df, aes(x = sample, y = y_value, fill = Condition)) +
    ggplot2::geom_bar(stat = "identity", col = "black") +
    ggplot2::scale_fill_manual(values = col_vector,
                               breaks = names(col_vector)) +
    # Style
    ggplot2::labs(x = "",
                  y = y_title,
                  title = fig_title) +
    ggplot2::scale_y_continuous(expand = c(0,0)) +
    ggplot2::theme_classic() +
    ggplot2::theme(plot.title = element_text(hjust = 0.5),
                   axis.text.x = element_text(angle = 90, hjust = 1),
                   # Transparent background
                   panel.background = ggplot2::element_blank(),
                   plot.background = ggplot2::element_blank(),
                   legend.background = ggplot2::element_blank())
  
  return(p)
}

# -----------------------------------------------------------------------------
# plotBoxplot
# Create a ggplot object corresponding to a boxplot
#
# Input:
#   df : dataframe
#   y_column : column in df for the Y-axis
#   y_title : Y-axis title
#   fig_title : figure title
#   colors_condition: named vector of colors
#
# Output:
#   p : ggplot object
# -----------------------------------------------------------------------------
plotBoxplot = function(df,
                       y_column,
                       y_title = "",
                       fig_title = "",
                       col_vector = colors_condition) {
  df$y_value = df[, y_column]
  
  p = ggplot2::ggplot(df, aes(x = sample, y = y_value, fill = Condition)) +
    ggplot2::geom_boxplot(col = "black") +
    ggplot2::scale_fill_manual(values = col_vector,
                               breaks = names(col_vector)) +
    # Style
    ggplot2::labs(x = "",
                  y = y_title,
                  title = fig_title) +
    ggplot2::scale_y_continuous(expand = c(0,0)) +
    ggplot2::theme_classic() +
    ggplot2::theme(plot.title = element_text(hjust = 0.5),
                   axis.text.x = element_text(angle = 90, hjust = 1),
                   # Transparent background
                   panel.background = ggplot2::element_blank(),
                   plot.background = ggplot2::element_blank(),
                   legend.background = ggplot2::element_blank())
  
  return(p)
}

# -----------------------------------------------------------------------------
# expressionColors
# Define a color panel for the heatmap
#
# Input:
#   range_values : a numerical vector with two values (min and max)
#
# Output:
#   expression_colors : a color or a function built with circlize::colorRamp2
# -----------------------------------------------------------------------------
expressionColors = function(range_values) {
  min_value = range_values[1]
  max_value = range_values[2]
  
  # min_value < 0 < max_value
  if (min_value < 0 & 0 < max_value) {
    expression_colors = circlize::colorRamp2(
      breaks = c(min_value, 0, max_value),
      colors = c("#224897", "#F7F7F7", "#D32A24"))
  } else
    
    # min_value < max_value < 0
    if (min_value < max_value & max_value < 0) {
      expression_colors = circlize::colorRamp2(
        breaks = c(min_value, 0),
        colors = c("#224897", "#F7F7F7"))
    } else
      
      # 0 < min_value < max_value
      if (0 < min_value & min_value < max_value) {
        expression_colors = circlize::colorRamp2(
          breaks = c(0, max_value),
          colors = c("#F7F7F7", "#D32A24"))
      } else {
        
        # min_value == max_value
        expression_colors = "#F7F7F7"
      }
  
  return(expression_colors)
}

# -----------------------------------------------------------------------------
# buildContrast
# Define the contrast vector to extract DE results from the dds object
#
# Input:
#   dds       : DESeqDataSet
#   group1    : list of character vectors, for instance:
#                           list(c("sexe", "F"), c("genotype", "HETERO")
#   group2    : list of character vectors, for instance:
#                           list(c("sexe", "F"), c("genotype", "HOMO")
#   weighted  : boolean     whether to weight the contrast matrix by the
#                           number of replicates per condition or not
#
# Output: a list of 3 elements
#   contrast        : a numerical vector
#   samples_group1  : a character vector containing sample names in group 1
#   samples_group2  : a character vector containing sample names in group 2
#
# Source:
# Modified from: https://www.r-bloggers.com/2024/05/a-guide-to-designs-and-contrasts-in-deseq2/
# -----------------------------------------------------------------------------
buildContrast = function(dds,
                         group1,
                         group2,
                         weighted = FALSE){
  #-- Model matrix for each sample 
  mod_mat = model.matrix(DESeq2::design(dds),
                         SummarizedExperiment::colData(dds))
  
