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Initial release of MOSAIC R package
Multi-Omic Sample-wise Analysis of Inter-feature Connectivity. Implements spectral co-embedding for 1-3 modalities, per-feature PERMANOVA-based differential connectivity testing, disease subgroup detection, and multi-modal simulation framework.
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‎.Rbuildignore‎

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^.*\.Rproj$
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^\.Rproj\.user$
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^LICENSE\.md$
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^README\.Rmd$
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^_pkgdown\.yml$
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^docs$
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^pkgdown$
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^_pkgdown\.yml$
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^\.github$

‎.github/workflows/pkgdown.yaml‎

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on:
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push:
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branches: [main]
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workflow_dispatch:
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name: pkgdown
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permissions:
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contents: write
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pages: write
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jobs:
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pkgdown:
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runs-on: ubuntu-latest
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env:
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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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steps:
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- uses: actions/checkout@v4
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- uses: r-lib/actions/setup-r@v2
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with:
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use-public-rspm: true
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- uses: r-lib/actions/setup-r-dependencies@v2
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with:
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extra-packages: any::pkgdown
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needs: website
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- name: Build site
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run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
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shell: Rscript {0}
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- name: Deploy to GitHub pages
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uses: JamesIves/github-pages-deploy-action@v4
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with:
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clean: false
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branch: gh-pages
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folder: docs

‎DESCRIPTION‎

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Package: MOSAIC
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Title: Multi-Omic Sample-Wise Analysis of Inter-Feature Connectivity
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Version: 1.0.0
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Authors@R: c(
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person("Chang", "Lu", email = "chang.lu@yale.edu", role = c("aut", "cre")),
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person("Rong", "Ma", email = "rongma@hsph.harvard.edu", role = "aut"),
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person("Yuval", "Kluger", email = "yuval.kluger@yale.edu", role = "aut")
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)
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Description: MOSAIC (Multi-Omic Sample-wise Analysis of Inter-feature
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Connectivity) is a spectral framework that learns a high-resolution
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feature-by-sample joint embedding from population-scale single-cell
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multi-omics data. For each individual, MOSAIC constructs a sample-specific
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coupling matrix capturing complete intra- and cross-modality feature
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interactions, then projects all samples into a shared latent space via
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spectral integration. The resulting embedding represents each feature by its
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connectivity profile relative to all other features, enabling direct
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comparison of regulatory network topology across individuals. MOSAIC supports
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differential connectivity analysis, unsupervised subgroup detection, and
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clinical outcome prediction.
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License: MIT + file LICENSE
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Encoding: UTF-8
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Roxygen: list(markdown = TRUE)
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RoxygenNote: 7.3.2
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Imports:
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Seurat (>= 5.0),
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Matrix,
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RSpectra,
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ggplot2,
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rlang,
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methods,
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parallel,
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doParallel,
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foreach,
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vegan,
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cluster,
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RColorBrewer
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Suggests:
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transport,
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testthat (>= 3.0.0),
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knitr,
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rmarkdown
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VignetteBuilder: knitr
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URL: https://github.com/KlugerLab/MOSAIC
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BugReports: https://github.com/KlugerLab/MOSAIC/issues

‎LICENSE‎

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YEAR: 2025
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COPYRIGHT HOLDER: Chang Lu

‎LICENSE.md‎

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# MIT License
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Copyright (c) 2025 Chang Lu
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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export(cal_peranova_per_feature_parallel_no_zero)
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export(calculate_empirical_pvalue)
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export(compute_module_similarity)
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export(find_elbow_kneedle)
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export(find_partition_hclust)
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export(generate_svd_simulated_data)
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export(plot_eigen)
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export(plot_mds_cluster)
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export(run_MOSAIC)
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importFrom(Matrix,Matrix)
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importFrom(RColorBrewer,brewer.pal)
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importFrom(RSpectra,eigs_sym)
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importFrom(Seurat,AddMetaData)
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importFrom(Seurat,CreateSeuratObject)
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importFrom(Seurat,DefaultAssay)
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importFrom(Seurat,ScaleData)
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importFrom(Seurat,SplitObject)
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importFrom(cluster,silhouette)
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importFrom(doParallel,registerDoParallel)
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importFrom(foreach,`%dopar%`)
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importFrom(foreach,foreach)
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importFrom(ggplot2,aes)
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importFrom(ggplot2,element_text)
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importFrom(ggplot2,geom_point)
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importFrom(ggplot2,ggplot)
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importFrom(ggplot2,labs)
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importFrom(ggplot2,scale_color_manual)
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importFrom(ggplot2,scale_fill_manual)
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importFrom(ggplot2,stat_ellipse)
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importFrom(ggplot2,theme)
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importFrom(ggplot2,theme_classic)
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importFrom(ggplot2,theme_minimal)
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importFrom(methods,as)
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importFrom(parallel,clusterExport)
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importFrom(parallel,detectCores)
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importFrom(parallel,makeCluster)
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importFrom(parallel,stopCluster)
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importFrom(rlang,.data)
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importFrom(stats,aggregate)
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importFrom(stats,as.dist)
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importFrom(stats,cmdscale)
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importFrom(stats,cor)
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importFrom(stats,cutree)
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importFrom(stats,dist)
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importFrom(stats,hclust)
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importFrom(stats,kmeans)
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importFrom(stats,median)
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importFrom(stats,p.adjust)
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importFrom(stats,rnorm)
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importFrom(vegan,adonis2)

‎R/MOSAIC-package.R‎

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#' @keywords internal
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"_PACKAGE"
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#' @importFrom Seurat ScaleData SplitObject DefaultAssay CreateSeuratObject AddMetaData
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#' @importFrom RSpectra eigs_sym
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#' @importFrom Matrix Matrix
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#' @importFrom ggplot2 ggplot aes geom_point theme_minimal theme_classic labs theme
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#' element_text stat_ellipse scale_color_manual scale_fill_manual
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#' @importFrom rlang .data
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#' @importFrom methods as
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#' @importFrom parallel detectCores makeCluster stopCluster clusterExport
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#' @importFrom doParallel registerDoParallel
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#' @importFrom foreach foreach `%dopar%`
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#' @importFrom vegan adonis2
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#' @importFrom cluster silhouette
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#' @importFrom RColorBrewer brewer.pal
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#' @importFrom stats cmdscale cor dist hclust cutree kmeans median rnorm
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#' p.adjust aggregate as.dist
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