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Initial echoAI package: AI/ML variant-level predictions
Migrate all IMPACT functions from echoannot and echolocatoR/R_tmp: - IMPACT_query() and IMPACT_process() moved from echoannot - IMPACT_files.rda dataset moved from echoannot - 13 additional functions from R_tmp/IMPACT.R refactored into 7 modular files (enrichment, plotting, heatmap, postprocessing, annotation download, LD scores) - Tests moved from echoannot - README, Docker vignette, and main vignette added Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎.Rbuildignore‎

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.*\.Rproj$
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^\.Rproj\.user$
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^README.Rmd
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^\.github$
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^doc$
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^Meta$
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^codecov\.yml$
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^_pkgdown\.yml$
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^docs$
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^pkgdown$
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Dockerfile
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^LICENSE\.md$
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node_modules$
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package-lock\.json$
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package\.json$

‎.github/workflows/rworkflows.yml‎

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name: rworkflows
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'on':
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push:
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branches:
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- master
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- main
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- devel
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- RELEASE_**
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pull_request:
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branches:
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- master
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- main
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- devel
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- RELEASE_**
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jobs:
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rworkflows:
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permissions: write-all
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runs-on: ${{ matrix.config.os }}
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name: ${{ matrix.config.os }} (${{ matrix.config.r }})
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container: ${{ matrix.config.cont }}
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strategy:
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fail-fast: ${{ false }}
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matrix:
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config:
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- os: ubuntu-latest
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bioc: devel
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r: auto
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cont: ghcr.io/bioconductor/bioconductor_docker:devel
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rspm: ~
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- os: macOS-latest
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bioc: release
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r: auto
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cont: ~
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rspm: ~
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- os: windows-latest
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bioc: release
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r: auto
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cont: ~
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rspm: ~
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steps:
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- uses: neurogenomics/rworkflows@master
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with:
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run_bioccheck: ${{ false }}
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run_rcmdcheck: ${{ true }}
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as_cran: ${{ true }}
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run_vignettes: ${{ true }}
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has_testthat: ${{ true }}
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run_covr: ${{ true }}
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run_pkgdown: ${{ true }}
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has_runit: ${{ false }}
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has_latex: ${{ false }}
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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run_docker: ${{ true }}
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docker_registry: ghcr.io

‎.gitignore‎

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# R project files
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*.Rproj
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.Rproj.user
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.Ruserdata
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# History files
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.Rhistory
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.Rapp.history
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# Session Data files
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.RData
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# User-specific files
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.Ruserdata
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# .DS_Store
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# find . -name .DS_Store -print0 | xargs -0 git rm -f --ignore-unmatch
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.DS_Store
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./.DS_Store
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./**/.DS_Store
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./**/**/.DS_Store
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./**/**/**/.DS_Store
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./**/**/**/**/.DS_Store
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./**/**/**/**/**/.DS_Store
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./**/**/**/**/**/**/.DS_Store
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# Example code in package build process
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*-Ex.R
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# Output files from R CMD build
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/*.tar.gz
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# Output files from R CMD check
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/*.Rcheck/
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# RStudio files
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.Rproj.user/
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# produced vignettes
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vignettes/*.html
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vignettes/*.pdf
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vignettes/*.R
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# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
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.httr-oauth
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# knitr and R markdown default cache directories
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*_cache/
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/cache/
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# Temporary files created by R markdown
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*.utf8.md
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*.knit.md
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# R Environment Variables
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.Renviron
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*.tbi
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Rplots.pdf

