bpa-jfdi-p2-publication

This repository contains scripts and documentation associated with the publication (currently in review):

Hernandez, K.M., Bramlett, K., Agius, P., ..., and Leiman, L.C. 2022. Contrived materials and a dataset for the evaluation of liquid biopsy tests: A Blood Profiling Atlas in Cancer (BLOODPAC) community study

The datasets

Results from the manuscript can be reproduced from a single CSV file that is registered to the BLOODPAC Data Commons. The GUID for this file is dg.5B0D/b47b8599-1488-49d1-9047-d5b3cc661806. However, the simplest way to access the dataset is to use the permalink: https://data.bloodpac.org/discovery/JFDI_P2/. This page contains metadata about the publication and at the bottom contains a direct download link. These data are publicly available and open access so you do not need to be logged in for access. For any help with downloading the file from the BLOODPAC Data Commons, there is an "Email Support" link at the top of the commons website.

Analysis

Most figures and tables from the manuscript can be reproduced by running the R-script provided in this repository.

The main script is bpa-jfdi-p2-results.R. To use this script, you need to have the dataset downloaded and make sure the required R packages are installed.

The R package requirements include:

• tidyverse
• ggpubr
• combat

R session information:

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 12.6

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] combinat_0.0-8  ggpubr_0.4.0    forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7
 [6] purrr_0.3.4     readr_2.0.1     tidyr_1.1.3     tibble_3.1.4    ggplot2_3.3.5
[11] tidyverse_1.3.1

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.1  haven_2.4.3       carData_3.0-4     colorspace_2.0-2
 [5] vctrs_0.3.8       generics_0.1.0    utf8_1.2.2        rlang_0.4.11
 [9] pillar_1.6.2      foreign_0.8-81    glue_1.4.2        withr_2.4.2
[13] DBI_1.1.1         dbplyr_2.1.1      modelr_0.1.8      readxl_1.3.1
[17] lifecycle_1.0.0   munsell_0.5.0     ggsignif_0.6.2    gtable_0.3.0
[21] cellranger_1.1.0  zip_2.2.0         rvest_1.0.1       rio_0.5.27
[25] tzdb_0.1.2        curl_4.3.2        fansi_0.5.0       broom_0.7.9
[29] Rcpp_1.0.7        scales_1.1.1      backports_1.2.1   jsonlite_1.7.2
[33] abind_1.4-5       fs_1.5.0          hms_1.1.0         openxlsx_4.2.4
[37] stringi_1.7.4     rstatix_0.7.0     grid_4.1.0        cli_3.0.1
[41] tools_4.1.0       magrittr_2.0.1    crayon_1.4.1      car_3.0-11
[45] pkgconfig_2.0.3   ellipsis_0.3.2    data.table_1.14.0 xml2_1.3.2
[49] reprex_2.0.1      lubridate_1.7.10  assertthat_0.2.1  httr_1.4.2
[53] rstudioapi_0.13   R6_2.5.1          compiler_4.1.0

After making sure you have the required files and packages, you can generate the figures by running:

Rscript bpa-jfdi-p2-results.R </path/to/dataset.csv>

The script will create a directory figures/ where all the figures will be saved as PNGs.