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November 8, 2024 16:17
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PDC API Test example 0
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| >>> what apis are available? | |
| thought: I will list all the available APIs to provide the user with the information they need. | |
| tool: AdhocApi.list_apis | |
| tool_input: None | |
| observation: {'Proteomic Data Commons': {'description': "The Proteomics Data Commons (PDC) is a comprehensive, open-access resource that stores,\nmanages, and shares large-scale proteomic | |
| data for cancer and other biomedical research.\nIt is part of the National Cancer Institute's broader data ecosystem, enabling researchers\nto access and analyze proteomic datasets, including | |
| mass spectrometry data and related\nmetadata. The PDC supports integrative research by providing standardized data formats\nand analysis tools, facilitating the discovery of protein | |
| biomarkers and insights into\ncancer biology, which helps in advancing personalized medicine and treatment approaches.\n"}} | |
| thought: I have retrieved the list of available APIs. There is one API available: | |
| tool: final_answer | |
| tool_input: 1. Proteomic Data Commons: A comprehensive, open-access resource for storing, managing, and sharing large-scale proteomic data for cancer and other biomedical research. It | |
| supports integrative research by providing standardized data formats and analysis tools. | |
| 1. Proteomic Data Commons: A comprehensive, open-access resource for storing, managing, and sharing large-scale proteomic data for cancer and other biomedical research. It supports integrative research by providing standardized data formats and analysis tools. | |
| >>> can you make a request that will determine what the list of possible experiment types are | |
| thought: To determine the list of possible experiment types, I will draft a request using the Proteomic Data Commons API. | |
| tool: AdhocApi.use_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'goal': 'Determine the list of possible experiment types.'} | |
| INFO {'api': 'Proteomic Data Commons', 'goal': 'Determine the list of possible experiment types.'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23690, min_total_token_count=32768. Running model without cache.'} | |
| INFO {'draft_code': 'import requests\nimport json\nimport pandas as pd\nimport os\n\nurl = \'https://pdc.cancer.gov/graphql\'\nheaders = {"Content-Type": "application/json"}\n\n\nquery = """{\n uiFilters{\n experiment_type {\n filterValue\n }\n }\n }\n """\n \nr = requests.post(url, json={\'query\': query}, headers=headers)\njson_data = json.loads(r.text)\n\nexp_types = json_data[\'data\'][\'uiFilters\'][\'experiment_type\']\nexp_type_list = [x[\'filterValue\'] for x in exp_types]\nprint(exp_type_list)\n'} | |
| INFO {'fixed_code': 'import requests\nimport json\nimport os\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\nurl = \'https://pdc.cancer.gov/graphql\'\nheaders = {"Content-Type": "application/json"}\n\nquery = """{\n uiFilters{\n experiment_type {\n filterValue\n }\n }\n }\n """\n\nr = requests.post(url, json={\'query\': query}, headers=headers)\njson_data = json.loads(r.text)\n\nexp_types = json_data[\'data\'][\'uiFilters\'][\'experiment_type\']\nexp_type_list = [x[\'filterValue\'] for x in exp_types]\nprint(exp_type_list)'} | |
| INFO {'info': 'directly returning code'} | |
| observation: import requests | |
| import json | |
| import os | |
| # Ensure the data directory exists | |
| os.makedirs('./data/', exist_ok=True) | |
| url = 'https://pdc.cancer.gov/graphql' | |
| headers = {"Content-Type": "application/json"} | |
| query = """{ | |
| uiFilters{ | |
| experiment_type { | |
| filterValue | |
| } | |
| } | |
| } | |
| """ | |
| r = requests.post(url, json={'query': query}, headers=headers) | |
| json_data = json.loads(r.text) | |
| exp_types = json_data['data']['uiFilters']['experiment_type'] | |
| exp_type_list = [x['filterValue'] for x in exp_types] | |
| print(exp_type_list) | |
| thought: I have drafted and executed the request to determine the list of possible experiment types using the Proteomic Data Commons API. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport json\nimport os\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\nurl = | |
| \'https://pdc.cancer.gov/graphql\'\nheaders = {"Content-Type": "application/json"}\n\nquery = """{\n uiFilters{\n experiment_type {\n filterValue\n }\n | |
| }\n }\n """\n\nr = requests.post(url, json={\'query\': query}, headers=headers)\njson_data = json.loads(r.text)\n\nexp_types = | |
| json_data[\'data\'][\'uiFilters\'][\'experiment_type\']\nexp_type_list = [x[\'filterValue\'] for x in exp_types]\nprint(exp_type_list)'} | |
| observation: [['Oral Squamous Cell Carcinoma Study - Proteome', 'TCGA Breast Cancer CompRef Samples - Proteome', 'TCGA Ovarian CompRef Samples PNNL Proteome', 'TCGA Breast Cancer CompRef | |
| Samples - Phosphoproteome', 'TCGA BRCA Proteome S015-1', 'TCGA BRCA Phosphoproteome S015-2', 'TCGA OV Glycoproteome S020-1', 'TCGA OV Proteome S020-2', 'TCGA OV Proteome S020-3', 'TCGA OV | |
| Phosphoproteome S020-4', 'TCGA Ovarian CompRef Samples PNNL Phosphoproteome Velos Qexactive', 'Therapeutic Targets in Breast Cancer Xenografts - Phosphoproteome', 'Proteogenomics of Gastric | |
| Cancer - Proteome', 'Proteogenomics of Gastric Cancer - Phosphoproteome', 'Proteogenomics of Gastric Cancer - Glycoproteome', 'Therapeutic Targets in Breast Cancer Xenografts - Proteome', | |
| 'TCGA Ovarian CompRef Samples JHU Proteome'], ['CPTAC CCRCC Confirmatory Study - DIA Intact Glycoproteome', 'CPTAC CCRCC Confirmatory Study - DIA Phosphoproteome', 'CPTAC GBM Discovery Study | |
| - Metabolome', 'CPTAC CCRCC Discovery Study - DIA Proteome', 'CPTAC PDA Discovery Study - DIA Proteome', 'CPTAC PDAC Proteins in Serum - Proteome', 'VU Normal Colon Epithelium - Proteome', | |
| 'CPTAC GBM Discovery Study - Lipidome', 'CPTAC CCRCC Confirmatory Study - Validation - Metabolome', 'CPTAC Deep Proteomics 2D-DIA', 'CPTAC PDAC Proteins in Serum - Glycoproteome', 'TCGA | |
| Colorectal Cancer CompRef Samples - Proteome', 'CPTAC CCRCC Confirmatory Study - Intratumor Heterogeneity - DIA Proteome', 'CPTAC CCRCC Confirmatory Study - Training - Metabolome', | |
| 'guo_kidney ST25730263', 'CPTAC non-ccRCC Study - Metabolome', 'CPTAC GBM Confirmatory Study - Metabolome', 'TCGA COAD Proteome S016-1', 'Prospective COAD Proteome S037-1', 'CPTAC CCRCC | |
| Confirmatory Study - Kinase Inhibition - DIA Phosphoproteome', 'CPTAC GBM Confirmatory Study - Lipidome', 'CPTAC CCRCC Confirmatory Study - DIA Proteome', 'CPTAC PDAC BioTExt - Proteome'], | |
| ['Broad Institute - Medulloblastoma - Phosphoproteome', 'AML Ex Vivo Drug Response - Sorafenib Treatment - Phosphoproteome', 'Broad Institute - Medulloblastoma - | |
| Phospho-tyrosine-enrichments', 'NCI-7 Cell Line Panel - Phosphoproteome', 'AML Ex Vivo Drug Response - Primary Cohort - Proteome', 'CPTAC LUAD Discovery Study - Acetylome', 'CPTAC LUAD | |
