cafferychen777
commented
8 months ago Hello @Aristotle1992,
You can modify the font size of your plots by adjusting the theme elements in your ggplot2 calls. For instance, if you want to increase the font size of the axis labels, you can modify the axis.text argument in the theme function. Here's an example:
ggplot2::theme(
axis.text = ggplot2::element_text(size = 12, color = "black"),
...
)In the pathway_errorbar function you provided, there are several places where the theme function is used, and you can adjust the font size in these places as needed. Here are the lines you would need to modify:
- Line 239:
axis.text = ggplot2::element_text(size = 10, color = "black") - Line 241:
axis.text.y = ggplot2::element_text(size = 10, color = "black", margin = ggplot2::margin(b = 6)) - Line 244:
axis.title.x = ggplot2::element_text(size = 10, color = "black", hjust = 0.5) - Line 249:
axis.text = ggplot2::element_text(size = 10, color = "black") - Line 252:
axis.text.x = ggplot2::element_text(size = 10, color = "black", margin = ggplot2::margin(b = 6)) - Line 255:
axis.title.x = ggplot2::element_text(size = 11, color = "black", hjust = 0.5) - Line 312:
axis.text = ggplot2::element_text(size = 10, color = "black") - Line 315:
axis.text.x = ggplot2::element_text(size = 10, color = "black", margin = ggplot2::margin(b = 6)) - Line 318:
axis.title.y = ggplot2::element_text(size = 11, color = "black", vjust = 0)
In each of these lines, you can increase the size argument to increase the font size. For example, you can change size = 10 to size = 12 to increase the font size.
pathway_errorbar <-
function(abundance,
daa_results_df,
Group,
ko_to_kegg = FALSE,
p_values_threshold = 0.05,
order = "group",
select = NULL,
p_value_bar = TRUE,
colors = NULL,
x_lab = NULL) {
# Identify pathways with missing annotation
missing_pathways <- daa_results_df[is.na(daa_results_df$pathway_name), "feature"]
# Inform the user about the missing annotations
if(length(missing_pathways) > 0) {
message("The following pathways are missing annotations and have been excluded: ",
paste(missing_pathways, collapse = ", "))
message("You can use the 'pathway_annotation' function to add annotations for these pathways.")
}
# Get the names of all columns in the data frame
column_names <- colnames(daa_results_df)
# Check if there are more than two 'group' columns
group_columns <- grepl("^group", column_names)
if (sum(group_columns) > 2) {
p_value_bar <- FALSE
message(
"There are more than two 'group' columns in the 'daa_results_df' data frame. As a result, it is not possible to compute the log2 fold values. The 'p_value_bar' has been automatically set to FALSE."
)
}
# Exclude rows with missing pathway annotation
daa_results_df <- daa_results_df[!is.na(daa_results_df[,x_lab]),]
if (is.null(x_lab)){
if (ko_to_kegg == TRUE){
x_lab <- "pathway_name"
}else{
x_lab <- "description"
}
if (is.null(daa_results_df$pathway_name)&is.null(daa_results_df$description)){
message(
"Please utilize the 'pathway_annotation' function to annotate the 'daa_results_df' data frame."
)
}
}
if (!(x_lab %in% colnames(daa_results_df))){
message(
"The 'x_lab' you defined does not exist as a column in the 'daa_results_df' data frame."
)
}
if (nlevels(factor(daa_results_df$method)) != 1) {
message(
"The 'method' column in the 'daa_results_df' data frame contains more than one method. Please filter it to contain only one method."
)
}
if (nlevels(factor(daa_results_df$group1)) != 1 || nlevels(factor(daa_results_df$group2)) != 1) {
message(
"The 'group1' or 'group2' column in the 'daa_results_df' data frame contains more than one group. Please filter each to contain only one group."
