R/plot.R
In rachaelcox/enrichr: Compute Protein Enrichment
Defines functions
zplot
psmplot
Documented in
psmplot
zplot
#' Visualize control PSMs versus test PSMs on a log-log plot.
#'
#' Graphs control PSMs versus test PSMs on a log-log plot. A pseudocount of 1 is adding to all PSMs for log-log visualization. Colored by Z-test threshold. See README for file formats and example usage on the \href{https://github.com/rachaelcox/enrichr}{enrichr GitHub Repository}.
#'
#' @param data A data frame produced by `enrich()`.
#' @param ylab Text for y-axis label; will correspond to test PSMs.
#' @param xlab Text for x-axis label; will correspond to control PSMs.
#' @param threshold Optional. FDR threshold to color points by. One of c(90, 95, 99). Default = 5% FDR (fdr_bh <= 0.05).
#' @param num_labs Optional. Number of annotation labels to display. Requires input to have an annotation column named `gene_names_primary`. Default = 0.
#' @param label_file Optional. Comma-separated file containing names of genes to be labeled on the plot; requires input to have an annotation column named `gene_names_primary` and labels must match format in the column.
#' @param outfile_prefix Optional. File prefix for output `.png` and `.pdf` files. Default = "output."
#' @return PSM log-log plots in `.png` and `.pdf` format.
#' @export
psmplot <- function(data, outfile_prefix, threshold, num_labs,
label_file, ylab, xlab, point_color){
theme_set(cowplot::theme_cowplot())
palette_pretty <- c("#0072B2","#E69F00","#009E24",
"#FF0000", "#979797","#5530AA", "#1E1E1E")
log10_minor_break <- function (...){
function(x) {
minx = floor(min(log10(x), na.rm=T))-1;
maxx = ceiling(max(log10(x), na.rm=T))+1;
n_major = maxx-minx+1;
major_breaks = seq(minx, maxx, by=1)
minor_breaks =
rep(log10(seq(1, 9, by=1)), times = n_major)+
rep(major_breaks, each = 9)
return(10^(minor_breaks))
}
}
if(missing(outfile_prefix)){
outfile_prefix = "output"
}
if(missing(num_labs)){
num_labs = 10
}
if(missing(threshold)){
threshold = 99
}
if(missing(point_color)){
point_color = palette_pretty[4]
}
xcol <- grep('ctrl_PSMs$', names(data), value = TRUE)
ycol <- grep('exp_PSMs$', names(data), value = TRUE)
zcol <- grep('_zscore$', names(data), value = TRUE)
acol <- grep('gene_names_primary$', names(data), value = TRUE)
# calculate thresholds
data_conf <- data %>%
dplyr::mutate(conf_90 = dplyr::case_when(fdr_bh <= 0.10 ~ TRUE,
TRUE ~ FALSE),
conf_95 = dplyr::case_when(fdr_bh <= 0.05 ~ TRUE,
TRUE ~ FALSE),
conf_99 = dplyr::case_when(fdr_bh <= 0.01 ~ TRUE,
TRUE ~ FALSE))
# add 1 to all PSMs for log-log visualization
print('Adding pseudocounts to PSMs...')