  #-- Samples in group1
  # A list of TRUE / FALSE
  # Each element of the list is of length the number of samples
  # The i-th element of the list corresponds to the i-th condition in group1
  grp1_rows = lapply(c(1:length(group1)), FUN = function(group1_i) {
    group1_i_name = group1[[group1_i]][1]
    group1_i_values = group1[[group1_i]][2:length(group1_i)]
    
    samples_in_group1_i = (SummarizedExperiment::colData(dds)[[group1_i_name]] %in% group1_i_values) # TRUE / FALSE
    
    return(samples_in_group1_i)
  })
  grp1_rows = Reduce(function(x, y) x & y, grp1_rows)
  
  grp2_rows = lapply(c(1:length(group2)), FUN = function(group2_i) {
    group2_i_name = group2[[group2_i]][1]
    group2_i_values = group2[[group2_i]][2:length(group2_i)]
    
    samples_in_group2_i = (SummarizedExperiment::colData(dds)[[group2_i_name]] %in% group2_i_values) # TRUE / FALSE
    
    return(samples_in_group2_i)
  })
  grp2_rows = Reduce(function(x, y) x & y, grp2_rows)
  
  #-- Model matrix with only samples of interest
  mod_mat1 = mod_mat[grp1_rows, , drop = FALSE]
  mod_mat2 = mod_mat[grp2_rows, , drop = FALSE]
  
  #-- Consider the weight or not
  if(!weighted){
    mod_mat1 = mod_mat1[!duplicated(mod_mat1), , drop = FALSE]
    mod_mat2 = mod_mat2[!duplicated(mod_mat2), , drop = FALSE]
  }
  
  #-- Output
  contrast = colMeans(mod_mat1)-colMeans(mod_mat2)
  samples_group1 = colnames(dds)[grp1_rows]
  samples_group2 = colnames(dds)[grp2_rows]
  output = list(contrast = contrast,
                samples_group1 = samples_group1,
                samples_group2 = samples_group2)
  
  return(output)
}
```

These functions are listed below.

```{r print_functions, echo = FALSE, results = 'asis'}
cat(paste0("- ", setdiff(ls(), c("hooks", "params", "hook_foldable", "design_tmp",
                                 "fig_save_folder", "text_to_print"))),
    sep = "\n")
```

## Parameters

The table below summarizes all the input parameters.

```{r input_parameter, class.source = "fold-hide"}
projectName     = params$projectName
designPath      = params$designPath
comparisonPath  = params$comparisonPath
diffanaTest     = as.logical(toupper(params$diffanaTest))
expHeader       = as.logical(toupper(params$expHeader))
deseqModel      = params$deseqModel
sizeFactorType  = params$sizeFactorType
fitType         = params$fitType
statisticTest   = params$statisticTest
weightContrast  = params$weightContrast
prefix          = params$prefix
plotInteractive = params$plotInteractive
leaveOnError    = params$leaveOnError

# Display them as a table
c("Project name (projectName)" = projectName,
  "Path to the design file (designPath)" = designPath,
  "Path to the comparison file (comparisonPath)" = comparisonPath,
  "Whether to perform the differential expression analysis or not (diffanaTest)" = diffanaTest,
  "Whether the design table has a header or not (expHeader)" = expHeader,
  "DESeq2 model (deseqModel)" = deseqModel,
  "Size factor type (sizeFactorType)" = sizeFactorType,
  "Fit type (fitType)" = fitType,
  "Statistic test (statisticTest)" = statisticTest,
  "Whether to weight the contrast vector by the number of samples or not (weightContrast)" = weightContrast,
  "Prefix to save files (prefix)" = prefix,
  "Whether to make interactive plots or not (plotInteractive)" = plotInteractive,
  "Whether to stop the rendering in case of error (leaveOnError)" = leaveOnError) %>%
  data.frame(Parameter = names(.),
             Value     = .,
             row.names = NULL) %>%
  knitr::kable("html", escape = FALSE) %>%
  kableExtra::kable_styling("striped", full_width = FALSE) %>%
  kableExtra::column_spec(1, bold = TRUE)
```

# Data preparation

The purpose of this section is to:

* load the design file, containing metadata about each sample ;
* load the individual sample count data, assemble them as a gene-by-sample count matrix, and save it as a TSV file ; and,
* build a dataset in a format compatible with the differential expression analysis (DESeq2-related format).

## Load metadata

We load the design file and display the 6 first rows. 