)
}
if (is.null(colors)) {
colors <- c("#d93c3e", "#3685bc", "#6faa3e", "#e8a825", "#c973e6", "#ee6b3d", "#2db0a7", "#f25292")[1:nlevels(as.factor(Group))]
}
errorbar_abundance_mat <- as.matrix(abundance)
daa_results_filtered_df <-
daa_results_df[daa_results_df$p_adjust < p_values_threshold,]
if (!is.null(select)) {
daa_results_filtered_sub_df <-
daa_results_filtered_df[daa_results_filtered_df$feature %in% select, ]
} else {
daa_results_filtered_sub_df <- daa_results_filtered_df
}
if (nrow(daa_results_filtered_sub_df) > 30) {
message(
paste0(
"The number of features with statistical significance exceeds 30, leading to suboptimal visualization. ",
"Please use 'select' to reduce the number of features.\n",
"Currently, you have these features: ",
paste(paste0('"', daa_results_filtered_sub_df$feature, '"'), collapse = ", "), ".\n",
"You can find the statistically significant features with the following command:\n",
"daa_results_df %>% filter(p_adjust < 0.05) %>% select(c(\"feature\",\"p_adjust\"))"
)
)
stop()
}
if (nrow(daa_results_filtered_sub_df) == 0){
stop(
"Visualization with 'pathway_errorbar' cannot be performed because there are no features with statistical significance. ",
"For possible solutions, please check the FAQ section of the tutorial."
)
}
# Convert to relative abundance
relative_abundance_mat <- apply(t(errorbar_abundance_mat), 1, function(x)
x / sum(x))
# Subset to only include the features present in daa_results_filtered_sub_df$feature
sub_relative_abundance_mat <- relative_abundance_mat[rownames(relative_abundance_mat) %in% daa_results_filtered_sub_df$feature,]
# Create a matrix for the error bars
error_bar_matrix <- cbind(
sample = colnames(sub_relative_abundance_mat),
group = Group,
t(sub_relative_abundance_mat)
)
error_bar_df <- as.data.frame(error_bar_matrix)
error_bar_df$group <- factor(Group,levels = levels(as.factor(Group)))
error_bar_pivot_longer_df <- tidyr::pivot_longer(error_bar_df,-c(sample, group))
error_bar_pivot_longer_tibble <-
mutate(error_bar_pivot_longer_df, group = as.factor(group))
error_bar_pivot_longer_tibble$sample <-
factor(error_bar_pivot_longer_tibble$sample)
error_bar_pivot_longer_tibble$name <-
factor(error_bar_pivot_longer_tibble$name)
error_bar_pivot_longer_tibble$value <-
as.numeric(error_bar_pivot_longer_tibble$value)
error_bar_pivot_longer_tibble_summarised <-
error_bar_pivot_longer_tibble %>% group_by(name, group) %>%
summarise(mean = mean(value), sd = stats::sd(value))
error_bar_pivot_longer_tibble_summarised <-
error_bar_pivot_longer_tibble_summarised %>% mutate(group2 = "nonsense")
switch(
order,
"p_values" = {
order <- order(daa_results_filtered_sub_df$p_adjust)
},
"name" = {
order <- order(daa_results_filtered_sub_df$feature)
},
"group" = {
daa_results_filtered_sub_df$pro <- 1
for (i in levels(error_bar_pivot_longer_tibble_summarised$name)) {
error_bar_pivot_longer_tibble_summarised_sub <-
error_bar_pivot_longer_tibble_summarised[error_bar_pivot_longer_tibble_summarised$name ==
i,]
pro_group <-
error_bar_pivot_longer_tibble_summarised_sub[error_bar_pivot_longer_tibble_summarised_sub$mean ==
max(error_bar_pivot_longer_tibble_summarised_sub$mean),]$group
pro_group <- as.vector(pro_group)
daa_results_filtered_sub_df[daa_results_filtered_sub_df$feature ==
i,]$pro <- pro_group
}
order <-
order(daa_results_filtered_sub_df$pro,
daa_results_filtered_sub_df$p_adjust)
},
"pathway_class" = {
if (!"pathway_class" %in% colnames(daa_results_filtered_sub_df)) {
stop(
"The 'pathway_class' column is missing in the 'daa_results_filtered_sub_df' data frame. ",
"Please use the 'pathway_annotation' function to annotate the 'pathway_daa' results."