data_conf[ycol] <- data_conf[ycol]+1
data_conf[xcol] <- data_conf[xcol]+1
data_conf %>%
dplyr::select(accession, xcol, ycol) %>%
print()
if(missing(label_file)){
label_subset <- data_conf %>%
dplyr::arrange(desc(zcol)) %>%
dplyr::slice_head(n = num_labs)
} else {
labels <- readr::read_csv(label_file, col_names = FALSE)
label_subset <- data_conf %>%
dplyr::filter(grepl(paste(labels$X1, collapse = "|"), get(acol),
ignore.case = TRUE))
}
print(sprintf('Labeled plot points for "%s_PSMloglog.png"', outfile_prefix))
label_subset %>%
dplyr::select(accession, zcol, acol) %>%
print()
if(threshold == 90){
p1 <- ggplot(data_conf, aes(x = get(xcol), y = get(ycol),
color = conf_90, label = get(acol))) +
geom_point(alpha = 0.75, size = 1.5) +
annotate(geom = "text", x = Inf, y = 1, vjust = 1, hjust = 1,
label = "10% FDR", color = point_color, size = 4)
} else if(threshold == 95) {
p1 <- ggplot(data_conf, aes(x = get(xcol), y = get(ycol),
color = conf_95, label = get(acol))) +
geom_point(alpha = 0.75, size = 1.5) +
annotate(geom = "text", x = Inf, y = 1, vjust = 1, hjust = 1,
label = "5% FDR", color = point_color, size = 4)
} else {
p1 <- ggplot(data_conf, aes(x = get(xcol), y = get(ycol),
color = conf_99, label = get(acol))) +
geom_point(alpha = 0.75, size = 1.5) +
annotate(geom = "text", x = Inf, y = 1, vjust = 1, hjust = 1,
label = "1% FDR", color = point_color, size = 4)
}
p1
p2 <- p1 + ggrepel::geom_text_repel(data = label_subset,
size = 6.5/.pt, # font size
fontface = "bold",
alpha = 1.0,
hjust = 0.2,
point.padding = unit(0.5, "lines"),
box.padding = unit(0.5, "lines"),
nudge_y = 0.3,
nudge_x = -0.3,
colour = palette_pretty[7],
set.seed(3333)) +
scale_x_log10(minor_breaks = log10_minor_break()) +
scale_y_log10(minor_breaks = log10_minor_break()) +
scale_color_manual(values = rev(c(point_color, palette_pretty[5]))) +
theme(legend.position = "none") +
theme(panel.grid.major = element_line(colour = palette_pretty[5], size = 0.1, linetype = "dashed"),
panel.grid.minor = element_line(colour = palette_pretty[5], size = 0.1, linetype = "dashed")) +
xlab(xlab) +
ylab(ylab)
p2
ggsave(sprintf("%s_PSMloglog.pdf", outfile_prefix), plot = p2,
device = "pdf", width = 8, height = 6, units = "in")
ggsave(sprintf("%s_PSMloglog.png", outfile_prefix), plot = p2,
device = "png", width = 8, height = 6, units = "in")
return(p2)
}
#' Plot Z-scores across biological replicates.
#'
#' Colored by a multiple hypothesis corrected false discovery rate (FDR) computed on a weighted Z-score. See README for file formats and example usage on the \href{https://github.com/rachaelcox/enrichr}{enrichr GitHub Repository}.
#'
#' @param data A data frame produced by `combine_reps()`.
#' @param ycol Name of column containing y-axis data; should correspond to replicate #1 Z-scores.
#' @param xcol Name of column containing x-axis data; should correspond to replicate #1 Z-scores.
#' @param ylab Text for y-axis label; should correspond to replicate #1 Z-scores.
#' @param xlab Text for x-axis label; should correspond to replicate #2 Z-scores.
#' @param threshold Optional. FDR threshold to color points by. One of c(90, 95, 99). Default = 5% FDR (fdr_bh <= 0.05).
#' @param num_labs Optional. Number of annotation labels to display. Requires input to have an annotation column named `gene_names_primary`. Default = 0.
#' @param label_file Optional. Comma-separated file containing names of genes to be labeled on the plot; requires input to have an annotation column named `gene_names_primary` and labels must match format in the column.
#' @param outfile_prefix Optional. File prefix for output `.png` and `.pdf` files. Default = "output."
#' @return Plot depicting replicate 1 z-score vs replicate 2 z-scores in `.png` and `.pdf` format. Colored by FDR.