| Discovery Study - CompRef Acetylome', 'CPTAC UCEC Discovery Study - Acetylome', 'CPTAC UCEC Discovery Study - CompRef Acetylome', 'Sampling techniques for enrichment of PDAC - Proteome', 'KU | |
| PDAC Discovery Study - Global proteome', 'Prospective Ovarian JHU Intact Glycoproteome', 'NCI-7 Cell Line Panel - Proteome', 'Prospective Ovarian JHU Proteome v2', 'KU PDAC Discovery Study - | |
| Phosphoproteome', 'NCI-7 Cell Line Panel Experimental Application - Proteome', 'Microscaled Proteogenomic Methods for Precision Oncology PDX bulk - Phosphoproteome', 'AML Ex Vivo Drug | |
| Response - Sorafenib Treatment - Proteome', 'Reproducible Proteome and Phosphoproteome Workflow JHU - Phosphoproteome', 'Microscaled Proteogenomic Methods for Precision Oncology PDX bulk - | |
| Proteome', 'Broad Institute - Medulloblastoma - Proteome', 'Academia Sinica LUAD100-Phosphoproteome', 'NCC iCC - Proteome', 'Academia Sinica LUAD100-Proteome v2', 'Prospective Breast BI | |
| Acetylome', 'Prospective Breast BI - CompRef Acetylome', 'Prospective Breast BI Proteome v2', 'Prospective Breast BI - CompRef Proteome', 'Prospective Breast BI Phosphoproteome v2', | |
| 'Prospective Breast BI - CompRef Phosphoproteome', 'Reproducible Proteome and Phosphoproteome Workflow PNNL - Phosphoproteome', 'Prospective COAD Proteome S037-2', 'Prospective COAD | |
| Phosphoproteome S037-3', 'Prospective OV Proteome S038-2', 'Prospective OV Phosphoproteome S038-3', 'Microscaled Proteogenomic Methods for Precision Oncology PDX cores - Phosphoproteome', | |
| 'UCEC Discovery - Proteome S043-1', 'UCEC Discovery - Phosphoproteome S043-2', 'AML Ex Vivo Drug Response - Primary Cohort - Phosphoproteome', 'Reproducible Proteome and Phosphoproteome | |
| Workflow PNNL - Proteome', 'Broad Institute - Medulloblastoma - Acetylome', 'Reproducible Proteome and Phosphoproteome Workflow BI - Phosphoproteome', 'CPTAC CCRCC Discovery Study - Proteome | |
| S044-1', 'CPTAC CCRCC Discovery Study - Phosphoproteome S044-2', 'UCEC Discovery - CompRef Proteome S043-1', 'UCEC Discovery - CompRef Phosphoproteome S043-2', 'CPTAC CCRCC Discovery Study - | |
| CompRef Proteome S044-1', 'CPTAC CCRCC Discovery Study - CompRef Phosphoproteome S044-2', 'CPTAC LUAD Discovery Study - CompRef Proteome', 'CPTAC LUAD Discovery Study - CompRef | |
| Phosphoproteome', 'Reproducible Proteome and Phosphoproteome Workflow BI - Proteome', 'CPTAC CCRCC Discovery Study - Intact Glycoproteome', 'Microscaled Proteogenomic Methods for Precision | |
| Oncology PDX cores - Proteome', 'NCC iCC - Phosphoproteome', 'Reproducible Proteome and Phosphoproteome Workflow JHU - Proteome', 'Prospective Ovarian JHU N-linked Glycosite-containing | |
| peptide v2', 'CPTAC LUAD Discovery Study - Proteome', 'CPTAC LUAD Discovery Study - Phosphoproteome'], ['AML Gilteritinib Resistance - Proteome', 'CPTAC UCEC Confirmatory Study - | |
| Phosphoproteome', 'CPTAC GBM Discovery Study - CompRef Phosphoproteome', 'CPTAC UCEC Confirmatory Study - Glycoproteome', 'CPTAC GBM Confirmatory Study - CompRef Proteome', 'CPTAC LSCC | |
| Discovery Study - Phosphoproteome', 'CPTAC LSCC Discovery Study - Acetylome', 'CPTAC LSCC Discovery Study - Proteome', 'PTRC HGSOC Frozen Validation - Proteome', 'PTRC HGSOC FFPE Discovery - | |
| Phosphoproteome', 'CPTAC PDA Discovery Study - Proteome', 'PTRC TNBC - Phosphoproteome', 'PTRC HGSOC FFPE Validation - Phosphoproteome', 'Microscaled Proteogenomic Methods for Precision | |
| Oncology DP1 Clinical Trial - Phosphoproteome', 'CPTAC UCEC Confirmatory Study - CompRef Phosphoproteome', 'AML Ex Vivo Drug Response - Combination Treatment - Phosphoproteome', 'AML | |
| Gilteritinib Resistance - Phosphoproteome', 'HBV-Related Hepatocellular Carcinoma - Phosphoproteome', 'AML Gilteritinib TimeCourse - Proteome', 'CPTAC UCEC Confirmatory Study - Proteome', | |
| 'Beat AML Baseline Clinical - Proteome', 'CPTAC GBM Confirmatory Study - Glycoproteome', 'CPTAC GBM Confirmatory Study - CompRef Acetylome', 'CPTAC UCEC Confirmatory Study - CompRef | |
| Acetylome', 'KNCC Glioblastoma Evolution - Proteome', 'CPTAC GBM Confirmatory Study - Acetylome', 'Pediatric Brain Cancer Pilot Study - Phosphoproteome', 'Pediatric Brain Cancer Pilot Study - | |
| Proteome', 'Beat AML Baseline Clinical - Phosphoproteome', 'AML Quizartinib Resistance - Proteome', 'CPTAC UCEC Confirmatory Study - CompRef Proteome', 'CPTAC PDA Discovery Study - | |
| Phosphoproteome', 'CPTAC UCEC Confirmatory Study - Acetylome', 'CPTAC non-ccRCC Study - Proteome', 'PTRC HGSOC Frozen Validation - Phosphoproteome', 'AML Quizartinib Resistance - | |
| Phosphoproteome', 'APOLLO LUAD - Phosphoproteome - FeNTA', 'CPTAC HNSCC Discovery Study - Proteome', 'CPTAC HNSCC Discovery Study - Phosphoproteome', 'PTRC HGSOC FFPE Discovery - Proteome', | |
| 'PTRC TNBC - Proteome', 'CPTAC GBM Discovery Study - CompRef Proteome', 'PTRC HGSOC FFPE Validation - Proteome', 'CPTAC GBM Confirmatory Study - CompRef Phosphoproteome', 'PTRC TNBC PDX - | |
| Proteome', 'CPTAC GBM Confirmatory Study - Proteome', 'CPTAC LSCC Discovery Study - Ubiquitylome', 'CPTAC GBM Discovery Study - Acetylome', 'CPTAC GBM Discovery Study - CompRef Acetylome', | |
| 'APOLLO LUAD - Proteome', 'APOLLO LUAD - Phosphoproteome - TiO2', 'Microscaled Proteogenomic Methods for Precision Oncology DP1 Clinical Trial - Proteome', 'CPTAC GBM Discovery Study - | |
| Proteome', 'AML Gilteritinib TimeCourse - Phosphoproteome', 'AML Ex Vivo Drug Response - Combination Treatment - Proteome', 'CPTAC non-ccRCC Study - Intact Glycoproteome', 'CPTAC non-ccRCC | |
| Study - Phosphoproteome', 'CPTAC GBM Confirmatory Study - Phosphoproteome', 'KNCC Glioblastoma Evolution - Phosphoproteome', 'CPTAC GBM Discovery Study - Phosphoproteome', 'HBV-Related | |
| Hepatocellular Carcinoma - Proteome', 'CPTAC PDA Discovery Study - Intact Glycoproteome'], ['Georgetown Lung Cancer Proteomics Study'], ['Buparlisib Treated Xenograft Tumors of TNBC - | |
| Proteome', 'Buparlisib Treated Xenograft Tumors of TNBC - Phosphoproteome']] | |
| thought: I have successfully retrieved the list of possible experiment types from the Proteomic Data Commons API. | |
| tool: final_answer | |
| tool_input: The list of possible experiment types includes: | |
| 1. Oral Squamous Cell Carcinoma Study - Proteome | |
| 2. TCGA Breast Cancer CompRef Samples - Proteome | |
| 3. TCGA Ovarian CompRef Samples PNNL Proteome | |
| 4. TCGA Breast Cancer CompRef Samples - Phosphoproteome | |
| 5. TCGA BRCA Proteome S015-1 | |
| 6. TCGA BRCA Phosphoproteome S015-2 | |
| 7. TCGA OV Glycoproteome S020-1 | |
| 8. TCGA OV Proteome S020-2 | |
| 9. TCGA OV Proteome S020-3 | |
| 10. TCGA OV Phosphoproteome S020-4 | |
| 11. TCGA Ovarian CompRef Samples PNNL Phosphoproteome Velos Qexactive | |
| 12. Therapeutic Targets in Breast Cancer Xenografts - Phosphoproteome | |
| 13. Proteogenomics of Gastric Cancer - Proteome | |
| 14. Proteogenomics of Gastric Cancer - Phosphoproteome | |
| 15. Proteogenomics of Gastric Cancer - Glycoproteome | |
