This repository holds software used to perform analyses and generate figures/tables for the 2021 American Society of Colon and Rectal Surgeons (ASCRS) Annual Scientific Meeting presentation, number SP45, titled: "Tumor Genotypes Account for Survival Differences in Right and Left-sided Colon Cancers". This presentation was presented in the Plenary Abstract Session III: Colorectal Cancer and Other Neoplasia. Additionally, it was submitted for publication consideration.
Only contains gene.csv, a CSV that contains data from The Cancer
Genome Atlas Program (TCGA) for colorectal cancers (CRC).
Empty directory that will contain generated R objects, tables, and figures.
All scripts expect that they are executed with a working directory of
src (e.g., should type ./script.R to execute, not src/script.R).
All tables produced are HTML tables. To use in Microsoft Word, it is best to open them in a browser then copy and paste them into Word.
All plots are in png format. Graphic size and format is easy to tweak by changing the script.
All file outputs are put into the ../output/ directory with self-explanatory
names.
Cleans and tidies the CSV holding information on all CRC obtained from
TCGA. Will save a serialized R object (.rds) for a tibble holding
tidied information with patient characteristics and genetics and
a separate tibble holding information on MSI. It only keeps patient data
when the patient has information on a primary CRC tumor and completed
genetic mutation data.
Produces two R objects from the previously tidied patient and MSI
data produced by tidy_tcga.R. Notably, it only keeps data for patients
with a right or left colon cancer and completed information for use in
survival analyses (so more than 0 months of follow-up) and completed T
and N staging. The first, analyses_data_tidy.rds, holds the subset of
data for R and L cancers with imputed missing LVI, R0 margin status,
and metastases status. This is tidy (one gene mutation per line). It
also produces a second R object, model_inputs.rds, that holds the data
widened, for use in modeling.
Performs the analyses and generates summary tables for them for
chi-squared tests of independence. Requires tidy_tcga.R then
make_inputs.R to be run first. It analyses:
- Tumor sidedness versus gene mutational status
- Nodal positivity (N0 vs N1+) versus gene mutational status
Requires tidy_tcga.R then make_inputs.R to be run first. This script
will take model_inputs.rds and output:
- Table with lasso regression for 5-yr survival results
Requires tidy_tcga.R then make_inputs.R to be run first. This script
will take model_inputs.rds and output:
- Table with total effect of R vs L on 5-yr survival Cox PH results
- Schoenfeld residual plots for the above analysis
- Table with total effect of genetics on 5-yr survival Cox PH results
- Schoenfeld residual plots for the above analysis
- Plot of KM curve of R vs L and 5-yr survival
- Table with survival statistics each year of follow-up
Requires the outputs from tidy_tcga.R and make_inputs.R. It takes
model_inputs.rds and tidied.rds and outputs:
- Table 1 in the paper
Contains the files needed to re-create the slides for the oral presentation
I gave at the meeting. Slides were created in R markdown using the
xarigan package.
All analyses were run in the following environment:
R version 4.0.3 (2020-10-10)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Fedora
64B7
33 (Workstation Edition)
Matrix products: default
BLAS/LAPACK: /usr/lib64/libflexiblas.so.3.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] survminer_0.4.8 ggpubr_0.4.0 gtsummary_1.3.5 survival_3.2-7
[5] glmnet_4.0-2 Matrix_1.2-18 forcats_0.5.0 stringr_1.4.0
[9] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[13] tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 nvimcom_0.9-102
loaded via a namespace (and not attached):
[1] fs_1.5.0 usethis_1.6.3
[3] lubridate_1.7.9 httr_1.4.2
[5] tools_4.0.3 backports_1.1.10
[7] utf8_1.1.4 R6_2.4.1
[9] adaptMCMC_1.3 DBI_1.1.0
[11] colorspace_1.4-1 withr_2.3.0
[13] tidyselect_1.1.0 gridExtra_2.3
[15] curl_4.3 compiler_4.0.3
[17] cli_2.1.0 rvest_0.3.6
[19] gt_0.2.2 xml2_1.3.2
[21] labeling_0.4.2 sass_0.2.0
[23] scales_1.1.1 checkmate_2.0.0
[25] survMisc_0.5.5 commonmark_1.7
[27] digest_0.6.26 foreign_0.8-80
[29] rio_0.5.16 pkgconfig_2.0.3
[31] htmltools_0.5.0 highr_0.8
[33] dbplyr_1.4.4 rlang_0.4.8
[35] readxl_1.3.1 selectiveInference_1.2.5
[37] rstudioapi_0.11 shape_1.4.5
[39] generics_0.0.2 farver_2.0.3
[41] zoo_1.8-8 jsonlite_1.7.1
[43] zip_2.1.1 car_3.0-10
[45] magrittr_1.5 Rcpp_1.0.5
[47] munsell_0.5.0 fansi_0.4.1
[49] clipr_0.7.1 abind_1.4-5
[51] lifecycle_0.2.0 stringi_1.5.3
[53] carData_3.0-4 MASS_7.3-53
[55] grid_4.0.3 blob_1.2.1
[57] parallel_4.0.3 crayon_1.3.4
[59] lattice_0.20-41 haven_2.3.1
[61] splines_4.0.3 hms_0.5.3
[63] knitr_1.30 ps_1.4.0
[65] pillar_1.4.6 tcltk_4.0.3
[67] ggsignif_0.6.0 codetools_0.2-16
[69] reprex_0.3.0 glue_1.4.2
[71] data.table_1.13.2 modelr_0.1.8
[73] vctrs_0.3.4 foreach_1.5.1
[75] cellranger_1.1.0 gtable_0.3.0
[77] km.ci_0.5-2 assertthat_0.2.1
[79] xfun_0.18 openxlsx_4.2.3
[81] xtable_1.8-4 broom_0.7.2
[83] Rmpfr_0.8-1 coda_0.19-4
[85] rstatix_0.6.0 intervals_0.15.2
[87] iterators_1.0.13 KMsurv_0.1-5
[89] gmp_0.6-1 ellipsis_0.3.1