{"id":32200652,"url":"https://github.com/diegommcc/spatialddls","last_synced_at":"2025-10-22T03:53:41.245Z","repository":{"id":156460980,"uuid":"595535856","full_name":"diegommcc/SpatialDDLS","owner":"diegommcc","description":"Deconvolution of spatial transcriptomics data based on Deep Learning","archived":false,"fork":false,"pushed_at":"2024-10-31T08:59:50.000Z","size":137024,"stargazers_count":5,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-22T03:53:31.071Z","etag":null,"topics":["deconvolution","deep-learning","neural-network","spatial-transcriptomics"],"latest_commit_sha":null,"homepage":"https://diegommcc.github.io/SpatialDDLS/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/diegommcc.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-01-31T09:33:35.000Z","updated_at":"2025-01-20T22:34:12.000Z","dependencies_parsed_at":"2024-10-29T17:40:57.583Z","dependency_job_id":null,"html_url":"https://github.com/diegommcc/SpatialDDLS","commit_stats":null,"previous_names":["diegommcc/spatialddls"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/diegommcc/SpatialDDLS","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegommcc%2FSpatialDDLS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegommcc%2FSpatialDDLS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegommcc%2FSpatialDDLS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegommcc%2FSpatialDDLS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/diegommcc","download_url":"https://codeload.github.com/diegommcc/SpatialDDLS/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegommcc%2FSpatialDDLS/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280376541,"owners_count":26320276,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-22T02:00:06.515Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deconvolution","deep-learning","neural-network","spatial-transcriptomics"],"created_at":"2025-10-22T03:53:37.829Z","updated_at":"2025-10-22T03:53:41.239Z","avatar_url":"https://github.com/diegommcc.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **SpatialDDLS** \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"120\"/\u003e\n\n[![R build status](https://github.com/diegommcc/SpatialDDLS/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/diegommcc/SpatialDDLS/actions) \n\u003c!-- [![codecov.io](https://codecov.io/github/diegommcc/SpatialDDLS/coverage.svg?branch=master)](https://app.codecov.io/gh/diegommcc/SpatialDDLS) --\u003e\n\n\n\u003cdiv style=\"text-align:left\"\u003e\n\u003cspan\u003e\n\u003ch4\u003eAn R package to deconvolute spatial transcriptomics data using single-cell RNA-seq and deep neural networks\u003c/h4\u003e\u003c/span\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\nThe **SpatialDDLS** R package provides a neural network-based solution for cell type deconvolution of spatial transcriptomics data. The package takes advantage of single-cell RNA sequencing (scRNA-seq) data to simulate mixed transcriptional profiles with known cell composition and train fully-connected neural networks to predict cell type composition of spatial transcriptomics spots. The resulting trained models can be applied to new spatial transcriptomics data to predict cell type proportions, allowing for a more accurate cell type identification and characterization of spatially-resolved transcriptomic data. Overall, SpatialDDLS is a powerful tool for cell type deconvolution in spatial transcriptomics data, providing a reliable, fast and flexible solution for researchers in the field.\n\nFor more details about the algorithm and functionalities implemented in this package, see \u003chttps://diegommcc.github.io/SpatialDDLS/\u003e.\n\n\n\u003cimg src=\"man/figures/summary.png\"/\u003e\n\n## Installation\n\n**SpatialDDLS** is already available on CRAN: \n\n```r\ninstall.packages(\"SpatialDDLS\")\n```\n\nThe version under development is available on GitHub and can be installed as follows:\n\n```r\nif (!requireNamespace(\"devtools\", quietly = TRUE))\n    install.packages(\"devtools\")\ndevtools::install_github(\"diegommcc/SpatialDDLS\")\n```\n\nThe package depends on the [tensorflow](https://cran.r-project.org/package=tensorflow) and [keras](https://cran.r-project.org/package=keras) R packages, so a working Python interpreter with the Tensorflow Python library installed is needed. The `installTFpython` function provides an easy way to install a conda environment named `spatialddls-env` with all necessary dependencies covered. We recommend installing the TensorFlow Python library in this way, although a custom installation is possible.\n\n```r\nlibrary(\"SpatialDDLS\")\ninstallTFpython(install.conda = TRUE)\n```\n\n\u003c!-- ## Usage\n\nIn the following figure, an outline of the package's workflow can be found: \n\n\u003cimg src=\"man/figures/workflow_readme.png\"/\u003e --\u003e\n\n\n## References\n\n\u003ctable\u003e\n  \u003ctr\u003e\u003ctd\u003e Mañanes, D., Rivero-García, I., Relaño, C., Jimenez-Carretero, D., Torres, M., Sancho, D., Torroja, C. and Sánchez-Cabo, F. (2024). SpatialDDLS: An R package to deconvolute spatial transcriptomics data using neural networks.\n  \u003ci\u003eBioinformatics\u003c/i\u003e\n   \u003cb\u003e40\u003c/b\u003e 2\n  \u003ca href='https://doi.org/10.1093/bioinformatics/btae072'\u003edoi:10.1093/bioinformatics/btae072\u003c/a\u003e\n  \u003c/td\u003e\u003c/tr\u003e\n\n  \u003ctr\u003e\u003ctd\u003eTorroja, C. and Sánchez-Cabo, F. (2019). digitalDLSorter: A Deep Learning algorithm to quantify immune cell populations based on scRNA-Seq data.\n  \u003ci\u003eFrontiers in Genetics\u003c/i\u003e\n  \u003cb\u003e10\u003c/b\u003e 978\n  \u003ca href='https://doi.org/10.3389/fgene.2019.00978'\u003edoi:10.3389/fgene.2019.00978\u003c/a\u003e\n  \u003c/td\u003e\u003c/tr\u003e\n\u003c/table\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdiegommcc%2Fspatialddls","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdiegommcc%2Fspatialddls","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdiegommcc%2Fspatialddls/lists"}