Tools to simulate genetic distance matrices, align and compare them via
multidimensional scaling (MDS) and Procrustes, and evaluate imputation with
the Bootstrapping Evaluation for Structural Missingness Imputation (BESMI)
framework. Methods align with Zhu et al. (2025)
Machine Learning Solutions for Integrating Partially Overlapped Genetic Datasets.
If you use DataFusion-GDM, please cite:
Author ORCID: https://orcid.org/0000-0002-9916-9732
In R:
if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("jiashuaiz/DataFusion-GDM")
library(DataFusionGDM)
# Simulate a GDM in memory and visualize
res <- run_genetic_scenario("island", n_pops = 40)
res$plots$heatmap()
res$plots$mds()
# Optionally export to CSV if needed (defaults to tempdir)
tmp <- export_simulated_gdm(scenario = "default", n_pops = 40, verbose = FALSE)
# unlink(tmp) # clean up when finished
# Simulate and visualize
source(system.file("examples/simulate_gdm_quick.R", package = "DataFusionGDM"), echo = TRUE)
# MDS + Procrustes
source(system.file("examples/mds_procrustes_demo.R", package = "DataFusionGDM"), echo = TRUE)
# BESMI batch (small demo)
source(system.file("examples/besmi_batch_quick.R", package = "DataFusionGDM"), echo = TRUE)
See the package vignettes for end-to-end guides:
Open vignettes in R:
browseVignettes("DataFusionGDM")
vignette("getting-started", package = "DataFusionGDM")
R/simulate_gdm.RR/mds_procrustes.RR/besmi*.Rvignettes/ (no bundled data; examples use in-memory/temp files)GPL-3.0