```{r load_design}
design = read.table(designPath,
                    sep = "\t",
                    header = TRUE,
                    dec = ".",
                    stringsAsFactors = FALSE)

# About a warning in DESeq2
design$Condition = stringr::str_replace_all(
  design$Condition,
  pattern = "-",
  replacement = "_")

# Add Reference if does not exist
if (!("Reference" %in% colnames(design))) {
  design$Reference = Inf
  complexMode = TRUE
} else {
  complexMode = FALSE
}

# Reorder samples based on Condition
design = design %>%
  dplyr::arrange(Condition, RepTechGroup, Name) %>%
  dplyr::mutate(Condition = factor(Condition, levels = unique(Condition))) %>%
  dplyr::mutate(RepTechGroup = factor(RepTechGroup, levels = unique(RepTechGroup))) %>%
  dplyr::mutate(Name = factor(Name, levels = unique(Name)))

# Remove columns out of interest
design = design[, !(colnames(design) %in% c("SampleId", "Description", "Date", "FastqFormat"))]

# Display
head(design) %>%
  knitr::kable("html") 
```


Description of the columns:

```{r print_metadata_help, echo = FALSE, results = 'asis'}
the_message = c(Name = "- **`Name`**: Unique identifier of each technical replicate.",
                expressionFile = "- **`expressionFile`**: File path to the count expression data. The **TSV** file contains two columns: one for the gene identifiers and one for the number of reads mapping to their genomic location. For compatibility with Excel, please use the **XLSX** file instead of the TSV file ([read more](https://pmc.ncbi.nlm.nih.gov/articles/PMC8357140/)). Furthermore, the XLSX file contains additional columns, such as a description of each gene.",
                Reads = "- **`Reads`**: File path to the sequencing reads, in FASTQ format ([read more](https://en.wikipedia.org/wiki/FASTQ_format)).",
                RepTechGroup = "- **`RepTechGroup`**: Samples with the same value in the `RepTechGroup` column are **technical replicates**. This occurs when the sequencing library is split over sequencing lanes. Each lane generates a technical replicate from the same biological library.",
                Condition = "- **`Condition`**: Samples with the same value in the `Condition` column are **biological replicates**. They are produced by the experimenter.",
                Reference = "- **`Reference`**: Numerical values used to define the pairwise comparisons between conditions in the differential expression analysis.")
the_message = the_message[names(the_message) %in% colnames(design)]

cat(paste0(the_message), sep = "\n")
```

We associate a color to each condition.

```{r add_colors}
colors_condition = unique(design$Condition) %>%
  setNames(nm = .,
           grDevices::hcl.colors(length(.), "Spectral"))

data.frame(Color = unname(colors_condition),
           Condition = names(colors_condition)) %>%
  knitr::kable("html",
               col.names = c("Color (hex code)", "Condition")) %>%
  kableExtra::column_spec(1, color = colors_condition) %>%
  kableExtra::kable_styling(full_width = FALSE)
```

## Build count matrix

From the individual count data, we build a gene-by-sample count matrix. The count value for a given gene corresponds to the number (integer value) of sequencing reads that map to its genomic region. We display the top left corner of this matrix, meaning 5 genes and 5 samples. The complete matrix is saved as a TSV file ([quick access](#save_count_matrix)).

```{r count_mat}
count_mat = buildCountMatrix(files = design$expressionFile,
                             sampleLabel = design$Name,
                             expHeader = expHeader)

# Reorder columns
count_mat = count_mat[, levels(design$Name)]

# Display
count_mat[c(1:5), c(1:5)] %>%
  knitr::kable("html") %>%
  kableExtra::kable_styling(full_width = FALSE)
```

The count matrix contains **`r nrow(count_mat)`** genes, **`r ncol(count_mat)`** samples and **`r round(100*mean(count_mat == 0),2)`**% of null values.

## Create DESeqDataSet object

We create the DESeqDataSet object that includes the count matrix and the design file.

```{r dds_object}
dds = DESeq2::DESeqDataSetFromMatrix(
  countData = count_mat,
  colData = design,
  design = as.formula(deseqModel))
```

## Save count matrix {#save_count_matrix}

We save the count matrix as a TSV file.