| 16. Therapeutic Targets in Breast Cancer Xenografts - Proteome | |
| 17. TCGA Ovarian CompRef Samples JHU Proteome | |
| 18. CPTAC CCRCC Confirmatory Study - DIA Intact Glycoproteome | |
| 19. CPTAC CCRCC Confirmatory Study - DIA Phosphoproteome | |
| 20. CPTAC GBM Discovery Study - Metabolome | |
| 21. CPTAC CCRCC Discovery Study - DIA Proteome | |
| 22. CPTAC PDA Discovery Study - DIA Proteome | |
| 23. CPTAC PDAC Proteins in Serum - Proteome | |
| 24. VU Normal Colon Epithelium - Proteome | |
| 25. CPTAC GBM Discovery Study - Lipidome | |
| 26. CPTAC CCRCC Confirmatory Study - Validation - Metabolome | |
| 27. CPTAC Deep Proteomics 2D-DIA | |
| 28. CPTAC PDAC Proteins in Serum - Glycoproteome | |
| 29. TCGA Colorectal Cancer CompRef Samples - Proteome | |
| 30. CPTAC CCRCC Confirmatory Study - Intratumor Heterogeneity - DIA Proteome | |
| 31. CPTAC CCRCC Confirmatory Study - Training - Metabolome | |
| 32. guo_kidney ST25730263 | |
| 33. CPTAC non-ccRCC Study - Metabolome | |
| 34. CPTAC GBM Confirmatory Study - Metabolome | |
| 35. TCGA COAD Proteome S016-1 | |
| 36. Prospective COAD Proteome S037-1 | |
| 37. CPTAC CCRCC Confirmatory Study - Kinase Inhibition - DIA Phosphoproteome | |
| 38. CPTAC GBM Confirmatory Study - Lipidome | |
| 39. CPTAC CCRCC Confirmatory Study - DIA Proteome | |
| 40. CPTAC PDAC BioTExt - Proteome | |
| 41. Broad Institute - Medulloblastoma - Phosphoproteome | |
| 42. AML Ex Vivo Drug Response - Sorafenib Treatment - Phosphoproteome | |
| 43. Broad Institute - Medulloblastoma - Phospho-tyrosine-enrichments | |
| 44. NCI-7 Cell Line Panel - Phosphoproteome | |
| 45. AML Ex Vivo Drug Response - Primary Cohort - Proteome | |
| 46. CPTAC LUAD Discovery Study - Acetylome | |
| 47. CPTAC LUAD Discovery Study - CompRef Acetylome | |
| 48. CPTAC UCEC Discovery Study - Acetylome | |
| 49. CPTAC UCEC Discovery Study - CompRef Acetylome | |
| 50. Sampling techniques for enrichment of PDAC - Proteome | |
| 51. KU PDAC Discovery Study - Global proteome | |
| 52. Prospective Ovarian JHU Intact Glycoproteome | |
| 53. NCI-7 Cell Line Panel - Proteome | |
| 54. Prospective Ovarian JHU Proteome v2 | |
| 55. KU PDAC Discovery Study - Phosphoproteome | |
| 56. NCI-7 Cell Line Panel Experimental Application - Proteome | |
| 57. Microscaled Proteogenomic Methods for Precision Oncology PDX bulk - Phosphoproteome | |
| 58. AML Ex Vivo Drug Response - Sorafenib Treatment - Proteome | |
| 59. Reproducible Proteome and Phosphoproteome Workflow JHU - Phosphoproteome | |
| 60. Microscaled Proteogenomic Methods for Precision Oncology PDX bulk - Proteome | |
| 61. Broad Institute - Medulloblastoma - Proteome | |
| 62. Academia Sinica LUAD100-Phosphoproteome | |
| 63. NCC iCC - Proteome | |
| 64. Academia Sinica LUAD100-Proteome v2 | |
| 65. Prospective Breast BI Acetylome | |
| 66. Prospective Breast BI - CompRef Acetylome | |
| 67. Prospective Breast BI Proteome v2 | |
| 68. Prospective Breast BI - CompRef Proteome | |
| 69. Prospective Breast BI Phosphoproteome v2 | |
| 70. Prospective Breast BI - CompRef Phosphoproteome | |
| 71. Reproducible Proteome and Phosphoproteome Workflow PNNL - Phosphoproteome | |
| 72. Prospective COAD Proteome S037-2 | |
| 73. Prospective COAD Phosphoproteome S037-3 | |
| 74. Prospective OV Proteome S038-2 | |
| 75. Prospective OV Phosphoproteome S038-3 | |
| 76. Microscaled Proteogenomic Methods for Precision Oncology PDX cores - Phosphoproteome | |
| 77. UCEC Discovery - Proteome S043-1 | |
| 78. UCEC Discovery - Phosphoproteome S043-2 | |
| 79. AML Ex Vivo Drug Response - Primary Cohort - Phosphoproteome | |
| 80. Reproducible Proteome and Phosphoproteome Workflow PNNL - Proteome | |
| 81. Broad Institute - Medulloblastoma - Acetylome | |
| 82. Reproducible Proteome and Phosphoproteome Workflow BI - Phosphoproteome | |
| 83. CPTAC CCRCC Discovery Study - Proteome S044-1 | |
| 84. CPTAC CCRCC Discovery Study - Phosphoproteome S044-2 | |
| 85. UCEC Discovery - CompRef Proteome S043-1 | |
| 86. UCEC Discovery - CompRef Phosphoproteome S043-2 | |
| 87. CPTAC CCRCC Discovery Study - CompRef Proteome S044-1 | |
| 88. CPTAC CCRCC Discovery Study - CompRef Phosphoproteome S044-2 | |
| 89. CPTAC LUAD Discovery Study - CompRef Proteome | |
| 90. CPTAC LUAD Discovery Study - CompRef Phosphoproteome | |
| 91. Reproducible Proteome and Phosphoproteome Workflow BI - Proteome | |
| 92. CPTAC CCRCC Discovery Study - Intact Glycoproteome | |
| 93. Microscaled Proteogenomic Methods for Precision Oncology PDX cores - Proteome | |
| 94. NCC iCC - Phosphoproteome | |
| 95. Reproducible Proteome and Phosphoproteome Workflow JHU - Proteome | |
| 96. Prospective Ovarian JHU N-linked Glycosite-containing peptide v2 | |
| 97. CPTAC LUAD Discovery Study - Proteome | |
| 98. CPTAC LUAD Discovery Study - Phosphoproteome | |
| 99. AML Gilteritinib Resistance - Proteome | |
| 100. CPTAC UCEC Confirmatory Study - Phosphoproteome | |
| 101. CPTAC GBM Discovery Study - CompRef Phosphoproteome | |
| 102. CPTAC UCEC Confirmatory Study - Glycoproteome | |
| 103. CPTAC GBM Confirmatory Study - CompRef Proteome | |
| 104. CPTAC LSCC Discovery Study - Phosphoproteome | |
| 105. CPTAC LSCC Discovery Study - Acetylome | |
| 106. CPTAC LSCC Discovery Study - Proteome | |
| 107. PTRC HGSOC Frozen Validation - Proteome | |
| 108. PTRC HGSOC FFPE Discovery - Phosphoproteome | |
| 109. CPTAC PDA Discovery Study - Proteome | |
| 110. PTRC TNBC - Phosphoproteome | |
| 111. PTRC HGSOC FFPE Validation - Phosphoproteome | |
| 112. Microscaled Proteogenomic Methods for Precision Oncology DP1 Clinical Trial - Phosphoproteome | |
| 113. CPTAC UCEC Confirmatory Study - CompRef Phosphoproteome | |
| 114. AML Ex Vivo Drug Response - Combination Treatment - Phosphoproteome | |
| 115. AML Gilteritinib Resistance - Phosphoproteome | |
| 116. HBV-Related Hepatocellular Carcinoma - Phosphoproteome | |
| 117. AML Gilteritinib TimeCourse - Proteome | |
| 118. CPTAC UCEC Confirmatory Study - Proteome | |
| 119. Beat AML Baseline Clinical - Proteome | |
| 120. CPTAC GBM Confirmatory Study - Glycoproteome | |
| 121. CPTAC GBM Confirmatory Study - CompRef Acetylome | |
| 122. CPTAC UCEC Confirmatory Study - CompRef Acetylome | |
| 123. KNCC Glioblastoma Evolution - Proteome | |
| 124. CPTAC GBM Confirmatory Study - Acetylome | |
| 125. Pediatric Brain Cancer Pilot Study - Phosphoproteome | |
| 126. Pediatric Brain Cancer Pilot Study - Proteome | |
| 127. Beat AML Baseline Clinical - Phosphoproteome | |
| 128. AML Quizartinib Resistance - Proteome | |
| 129. CPTAC UCEC Confirmatory Study - CompRef Proteome | |
| 130. CPTAC PDA Discovery Study - Phosphoproteome | |
| 131. CPTAC UCEC Confirmatory Study - Acetylome | |
| 132. CPTAC non-ccRCC Study - Proteome | |
| 133. PTRC HGSOC Frozen Validation - Phosphoproteome | |
| 134. AML Quizartinib Resistance - Phosphoproteome | |
| 135. APOLLO LUAD - Phosphoproteome - FeNTA | |
| 136. CPTAC HNSCC Discovery Study - Proteome | |
| 137. CPTAC HNSCC Discovery Study - Phosphoproteome | |
| 138. PTRC HGSOC FFPE Discovery - Proteome | |
