{"id":21077016,"url":"https://github.com/drisso/scalablepca","last_synced_at":"2025-03-14T04:09:46.165Z","repository":{"id":89938005,"uuid":"355894873","full_name":"drisso/scalablePCA","owner":"drisso","description":"testing","archived":false,"fork":false,"pushed_at":"2021-06-11T10:11:33.000Z","size":46,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-20T23:13:51.688Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"billila/scalablePCA","license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/drisso.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-04-08T12:17:34.000Z","updated_at":"2021-06-11T10:11:35.000Z","dependencies_parsed_at":"2024-11-12T01:54:01.667Z","dependency_job_id":null,"html_url":"https://github.com/drisso/scalablePCA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drisso%2FscalablePCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drisso%2FscalablePCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drisso%2FscalablePCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drisso%2FscalablePCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/drisso","download_url":"https://codeload.github.com/drisso/scalablePCA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243521281,"owners_count":20304186,"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":[],"created_at":"2024-11-19T19:34:38.620Z","updated_at":"2025-03-14T04:09:46.146Z","avatar_url":"https://github.com/drisso.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# scalablePCA\n\nThis analysis wants to benchmark 7 differt PCA's methods. This repository contains code for reproducing the benchmark of the PCA's methods.\\\nThe dataset is available on https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons. \n\nIn the \"scanpy10x.py\" script we normalize with total UMI count per cell, we filter genes with more than 1 count and select highly-variable genes, we log-tranform the data and then scale to unit variance and shift to zero mean.\nFinally we save the preprocessed object using \"adata.write()\" \n\nNext, in the file \"time_7_metodi/time_subset.R\" we create downsample sizes of datasets (sizes 100k,500k, 1M) from the preprocessed object described above. \n\nWe use seven different methods to compute PCA:\n* BiocSingular_Random\n* BiocSingular_Irlba\n* BiocSingular_Exact\n* Scanpy_in_R\n* Scanpy_in_Python\n* BiocSklearn_in_R\n* BiocSklearn_in_Python\n\n\nIn the folder \"time_7_metodi\" you can find the script to reproduce e\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrisso%2Fscalablepca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdrisso%2Fscalablepca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrisso%2Fscalablepca/lists"}