#' @export
zplot <- function(data, outfile_prefix, ylab, xlab, ycol, xcol,
threshold, label_file, num_labels){
theme_set(cowplot::theme_cowplot())
palette_pretty <- c("#1E1E1E", "#FF0000", "#0072B2",
"#E69F00", "#009E24", "#979797","#5530AA", "#1E1E1E")
if(missing(outfile_prefix)){
outfile_prefix = "output"
}
if(missing(num_labels)){
num_labels = 10
}
if(missing(threshold)){
threshold = 95
}
acol <- grep('gene_names_primary$', names(data), value = TRUE)
zcol <- grep('joint_zscore', names(data), value = TRUE)
if(length(zcol) > 1){
zcol <- zcol[2]
}
if(missing(label_file)){ # take top n labels if no label file
label_subset <- data %>%
dplyr::arrange(desc(zcol)) %>%
dplyr::slice_head(n = num_labels)
} else {
labels <- readr::read_csv(label_file, col_names = FALSE)
label_subset <- data %>%
dplyr::filter(grepl(paste(labels$X1, collapse = "|"), get(acol), ignore.case = TRUE))
}
print(sprintf('Labeled plot points for "%s_zscore_comparison.png & %s_zscore_comparison.pdf:"', outfile_prefix, outfile_prefix))
label_subset %>%
dplyr::select(accession, matches("zscore"), matches("gene_names_primary")) %>%
print()
# z-scores vs z-scores plot
if(threshold == 90){
p_base <- ggplot(data, aes(x = get(xcol), y = get(ycol),
color = conf_90, label = get(acol))) +
annotate(geom = "text",
x = max(data[xcol]),
y = min(data[ycol]),
vjust = 1,
label = "10% FDR",
color = palette_pretty[2], size = 4) +
geom_hline(yintercept = 1.282, alpha = 0.6) +
geom_hline(yintercept = -1.282, alpha = 0.6) +
geom_vline(xintercept = 1.282, alpha = 0.6) +
geom_vline(xintercept = -1.282, alpha = 0.6) +
scale_x_continuous(breaks = c(-5, -1.282, 0, 1.282, 5, 10),
labels = c(-5, -1.282, 0, 1.282, 5, 10)) +
scale_y_continuous(breaks = c(-5, -1.282, 0, 1.282, 5, 10),
labels = c(-5, -1.282, 0, 1.282, 5, 10))
} else if(threshold == 95) {
p_base <- ggplot(data, aes(x = get(xcol), y = get(ycol),
color = conf_95, label = get(acol))) +
#labs(color = "95% Confidence\n") +
annotate(geom = "text",
x = max(data[xcol]),
y = min(data[ycol]),
vjust = 1,
label = "5% FDR",
color = palette_pretty[2], size = 4) +
geom_hline(yintercept = 1.645, alpha = 0.6) +
geom_hline(yintercept = -1.645, alpha = 0.6) +
geom_vline(xintercept = 1.645, alpha = 0.6) +
geom_vline(xintercept = -1.645, alpha = 0.6) +
scale_x_continuous(breaks = c(-5, -1.645, 0, 1.645, 5, 10),
labels = c(-5, -1.645, 0, 1.645, 5, 10)) +
scale_y_continuous(breaks = c(-5, -1.645, 0, 1.645, 5, 10),
labels = c(-5, -1.645, 0, 1.645, 5, 10))
} else {
p_base <- ggplot(data, aes(x = get(xcol), y = get(ycol),
color = conf_99, label = get(acol))) +
annotate(geom = "text",
x = max(data[xcol]),
y = min(data[ycol]),
vjust = 1,
label = "1% FDR",
color = palette_pretty[2], size = 4) +
geom_hline(yintercept = 2.33, alpha = 0.6) +
geom_hline(yintercept = -2.33, alpha = 0.6) +
geom_vline(xintercept = 2.33, alpha = 0.6) +
geom_vline(xintercept = -2.33, alpha = 0.6) +
scale_x_continuous(breaks = c(-5, -2.33, 0, 2.33, 5, 10),
labels = c(-5, -2.33, 0, 2.33, 5, 10)) +
scale_y_continuous(breaks = c(-5, -2.33, 0, 2.33, 5, 10),
labels = c(-5, -2.33, 0, 2.33, 5, 10))
}
pz <- p_base +
geom_point(alpha = 0.75, size = 1.5) +
ggrepel::geom_text_repel(data = label_subset,
size = 6.5/.pt, # font size
fontface = "bold",
alpha = 1.0,
hjust = 0.2,
point.padding = unit(0.5, "lines"),
box.padding = unit(0.5, "lines"),
nudge_y = -0.3,
nudge_x = 0.8,
colour = palette_pretty[1],
set.seed(3333)) +
scale_color_manual(values = palette_pretty) +
theme(legend.position = "none") +
#theme(legend.position = "bottom") +
#theme(legend.position = c(0.7, 0.15),
# legend.title = element_blank()) +
#guides(color = guide_legend(ncol = 1, label.theme = element_text(size = 9))) +
theme(panel.grid.major = element_line(colour = palette_pretty[6],
size = 0.1, linetype = "dashed"),
panel.grid.minor = element_line(colour = palette_pretty[6],
size = 0.1, linetype = "dashed")) +
xlab(xlab) +
ylab(ylab)
ggsave(sprintf("%s_zscore_comparison.pdf", outfile_prefix), plot = pz,
width = 8, height = 6, units = "in")
ggsave(sprintf("%s_zscore_comparison.png", outfile_prefix), plot = pz,
width = 8, height = 6, units = "in")
print(pz)
return(pz)
}
rachaelcox/enrichr documentation built on Jan. 19, 2021, 2:11 p.m.
