v2025.1

Author: ToledoEM

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: msigdf
 Type: Package
 Title: MSigDB in a data frame
-Version: 2024.1
+Version: 2025.1
 Authors@R: c(
     person("Stephen", "Turner", email = "vustephen@gmail.com", role = "aut"),
     person("Toledo", "Enrique", email = "enriquetoledo@gmail.com", role = c("ctb","cre")))
@@ -11,7 +11,9 @@ License: CC0
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.3.2
-Depends: R (>= 3.1.2)
+Depends: 
+    R (>= 3.5)
 Imports: tibble
 Suggests: knitr, plyr, tidyverse, rmarkdown, BiocStyle
 VignetteBuilder: knitr
+LazyDataCompression: xz

README.md

@@ -1,12 +1,12 @@
 # MSigDF
 
-The [Molecular Signatures Database (MSigDB)](http://www.broad.mit.edu/gsea/msigdb/index.jsp) in a tidy data frame.    
+The [Molecular Signatures Database (MSigDB)](https://www.gsea-msigdb.org/gsea/msigdb/index.jsp) in a tidy data frame.    
 
 
 This is the updated version of the archived repo of [@stephenturner](https://github.com/stephenturner/msigdf/pull/1)   
 
 
-Current version: [v2024.1.Hs](https://docs.gsea-msigdb.org/#MSigDB/Release_Notes/MSigDB_Latest/) (Aug 2024).
+Current version: [v2025.1.Hs](https://docs.gsea-msigdb.org/#MSigDB/Release_Notes/MSigDB_Latest/) (June 2025).
 
 ## Installation
 

examples/msigdf_examples.R

@@ -0,0 +1,67 @@
+# Example usage of msigdf package with improved and parallel code for human and mouse data
+
+library(tidyverse)
+library(msigdf)
+
+# Show first few rows for both human and mouse gene sets
+msigdf.human %>% slice_head(n = 5)
+msigdf.mouse %>% slice_head(n = 5)
+
+# Filter for a specific KEGG pathway in both human and mouse
+msigdf.human %>%
+  filter(geneset == "KEGG_NON_HOMOLOGOUS_END_JOINING")
+
+msigdf.mouse %>%
+  filter(geneset == "KEGG_NON_HOMOLOGOUS_END_JOINING")
+
+# Get gene symbols as a named list for Hallmark sets (human) and for mouse
+human_hallmark <- msigdf.human %>%
+  filter(category_code == "hallmark") %>%
+  select(geneset, symbol) %>%
+  group_by(geneset) %>%
+  summarize(symbols = list(symbol), .groups = "drop") %>%
+  deframe()
+
+mouse_hallmark <- msigdf.mouse %>%
+  filter(category_code == "hallmark") %>%
+  select(geneset, symbol) %>%
+  group_by(geneset) %>%
+  summarize(symbols = list(symbol), .groups = "drop") %>%
+  deframe()
+
+# Show first 3 gene sets and their first 3 symbols
+purrr::map(human_hallmark[1:3], head, 3)
+purrr::map(mouse_hallmark[1:3], head, 3)
+
+# Count gene sets by collection and sub-collection for both species
+msigdf.human %>%
+  count(category_code, category_subcode, sort = TRUE)
+
+msigdf.mouse %>%
+  count(category_code, category_subcode, sort = TRUE)
+
+# Join with URLs for a specific hallmark set (Notch signaling) in human and mouse
+human_notch_url <- msigdf.human %>%
+  filter(geneset == "HALLMARK_NOTCH_SIGNALING") %>%
+  distinct(geneset) %>%
+  left_join(msigdf.urls, by = "geneset") %>%
+  pull(url)
+
+mouse_notch_url <- msigdf.mouse %>%
+  filter(geneset == "HALLMARK_NOTCH_SIGNALING") %>%
+  distinct(geneset) %>%
+  left_join(msigdf.mouse.urls, by = "geneset") %>%
+  pull(url)
+
+# Print URLs
+print(human_notch_url)
+print(mouse_notch_url)
+
+# List KEGG pathways sorted by number of genes (human and mouse)
+msigdf.human %>%
+  filter(category_code == "c2", str_starts(geneset, "KEGG_")) %>%
+  count(geneset, sort = TRUE)
+
+msigdf.mouse %>%
+  filter(category_code == "m2", str_starts(geneset, "KEGG_")) %>%
+  count(geneset, sort = TRUE)