```{r save_count_matrix}
filename = paste0(prefix, projectName, "-normalisation_rawCountMatrix.tsv")

saveRawCountMatrix(dds,
                   file = filename)
```

Output file name:<br>*`r filename`*

# Quality control

The aim of this section is to better apprehend the variability between the technical replicates. For information on the correspondence between the identifiers of technical replicates and sample names, please refer to the design file.

## Raw counts dendrogram

We compute euclidean distances between each pair of samples, considering the raw count values.

```{r dendrogram_raw_count_matrix}
dist.mat = dist(t(count_mat))
hc = stats::hclust(dist.mat)
hcdata = ggdendro::dendro_data(hc, type = "rectangle")

# Add grouping for colors
hcdata$labels = dplyr::left_join(x = hcdata$labels,
                                 y = design[, c("Name", "Condition")],
                                 by = c("label" = "Name"))
```

We visualise these distances through a dendrogram. It informs whether technical replicates or closely related conditions are closer together than with other samples.

```{r fig-normalisation_unpooled_clustering, fig.width = 10, fig.height = 6}
plotDendrogram(hc_data,
               fig_title = paste0("Dendrogram on the raw count matrix - ",
                                  projectName))
```

## Raw counts PCA

The **`r ncol(count_mat)`** samples are represented in a space of **`r nrow(count_mat)`** dimensions, corresponding to genes. We perform a Principal Component Analysis (PCA) to visualise samples on a two-dimension space such that it represents the highest variability between samples.

```{r run_pca}
pcaCount = count_mat %>%
  t() %>%
  FactoMineR::PCA(., graph = FALSE)
```

We visualise the samples on the two-dimensional projection. The coordinates of each sample depend on those of the other samples. This projection captures **`r round(pcaCount$eig[2, 3], 2)`**% of the total variability between samples. Therefore, it lacks **`r 100-round(pcaCount$eig[2, 3], 2)`**% of the initial information. In this representation, two points (samples) are close together if they have similar global transcriptomic profiles. We expect the technical and biological replicates to be closer to each other than to the other samples and conditions.

```{r fig-normalisation_unpooled_PCA, fig.width = 10, fig.height = 6}
pcaCount$ind$coord %>%
  as.data.frame() %>%
  dplyr::mutate(sample = rownames(.)) %>%
  dplyr::left_join(x = .,
                   y = unique(design[, c("Name", "Condition")]),
                   by = c("sample" = "Name")) %>%
  plotPCA(.,
          fig_title =  paste0("PCA of the raw count matrix - ", projectName))
```

## Null counts barplot

This figure displays the proportion of null counts per sample, meaning, the genes that are not being expressed or not captured by the protocol, within the biological samples. We expect samples from the same condition to be similar.

```{r fig-normalisation_null_counts, fig.width = 10, fig.height = 6}
colMeans(count_mat == 0) %>%
  data.frame(sample = factor(names(.), levels = levels(design$Name)),
             prop_null_counts = .) %>%
  dplyr::mutate(prop_null_counts = 100*prop_null_counts) %>%
  dplyr::left_join(x = .,
                   y = design[, c("Name", "Condition")],
                   by = c("sample" = "Name")) %>%
  # Plot
  plotBarplot(.,
              y_column = "prop_null_counts",
              y_title = "Proportion of null counts (%)",
              fig_title = paste0("Proportion of null counts per sample - ",
                                 projectName))
```

## Raw counts barplot

This figure displays the total number of reads per sample, also known as library size. We can assess if some samples have a low library size compared to others, and whether technical replicates share a similar number of reads or not.

```{r fig-normalisation_barplot_counts, fig.width = 10, fig.height = 6}
colSums(DESeq2::counts(dds)) %>%
  data.frame(sample = factor(names(.), levels = levels(design$Name)),
             read_counts = .) %>%
  dplyr::left_join(x = .,
                   y = design[, c("Name", "Condition")],
                   by = c("sample" = "Name")) %>%
  # Plot
  plotBarplot(.,
              y_column = "read_counts",
              y_title = "Total read counts",
              fig_title = paste0("Number of reads per sample - ", projectName))
```

## Raw counts boxplot

This figure displays, as a boxplot, the number of raw counts for each gene, per sample. It informs whether the genes show a similar count distribution between samples and conditions.