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Defines functions zplot psmplot
Documented in psmplot zplot
#' Visualize control PSMs versus test PSMs on a log-log plot.
#'
#' Graphs control PSMs versus test PSMs on a log-log plot. A pseudocount of 1 is adding to all PSMs for log-log visualization. Colored by Z-test threshold. See README for file formats and example usage on the \href{https://github.com/rachaelcox/enrichr}{enrichr GitHub Repository}.
#'
#' @param data A data frame produced by `enrich()`.
#' @param ylab Text for y-axis label; will correspond to test PSMs.
#' @param xlab Text for x-axis label; will correspond to control PSMs.
#' @param threshold Optional. FDR threshold to color points by. One of c(90, 95, 99). Default = 5% FDR (fdr_bh <= 0.05).
#' @param num_labs Optional. Number of annotation labels to display. Requires input to have an annotation column named `gene_names_primary`. Default = 0.
#' @param label_file Optional. Comma-separated file containing names of genes to be labeled on the plot; requires input to have an annotation column named `gene_names_primary` and labels must match format in the column.
#' @param outfile_prefix Optional. File prefix for output `.png` and `.pdf` files. Default = "output."
#' @return PSM log-log plots in `.png` and `.pdf` format.
#' @export
psmplot <- function(data, outfile_prefix, threshold, num_labs,
label_file, ylab, xlab, point_color){
theme_set(cowplot::theme_cowplot())
palette_pretty <- c("#0072B2","#E69F00","#009E24",
"#FF0000", "#979797","#5530AA", "#1E1E1E")
log10_minor_break <- function (...){
function(x) {
minx = floor(min(log10(x), na.rm=T))-1;
maxx = ceiling(max(log10(x), na.rm=T))+1;
n_major = maxx-minx+1;
major_breaks = seq(minx, maxx, by=1)
minor_breaks =
rep(log10(seq(1, 9, by=1)), times = n_major)+
rep(major_breaks, each = 9)
return(10^(minor_breaks))
}
}
if(missing(outfile_prefix)){
outfile_prefix = "output"
}
if(missing(num_labs)){
num_labs = 10
}
if(missing(threshold)){
threshold = 99
}
if(missing(point_color)){
point_color = palette_pretty[4]
}
xcol <- grep('ctrl_PSMs$', names(data), value = TRUE)
ycol <- grep('exp_PSMs$', names(data), value = TRUE)
zcol <- grep('_zscore$', names(data), value = TRUE)
acol <- grep('gene_names_primary$', names(data), value = TRUE)
# calculate thresholds
data_conf <- data %>%
dplyr::mutate(conf_90 = dplyr::case_when(fdr_bh <= 0.10 ~ TRUE,
TRUE ~ FALSE),
conf_95 = dplyr::case_when(fdr_bh <= 0.05 ~ TRUE,
TRUE ~ FALSE),
conf_99 = dplyr::case_when(fdr_bh <= 0.01 ~ TRUE,
TRUE ~ FALSE))
# add 1 to all PSMs for log-log visualization
print('Adding pseudocounts to PSMs...')
