This directory contains the code required to generate individual panels for main and supplemental display items.
It is designed such that a single shell script will run all required data processing or analysis steps prior to plotting.
There are two main strategies for creating individual panels: 1. scripts that are exclusively created for publication-ready display (located in figures/scripts) or 2. copying publication-ready plots that are generated within a given analysis module (i.e., analyses) to the correct location in figures/.
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Follow these steps to refresh all publication-ready figures. All steps assume your current directory is the top of this repository.
See these instructions to obtain the current data release.
We recommend using the download script to obtain data because this will automatically create symlinks in data/ to the latest files.
See these instructions for setting up the project Docker container.
Briefly, the latest version of the project Docker image, which is updated upon commit to master, can be obtained and run via:
docker pull ccdlopenpbta/open-pbta:latest
docker run \
-e PASSWORD=<password> \
-p 8787:8787 \
-v $(pwd):/home/rstudio/kitematic \
ccdlopenpbta/open-pbta:latestYou may choose to use docker exec to interact with the container from there or if you'd prefer the RStudio interface, you can navigate to localhost:8787 and enter username rstudio and the password you set with the run command above.
RUN_LOCAL=1 for local testing that skips the RAM-intensive steps.
This script runs all the intermediate steps needed to generate figures starting with the files in the data release.
Earlier versions of this script ran the consensus SNV modules, but the consensus SNV files are included in the data download and the downstream modules use the files in the data/ directory, so this is no longer necessary.
bash figures/generate-figures.shIn some cases, the shell script will copy plots to the appropriate locations in the figures/ directory.
All figures need to adhere to the guidelines in this README and need to be generated, including any upstream steps, via figures/generate-figures.sh.
This section includes details for file organization and guidelines that are not covered in the section on style.
Individual panels, including any standalone legends, intended for the main text should be saved as PDFs in figures/pdfs/<figure>/panels.
Each figure has its own subdirectory within figures/pdfs; individual panels for Figure 1 would be saved in figures/pdfs/fig1/panels, for Figure 2 in figures/pdfs/fig2/panels and so on and so forth.
Similarly, panels and standalone legends intended for the supplementary materials should be saved as PDFs in figures/pdfs/supp/<figure>/panels.
As for main text figures, individual panels for Figure S1 would be saved in figures/pdfs/supp/figs1/panels, and so on.
Multipanel figures are compiled with Adobe Illustrator and saved as PDFs in their respective subdirectories.
For example, the compiled, PDF version of Figure 1 is saved as figures/pdf/fig1/figure1.pdf.
Similarly for supplementary figures, the compiled PDF version of Figure S1 is saved as figures/pdf/supp/figs1/figureS1.pdf.
(Original .ai files are available in Google Drive and can be shared as necessary -- contact @jaclyn-taroni!)
PNG versions of figures are exported using the PDF file and saved to the figures/pngs/ folder.
PNGs version can be linked in the accompanying manuscript repository AlexsLemonade/OpenPBTA-manuscript.
Each figure should include its own associated README in the appropriate PDF subdirectory, e.g., the documentation for Figure 4 can be found in figures/pdfs/fig4/README.md.
That README should detail what analysis modules or individual scripts are required to generate the individual panels for that figure.
Scripts that are exclusively for creating publication-ready plots, both main text and supplementary, are available in figures/scripts and should be documented in the figures/scripts/README.md.
Scripts that are only used to create supplementary figures and do not export any main text figures should begin with the prefix supp-.
This project has a set of unified color palettes.
There are 6 sets of hex color keys to be used for all final figures, stored as 6 TSV files in the figures/palettes folder.
hex_codes contains the colors to be passed to your plotting code and color_names contains short descriptors of each color (e.g. gradient_1, or divergent_neutral).
Palettes for numeric or binary data contain an na_color that is uniform across palettes.
This color should be used for all NA values.
na_color is always the last value in the palette.
If na_color is not needed or is supplied separately to a plotting function, you can use a dplyr::filter(hex_code != "na_color") to remove na_color.
To see a summary of what colors are used for histology labeling, see mapping-histology-labels.nb.html
| Palette File Name | Color Notes | Variable application |
|---|---|---|
gradient_col_palette.tsv |
10 hex_codes where gradient_0 is for an absolute 0 but may need to be removed from the palette depending on the application |
For numeric data being plotted e.g., tumor mutation burden |
divergent_col_palette.tsv |
12 hex codes where the numbers in the name indicate distance from divergent_neutral. |
For data has that is bidirectional e.g., Amplification/Deletion values like seg.mean |
binary_col_palette.tsv |
A vector of two hex codes | For binary variables e.g., presence/absence or Amp/Del as statuses |
oncoprint_color_palette.tsv |
A named vector of hex codes assigned to each short_histology and to each CNV, SNV and Fusion category |
For plotting an oncoprint figure, this vector provides hex codes for specific alterations |
tumor_descriptor_palette.tsv |
A named vector of hex codes assigned to each tumor_descriptor |
For plotting sample distributions, this vector provides color for tumor descriptor categories |
broad_histology_cancer_group_palette.tsv |
Contains multiple columns having to do with the display by disease label (i.e., broad_histology or cancer_group) |
To be used for any plots that require coloring by broad_histology or cancer_group; please see figures/mapping-histology-labels.Rmd for more information |
histology_label_color_table.tsv is a deprecated version of the palettes used for broad_histology and cancer_group.