| 139. PTRC TNBC - Proteome | |
| 140. CPTAC GBM Discovery Study - CompRef Proteome | |
| 141. PTRC HGSOC FFPE Validation - Proteome | |
| 142. CPTAC GBM Confirmatory Study - CompRef Phosphoproteome | |
| 143. PTRC TNBC PDX - Proteome | |
| 144. CPTAC GBM Confirmatory Study - Proteome | |
| 145. CPTAC LSCC Discovery Study - Ubiquitylome | |
| 146. CPTAC GBM Discovery Study - Acetylome | |
| 147. CPTAC GBM Discovery Study - CompRef Acetylome | |
| 148. APOLLO LUAD - Proteome | |
| 149. APOLLO LUAD - Phosphoproteome - TiO2 | |
| 150. Microscaled Proteogenomic Methods for Precision Oncology DP1 Clinical Trial - Proteome | |
| 151. CPTAC GBM Discovery Study - Proteome | |
| 152. AML Gilteritinib TimeCourse - Phosphoproteome | |
| 153. AML Ex Vivo Drug Response - Combination Treatment - Proteome | |
| 154. CPTAC non-ccRCC Study - Intact Glycoproteome | |
| 155. CPTAC non-ccRCC Study - Phosphoproteome | |
| 156. CPTAC GBM Confirmatory Study - Phosphoproteome | |
| 157. KNCC Glioblastoma Evolution - Phosphoproteome | |
| 158. CPTAC GBM Discovery Study - Phosphoproteome | |
| 159. HBV-Related Hepatocellular Carcinoma - Proteome | |
| 160. CPTAC PDA Discovery Study - Intact Glycoproteome | |
| 161. Georgetown Lung Cancer Proteomics Study | |
| 162. Buparlisib Treated Xenograft Tumors of TNBC - Proteome | |
| 163. Buparlisib Treated Xenograft Tumors of TNBC - Phosphoproteome | |
| The list of possible experiment types includes: | |
| 1. Oral Squamous Cell Carcinoma Study - Proteome | |
| 2. TCGA Breast Cancer CompRef Samples - Proteome | |
| 3. TCGA Ovarian CompRef Samples PNNL Proteome | |
| 4. TCGA Breast Cancer CompRef Samples - Phosphoproteome | |
| 5. TCGA BRCA Proteome S015-1 | |
| 6. TCGA BRCA Phosphoproteome S015-2 | |
| 7. TCGA OV Glycoproteome S020-1 | |
| 8. TCGA OV Proteome S020-2 | |
| 9. TCGA OV Proteome S020-3 | |
| 10. TCGA OV Phosphoproteome S020-4 | |
| 11. TCGA Ovarian CompRef Samples PNNL Phosphoproteome Velos Qexactive | |
| 12. Therapeutic Targets in Breast Cancer Xenografts - Phosphoproteome | |
| 13. Proteogenomics of Gastric Cancer - Proteome | |
| 14. Proteogenomics of Gastric Cancer - Phosphoproteome | |
| 15. Proteogenomics of Gastric Cancer - Glycoproteome | |
| 16. Therapeutic Targets in Breast Cancer Xenografts - Proteome | |
| 17. TCGA Ovarian CompRef Samples JHU Proteome | |
| 18. CPTAC CCRCC Confirmatory Study - DIA Intact Glycoproteome | |
| 19. CPTAC CCRCC Confirmatory Study - DIA Phosphoproteome | |
| 20. CPTAC GBM Discovery Study - Metabolome | |
| 21. CPTAC CCRCC Discovery Study - DIA Proteome | |
| 22. CPTAC PDA Discovery Study - DIA Proteome | |
| 23. CPTAC PDAC Proteins in Serum - Proteome | |
| 24. VU Normal Colon Epithelium - Proteome | |
| 25. CPTAC GBM Discovery Study - Lipidome | |
| 26. CPTAC CCRCC Confirmatory Study - Validation - Metabolome | |
| 27. CPTAC Deep Proteomics 2D-DIA | |
| 28. CPTAC PDAC Proteins in Serum - Glycoproteome | |
| 29. TCGA Colorectal Cancer CompRef Samples - Proteome | |
| 30. CPTAC CCRCC Confirmatory Study - Intratumor Heterogeneity - DIA Proteome | |
| 31. CPTAC CCRCC Confirmatory Study - Training - Metabolome | |
| 32. guo_kidney ST25730263 | |
| 33. CPTAC non-ccRCC Study - Metabolome | |
| 34. CPTAC GBM Confirmatory Study - Metabolome | |
| 35. TCGA COAD Proteome S016-1 | |
| 36. Prospective COAD Proteome S037-1 | |
| 37. CPTAC CCRCC Confirmatory Study - Kinase Inhibition - DIA Phosphoproteome | |
| 38. CPTAC GBM Confirmatory Study - Lipidome | |
| 39. CPTAC CCRCC Confirmatory Study - DIA Proteome | |
| 40. CPTAC PDAC BioTExt - Proteome | |
| 41. Broad Institute - Medulloblastoma - Phosphoproteome | |
| 42. AML Ex Vivo Drug Response - Sorafenib Treatment - Phosphoproteome | |
| 43. Broad Institute - Medulloblastoma - Phospho-tyrosine-enrichments | |
| 44. NCI-7 Cell Line Panel - Phosphoproteome | |
| 45. AML Ex Vivo Drug Response - Primary Cohort - Proteome | |
| 46. CPTAC LUAD Discovery Study - Acetylome | |
| 47. CPTAC LUAD Discovery Study - CompRef Acetylome | |
| 48. CPTAC UCEC Discovery Study - Acetylome | |
| 49. CPTAC UCEC Discovery Study - CompRef Acetylome | |
| 50. Sampling techniques for enrichment of PDAC - Proteome | |
| 51. KU PDAC Discovery Study - Global proteome | |
| 52. Prospective Ovarian JHU Intact Glycoproteome | |
| 53. NCI-7 Cell Line Panel - Proteome | |
| 54. Prospective Ovarian JHU Proteome v2 | |
| 55. KU PDAC Discovery Study - Phosphoproteome | |
| 56. NCI-7 Cell Line Panel Experimental Application - Proteome | |
| 57. Microscaled Proteogenomic Methods for Precision Oncology PDX bulk - Phosphoproteome | |
| 58. AML Ex Vivo Drug Response - Sorafenib Treatment - Proteome | |
| 59. Reproducible Proteome and Phosphoproteome Workflow JHU - Phosphoproteome | |
| 60. Microscaled Proteogenomic Methods for Precision Oncology PDX bulk - Proteome | |
| 61. Broad Institute - Medulloblastoma - Proteome | |
| 62. Academia Sinica LUAD100-Phosphoproteome | |
| 63. NCC iCC - Proteome | |
| 64. Academia Sinica LUAD100-Proteome v2 | |
| 65. Prospective Breast BI Acetylome | |
| 66. Prospective Breast BI - CompRef Acetylome | |
| 67. Prospective Breast BI Proteome v2 | |
| 68. Prospective Breast BI - CompRef Proteome | |
| 69. Prospective Breast BI Phosphoproteome v2 | |
| 70. Prospective Breast BI - CompRef Phosphoproteome | |
| 71. Reproducible Proteome and Phosphoproteome Workflow PNNL - Phosphoproteome | |
| 72. Prospective COAD Proteome S037-2 | |
| 73. Prospective COAD Phosphoproteome S037-3 | |
| 74. Prospective OV Proteome S038-2 | |
| 75. Prospective OV Phosphoproteome S038-3 | |
| 76. Microscaled Proteogenomic Methods for Precision Oncology PDX cores - Phosphoproteome | |
| 77. UCEC Discovery - Proteome S043-1 | |
| 78. UCEC Discovery - Phosphoproteome S043-2 | |
| 79. AML Ex Vivo Drug Response - Primary Cohort - Phosphoproteome | |
| 80. Reproducible Proteome and Phosphoproteome Workflow PNNL - Proteome | |
| 81. Broad Institute - Medulloblastoma - Acetylome | |
| 82. Reproducible Proteome and Phosphoproteome Workflow BI - Phosphoproteome | |
| 83. CPTAC CCRCC Discovery Study - Proteome S044-1 | |
| 84. CPTAC CCRCC Discovery Study - Phosphoproteome S044-2 | |
| 85. UCEC Discovery - CompRef Proteome S043-1 | |
| 86. UCEC Discovery - CompRef Phosphoproteome S043-2 | |
| 87. CPTAC CCRCC Discovery Study - CompRef Proteome S044-1 | |
| 88. CPTAC CCRCC Discovery Study - CompRef Phosphoproteome S044-2 | |
| 89. CPTAC LUAD Discovery Study - CompRef Proteome | |
| 90. CPTAC LUAD Discovery Study - CompRef Phosphoproteome | |
| 91. Reproducible Proteome and Phosphoproteome Workflow BI - Proteome | |
| 92. CPTAC CCRCC Discovery Study - Intact Glycoproteome | |
| 93. Microscaled Proteogenomic Methods for Precision Oncology PDX cores - Proteome | |
| 94. NCC iCC - Phosphoproteome | |
| 95. Reproducible Proteome and Phosphoproteome Workflow JHU - Proteome | |
| 96. Prospective Ovarian JHU N-linked Glycosite-containing peptide v2 | |
| 97. CPTAC LUAD Discovery Study - Proteome | |
| 98. CPTAC LUAD Discovery Study - Phosphoproteome | |
| 99. AML Gilteritinib Resistance - Proteome | |
