{"id":20837764,"url":"https://github.com/astrazeneca/persist","last_synced_at":"2026-03-07T08:31:30.926Z","repository":{"id":255036997,"uuid":"846458142","full_name":"AstraZeneca/persist","owner":"AstraZeneca","description":"This provides the necessary code and a short tutorial to run PersiST, an exploratory tool for spatial 'omics datasets.","archived":false,"fork":false,"pushed_at":"2024-08-27T08:35:39.000Z","size":8544,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-09T11:52:26.781Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","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/AstraZeneca.png","metadata":{"files":{"readme":"README.ipynb","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":"2024-08-23T08:53:08.000Z","updated_at":"2025-07-05T09:46:50.000Z","dependencies_parsed_at":"2024-08-27T17:38:22.351Z","dependency_job_id":null,"html_url":"https://github.com/AstraZeneca/persist","commit_stats":null,"previous_names":["astrazeneca/persist"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AstraZeneca/persist","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fpersist","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fpersist/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fpersist/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fpersist/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AstraZeneca","download_url":"https://codeload.github.com/AstraZeneca/persist/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fpersist/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":277450940,"owners_count":25819971,"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":"2025-09-28T02:00:08.834Z","response_time":79,"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":[],"created_at":"2024-11-18T01:08:31.928Z","updated_at":"2025-09-29T00:33:25.111Z","avatar_url":"https://github.com/AstraZeneca.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5284d763-ed60-494e-b538-a5db32fcee5d\",\n   \"metadata\": {},\n   \"source\": [\n    \"\u003cimg src='README_files/PersiST_Logo.png' width='300' \u003e \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6eeb13d2-4523-4892-8afc-81b7d9061ce7\",\n   \"metadata\": {},\n   \"source\": [\n    \"# PersiST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"22952da3-66c6-4284-ae00-f95f298ac032\",\n   \"metadata\": {},\n   \"source\": [\n    \"PersiST is an exploratory method for analysing spatial transcriptomics (and other 'omics) datsets. Given a spatial transcriptomics data set containing expression data on multiple genes resolved to a shared set of co-orindates, PerisST computes a single score for each gene that measures the amount of spatial structure that gene shows in it's expression pattern, called the *Coefficient of Spatial Structure* (CoSS). This score can be used for multiple analytical tasks, as we show below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4a746ad9-cc9a-4ff9-a702-0bb259fc84c0\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Spatially Variable Gene Identification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6fa69eaf-a85a-4077-a754-5415d6baeed9\",\n   \"metadata\": {},\n   \"source\": [\n    \"For this tutorial, we shall be looking at spatial transcriptomics data on a sample from the Kidney Precision Medicine Project[1]. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"be9cc89d-65fc-43cc-a011-7c1e21083733\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\u003cdiv\u003e\\n\",\n       \"\u003cstyle scoped\u003e\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"  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0.0   0.00000   \\n\",\n       \"1    0.589610    0.809106    0.00000   0.0    0.000000    0.0   0.00000   \\n\",\n       \"2    0.571644    0.166174   75.90709   0.0   75.907090    0.0   0.00000   \\n\",\n       \"3    0.539074    0.714422  382.89725   0.0  127.632416    0.0   0.00000   \\n\",\n       \"4    0.570493    0.468741   82.88438   0.0    0.000000    0.0  82.88438   \\n\",\n       \"\\n\",\n       \"          FGR        CFH     FUCA2  ...  ENSG00000288156  ENSG00000288162  \\\\\\n\",\n       \"0  117.633220    0.00000   0.00000  ...              0.0              0.0   \\n\",\n       \"1   86.865880  173.73177  86.86588  ...              0.0              0.0   \\n\",\n       \"2    0.000000  151.81418   0.00000  ...              0.0              0.0   \\n\",\n       \"3  127.632416    0.00000   0.00000  ...              0.0              0.0   \\n\",\n       \"4    0.000000   82.88438   0.00000  ...              0.0              0.0   \\n\",\n       \"\\n\",\n       \"   ENSG00000288172  ENSG00000288187  ENSG00000288234  ENSG00000288253  \\\\\\n\",\n       \"0              0.0              0.0              0.0              0.0   \\n\",\n       \"1              0.0              0.0              0.0              0.0   \\n\",\n       \"2              0.0              0.0              0.0              0.0   \\n\",\n       \"3              0.0              0.0              0.0              0.0   \\n\",\n       \"4              0.0              0.0              0.0              0.0   \\n\",\n       \"\\n\",\n       \"   ENSG00000288302  ENSG00000288380  ENSG00000288398       SOD2  \\n\",\n       \"0              0.0              0.0              0.0  1058.6990  \\n\",\n       \"1              0.0              0.0              0.0  1737.3176  \\n\",\n       \"2              0.0              0.0              0.0  2201.3057  \\n\",\n       \"3              0.0              0.0              0.0  1148.6918  \\n\",\n       \"4              0.0              0.0              0.0  1989.2250  \\n\",\n       \"\\n\",\n       \"[5 rows x 26026 columns]\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"df = pd.read_csv('data/kpmp_30-10125_spatial_expression.csv')\\n\",\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b076d2a6-ebb8-4466-9ae3-f1029ef9dc60\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is a pandas DataFrame where the first two columns correspond to the well co-ordinates, and the remaining columns contain the expression of each gene. This is the format PersiST expects spatial transcriptomics data to come in.