It has been retained, for the moment, to prevent the introduction of breaking changes to multiple analysis modules.
It is not to be used for any future development and the relationships between individual biospecimens and disease labels may be out of date.
The non-histologies color palette TSV files are created by running scripts/color_palettes.R, which can be called by Rscript scripts/color_palettes.R.
Hex codes for the palettes are hard-coded in this script.
Do not manually edit palette TSV files.
The script can be called from anywhere in this repository (will look for the .git file).
The hex codes table in figures/README.md and its swatches should also be updated by using the swatches_table function at the end of the script and copy and pasting this function's output to the appropriate place in the table.
The histology color palette file is created by running Rscript -e "rmarkdown::render('figures/mapping-histology-labels.Rmd', clean = TRUE)".
Step 1) Read in color palette file and create a named vector of hex codes
Here's an example for cancer_group, specifically:
# Get palette for cancer group
cancer_group_palette <- readr::read_tsv(
"figures/palettes/broad_histology_cancer_group_palette.tsv")
) %>%
dplyr::select(cancer_group, cancer_group_hex) %>%
# Remove NA values -- a cancer group hex value will be NA only if the
# cancer group is NA
dplyr::filter(complete.cases(.))
# Make color palette suitable for use with ggplot
annotation_colors <- cancer_group_palette$cancer_group_hex
names(annotation_colors) <- cancer_group_palette$cancer_group Step 2) Make your ggplot using the named vector as a manual palette
You will be able to use the named vector with ggplot2 functions such as scale_fill_manual() or scale_color_manual(), like so:
ggplot2::ggplot(
ggplot2::aes(x = cancer_group,
y = y_value,
fill = cancer_group)
) +
geom_boxplot() +
scale_fill_manual(values = annotation_colors)Step 1) Import the palette.
You may want to remove the na_color at the end of the list depending on whether your data include NAs or if the plotting function you are using has the na_color supplied separately.
gradient_col_palette <- readr::read_tsv(
file.path(figures_dir, "palettes", "gradient_color_palette.tsv")
)If we need the NA color separated, like for use with ComplexHeatmap which has a separate argument for the color for NA values.
na_color <- gradient_col_palette %>%
dplyr::filter(color_names == "na_color")
gradient_col_palette <- gradient_col_palette %>%
dplyr::filter(color_names != "na_color")Step 2) Make a color function.
In this example, we are building a colorRamp2 function based on a regular interval between the minimum and maximum of our variable df$variable by using seq.
However, depending on your data's distribution a regular interval based palette might not represent your data well on the plot.
You can provide any numeric vector to color code a palette using circlize::colorRamp2 as long as that numeric vector is the same length as the palette itself.
gradient_col_val <- seq(from = min(df$variable), to = max(df$variable),
length.out = nrow(gradient_col_palette))
col_fun <- circlize::colorRamp2(gradient_col_val,
gradient_col_palette$hex_codes)Step 3) Apply to numeric data, or supply to your plotting code.
This step depends on how your main plotting function would like the data supplied.
For example, ComplexHeatmap wants a function to be supplied to their col argument.
# Apply to variable directly and make a new column
df <- df %>%
dplyr::mutate(color_key = col_fun(variable))
## OR ##
# Some plotting packages want a color function
ComplexHeatmap::Heatmap(
df,
col = col_fun,
na_col = na_color$hex_codes
)In general, we will use the ggpubr package with ggtheme = theme_pubr()) and color palette simpsons from package ggsci since it has 16 levels and can accommodate the levels in groups such as molecular_subtype.
To view the palette:
scales::show_col(ggsci::pal_simpsons("springfield")(16))For 2+ group comparisons, we will use violin or boxplots with jitter.
Some modules perform group-wise comparisons.
For the manuscript, we may want to output tables of the statistics and/or print the statistical test and p-value directly on the plot.
We use the functions ggpubr::compare_means() and ggpubr::stat_compare_means() for this.
Below are the default tests, parameters, and method options for 2 groups or more than two groups for your convenience.
Caution: the default p-values on the plots are uncorrected.
| 2 groups | 3+ groups | |
|---|---|---|
| Default test (method) | Wilcoxon | Kruskal-wallis |
| Allowed methods | "wilcox.test" (non-parametric) "t.test" (parametric) | "kruskal.test" (non-parametric) "anova" (parametric) |
| Default multiple testing (p.adjust.method) | NA | yes, but not bonferroni |
| Allowed p.adjust.method | NA | "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" |
Below is an example for creating a violin plot with boxplot, jitter, and appropriate statistics.
if(length(unique(df$var_x)) > 2){
method <- "kruskal.test"
} else {
method <- "wilcox.test"
}
p <- ggviolin(df, x = "var_x", y = "var_y",
color = "var_color",
palette = "simpsons",
order = c("a", "b", "c"),
add = c("boxplot", "jitter"),
ggtheme = theme_pubr()) +
# Add pairwise comparisons p-value
stat_compare_means(method = method, label.y = 1.2, label.x.npc = "center") +
xlab("xlab_text") +
ylab("ylab_text") +
rremove("legend")