| 100. CPTAC UCEC Confirmatory Study - Phosphoproteome | |
| 101. CPTAC GBM Discovery Study - CompRef Phosphoproteome | |
| 102. CPTAC UCEC Confirmatory Study - Glycoproteome | |
| 103. CPTAC GBM Confirmatory Study - CompRef Proteome | |
| 104. CPTAC LSCC Discovery Study - Phosphoproteome | |
| 105. CPTAC LSCC Discovery Study - Acetylome | |
| 106. CPTAC LSCC Discovery Study - Proteome | |
| 107. PTRC HGSOC Frozen Validation - Proteome | |
| 108. PTRC HGSOC FFPE Discovery - Phosphoproteome | |
| 109. CPTAC PDA Discovery Study - Proteome | |
| 110. PTRC TNBC - Phosphoproteome | |
| 111. PTRC HGSOC FFPE Validation - Phosphoproteome | |
| 112. Microscaled Proteogenomic Methods for Precision Oncology DP1 Clinical Trial - Phosphoproteome | |
| 113. CPTAC UCEC Confirmatory Study - CompRef Phosphoproteome | |
| 114. AML Ex Vivo Drug Response - Combination Treatment - Phosphoproteome | |
| 115. AML Gilteritinib Resistance - Phosphoproteome | |
| 116. HBV-Related Hepatocellular Carcinoma - Phosphoproteome | |
| 117. AML Gilteritinib TimeCourse - Proteome | |
| 118. CPTAC UCEC Confirmatory Study - Proteome | |
| 119. Beat AML Baseline Clinical - Proteome | |
| 120. CPTAC GBM Confirmatory Study - Glycoproteome | |
| 121. CPTAC GBM Confirmatory Study - CompRef Acetylome | |
| 122. CPTAC UCEC Confirmatory Study - CompRef Acetylome | |
| 123. KNCC Glioblastoma Evolution - Proteome | |
| 124. CPTAC GBM Confirmatory Study - Acetylome | |
| 125. Pediatric Brain Cancer Pilot Study - Phosphoproteome | |
| 126. Pediatric Brain Cancer Pilot Study - Proteome | |
| 127. Beat AML Baseline Clinical - Phosphoproteome | |
| 128. AML Quizartinib Resistance - Proteome | |
| 129. CPTAC UCEC Confirmatory Study - CompRef Proteome | |
| 130. CPTAC PDA Discovery Study - Phosphoproteome | |
| 131. CPTAC UCEC Confirmatory Study - Acetylome | |
| 132. CPTAC non-ccRCC Study - Proteome | |
| 133. PTRC HGSOC Frozen Validation - Phosphoproteome | |
| 134. AML Quizartinib Resistance - Phosphoproteome | |
| 135. APOLLO LUAD - Phosphoproteome - FeNTA | |
| 136. CPTAC HNSCC Discovery Study - Proteome | |
| 137. CPTAC HNSCC Discovery Study - Phosphoproteome | |
| 138. PTRC HGSOC FFPE Discovery - Proteome | |
| 139. PTRC TNBC - Proteome | |
| 140. CPTAC GBM Discovery Study - CompRef Proteome | |
| 141. PTRC HGSOC FFPE Validation - Proteome | |
| 142. CPTAC GBM Confirmatory Study - CompRef Phosphoproteome | |
| 143. PTRC TNBC PDX - Proteome | |
| 144. CPTAC GBM Confirmatory Study - Proteome | |
| 145. CPTAC LSCC Discovery Study - Ubiquitylome | |
| 146. CPTAC GBM Discovery Study - Acetylome | |
| 147. CPTAC GBM Discovery Study - CompRef Acetylome | |
| 148. APOLLO LUAD - Proteome | |
| 149. APOLLO LUAD - Phosphoproteome - TiO2 | |
| 150. Microscaled Proteogenomic Methods for Precision Oncology DP1 Clinical Trial - Proteome | |
| 151. CPTAC GBM Discovery Study - Proteome | |
| 152. AML Gilteritinib TimeCourse - Phosphoproteome | |
| 153. AML Ex Vivo Drug Response - Combination Treatment - Proteome | |
| 154. CPTAC non-ccRCC Study - Intact Glycoproteome | |
| 155. CPTAC non-ccRCC Study - Phosphoproteome | |
| 156. CPTAC GBM Confirmatory Study - Phosphoproteome | |
| 157. KNCC Glioblastoma Evolution - Phosphoproteome | |
| 158. CPTAC GBM Discovery Study - Phosphoproteome | |
| 159. HBV-Related Hepatocellular Carcinoma - Proteome | |
| 160. CPTAC PDA Discovery Study - Intact Glycoproteome | |
| 161. Georgetown Lung Cancer Proteomics Study | |
| 162. Buparlisib Treated Xenograft Tumors of TNBC - Proteome | |
| 163. Buparlisib Treated Xenograft Tumors of TNBC - Phosphoproteome | |
| >>> Find patient cases with tumor samples with both processed protein abundance data (this does not need to include abundance data for phosphorylated or acetylated forms) and genomic data for identifying single nucleotide polymorphisms (either whole genome sequencing data or genotyping array data). Download metadata about these patient cases, specimens, processed protein abundance data files, and genomic data files. | |
| thought: To fulfill this request, I need to use the Proteomic Data Commons API to find patient cases with the specified criteria and download the relevant metadata and data files. | |
| tool: AdhocApi.use_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'goal': 'Find patient cases with tumor samples that have both processed protein abundance data and genomic data for identifying single nucleotide | |
| polymorphisms. Download metadata about these patient cases, specimens, processed protein abundance data files, and genomic data files.'} | |
| INFO {'api': 'Proteomic Data Commons', 'goal': 'Find patient cases with tumor samples that have both processed protein abundance data and genomic data for identifying single nucleotide polymorphisms. Download metadata about these patient cases, specimens, processed protein abundance data files, and genomic data files.'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23690, min_total_token_count=32768. Running model without cache.'} | |
| INFO {'draft_code': 'import requests\nimport json\nimport os\n\n# Base URL for the Proteomic Data Commons GraphQL API\nbase_url = "https://pdc.cancer.gov/graphql"\n\n# GraphQL query to find cases with both proteomics and SNP data\nquery = """\n{\n cases {\n case_id\n samples {\n sample_id\n files {\n file_id\n data_category\n platform\n }\n }\n }\n}\n"""\n\n# Make the API request\nresponse = requests.post(base_url, json={"query": query})\n\n# Check for successful response\nif response.status_code == 200:\n data = json.loads(response.text)\n\n # Extract case and file information\n cases_with_both_data = []\n for case in data["data"]["cases"]:\n case_id = case["case_id"]\n samples = case["samples"]\n\n # Check if the case has both proteomics and SNP data\n has_proteomics = False\n has_snp = False\n for sample in samples:\n sample_id = sample["sample_id"]\n files = sample["files"]\n for file in files:\n if file["data_category"] == "Protein Expression Quantification" and file["platform"] == "Mass Spectrometry":\n has_proteomics = True\n if file["data_category"] == "Single Nucleotide Variation":\n has_snp = True\n\n # If both data types are found, add the case and sample info\n if has_proteomics and has_snp:\n cases_with_both_data.append({"case_id": case_id, "sample_id": sample_id})\n break # Move on to the next case once both data types are found\n\n # Print the cases with both data types\n print(f"Found {len(cases_with_both_data)} cases with both proteomics and SNP data:")\n for case in cases_with_both_data:\n print(case)\n\n # TODO: Add logic to download metadata and files for the identified cases and samples\n # For example, you can use the case_id and sample_id to query the API for more details\n # and use the file_id to download the actual data files.\n\nelse:\n print(f"Error: API request failed with status code {response.status_code}")'} | |
