In sciencepeak/rTCRBCRr: Repertoire Analysis of the Detected Clonotype

Environment setup.

rm(list = ls())
ptm <- proc.time()
# proc.time() - ptm
options(stringsAsFactors = F)
library("magrittr")
# library("stringr")
# library("dplyr")
# library("tidyr")
# library("tibble")
# library("purrr")
# library("readr")
# 
# library("fs")
# library("R.utils") # provide bzip2 function

https://mvuorre.github.io/exampleRPackage/

Initial commit using Git (for Windows)

usethis::use_git() # This is to make commit files locally without Github GUI.
# There is no need to run this command if you use GitHub GUI desktop.
file.remove(".git/hooks/pre-commit")

Basic setting

package_name <- 'rTCRBCRr'

# available::available(package_name) # Check availability of the package name

Make the hexagon sticker as the package logo

library(ggplot2)
library(hexSticker)

dir.create("man/figures")

dir(system.file("man/figures", package = package_name))

image_path <- "man/figures/ggplot2_logo.png"

p <- ggplot(aes(x = Sepal.Width, y = Petal.Length), data = iris) + geom_point()
p <- p + theme_void() + theme_transparent()

sticker(p, package=package_name, p_size=20, s_x=1, s_y=.75, s_width=1.3, s_height=1,
        filename=image_path)

usethis::use_logo(image_path)

The helper use_r() creates and/or opens a script below R/. It really shines in a more mature package, when navigating between .R files and the associated test file.

# usethis::use_r()

Adds a CRAN package dependency to DESCRIPTION

usethis::use_package("magrittr")
usethis::use_package("tidyr")
usethis::use_package("tidyselect")
usethis::use_package("readr")

Add the directory for package data preparation codes

usethis::use_data_raw()

devtools::load_all()

prepare internal data for @examples tag of each exported function.

present_tool <- c("trust", "mixcr")[1]
example_data_directory <- system.file(paste("extdata", present_tool, sep = "/"), package = "rTCRBCRr")
input_paths <- dir(example_data_directory, full.names = TRUE)
input_files <- dir(example_data_directory, full.names = FALSE)
input_files

sample_names <- sub(".tsv.*", "", input_files)
sample_names

raw_clonotype_dataframe_list <- lapply(input_paths, readr::read_tsv) %>%
    magrittr::set_names(., value = sample_names)
raw_clonotype_dataframe_list

Prepare individual example data.

raw_input_clonotype_dataframe <- raw_clonotype_dataframe_list[["sample_01"]]

immunarch_style_dataframe <- raw_input_clonotype_dataframe %>%
    format_clonotype_to_immunarch_style(., clonotyping_tool = present_tool)

nonproductive_CDR3aa_removed_dataframe <- immunarch_style_dataframe %>%
    remove_nonproductive_CDR3aa

chain_separated_dataframe <- nonproductive_CDR3aa_removed_dataframe %>%
    annotate_chain_name_and_isotype_name

convergent_clonotype_merged_dataframe <- chain_separated_dataframe %>%
    merge_convergent_clonotype

# input_dataframe <- nonproductive_CDR3aa_removed_dataframe

Prepare a list of processed dataframes.

the_divergent_clonotype_dataframe_list <- raw_clonotype_dataframe_list %>%
    lapply(., format_clonotype_to_immunarch_style, clonotyping_tool = present_tool) %>%
    lapply(., remove_nonproductive_CDR3aa) %>%
    lapply(., annotate_chain_name_and_isotype_name) %>%
    lapply(., merge_convergent_clonotype)

# handle repertoire metrics for all the chains.
all_sample_all_chain_all_metrics_wide_format_dataframe_list <- the_divergent_clonotype_dataframe_list %>%
    lapply(., compute_repertoire_metrics_by_chain_name)

all_sample_all_chain_all_metrics_wide_format_dataframe_list

all_sample_all_chain_all_metrics_wide_format_dataframe <- all_sample_all_chain_all_metrics_wide_format_dataframe_list %>%
    combine_all_sample_repertoire_metrics

all_sample_all_chain_all_metrics_wide_format_dataframe

all_sample_all_chain_individual_metrics_dataframe_list <- all_sample_all_chain_all_metrics_wide_format_dataframe %>%
    get_item_name_x_sample_name_for_each_metric

all_sample_all_chain_individual_metrics_dataframe_list

# handle repertoire metrics all all the isotypes of IGH chain.
all_sample_IGH_chain_all_metrics_wide_format_dataframe_list <- the_divergent_clonotype_dataframe_list %>%
    lapply(., calculate_IGH_isotype_proportion)

all_sample_IGH_chain_all_metrics_wide_format_dataframe_list

all_sample_IGH_chain_all_metrics_wide_format_dataframe <- all_sample_IGH_chain_all_metrics_wide_format_dataframe_list %>%
    combine_all_sample_repertoire_metrics

all_sample_IGH_chain_all_metrics_wide_format_dataframe

all_sample_IGH_chain_individual_metrics_dataframe_list <- all_sample_IGH_chain_all_metrics_wide_format_dataframe %>%
    get_item_name_x_sample_name_for_each_metric

all_sample_IGH_chain_individual_metrics_dataframe_list

Save the object for examples used in function examples.

Remember to manully add documentation in the /R/data.R file

usethis::use_data(raw_input_clonotype_dataframe, overwrite = T)
usethis::use_data(immunarch_style_dataframe, overwrite = T)
usethis::use_data(nonproductive_CDR3aa_removed_dataframe, overwrite = T)
usethis::use_data(chain_separated_dataframe, overwrite = T)
usethis::use_data(convergent_clonotype_merged_dataframe, overwrite = T)

usethis::use_data(the_divergent_clonotype_dataframe_list, overwrite = T)

usethis::use_data(all_sample_all_chain_all_metrics_wide_format_dataframe_list, overwrite = T)
usethis::use_data(all_sample_all_chain_all_metrics_wide_format_dataframe, overwrite = T)
usethis::use_data(all_sample_all_chain_individual_metrics_dataframe_list, overwrite = T)
usethis::use_data(all_sample_IGH_chain_all_metrics_wide_format_dataframe_list, overwrite = T)
usethis::use_data(all_sample_IGH_chain_all_metrics_wide_format_dataframe, overwrite = T)
usethis::use_data(all_sample_IGH_chain_individual_metrics_dataframe_list, overwrite = T)

The document for the package itself was created from the description file.

https://combine-australia.github.io/r-pkg-dev/other-documentation.html

usethis::use_package_doc()

Copy R scripts

Copy inst/extdata files

devtools::document()

Render README rmarkdown file to README.md for github.

You'll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.

# usethis::use_readme_rmd() # Make README file via rmarkdown.
devtools::build_readme()

# usethis::use_vignette("Quick_Start")
devtools::build_vignettes()

usethis::use_mit_license()

need to make custom change to make the workflow work.

usethis::use_github_action("check-standard")
# The r.yml workflow is created by the github actions webpage by clicking on new workflow and then create it online. But the online version lacks the pandoc steps, so we need to use our previously manually adjusted r.yml file and make some other change like "master" to "main" to make the stitched r.yml working.

devtools::check()

devtools::install()

devtools::build()

proc.time() - ptm

sciencepeak/rTCRBCRr documentation built on March 11, 2023, 7:23 p.m.