```{r fig-normalisation_boxplot_count, fig.width = 10, fig.height = 6}
log2(DESeq2::counts(dds)+1) %>%
  reshape2::melt() %>%
  `colnames<-`(c("gene_ID", "sample", "log2_counts")) %>%
  dplyr::mutate(sample = factor(sample, levels = levels(design$Name))) %>%
  dplyr::left_join(x = .,
                   y = design[, c("Name", "Condition")],
                   by = c("sample" = "Name")) %>%
  # Plot
  plotBoxplot(.,
              y_column = "log2_counts",
              y_title = "log_2 (counts+1)",
              fig_title = paste0("Raw counts distribution per sample - ", projectName))
```


<!-- Conditional execution in case technical replicates are present -->
<br>

```{r tech_rep_present, echo = FALSE}
tech_rep_present = (max(table(design$RepTechGroup, design$Condition)) > 1)

if (tech_rep_present) {
  the_message = "In this context, there are technical replicates."
} else {
  the_message = "In this context, there is no technical replicate."
  
  # Anyway, we change the sample labels to the biological meaningful ones.
  dds = DESeq2::collapseReplicates(dds,
                                   groupby = dds$RepTechGroup)
}
```

`r the_message`

```{r collapse_replicates, echo = FALSE, results = 'asis', eval = tech_rep_present}
res = knitr::knit_child(
  input = '02_child_collapse_replicates.Rmd',
  quiet = TRUE)

cat(res, sep = '\n')
```

# Normalisation

The aim of this section is to normalise the count matrix, before performing the differential expression analysis. The normalisation method considers the sequencing depth and RNA composition of each sample to compute normalised count values.

For more details, please visit:

[https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html](https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html)

## Data handling

We normalise the count matrix. We display the top left corner of this matrix, meaning 5 genes and 5 samples. Unlike count data, normalised count values are not necessarily integers.

```{r estimateSizeFactors}
dds = DESeq2::estimateSizeFactors(dds,
                                  type = sizeFactorType)

DESeq2::counts(dds, normalized = TRUE)[c(1:5), c(1:5)] %>%
  knitr::kable("html") %>%
  kableExtra::kable_styling(full_width = FALSE)
```

We save the normalised count matrix as a TSV file.

```{r save_norm_count_matrix}
filename = paste0(prefix, projectName,
                  "-normalisation_normalisedCountMatrix.tsv")

saveTable(DESeq2::counts(dds, normalized = TRUE),
          fileName = filename)
```

Output file name:<br>*`r filename`*

## Visualisation {#normalisation_visu}

### Normalised counts dendrogram

We compute euclidean distances between each pair of samples, considering the normalised count values.

```{r dendrogram_pooled_normalised_count_matrix}
ddsStabilized = dds %>%
  DESeq2::varianceStabilizingTransformation() %>%
  SummarizedExperiment::assay()
dist.mat = dist(t(ddsStabilized))
hc = stats::hclust(dist.mat)
hcdata = ggdendro::dendro_data(hc, type = "rectangle")

# Add grouping for colors
hcdata$labels = dplyr::left_join(
  x = hcdata$labels,
  y = unique(design[, c("RepTechGroup", "Condition")]),
  by = c("label" = "RepTechGroup"))
```

We visualise these distances through a dendrogram. It informs whether the samples share a similar transcriptomic profile per condition or not.

```{r fig-normalisation_pooled_clustering, fig.width = 10, fig.height = 6}
plotDendrogram(
  hc_data,
  fig_title = paste0("Dendrogram on the normalised count matrix - ", projectName))
```

### Normalised counts PCA

We perform a PCA for all samples using the normalised count matrix.

```{r run_pca2}
pcaCount = dds %>%
  DESeq2::counts(., normalized = TRUE) %>%
  t() %>%
  FactoMineR::PCA(., graph = FALSE)
```

We visualise the samples on the two-dimensional projection. The coordinates of each sample depend on those of the other samples. This projection captures **`r round(pcaCount$eig[2, 3], 2)`**% of the total variability between samples. Therefore, it lacks **`r 100-round(pcaCount$eig[2, 3], 2)`**% of the initial information. In this representation, two points (samples) are close together if they have similar global transcriptomic profiles. We expect samples from the same biological condition to be closer to each other than to samples from other conditions.

```{r fig-normalisation_pooled_PCA, fig.width = 10, fig.height = 6}
pcaCount$ind$coord %>%
  as.data.frame() %>%
  dplyr::mutate(sample = rownames(.)) %>%
  dplyr::left_join(x = .,
                   y = unique(design[, c("RepTechGroup", "Condition")]),
                   by = c("sample" = "RepTechGroup")) %>%
  plotPCA(.,
          fig_title =  paste0("PCA of the normalised count matrix - ", projectName))
```

### Normalised counts boxplot

This figure displays, as a boxplot, the number of normalised counts for each gene, per sample.