)
}
order <- order(
daa_results_filtered_sub_df$pathway_class,
daa_results_filtered_sub_df$p_adjust
)
},
{
order <- order
}
)
daa_results_filtered_sub_df <-
daa_results_filtered_sub_df[order,]
error_bar_pivot_longer_tibble_summarised_ordered <-
data.frame(
name = NULL,
group = NULL,
mean = NULL,
sd = NULL
)
for (i in daa_results_filtered_sub_df$feature) {
error_bar_pivot_longer_tibble_summarised_ordered <-
rbind(
error_bar_pivot_longer_tibble_summarised_ordered,
error_bar_pivot_longer_tibble_summarised[error_bar_pivot_longer_tibble_summarised$name ==
i,]
)
}
if (ko_to_kegg == FALSE){
error_bar_pivot_longer_tibble_summarised_ordered[, x_lab] <-
rep(daa_results_filtered_sub_df[, x_lab], each = length(levels(
factor(error_bar_pivot_longer_tibble_summarised_ordered$group)
)))
}
if (ko_to_kegg == TRUE) {
error_bar_pivot_longer_tibble_summarised_ordered$pathway_class <-
rep(daa_results_filtered_sub_df$pathway_class,
each = length(levels(
factor(error_bar_pivot_longer_tibble_summarised_ordered$group)
)))
}
error_bar_pivot_longer_tibble_summarised_ordered$name <- factor(error_bar_pivot_longer_tibble_summarised_ordered$name, levels = rev(daa_results_filtered_sub_df$feature))
bar_errorbar <-
ggplot2::ggplot(error_bar_pivot_longer_tibble_summarised_ordered, # nolint: object_usage_linter.
ggplot2::aes(mean, name, fill = group)) + # nolint
ggplot2::geom_errorbar(
ggplot2::aes(xmax = mean + sd, xmin = 0),
position = ggplot2::position_dodge(width = 0.8),
width = 0.5,
size = 0.5,
color = "black"
) +
ggplot2::geom_bar(stat = "identity",
position = ggplot2::position_dodge(width = 0.8),
width = 0.8) +
GGally::geom_stripped_cols(width = 10) +
ggplot2::scale_fill_manual(values = colors) +
ggplot2::scale_color_manual(values = colors) +
ggprism::theme_prism() +
ggplot2::scale_x_continuous(expand = c(0, 0),
guide = "prism_offset_minor",) +
ggplot2::scale_y_discrete(labels = rev(daa_results_filtered_sub_df[, x_lab])) +
ggplot2::labs(x = "Relative Abundance", y = NULL) +
ggplot2::theme(
axis.ticks.y = ggplot2::element_blank(),
axis.line.y = ggplot2::element_blank(),
axis.line.x = ggplot2::element_line(size = 0.5),
axis.ticks.x = ggplot2::element_line(size = 0.5),
panel.grid.major.y = ggplot2::element_blank(),
panel.grid.major.x = ggplot2::element_blank(),
axis.text = ggplot2::element_text(size = 10, color = "black"), # nolint
axis.text.x = ggplot2::element_text(margin = ggplot2::margin(r = 0)), # nolint
axis.text.y = ggplot2::element_text(
size = 10,
color = "black",
margin = ggplot2::margin(b = 6)
),
axis.title.x = ggplot2::element_text(
size = 10,
color = "black",
hjust = 0.5
),
legend.position = "top",
legend.key.size = ggplot2::unit(0.1, "cm"),
legend.direction = "vertical",
legend.justification = "left",
legend.text = ggplot2::element_text(size = 8, face = "bold"),
legend.box.just = "right",
plot.margin = ggplot2::margin(0, 0.5, 0.5, 0, unit = "cm")
) + ggplot2::coord_cartesian(clip = "off")
if (ko_to_kegg == TRUE) {
pathway_class_group_mat <-
daa_results_filtered_sub_df$pathway_class %>%
table() %>%
data.frame() %>% column_to_rownames(".")