‎DESCRIPTION‎

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Package: echoAI
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Type: Package
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Title: Echoverse Module: AI/ML Variant-Level Predictions
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Version: 0.99.0
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Date: 2026-03-14
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Authors@R:
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c(person(given = "Brian",
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family = "Schilder",
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role = c("aut","cre"),
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email = "brian_schilder@alumni.brown.edu",
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comment = c(ORCID = "0000-0001-5949-2191")),
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person(given = "Jack",
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family = "Humphrey",
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role = c("aut"),
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email = "Jack.Humphrey@mssm.edu",
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comment = c(ORCID = "0000-0002-6274-6620")),
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person(given = "Towfique",
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family = "Raj",
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role = c("aut"),
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email = "towfique.raj@mssm.edu",
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comment = c(ORCID = "0000-0002-9355-5704"))
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)
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Description: Echoverse module: API access to variant-level AI/ML predictions
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including IMPACT (Inference and Modeling of Phenotype-related ACtive
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Transcription) for immune cell annotations. Part of the echoverse suite
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for genomic fine-mapping.
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URL: https://github.com/RajLabMSSM/echoAI
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BugReports: https://github.com/RajLabMSSM/echoAI/issues
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Encoding: UTF-8
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Depends: R (>= 4.1)
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biocViews: Software, Genetics, FunctionalGenomics, SystemsBiology
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Imports:
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echotabix,
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echodata,
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downloadR,
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data.table,
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dplyr,
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stats,
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utils,
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methods,
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ggplot2
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Suggests:
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rworkflows,
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markdown,
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rmarkdown,
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remotes,
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knitr,
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BiocStyle,
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covr,
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testthat (>= 3.0.0),
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ggpubr,
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patchwork,
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ggrepel,
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ggridges,
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ComplexHeatmap,
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RColorBrewer,
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pals,
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shades,
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reshape2,
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stringr,
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R.utils,
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tidyr
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Remotes:
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github::RajLabMSSM/echodata,
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github::RajLabMSSM/echotabix,
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github::RajLabMSSM/downloadR
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RoxygenNote: 7.3.3
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VignetteBuilder: knitr
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License: GPL-3
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Config/testthat/edition: 3
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LazyData: true

‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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export(IMPACT_compute_enrichment)
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export(IMPACT_get_annotation_key)
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export(IMPACT_get_annotations)
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export(IMPACT_get_ldscores)
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export(IMPACT_get_top_annotations)
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export(IMPACT_heatmap)
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export(IMPACT_iterate_enrichment)
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export(IMPACT_iterate_get_annotations)
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export(IMPACT_plot_enrichment)
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export(IMPACT_plot_impact_score)
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export(IMPACT_postprocess_annotations)
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export(IMPACT_query)
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export(IMPACT_snp_group_boxplot)
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importFrom(data.table,":=")
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importFrom(data.table,data.table)
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importFrom(data.table,dcast)
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importFrom(data.table,fread)
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importFrom(data.table,fwrite)
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importFrom(data.table,melt.data.table)
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importFrom(data.table,merge.data.table)
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importFrom(data.table,rbindlist)
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importFrom(downloadR,zenodo_upload)
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importFrom(dplyr,arrange)
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importFrom(dplyr,group_by)
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importFrom(dplyr,mutate)
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importFrom(dplyr,mutate_at)
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importFrom(dplyr,n_distinct)
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importFrom(dplyr,select)
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importFrom(dplyr,slice)
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importFrom(dplyr,slice_head)
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importFrom(dplyr,summarise)
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importFrom(dplyr,summarise_at)
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importFrom(dplyr,top_n)
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importFrom(echodata,find_consensus_snps_no_polyfun)
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importFrom(echodata,snp_group_colorDict)
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importFrom(echodata,snp_group_filters)
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importFrom(echotabix,construct_query)
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importFrom(echotabix,convert)
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importFrom(echotabix,query)
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importFrom(ggplot2,aes)
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importFrom(ggplot2,element_blank)
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importFrom(ggplot2,element_text)
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importFrom(ggplot2,facet_grid)
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importFrom(ggplot2,geom_boxplot)
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importFrom(ggplot2,geom_col)
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importFrom(ggplot2,geom_density)
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importFrom(ggplot2,geom_hline)
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importFrom(ggplot2,geom_jitter)
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importFrom(ggplot2,geom_point)
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importFrom(ggplot2,geom_violin)
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importFrom(ggplot2,geom_vline)
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importFrom(ggplot2,ggplot)
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importFrom(ggplot2,ggsave)
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importFrom(ggplot2,labs)
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importFrom(ggplot2,scale_color_viridis_c)
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importFrom(ggplot2,scale_fill_manual)
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importFrom(ggplot2,theme)
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importFrom(ggplot2,theme_bw)
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importFrom(ggplot2,ylim)
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importFrom(stats,median)
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importFrom(stats,setNames)
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importFrom(utils,combn)
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importFrom(utils,data)

