{"id":20969895,"url":"https://github.com/jumphone/gca","last_synced_at":"2025-03-13T08:15:07.456Z","repository":{"id":122690704,"uuid":"137090411","full_name":"jumphone/GCA","owner":"jumphone","description":"GCA: Gene Correlation Analysis for Single-cell RNA-seq data","archived":false,"fork":false,"pushed_at":"2018-08-14T01:20:10.000Z","size":31860,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-20T03:56:41.547Z","etag":null,"topics":["bioinformatics","single-cell","single-cell-analysis","single-cell-rna-seq"],"latest_commit_sha":null,"homepage":"","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/jumphone.png","metadata":{"files":{"readme":"README.md","changelog":null,"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}},"created_at":"2018-06-12T15:17:17.000Z","updated_at":"2019-06-06T01:38:03.000Z","dependencies_parsed_at":"2023-03-13T12:19:15.364Z","dependency_job_id":null,"html_url":"https://github.com/jumphone/GCA","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jumphone%2FGCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jumphone%2FGCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jumphone%2FGCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jumphone%2FGCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jumphone","download_url":"https://codeload.github.com/jumphone/GCA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243365640,"owners_count":20279215,"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","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":["bioinformatics","single-cell","single-cell-analysis","single-cell-rna-seq"],"created_at":"2024-11-19T03:53:27.494Z","updated_at":"2025-03-13T08:15:07.419Z","avatar_url":"https://github.com/jumphone.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GCA: Gene Correlation Analysis for Single-cell RNA-seq data\n\nAuthor: Feng Zhang\n\nEmail: 15110700005@fudan.edu.cn\n\n# Demo\n\nDemo list: http://omics.fudan.edu.cn/static/demo/index.html (including Human oligodendrogliomas)\n\n![image](/image.png \"image\")\n\n# Requirements\n\n    Operating system: Mac or Linux, python=2.7, R=3.5    \n    Python packages: scipy    \n    R packages: stringr, pastecs, mixtools, igraph\n    \n    Running time (GSE70630): ~15 min (from step1 to step4), 4 threads, Centos7, Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz \n\n# Tips\n\n1. Get normalized expression matrix:  [sup1_normData.R](/sup1_normData.R) \n\n2. Downstream analysis (cluster \u0026 regulatory graph) of regulatory feature binary matrix: [sup2_Downstream.R](/sup2_Downstream.R)\n\n# How to run GCA\n\ni) Build expression index ( Python )\n\n    python | step1_BuildIndex.py | PAIR_LIST | EXP | OUTPUT_INDEX | RATE\n    \n    PAIR_LIST: a list of tab-delimited gene pairs \n    EXP: normalized expression matrix (without quotation marks)    \n    OUTPUT_INDEX: the index file name, given by the user   \n    RATE: the cutoff for the non-zero rate. For each gene pair, non-zero rate equals to the proportion of cells of which two genes both are detected. \n\nii) Generate Z-value matrix ( Python )\n\n    python | step2_CalZmat.py | OUTPUT_INDEX | OUTPUT_ZMAT | CPU    \n    \n    OUTPUR_INDEX: the index file generated in the first step    \n    OUTPUT_ZMAT: the z-value matrix file name, given by the user   \n    CPU: the number of threads to run GCA \n\niii) Mixture models analysis ( R )\n\n    Rscript | step3_deMix.R | EXP | OUTPUT_ZMAT | OUTPUT | CPU | SEED | CUTOFF\n    \n    EXP: the expression matrix used in the first step\n    OUTPUT_ZMAT: the z-value matrix generated in the second step\n    OUTPUT: the name of the output directory, given by the user\n    CPU: the number of threads to run GCA \n    SEED: seed for random function in R\n    CUTOFF: the cutoff of edge score (HCS) to draw the gene graph\n    \niv) Get regulatory feature binary matrix ( Python )\n\n    python | step4_getRegFeature.py | MODE | GCA_OUTPUT | OUTPUT\n    \n    MODE: a list of tab-delimited gene pairs with regulatory mode information (TRRUST file or your own file in the same format)\n    GCA_OUTPUT: the OUTPUT directory of GCA\n    OUTPUT: the regulatory feature binary matrix\n    \n    \n# Result introduction\n\nThe result summary HTML file named “index.html” locates in the output directory. \n\nThe left side of this page records the contribution score of each gene, and the right side presents the edge score of each gene pair.\n\nLinks:\n\n    “Graph” links to the gene graph that shows the path of genes that might contribute to the expression heterogeneity. \n    “Args” links to the txt file that records GCA running options \n    “txt” in “MixInfo” links to the txt file that records the parameters of ‘mixtools’ (Benaglia, et al., 2010).\n    “txt” in “Cluster” links to the txt file that records the cell labels, z-values, sub-group labels, color keys, and gene’s expression values.\n    “pdf” in “Figure” links to the pdf file that shows all the figure result for each gene pair.\n\n# Adjust figures generated by GCA \n\nUsers can simply adjust the figure by loading the “OUTPUT.saved_RData” into R, and generate different figures following the scripts in: step3_deMix.R\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjumphone%2Fgca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjumphone%2Fgca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjumphone%2Fgca/lists"}