pathway_class_group <- data.frame(.= unique(daa_results_filtered_sub_df$pathway_class),Freq = pathway_class_group_mat[unique(daa_results_filtered_sub_df$pathway_class),])
start <-
c(1, rev(pathway_class_group$Freq)[1:(length(pathway_class_group$Freq) - 1)]) %>%
cumsum()
end <- cumsum(rev(pathway_class_group$Freq))
ymin <- start - 1 / 2
ymax <- end + 1 / 2
nPoints <- length(start)
pCol <- c("#D51F26","#272E6A","#208A42","#89288F","#F47D2B",
"#FEE500","#8A9FD1","#C06CAB","#E6C2DC","#90D5E4",
"#89C75F","#F37B7D","#9983BD","#D24B27","#3BBCA8",
"#6E4B9E","#0C727C", "#7E1416","#D8A767","#3D3D3D")[1:nPoints]
pFill <- pCol
for (i in 1:nPoints) {
bar_errorbar <- bar_errorbar +
ggplot2::annotation_custom(
grob = grid::rectGrob(
gp = grid::gpar(
col = pCol[i],
fill = pFill[i],
lty = NULL,
lwd = NULL,
alpha = 0.2
)
),
xmin = ggplot2::unit(-2, 'native'),
xmax = ggplot2::unit(0, 'native'),
ymin = ggplot2::unit(ymin[i], 'native'),
ymax = ggplot2::unit(ymax[i], 'native')
)
}
}
daa_results_filtered_sub_df <-
cbind(
daa_results_filtered_sub_df,
negative_log10_p = -log10(daa_results_filtered_sub_df$p_adjust),
group_nonsense = "nonsense",
log_2_fold_change = NA
)
for (i in daa_results_filtered_sub_df$feature){
mean <- error_bar_pivot_longer_tibble_summarised_ordered[error_bar_pivot_longer_tibble_summarised_ordered$name %in% i,]$mean
daa_results_filtered_sub_df[daa_results_filtered_sub_df$feature==i,]$log_2_fold_change <- log2(mean[1]/mean[2])
}
daa_results_filtered_sub_df$feature <- factor(daa_results_filtered_sub_df$feature,levels = rev(daa_results_filtered_sub_df$feature))
p_values_bar <- daa_results_filtered_sub_df %>%
ggplot2::ggplot(ggplot2::aes(feature, log_2_fold_change, fill = group_nonsense)) +
ggplot2::geom_bar(stat = "identity",
position = ggplot2::position_dodge(width = 0.8),
width = 0.8) +
ggplot2::labs(y = "log2 fold change", x = NULL) +
GGally::geom_stripped_cols() +
ggplot2::scale_fill_manual(values = "#87ceeb") +
ggplot2::scale_color_manual(values = "#87ceeb") +
ggplot2::geom_hline(ggplot2::aes(yintercept = 0),
linetype = 'dashed',
color = 'black') +
ggprism::theme_prism() +
ggplot2::scale_y_continuous(expand = c(0, 0),
guide = "prism_offset_minor") +
ggplot2::theme(
axis.ticks.y = ggplot2::element_blank(),
axis.line.y = ggplot2::element_blank(),
axis.line.x = ggplot2::element_line(size = 0.5),
axis.ticks.x = ggplot2::element_line(size = 0.5),
panel.grid.major.y = ggplot2::element_blank(),
panel.grid.major.x = ggplot2::element_blank(),
axis.text = ggplot2::element_text(size = 10, color = "black"),
axis.text.y = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(
size = 10,
color = "black",
margin = ggplot2::margin(b = 6)
),
axis.title.x = ggplot2::element_text(
size = 11,
color = "black",
hjust = 0.5
),
legend.position = "non"
) +
ggplot2::coord_flip()
if (ko_to_kegg == TRUE) {
pathway_class_y <- (ymax + ymin) / 2 - 0.5
pathway_class_plot_df <-
as.data.frame(
cbind(
nonsense = "nonsense",
pathway_class_y = pathway_class_y,
pathway_class = rev(unique(
daa_results_filtered_sub_df$pathway_class
))
)
)
pathway_class_plot_df$pathway_class_y <-
as.numeric(pathway_class_plot_df$pathway_class_y)
pathway_class_annotation <-
pathway_class_plot_df %>% ggplot2::ggplot(ggplot2::aes(nonsense, pathway_class_y)) + ggplot2::geom_text(
ggplot2::aes(nonsense, pathway_class_y, label = pathway_class),
size = 3.5,
color = "black",
fontface = "bold",
family = "sans"
) +
ggplot2::scale_y_discrete(position = "right") +
ggprism::theme_prism() +
ggplot2::theme(
axis.ticks = ggplot2::element_blank(),
axis.line = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
panel.grid.major.x = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
axis.text = ggplot2::element_blank(),
plot.margin = ggplot2::unit(c(0, 0.2, 0, 0), "cm"),
axis.title.y = ggplot2::element_blank(),
axis.title.x = ggplot2::element_blank(),
legend.position = "non"
)
}
daa_results_filtered_sub_df$p_adjust <-
as.character(daa_results_filtered_sub_df$p_adjust)
daa_results_filtered_sub_df$unique <-