```{r fig-normalisation_normalised_boxplot_count, fig.width = 10, fig.height = 6}
log2(DESeq2::counts(dds, normalized = TRUE)+1) %>%
  reshape2::melt() %>%
  `colnames<-`(c("gene_ID", "sample", "log2_counts")) %>%
  dplyr::mutate(sample = factor(sample, levels = levels(design$RepTechGroup))) %>%
  dplyr::left_join(x = .,
                   y = unique(design[, c("RepTechGroup", "Condition")]),
                   by = c("sample" = "RepTechGroup")) %>%
  # Plot
  plotBoxplot(
    .,
    y_column = "log2_counts",
    y_title = "log_2 (counts+1)",
    fig_title = paste0("Normalised counts distribution per sample - ", projectName))
```

### Most expressed genes plot

We identified the most expressed gene in each sample.

```{r identify_most_expressed_genes}
# preparation of 2 data frame with the same number of column than
# the dds count matrix
maxCounts = DESeq2::counts(dds)[1,]
transcriptNames = DESeq2::counts(dds)[1,]

# for each sample (column)
for (i in 1:ncol(DESeq2::counts(dds))) {
  
  # selection of the maximum number of count
  maxCounts[i] = (max(DESeq2::counts(dds, normalized = TRUE)[,i])/
                    sum(DESeq2::counts(dds, normalized = TRUE)[,i]))*100
  
  # selection of the name of the genes this the maximum of count
  transcriptNames[i] = row.names(
    subset(DESeq2::counts(dds, normalized = TRUE),
           DESeq2::counts(dds, normalized = TRUE)[,i] == 
             max(DESeq2::counts(dds, normalized = TRUE)[,i])))
}
```

We represent the information as a barplot.

```{r fig-normalisation_most_expressed_genes, fig.width = 10, fig.height = 2+ncol(count_mat)/4}
dplyr::left_join(x = data.frame(sample = names(maxCounts),
                                max_counts = maxCounts),
                 y = data.frame(sample = names(transcriptNames),
                                gene_id = transcriptNames),
                 by = "sample") %>%
  dplyr::mutate(sample = factor(sample, levels = levels(design$RepTechGroup))) %>%
  dplyr::left_join(x = .,
                   y = unique(design[, c("RepTechGroup", "Condition")]),
                   by = c("sample" = "RepTechGroup")) %>%
  # Plot
  ggplot2::ggplot(., aes(x = sample, y = max_counts,
                         fill = Condition, label = gene_id)) +
  ggplot2::geom_bar(stat = "identity", col = "black") +
  ggplot2::geom_label(mapping = aes(x = sample, y = 0),
                      linewidth = 0, fill = NA, hjust = 0) +
  ggplot2::scale_fill_manual(values = colors_condition,
                             breaks = names(colors_condition)) +
  # Style
  ggplot2::labs(y = "Proportion of reads (%)",
                title = paste0("Most expressed genes - ", projectName)) +
  ggplot2::scale_y_continuous(expand = c(0,0)) +
  ggplot2::coord_flip() +
  ggplot2::theme_classic() +
  ggplot2::theme(plot.title = element_text(hjust = 0.5),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 90, hjust = 1),
                 # Transparent background
                 panel.background = ggplot2::element_blank(),
                 plot.background = ggplot2::element_blank(),
                 legend.background = ggplot2::element_blank())
```


<!-- Conditional execution in case differential expression analysis should be done -->

```{r diffana, echo = FALSE, results = 'asis', eval = diffanaTest}
res = knitr::knit_child(
  input = '03_child_differential_expression.Rmd',
  quiet = TRUE)

cat(res, sep = '\n')
```


# Versions

```{r sessioninfo, echo = FALSE, fold_output = TRUE, results = "hold"}
print("Packages location:")
.libPaths()
print("")
print("Session Information:")
sessionInfo()
```

# Reference

The differential expression analysis uses the `DESeq2` R package ([vignette](https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html)).

> Love, M.I., Huber, W. & Anders, S., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014). DOI: [10.1186/s13059-014-0550-8](https://doi.org/10.1186/s13059-014-0550-8)