‎R/IMPACT_enrichment.R‎

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#' Compute enrichment of IMPACT scores
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#'
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#' Conduct IMPACT enrichment between SNP groups and fine-mapping methods.
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#' Enrichment is computed as the ratio of IMPACT signal in a SNP group
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#' to the proportion of SNPs in that group.
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#'
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#' @param annot_melt A melted \code{data.table} of IMPACT annotations
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#' with columns including IMPACT_score, SNP, TF, Tissue, Cell, CellDeriv,
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#' and various fine-mapping result columns.
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#' @param locus Optional locus name to add to the output.
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#'
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#' @return A \code{data.table} of enrichment results per SNP group and
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#' annotation.
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#'
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#' @export
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#' @family IMPACT
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#' @importFrom dplyr group_by summarise arrange n_distinct
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#' @importFrom data.table rbindlist
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#' @examples
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#' \dontrun{
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#' enrich <- IMPACT_compute_enrichment(annot_melt = annot_melt,
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#' locus = "BST1")
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#' }
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IMPACT_compute_enrichment <- function(annot_melt,
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locus = NULL) {
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TF <- Tissue <- Cell <- CellDeriv <- IMPACT_score <- SNP <- NULL;
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leadSNP <- Support <- ABF.CS <- FINEMAP.CS <- SUSIE.CS <- NULL;
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POLYFUN_SUSIE.CS <- Consensus_SNP <- enrichment <- SNP.group <- NULL;
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annot_melt[is.na(annot_melt$IMPACT_score), "IMPACT_score"] <- 0
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SNP.groups <- list(
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"leadGWAS" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[leadSNP], na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[leadSNP], na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE))),
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"UCS" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[Support > 0], na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[Support > 0], na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE))),
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"ABF_CS" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[ABF.CS > 0], na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[ABF.CS > 0], na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE))),
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"FINEMAP_CS" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[FINEMAP.CS > 0],
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na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[FINEMAP.CS > 0], na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE))),
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"SUSIE_CS" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[SUSIE.CS > 0],
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na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[SUSIE.CS > 0], na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE))),
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"POLYFUN_CS" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[POLYFUN_SUSIE.CS > 0],
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na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[POLYFUN_SUSIE.CS > 0],
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na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE))),
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"Consensus" = annot_melt |>
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dplyr::group_by(TF, Tissue, Cell, CellDeriv) |>
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dplyr::summarise(
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enrichment = (sum(IMPACT_score[Consensus_SNP],
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na.rm = TRUE) /
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sum(IMPACT_score, na.rm = TRUE)) /
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(dplyr::n_distinct(SNP[Consensus_SNP], na.rm = TRUE) /
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dplyr::n_distinct(SNP, na.rm = TRUE)))
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)
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enrich <- data.table::rbindlist(SNP.groups, idcol = "SNP.group") |>
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dplyr::arrange(-enrichment)
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enrich <- cbind(Locus = locus, enrich)
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enrich$TF <- factor(enrich$TF, ordered = TRUE)
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enrich$SNP.group <- factor(enrich$SNP.group,
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levels = names(SNP.groups),
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ordered = TRUE)
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return(enrich)
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}
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#' Iterate IMPACT enrichment tests
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#'
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#' Run \code{\link{IMPACT_compute_enrichment}} across all unique loci
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#' in the ANNOT_MELT dataset.
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#'
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#' @param ANNOT_MELT A melted \code{data.table} of IMPACT annotations
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#' that must include a \code{Locus} column.
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#' @param verbose Print messages.
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#'
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#' @return A \code{data.table} of enrichment results across all loci.
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#'
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#' @export
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#' @family IMPACT
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#' @importFrom data.table rbindlist
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#' @examples
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#' \dontrun{
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#' ENRICH <- IMPACT_iterate_enrichment(ANNOT_MELT = ANNOT_MELT)
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#' }
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IMPACT_iterate_enrichment <- function(ANNOT_MELT,
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verbose = TRUE) {
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Locus <- NULL;
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ENRICH <- lapply(unique(ANNOT_MELT$Locus), function(locus) {
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messager("+ IMPACT:: Locus =", locus, v = verbose)
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annot_melt <- subset(ANNOT_MELT, Locus == locus)
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enrich <- IMPACT_compute_enrichment(annot_melt = annot_melt,
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locus = locus)
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return(enrich)
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})
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ENRICH <- data.table::rbindlist(ENRICH)
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return(ENRICH)
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}

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