data_conf[ycol] <- data_conf[ycol]+1
data_conf[xcol] <- data_conf[xcol]+1
data_conf %>%
dplyr::select(accession, xcol, ycol) %>%
print()
if(missing(label_file)){
label_subset <- data_conf %>%
dplyr::arrange(desc(zcol)) %>%
dplyr::slice_head(n = num_labs)
} else {
labels <- readr::read_csv(label_file, col_names = FALSE)
label_subset <- data_conf %>%
dplyr::filter(grepl(paste(labels$X1, collapse = "|"), get(acol),
ignore.case = TRUE))
}
print(sprintf('Labeled plot points for "%s_PSMloglog.png"', outfile_prefix))
label_subset %>%
dplyr::select(accession, zcol, acol) %>%
print()
if(threshold == 90){
p1 <- ggplot(data_conf, aes(x = get(xcol), y = get(ycol),
color = conf_90, label = get(acol))) +
geom_point(alpha = 0.75, size = 1.5) +
annotate(geom = "text", x = Inf, y = 1, vjust = 1, hjust = 1,
label = "10% FDR", color = point_color, size = 4)
} else if(threshold == 95) {
p1 <- ggplot(data_conf, aes(x = get(xcol), y = get(ycol),
color = conf_95, label = get(acol))) +
geom_point(alpha = 0.75, size = 1.5) +
annotate(geom = "text", x = Inf, y = 1, vjust = 1, hjust = 1,
label = "5% FDR", color = point_color, size = 4)
} else {
p1 <- ggplot(data_conf, aes(x = get(xcol), y = get(ycol),
color = conf_99, label = get(acol))) +
geom_point(alpha = 0.75, size = 1.5) +
annotate(geom = "text", x = Inf, y = 1, vjust = 1, hjust = 1,
label = "1% FDR", color = point_color, size = 4)
}
p1
p2 <- p1 + ggrepel::geom_text_repel(data = label_subset,
size = 6.5/.pt, # font size
fontface = "bold",
alpha = 1.0,
hjust = 0.2,
point.padding = unit(0.5, "lines"),
box.padding = unit(0.5, "lines"),
nudge_y = 0.3,
nudge_x = -0.3,
colour = palette_pretty[7],
set.seed(3333)) +
scale_x_log10(minor_breaks = log10_minor_break()) +
scale_y_log10(minor_breaks = log10_minor_break()) +
scale_color_manual(values = rev(c(point_color, palette_pretty[5]))) +
theme(legend.position = "none") +
theme(panel.grid.major = element_line(colour = palette_pretty[5], size = 0.1, linetype = "dashed"),
panel.grid.minor = element_line(colour = palette_pretty[5], size = 0.1, linetype = "dashed")) +
xlab(xlab) +
ylab(ylab)
p2
ggsave(sprintf("%s_PSMloglog.pdf", outfile_prefix), plot = p2,
device = "pdf", width = 8, height = 6, units = "in")
ggsave(sprintf("%s_PSMloglog.png", outfile_prefix), plot = p2,
device = "png", width = 8, height = 6, units = "in")
return(p2)
}
#' Plot Z-scores across biological replicates.
#'
#' Colored by a multiple hypothesis corrected false discovery rate (FDR) computed on a weighted Z-score. See README for file formats and example usage on the \href{https://github.com/rachaelcox/enrichr}{enrichr GitHub Repository}.
#'
#' @param data A data frame produced by `combine_reps()`.
#' @param ycol Name of column containing y-axis data; should correspond to replicate #1 Z-scores.
#' @param xcol Name of column containing x-axis data; should correspond to replicate #1 Z-scores.
#' @param ylab Text for y-axis label; should correspond to replicate #1 Z-scores.
#' @param xlab Text for x-axis label; should correspond to replicate #2 Z-scores.
#' @param threshold Optional. FDR threshold to color points by. One of c(90, 95, 99). Default = 5% FDR (fdr_bh <= 0.05).
#' @param num_labs Optional. Number of annotation labels to display. Requires input to have an annotation column named `gene_names_primary`. Default = 0.
#' @param label_file Optional. Comma-separated file containing names of genes to be labeled on the plot; requires input to have an annotation column named `gene_names_primary` and labels must match format in the column.
#' @param outfile_prefix Optional. File prefix for output `.png` and `.pdf` files. Default = "output."
#' @return Plot depicting replicate 1 z-score vs replicate 2 z-scores in `.png` and `.pdf` format. Colored by FDR.