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9b39daf0-5769-4e9f-97a8-bb43fbaf7b5d\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compute CoSS scores for all the genes in this sample (this will take about 10 - 20 minutes).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"2573ba77-5f6b-4635-b758-3873608259a0\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from compute_persistence import run_persistence\\n\",\n    \"metrics = run_persistence(df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fef67382-0b04-4076-8d68-d7c0034a6efd\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a look at those genes with the highest CoSS scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"eca70347-94d7-4b32-ab91-94823e706c04\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\u003cdiv\u003e\\n\",\n       \"\u003cstyle scoped\u003e\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"\u003c/style\u003e\\n\",\n       \"\u003ctable border=\\\"1\\\" class=\\\"dataframe\\\"\u003e\\n\",\n       \"  \u003cthead\u003e\\n\",\n       \"    \u003ctr style=\\\"text-align: right;\\\"\u003e\\n\",\n       \"      \u003cth\u003e\u003c/th\u003e\\n\",\n       \"      \u003cth\u003egene\u003c/th\u003e\\n\",\n       \"      \u003cth\u003eCoSS\u003c/th\u003e\\n\",\n       \"      \u003cth\u003eratio\u003c/th\u003e\\n\",\n       \"      \u003cth\u003egene_rank\u003c/th\u003e\\n\",\n       \"      \u003cth\u003epossible_artefact\u003c/th\u003e\\n\",\n       \"      \u003cth\u003esvg\u003c/th\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"  \u003c/thead\u003e\\n\",\n       \"  \u003ctbody\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e16443\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eIGLC1\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.141620\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.651803\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e1.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e16483\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eIGHG1\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.114255\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.467722\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e2.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e5372\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eMT1G\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.105850\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.335738\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e3.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e10798\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eDEFB1\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.103534\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.376595\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e4.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e12467\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eCCL19\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.101025\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.649770\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e5.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e22516\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eC17orf113\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.098336\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.574433\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e6.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e6980\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eALDOB\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.096201\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.271491\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e7.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e5750\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003ePODXL\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.095475\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.327815\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e8.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e1102\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eSLC12A3\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.095306\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.352575\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e9.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e11812\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003eUMOD\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.094709\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.401716\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e10.0\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eNo\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003eYes\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"  \u003c/tbody\u003e\\n\",\n       \"\u003c/table\u003e\\n\",\n       \"\u003c/div\u003e\"\n      ],\n      \"text/plain\": [\n       \"            gene      CoSS     ratio  gene_rank possible_artefact  svg\\n\",\n       \"16443      IGLC1  0.141620  0.651803        1.0                No  Yes\\n\",\n       \"16483      IGHG1  0.114255  0.467722        2.0                No  Yes\\n\",\n       \"5372        MT1G  0.105850  0.335738        3.0                No  Yes\\n\",\n       \"10798      DEFB1  0.103534  0.376595        4.0                No  Yes\\n\",\n       \"12467      CCL19  0.101025  0.649770        5.0                No  Yes\\n\",\n       \"22516  C17orf113  0.098336  0.574433        6.0                No  Yes\\n\",\n       \"6980       ALDOB  0.096201  0.271491        7.0                No  Yes\\n\",\n       \"5750       PODXL  0.095475  0.327815        8.0                No  Yes\\n\",\n       \"1102     SLC12A3  0.095306  0.352575        9.0                No  Yes\\n\",\n       \"11812       UMOD  0.094709  0.401716       10.0                No  Yes\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"metrics = metrics.sort_values('CoSS', ascending=False)\\n\",\n    \"metrics.iloc[:10,:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"b4a3fe6b-3632-4e4e-acd4-42efb2eaac5f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrazeneca%2Fpersist","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fastrazeneca%2Fpersist","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrazeneca%2Fpersist/lists"}