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part4.R
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part4.R
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## Dataviz2.0 - part4
install.packages("ggsci")
install.packages("tidyverse")
install.packages("ggpubr")
install.packages("ggrepel")
install.packages("cowplot")
install.packages("gridExtra")
library(ggsci)
library(tidyverse)
library(ggpubr)
library(ggrepel)
library(cowplot)
library(gridExtra)
#Create the data
#We'll use the plots you've already created in part3: box1, ml_scatter, scatter2
## Data Import
gene_loc <- read.table("GSE69360.gene-locations.txt",
header = T)
## Plotting the data
scatter <- ggplot(gene_loc, aes(x=End-Start, y=Length, group=Chr, color=Chr)) +
geom_point()
scatter
### It is hard to visualize the entire data.
### Let's pretend we are only interested in a small set of chromosomes.
### Let's subset the data and add a few variables!
target <- c("chrX", "chrY", "chrM", "chr17")
gene_loc2 <- filter(gene_loc, Chr %in% target)
log_EndStart <- log10(gene_loc2$End-gene_loc2$Start)
log_length <- log10(gene_loc2$Length)
gene_loc2$log_length <- log_length
gene_loc2$log_EndStart <- log_EndStart
head(gene_loc2)
###################################################
##Combine boxplot and regression plot previously created
### Boxplot
box1 <- ggplot(gene_loc2, aes(x = Chr, y = Length,
group=Chr, color=Chr)) +
geom_boxplot() +
xlab("Chr")+
theme_bw()
box1 <- box1 + scale_color_jco()+
theme(legend.position = "none") # remove legend
#box1 + scale_fill_discrete(name = "Chromosome") #rename legend
## Scatterplot + regression line
ml_scatter <- ggscatter(gene_loc2, x = "log_EndStart", y = "log_length",
color = "Chr", palette = "jco",
add = "reg.line", add.params = list(color = "black"), # customize regression line
fullrange = TRUE) +
facet_wrap(~Chr) +
stat_cor(label.y = 4.4) +
stat_regline_equation(label.y = 4.2)
ml_scatter
# arrange facet grid as 1x4
ml_scatter <- ml_scatter + facet_grid(cols = vars(Chr))
#Arranging boxplot and scatterplot on one page
## we’ll use the function ggarrange()[in ggpubr], which is a wrapper around the function plot_grid() [in cowplot package].
## Compared to the standard function plot_grid(), ggarange() can arrange multiple ggplots over multiple pages.
## use either
fig <- ggarrange(ml_scatter, box1 + rremove ("x.text"),
labels = c("A", "B"),
ncol =1, nrow = 2,
common.legend = TRUE)
fig
#Annotate the arranged figure
annotate_figure(fig,
top = text_grob("Visualizing gene length", color = "red", face = "bold", size = 14), #grob -- graphical object
bottom = text_grob("Data source: \n gse69360 data set", color = "blue", #customize text
hjust = 1, x = 1, face = "italic", size = 10), # horizontal justification: 0= left align, 1= right align
left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90), # rot = angle to rotate the text
right = "I'm done, thanks :-)!",
fig.lab = "Figure 1", fig.lab.face = "bold"
)
#Adding descriptive text
#Text to be added
text <- paste("gse69360 data set is a resource of ribosomal",
"RNA-depleted RNA-Seq data from different normal",
"adults and fetal human tissues. The dataset was first",
"published in https://www.nature.com/articles/sdata201563", sep = " ")
text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")
final_plt <- ggarrange(ml_scatter, box1, text.p + rremove ("x.text"),
labels = c("A", "B"),
ncol =1, nrow = 3,
common.legend = TRUE)
final_plt
#Export plot
ggsave(mean_lth_viz.pdf, width = 5, height = 5, units = "in")
# Place scatterplot together with density plot
# Scatter plot colored by groups ("Chr")
sp <- gene_loc2 %>%
ggscatter("log_EndStart", "log_length",
color = "Chr", palette = "jco",
size = 1, alpha = 0.2)+
border()
# Marginal density plot of x (top panel) and y (right panel)
xplot <- gene_loc2 %>%
ggdensity("log_EndStart", fill = "Chr", palette = "jco")
yplot <- gene_loc2 %>%
ggdensity("log_length", fill = "Chr", palette = "jco")+
rotate()
# Cleaning the plots
yplot <- yplot + clean_theme()
xplot <- xplot + clean_theme()
# Arranging the plot
fig_2 <- ggarrange(xplot, NULL, sp, yplot,
ncol = 2, nrow = 2, align = "hv",
widths = c(2, 1), heights = c(1, 2),
common.legend = TRUE)
# Annotating the plot
scat_density_plt <- annotate_figure(fig_2,
top = text_grob("Scatter plot with marginal density", color = "red", face = "bold", size = 14), #grob -- graphical object
bottom = text_grob("Data source: \n gse69360 data set", color = "blue", #customize text
hjust = 1, x = 1, face = "italic", size = 10), # horizontal justification: 0= left align, 1= right align
left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90), # rot = angle to rotate the text
right = "I'm done, thanks :-)!",
fig.lab = "Figure 1", fig.lab.face = "bold"
)
scat_density_plt
ggsave(marginal_distribution.pdf, width = 5, height = 5, units = "in")
################################
#Insert a table into a plot
#Regression plot summary data with labels & confidence interval made in part 2 using code below.
a <- gene_loc %>%
group_by(Chr) %>%
summarize(meanLength = mean(Length), numGenes = n())
head(a)
scatter2 <- ggplot(a, aes(x = numGenes, y = meanLength)) +
geom_point()+
theme_bw()+
geom_text_repel(aes(label = Chr), color="red", segment.color="blue")+
geom_smooth(method = loess, color = "lightblue", alpha = 0.1)
scatter2
# Compute descriptive statistics by groups
stable <- gene_loc2 %>%
desc_statby(measure.var = "Length", #Column containing variable to be sumarized
grps = "Chr") # Group
stable <- stable[, c("Chr", "length", "mean", "sd")]
# Summary table plot, medium blue theme
stable.p <- ggtexttable(stable, rows = NULL,
theme = ttheme("mBlue"))
# Arranging the plot
fig_3 <- ggarrange(scatter2, stable.p, text.p + rremove ("x.text"),
ncol =1, nrow = 3,
heights = c(1, 0.5, 0.2),
common.legend = TRUE)
fig_3
#Place the table within the plot using annotate_custom in ggplot
fig_4 <- scatter2 + annotation_custom(ggplotGrob(stable.p),
xmin = 2000, ymax = 1500,
xmax = 5000)
# Annotating the plot
final_scatter2 <- annotate_figure(fig_4,
top = text_grob("Scatter plot with summary table", color = "red", face = "bold", size = 14), #grob -- graphical object
bottom = text_grob("Data source: \n gse69360 data set", color = "blue", #customize text
hjust = 1, x = 1, face = "italic", size = 10), # horizontal justification: 0= left align, 1= right align
left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90), # rot = angle to rotate the text
right = "I'm done, thanks :-)!",
fig.lab = "Figure 1", fig.lab.face = "bold"
)
final_scatter2
#Export plot
ggsave(mean_length.pdf, width = 5, height = 5, units = "in")
##References
# http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/78-perfect-scatter-plots-with-correlation-and-marginal-histograms/
# http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/#change-columnrow-span-of-a-plot
# http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/77-facilitating-exploratory-data-visualization-application-to-tcga-genomic-data/
# http://www.sthda.com/english/wiki/be-awesome-in-ggplot2-a-practical-guide-to-be-highly-effective-r-software-and-data-visualization#blog-posts