#' @export
zplot <- function(data, outfile_prefix, ylab, xlab, ycol, xcol,
threshold, label_file, num_labels){
theme_set(cowplot::theme_cowplot())
palette_pretty <- c("#1E1E1E", "#FF0000", "#0072B2",
"#E69F00", "#009E24", "#979797","#5530AA", "#1E1E1E")
if(missing(outfile_prefix)){
outfile_prefix = "output"
}
if(missing(num_labels)){
num_labels = 10
}
if(missing(threshold)){
threshold = 95
}
acol <- grep('gene_names_primary$', names(data), value = TRUE)
zcol <- grep('joint_zscore', names(data), value = TRUE)
if(length(zcol) > 1){
zcol <- zcol[2]
}
if(missing(label_file)){ # take top n labels if no label file
label_subset <- data %>%
dplyr::arrange(desc(zcol)) %>%
dplyr::slice_head(n = num_labels)
} else {
labels <- readr::read_csv(label_file, col_names = FALSE)
label_subset <- data %>%
dplyr::filter(grepl(paste(labels$X1, collapse = "|"), get(acol), ignore.case = TRUE))
}
print(sprintf('Labeled plot points for "%s_zscore_comparison.png & %s_zscore_comparison.pdf:"', outfile_prefix, outfile_prefix))
label_subset %>%
dplyr::select(accession, matches("zscore"), matches("gene_names_primary")) %>%
print()
# z-scores vs z-scores plot
if(threshold == 90){
p_base <- ggplot(data, aes(x = get(xcol), y = get(ycol),
color = conf_90, label = get(acol))) +
annotate(geom = "text",
x = max(data[xcol]),
y = min(data[ycol]),
vjust = 1,
label = "10% FDR",
color = palette_pretty[2], size = 4) +
geom_hline(yintercept = 1.282, alpha = 0.6) +
geom_hline(yintercept = -1.282, alpha = 0.6) +
geom_vline(xintercept = 1.282, alpha = 0.6) +
geom_vline(xintercept = -1.282, alpha = 0.6) +
scale_x_continuous(breaks = c(-5, -1.282, 0, 1.282, 5, 10),
labels = c(-5, -1.282, 0, 1.282, 5, 10)) +
scale_y_continuous(breaks = c(-5, -1.282, 0, 1.282, 5, 10),
labels = c(-5, -1.282, 0, 1.282, 5, 10))
} else if(threshold == 95) {
p_base <- ggplot(data, aes(x = get(xcol), y = get(ycol),
color = conf_95, label = get(acol))) +
#labs(color = "95% Confidence\n") +
annotate(geom = "text",
x = max(data[xcol]),
y = min(data[ycol]),
vjust = 1,
label = "5% FDR",
color = palette_pretty[2], size = 4) +
geom_hline(yintercept = 1.645, alpha = 0.6) +
geom_hline(yintercept = -1.645, alpha = 0.6) +
geom_vline(xintercept = 1.645, alpha = 0.6) +
geom_vline(xintercept = -1.645, alpha = 0.6) +
scale_x_continuous(breaks = c(-5, -1.645, 0, 1.645, 5, 10),
labels = c(-5, -1.645, 0, 1.645, 5, 10)) +
scale_y_continuous(breaks = c(-5, -1.645, 0, 1.645, 5, 10),
labels = c(-5, -1.645, 0, 1.645, 5, 10))
} else {
p_base <- ggplot(data, aes(x = get(xcol), y = get(ycol),
color = conf_99, label = get(acol))) +
annotate(geom = "text",
x = max(data[xcol]),
y = min(data[ycol]),
vjust = 1,
label = "1% FDR",
color = palette_pretty[2], size = 4) +
geom_hline(yintercept = 2.33, alpha = 0.6) +
geom_hline(yintercept = -2.33, alpha = 0.6) +
geom_vline(xintercept = 2.33, alpha = 0.6) +
geom_vline(xintercept = -2.33, alpha = 0.6) +
scale_x_continuous(breaks = c(-5, -2.33, 0, 2.33, 5, 10),
labels = c(-5, -2.33, 0, 2.33, 5, 10)) +
scale_y_continuous(breaks = c(-5, -2.33, 0, 2.33, 5, 10),
labels = c(-5, -2.33, 0, 2.33, 5, 10))
}
pz <- p_base +
geom_point(alpha = 0.75, size = 1.5) +
ggrepel::geom_text_repel(data = label_subset,
size = 6.5/.pt, # font size
fontface = "bold",
alpha = 1.0,
hjust = 0.2,
point.padding = unit(0.5, "lines"),
box.padding = unit(0.5, "lines"),
nudge_y = -0.3,
nudge_x = 0.8,
colour = palette_pretty[1],
set.seed(3333)) +
scale_color_manual(values = palette_pretty) +
theme(legend.position = "none") +
#theme(legend.position = "bottom") +
#theme(legend.position = c(0.7, 0.15),
# legend.title = element_blank()) +
#guides(color = guide_legend(ncol = 1, label.theme = element_text(size = 9))) +
theme(panel.grid.major = element_line(colour = palette_pretty[6],
size = 0.1, linetype = "dashed"),
panel.grid.minor = element_line(colour = palette_pretty[6],
size = 0.1, linetype = "dashed")) +
xlab(xlab) +
ylab(ylab)
ggsave(sprintf("%s_zscore_comparison.pdf", outfile_prefix), plot = pz,
width = 8, height = 6, units = "in")
ggsave(sprintf("%s_zscore_comparison.png", outfile_prefix), plot = pz,
width = 8, height = 6, units = "in")
print(pz)
return(pz)
}
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