nrow(daa_results_filtered_sub_df) - seq_len(nrow(daa_results_filtered_sub_df)) + 1
daa_results_filtered_sub_df$p_adjust <-
substr(daa_results_filtered_sub_df$p_adjust, 1, 5)
p_annotation <- daa_results_filtered_sub_df %>%
ggplot2::ggplot(ggplot2::aes(group_nonsense, p_adjust)) +
ggplot2::geom_text(
ggplot2::aes(group_nonsense, unique, label = p_adjust),
size = 3.5,
color = "black",
fontface = "bold",
family = "sans"
) +
ggplot2::labs(y = "p-value (adjusted)") +
ggplot2::scale_y_discrete(position = "right") +
ggprism::theme_prism() +
ggplot2::theme(
axis.ticks = ggplot2::element_blank(),
axis.line = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
panel.grid.major.x = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
axis.text = ggplot2::element_blank(),
plot.margin = ggplot2::unit(c(0, 0.2, 0, 0), "cm"),
axis.title.y = ggplot2::element_text(
size = 11,
color = "black",
vjust = 0
),
axis.title.x = ggplot2::element_blank(),
legend.position = "non"
)
if (p_value_bar == TRUE) {
if (ko_to_kegg == TRUE) {
combination_bar_plot <-
pathway_class_annotation + bar_errorbar + p_values_bar + p_annotation + patchwork::plot_layout(ncol = 4, widths =
c(1, 1.2, 0.5, 0.1))
}
else{
combination_bar_plot <-
bar_errorbar + p_values_bar + p_annotation + patchwork::plot_layout(ncol = 3, widths = c(2.3, 0.7, 0.3))
}
}else{
if (ko_to_kegg == TRUE) {
combination_bar_plot <-
pathway_class_annotation + bar_errorbar + p_annotation + patchwork::plot_layout(ncol = 3, widths =
c(1, 1.2, 0.1))
}
else{
combination_bar_plot <-
bar_errorbar + p_annotation + patchwork::plot_layout(ncol = 2, widths = c(2.5, 0.2))
}
}
return(combination_bar_plot)
}Please note that this will only increase the font size of the text elements specified. If you want to increase the font size of other elements (like the title, legend, etc.), you will need to adjust the corresponding theme arguments.
I hope this helps! Let me know if you have any other questions.
Aristotle1992
Hi Chen, I have no issues with the package and I was able to carry out my analysis without having any hitch. However, is it possible to increase the font size of the image generated especially from the pathway error bar plot? In essence is it possible to edit this image using ggplot2? I checked through the documentation and couldn't find any way to go about this. What will be your best guess or ways to resolve this? Below is my code.
for rhizosphere sample
Load metadata as a tibble
data(metadata)
metadata <- read_delim("C:/Microbiome/picrust/rhizo/check/rhizo_metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE)
make the growing degree days a factor
metadata$Growing_Degree_Days <- as.factor(metadata$Growing_Degree_Days)
Load KEGG pathway abundance
data(kegg_abundance)
kegg_abundance <- ko2kegg_abundance("C:/Microbiome/picrust/rhizo/check/final_rhizo.tsv")
if you use edger or deseq2 it assumes no significance but if you use limma voom it assumes significant
Perform pathway differential abundance analysis (DAA) using ALDEx2 method
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "Growing_Degree_Days", daa_method = "DESeq2", select = NULL, reference = NULL)
Filter results for ALDEx2_Welch's t test method
Please check the unique(daa_results_df$method) and choose one
daa_sub_method_results_df <- daa_results_df[daa_results_df$method == "DESeq2", ]
Annotate pathway results using KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)
Generate pathway error bar plot
p_rhizo <- pathway_errorbar(abundance = kegg_abundance, daa_results_df = daa_annotated_sub_method_results_df, Group = metadata$Growing_Degree_Days, p_values_threshold = 0.05, order = "pathway_class", select = c("ko00627", "ko00940", "ko00531", "ko00460", "ko03020", "ko04070", "ko00770"), ko_to_kegg = TRUE, p_value_bar = TRUE, colors = NULL, x_lab = "pathway_name")
p_rhizo
next I will need to plot the heat map and PCA
save the images for rhizo
ggsave("p_rhizo.png", plot = p_rhizo, path = "C:/Manuscript/Images", dpi = 700, width = 15, height = 10, units = c("in"), device = "png")
Thanks