| INFO {'fixed_code': 'import requests\nimport json\nimport os\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Base URL for the Proteomic Data Commons GraphQL API\nbase_url = "https://pdc.cancer.gov/graphql"\n\n# GraphQL query to find cases with both proteomics and SNP data\nquery = """\n{\n cases {\n case_id\n samples {\n sample_id\n files {\n file_id\n data_category\n platform\n }\n }\n }\n}\n"""\n\n# Make the API request\nresponse = requests.post(base_url, json={"query": query})\n\n# Check for successful response\nif response.status_code == 200:\n data = json.loads(response.text)\n\n # Extract case and file information\n cases_with_both_data = []\n for case in data["data"]["cases"]:\n case_id = case["case_id"]\n samples = case["samples"]\n\n # Check if the case has both proteomics and SNP data\n has_proteomics = False\n has_snp = False\n for sample in samples:\n sample_id = sample["sample_id"]\n files = sample["files"]\n for file in files:\n if file["data_category"] == "Protein Expression Quantification" and file["platform"] == "Mass Spectrometry":\n has_proteomics = True\n if file["data_category"] == "Single Nucleotide Variation":\n has_snp = True\n\n # If both data types are found, add the case and sample info\n if has_proteomics and has_snp:\n cases_with_both_data.append({"case_id": case_id, "sample_id": sample_id})\n break # Move on to the next case once both data types are found\n\n # Print the cases with both data types\n print(f"Found {len(cases_with_both_data)} cases with both proteomics and SNP data:")\n for case in cases_with_both_data:\n print(case)\n\n # TODO: Add logic to download metadata and files for the identified cases and samples\n # For example, you can use the case_id and sample_id to query the API for more details\n # and use the file_id to download the actual data files.\n\nelse:\n print(f"Error: API request failed with status code {response.status_code}")'} | |
| INFO {'info': 'directly returning code'} | |
| observation: import requests | |
| import json | |
| import os | |
| # Ensure the data directory exists | |
| os.makedirs('./data/', exist_ok=True) | |
| # Base URL for the Proteomic Data Commons GraphQL API | |
| base_url = "https://pdc.cancer.gov/graphql" | |
| # GraphQL query to find cases with both proteomics and SNP data | |
| query = """ | |
| { | |
| cases { | |
| case_id | |
| samples { | |
| sample_id | |
| files { | |
| file_id | |
| data_category | |
| platform | |
| } | |
| } | |
| } | |
| } | |
| """ | |
| # Make the API request | |
| response = requests.post(base_url, json={"query": query}) | |
| # Check for successful response | |
| if response.status_code == 200: | |
| data = json.loads(response.text) | |
| # Extract case and file information | |
| cases_with_both_data = [] | |
| for case in data["data"]["cases"]: | |
| case_id = case["case_id"] | |
| samples = case["samples"] | |
| # Check if the case has both proteomics and SNP data | |
| has_proteomics = False | |
| has_snp = False | |
| for sample in samples: | |
| sample_id = sample["sample_id"] | |
| files = sample["files"] | |
| for file in files: | |
| if file["data_category"] == "Protein Expression Quantification" and file["platform"] == "Mass Spectrometry": | |
| has_proteomics = True | |
| if file["data_category"] == "Single Nucleotide Variation": | |
| has_snp = True | |
| # If both data types are found, add the case and sample info | |
| if has_proteomics and has_snp: | |
| cases_with_both_data.append({"case_id": case_id, "sample_id": sample_id}) | |
| break # Move on to the next case once both data types are found | |
| # Print the cases with both data types | |
| print(f"Found {len(cases_with_both_data)} cases with both proteomics and SNP data:") | |
| for case in cases_with_both_data: | |
| print(case) | |
| # TODO: Add logic to download metadata and files for the identified cases and samples | |
| # For example, you can use the case_id and sample_id to query the API for more details | |
| # and use the file_id to download the actual data files. | |
| else: | |
| print(f"Error: API request failed with status code {response.status_code}") | |
| thought: I have drafted and executed the request to find patient cases with both processed protein abundance data and genomic data for identifying single nucleotide polymorphisms. Now, I will | |
| run the code to get the results. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport json\nimport os\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Base URL for the Proteomic Data Commons | |
| GraphQL API\nbase_url = "https://pdc.cancer.gov/graphql"\n\n# GraphQL query to find cases with both proteomics and SNP data\nquery = """\n{\n cases {\n case_id\n samples {\n | |
| sample_id\n files {\n file_id\n data_category\n platform\n }\n }\n }\n}\n"""\n\n# Make the API request\nresponse = requests.post(base_url, json={"query": | |
| query})\n\n# Check for successful response\nif response.status_code == 200:\n data = json.loads(response.text)\n\n # Extract case and file information\n cases_with_both_data = []\n | |
| for case in data["data"]["cases"]:\n case_id = case["case_id"]\n samples = case["samples"]\n\n # Check if the case has both proteomics and SNP data\n | |
| has_proteomics = False\n has_snp = False\n for sample in samples:\n sample_id = sample["sample_id"]\n files = sample["files"]\n for file in | |
| files:\n if file["data_category"] == "Protein Expression Quantification" and file["platform"] == "Mass Spectrometry":\n has_proteomics = True\n | |
| if file["data_category"] == "Single Nucleotide Variation":\n has_snp = True\n\n # If both data types are found, add the case and sample info\n if | |
| has_proteomics and has_snp:\n cases_with_both_data.append({"case_id": case_id, "sample_id": sample_id})\n break # Move on to the next case once both data types | |
| are found\n\n # Print the cases with both data types\n print(f"Found {len(cases_with_both_data)} cases with both proteomics and SNP data:")\n for case in cases_with_both_data:\n | |
| print(case)\n\n # TODO: Add logic to download metadata and files for the identified cases and samples\n # For example, you can use the case_id and sample_id to query the API for more | |
| details\n # and use the file_id to download the actual data files.\n\nelse:\n print(f"Error: API request failed with status code {response.status_code}")'} | |
| observation: Error: API request failed with status code 400 | |
| thought: The API request failed with a status code 400, indicating a bad request. I need to investigate the query and correct any issues. | |
| tool: AdhocApi.ask_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'query': 'What is the correct GraphQL query format to find patient cases with both processed protein abundance data and genomic data for | |
| identifying single nucleotide polymorphisms?'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23617, min_total_token_count=32768. Running model without cache.'} | |
| observation: The provided schema doesn't contain enough information to directly answer your request. In order to construct a query that satisfies your request, more information is required | |
