Corrmotif overlap
Last updated: 2025-03-04
Checks: 6 1
Knit directory: CX5461_Project/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R
Markdown file created these results, you’ll want to first commit it to
the Git repo. If you’re still working on the analysis, you can ignore
this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20250129) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 54b5c39. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .RData
Ignored: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: analysis/Corrmotif_overlap.Rmd
Untracked: data/all_GO/
Unstaged changes:
Modified: Enriched_BP_Terms.csv
Modified: Enriched_CC_Terms.csv
Modified: Enriched_MF_Terms.csv
Modified: analysis/index.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
There are no past versions. Publish this analysis with
wflow_publish() to start tracking its development.
📌 Overlap of GO functions between Corrmotif all and corrmotif Conc.
library(UpSetR)
library(dplyr)
library(tools) # Required for file name processing
# Set the folder path
folder_path <- "data/all_GO"
# Get a list of all CSV files in the folder
csv_files <- list.files(folder_path, pattern = "\\.csv$", full.names = TRUE)
# Loop through each file and assign it as a variable in the global environment
for (file in csv_files) {
# Generate a valid R variable name from the file name (remove extension and replace spaces)
file_name <- tools::file_path_sans_ext(basename(file))
file_name <- gsub(" ", "_", file_name) # Replace spaces with underscores
file_name <- make.names(file_name) # Ensure the name is valid in R
# Assign the CSV file as a variable in the environment
assign(file_name, read.csv(file, stringsAsFactors = FALSE))
}
# Define datasets (lists of Entrez Gene IDs)
sets <- list(
"Non response all" = prob_all_1$ID,
"CX_DOX shared late response all" = prob_all_2$ID,
"Dox specific response all" = prob_all_3$ID,
"Late high dose DOX specific response all" = prob_all_4$ID,
"Non response (0.1)" = prob_1_0.1$ID,
"DOX only mid-late (0.1)" = prob_2_0.1$ID,
"CX_DOX mid-late (0.1)" = prob_3_0.1$ID,
"Non response (0.5)" = prob_1_0.5$ID,
"DOX only early-mid (0.5)" = prob_2_0.5$ID,
"DOX only mid-late (0.5)" = prob_3_0.5$ID,
"CX only mid-late (0.5)" = prob_4_0.5$ID,
"CX_DOX mid-late (0.5)" = prob_5_0.5$ID
)
# Create a binary matrix for UpSet plot
all_genes <- unique(unlist(sets)) # Get all unique Entrez Gene IDs
binary_matrix <- data.frame(Gene_ID = all_genes) # Initialize DataFrame
# Convert gene lists into a presence/absence matrix (1 = present, 0 = absent)
for (set_name in names(sets)) {
binary_matrix[[set_name]] <- as.integer(all_genes %in% sets[[set_name]])
}
# Remove Gene_ID column as UpSetR only needs the binary matrix
binary_matrix <- binary_matrix[, -1]
upset(binary_matrix,
sets = names(sets),
order.by = "freq",
sets.bar.color = "#56B4E9", # Blue bars for set sizes
mainbar.y.label = "Number of Shared Functions",
sets.x.label = "GO terms per set",
text.scale = 1.2,
nintersects = 30)
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] tools stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] dplyr_1.1.4 UpSetR_1.4.0
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_1.8.9 compiler_4.3.0 promises_1.3.0
[5] tidyselect_1.2.1 Rcpp_1.0.12 stringr_1.5.1 git2r_0.35.0
[9] gridExtra_2.3 later_1.3.2 jquerylib_0.1.4 scales_1.3.0
[13] yaml_2.3.10 fastmap_1.1.1 plyr_1.8.9 ggplot2_3.5.1
[17] R6_2.5.1 labeling_0.4.3 generics_0.1.3 workflowr_1.7.1
[21] knitr_1.49 tibble_3.2.1 munsell_0.5.1 rprojroot_2.0.4
[25] bslib_0.8.0 pillar_1.10.1 rlang_1.1.3 cachem_1.0.8
[29] stringi_1.8.3 httpuv_1.6.15 xfun_0.50 fs_1.6.3
[33] sass_0.4.9 cli_3.6.1 withr_3.0.2 magrittr_2.0.3
[37] digest_0.6.34 grid_4.3.0 rstudioapi_0.17.1 lifecycle_1.0.4
[41] vctrs_0.6.5 evaluate_1.0.3 glue_1.7.0 farver_2.1.2
[45] colorspace_2.1-0 rmarkdown_2.29 pkgconfig_2.0.3 htmltools_0.5.8.1