| regarding how protein abundance data and genomic data are linked to the case id. For instance, we need to know what fields or object types connect these data types. | |
| However, I can outline a general approach and provide a template query assuming some hypothetical connections: | |
| **Assumptions:** | |
| * **Data Category Connection:** Let's assume there is a field `data_categories` within the `uiCaseSummary` object that lists the data categories available for a case. | |
| * **Data Category Values:** And, the values within `data_categories` could be "Proteomic Profiling" for processed protein abundance and "Single Nucleotide Variation" for SNP data. | |
| **Template Query:** | |
| ```graphql | |
| query FilteredStudiesData($case_id: String!, $source: String!) { | |
| uiCaseSummary(case_id: $case_id, source: $source) { | |
| case_id | |
| data_categories # Hypothetical field to retrieve data categories | |
| # ... other fields you need | |
| } | |
| } | |
| ``` | |
| **Logic:** | |
| 1. **Query `uiCaseSummary`:** We start with the `uiCaseSummary` object, as it seems to hold comprehensive information about a case. | |
| 2. **Retrieve `data_categories`:** This hypothetical field would return a list of available data categories for the given case. | |
| 3. **Filter in your application:** You would then need to process the results of this query in your Python application. Iterate through the returned `data_categories` and identify cases that | |
| have both "Proteomic Profiling" and "Single Nucleotide Variation". | |
| **To refine this query:** | |
| * **Provide more context:** Explain how protein abundance data and SNP data are structured and linked in the API. Are there specific file types, object relationships, or other fields that | |
| indicate this information? | |
| * **Specify desired fields:** Let me know what specific data points you need from cases matching your criteria. | |
| Once I have more information, I can help you craft a more precise and effective GraphQL query. | |
| thought: The API response indicates that I need more information about how protein abundance data and genomic data are linked to the case ID in the Proteomic Data Commons API. I will ask the | |
| user for more context. | |
| tool: ask_user | |
| tool_input: {'query': 'Could you provide more context on how protein abundance data and genomic data are structured and linked in the API? Are there specific file types, object relationships, | |
| or other fields that indicate this information?'} | |
| Could you provide more context on how protein abundance data and genomic data are structured and linked in the API? Are there specific file types, object relationships, or other fields that indicate this information? $ I don't know. have the api look up possible fields | |
| observation: I don't know. have the api look up possible fields | |
| thought: I will ask the API to provide information about possible fields related to protein abundance data and genomic data in the Proteomic Data Commons API. | |
| tool: AdhocApi.ask_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'query': 'What are the possible fields related to protein abundance data and genomic data in the API schema?'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23617, min_total_token_count=32768. Running model without cache.'} | |
| observation: The PDC API, as it's currently presented in the schema, focuses heavily on clinical and file-related data rather than directly providing protein abundance or genomic data points. | |
| However, there are fields that give clues about how to access related information: | |
| **For Protein Abundance:** | |
| * **`spectral_counts` within the `uiProtein` and `uiGeneSpectralCount` queries:** These fields seem to provide spectral count data, a proxy for protein abundance, linked to specific genes or | |
| proteins. You can query based on gene name or protein name. | |
| * Example fields within `spectral_counts`: | |
| * `project_submitter_id`: Identifies the project. | |
| * `plex`: Multiplexing information. | |
| * `spectral_count`: The actual spectral count value. | |
| * `distinct_peptide`: Number of distinct peptides identified. | |
| * `unshared_peptide`: Number of unshared peptides identified. | |
| **For Genomic Data:** | |
| * The API schema itself **doesn't directly expose genomic data** like mutation or expression profiles. | |
| * **`file_name` and `file_type` within various file queries:** You can potentially filter for files containing genomic data based on their names or types (e.g., "vcf" for variant files, "bam" | |
| for alignment files). However, you'd then need to download and analyze these files separately. | |
| **To get a more definitive answer on accessing specific protein abundance or genomic data points, you might need to:** | |
| * **Explore the PDC website:** Check for documentation beyond the provided schema. | |
| * **Contact the PDC support:** They can provide the most accurate guidance on data availability and access methods. | |
| Let me know if you have other questions or would like me to draft example queries related to the existing protein abundance fields. | |
| thought: The API schema provides some fields related to protein abundance and genomic data. I will draft a query to explore these fields further and identify cases with both data types. | |
| tool: AdhocApi.use_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'goal': 'Draft a query to explore fields related to protein abundance (using spectral counts) and genomic data (using file names and types) to | |
| identify cases with both data types.'} | |
| INFO {'api': 'Proteomic Data Commons', 'goal': 'Draft a query to explore fields related to protein abundance (using spectral counts) and genomic data (using file names and types) to identify cases with both data types.'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23690, min_total_token_count=32768. Running model without cache.'} | |
| INFO {'draft_code': 'import requests\nimport json\nimport pandas as pd\nimport os\n\ndef pdc_graphql_query(query_text):\n """\n This function sends a GraphQL query to the PDC API and returns the JSON response.\n\n Args:\n query_text (str): The GraphQL query.\n\n Returns:\n dict: The JSON response from the PDC API.\n """\n\n api_url = "https://pdc.cancer.gov/graphql"\n response = requests.post(api_url, json={\'query\': query_text})\n\n if response.status_code == 200:\n return response.json()\n else:\n raise Exception(f"Query failed with code {response.status_code}: {response.text}")\n\n# GraphQL query to fetch protein abundance (spectral counts) and genomic data\nquery = """{\n cases {\n case_id\n samples {\n sample_id\n files {\n file_id\n file_name\n data_category\n }\n }\n geneSpectralCounts {\n gene_id\n spectral_counts {\n spectral_count\n }\n }\n }\n}\n"""\n\n# Execute the query\nresponse = pdc_graphql_query(query)\n\n# Extract data and create DataFrames\ncases_df = pd.json_normalize(response[\'data\'][\'cases\'])\n\n# Explode files and spectral counts to get individual rows per file and gene\ncases_df = cases_df.explode(\'samples\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'samples\'].apply(pd.Series)], axis=1).drop(\'samples\', axis=1)\ncases_df = cases_df.explode(\'files\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'files\'].apply(pd.Series)], axis=1).drop(\'files\', axis=1)\ncases_df = cases_df.explode(\'geneSpectralCounts\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'geneSpectralCounts\'].apply(pd.Series)], axis=1).drop(\'geneSpectralCounts\', axis=1)\n\n# Filter for cases with both genomic and proteomic data\ncases_with_both_data = cases_df[cases_df[\'data_category\'].notna() & cases_df[\'spectral_count\'].notna()]\n\n# Print or process the results\nprint(cases_with_both_data[[\'case_id\', \'sample_id\', \'file_id\', \'file_name\', \'data_category\', \'gene_id\', \'spectral_count\']])'} | |
| INFO {'fixed_code': 'import requests\nimport pandas as pd\nimport os\n\ndef pdc_graphql_query(query_text):\n """\n This function sends a GraphQL query to the PDC API and returns the JSON response.\n\n Args:\n query_text (str): The GraphQL query.\n\n Returns:\n dict: The JSON response from the PDC API.\n """\n\n api_url = "https://pdc.cancer.gov/graphql"\n response = requests.post(api_url, json={\'query\': query_text})\n\n if response.status_code == 200:\n return response.json()\n else:\n raise Exception(f"Query failed with code {response.status_code}: {response.text}")\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# GraphQL query to fetch protein abundance (spectral counts) and genomic data\nquery = """{\n cases {\n case_id\n samples {\n sample_id\n files {\n file_id\n file_name\n data_category\n }\n }\n geneSpectralCounts {\n gene_id\n spectral_counts {\n spectral_count\n }\n }\n }\n}\n"""\n\n# Execute the query\nresponse = pdc_graphql_query(query)\n\n# Extract data and create DataFrames\ncases_df = pd.json_normalize(response[\'data\'][\'cases\'])\n\n# Explode files and spectral counts to get individual rows per file and gene\ncases_df = cases_df.explode(\'samples\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'samples\'].apply(pd.Series)], axis=1).drop(\'samples\', axis=1)\ncases_df = cases_df.explode(\'files\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'files\'].apply(pd.Series)], axis=1).drop(\'files\', axis=1)\ncases_df = cases_df.explode(\'geneSpectralCounts\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'geneSpectralCounts\'].apply(pd.Series)], axis=1).drop(\'geneSpectralCounts\', axis=1)\n\n# Filter for cases with both genomic and proteomic data\ncases_with_both_data = cases_df[cases_df[\'data_category\'].notna() & cases_df[\'spectral_count\'].notna()]\n\n# Print or process the results\nprint(cases_with_both_data[[\'case_id\', \'sample_id\', \'file_id\', \'file_name\', \'data_category\', \'gene_id\', \'spectral_count\']])'} | |
| INFO {'info': 'directly returning code'} | |
| observation: import requests | |
| import pandas as pd | |
| import os | |
| def pdc_graphql_query(query_text): | |
| """ | |
| This function sends a GraphQL query to the PDC API and returns the JSON response. | |
| Args: | |
| query_text (str): The GraphQL query. | |
| Returns: | |
| dict: The JSON response from the PDC API. | |
| """ | |
| api_url = "https://pdc.cancer.gov/graphql" | |
| response = requests.post(api_url, json={'query': query_text}) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| raise Exception(f"Query failed with code {response.status_code}: {response.text}") | |
| # Ensure the data directory exists | |
| os.makedirs('./data/', exist_ok=True) | |
| # GraphQL query to fetch protein abundance (spectral counts) and genomic data | |
| query = """{ | |
| cases { | |
| case_id | |
| samples { | |
| sample_id | |
| files { | |
| file_id | |
| file_name | |
| data_category | |
| } | |
| } | |
| geneSpectralCounts { | |
| gene_id | |
| spectral_counts { | |
| spectral_count | |
| } | |
| } | |
| } | |
| } | |
| """ | |
| # Execute the query | |
| response = pdc_graphql_query(query) | |
| # Extract data and create DataFrames | |
| cases_df = pd.json_normalize(response['data']['cases']) | |
| # Explode files and spectral counts to get individual rows per file and gene | |
| cases_df = cases_df.explode('samples', ignore_index=True) | |
| cases_df = pd.concat([cases_df, cases_df['samples'].apply(pd.Series)], axis=1).drop('samples', axis=1) | |
| cases_df = cases_df.explode('files', ignore_index=True) | |
| cases_df = pd.concat([cases_df, cases_df['files'].apply(pd.Series)], axis=1).drop('files', axis=1) | |
| cases_df = cases_df.explode('geneSpectralCounts', ignore_index=True) | |
| cases_df = pd.concat([cases_df, cases_df['geneSpectralCounts'].apply(pd.Series)], axis=1).drop('geneSpectralCounts', axis=1) | |
| # Filter for cases with both genomic and proteomic data | |
| cases_with_both_data = cases_df[cases_df['data_category'].notna() & cases_df['spectral_count'].notna()] | |
| # Print or process the results | |
| print(cases_with_both_data[['case_id', 'sample_id', 'file_id', 'file_name', 'data_category', 'gene_id', 'spectral_count']]) | |
| thought: I have drafted the query to explore fields related to protein abundance and genomic data. Now, I will run the code to get the results. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport pandas as pd\nimport os\n\ndef pdc_graphql_query(query_text):\n """\n This function sends a GraphQL query to the PDC API and returns the | |
| JSON response.\n\n Args:\n query_text (str): The GraphQL query.\n\n Returns:\n dict: The JSON response from the PDC API.\n """\n\n api_url = | |
| "https://pdc.cancer.gov/graphql"\n response = requests.post(api_url, json={\'query\': query_text})\n\n if response.status_code == 200:\n return response.json()\n else:\n | |
| raise Exception(f"Query failed with code {response.status_code}: {response.text}")\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# GraphQL query to fetch | |
| protein abundance (spectral counts) and genomic data\nquery = """{\n cases {\n case_id\n samples {\n sample_id\n files {\n file_id\n file_name\n | |
| data_category\n }\n }\n geneSpectralCounts {\n gene_id\n spectral_counts {\n spectral_count\n }\n }\n }\n}\n"""\n\n# Execute the query\nresponse = | |
| pdc_graphql_query(query)\n\n# Extract data and create DataFrames\ncases_df = pd.json_normalize(response[\'data\'][\'cases\'])\n\n# Explode files and spectral counts to get individual rows per | |
| file and gene\ncases_df = cases_df.explode(\'samples\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'samples\'].apply(pd.Series)], axis=1).drop(\'samples\', | |
| axis=1)\ncases_df = cases_df.explode(\'files\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'files\'].apply(pd.Series)], axis=1).drop(\'files\', axis=1)\ncases_df = | |
| cases_df.explode(\'geneSpectralCounts\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'geneSpectralCounts\'].apply(pd.Series)], axis=1).drop(\'geneSpectralCounts\', | |
| axis=1)\n\n# Filter for cases with both genomic and proteomic data\ncases_with_both_data = cases_df[cases_df[\'data_category\'].notna() & cases_df[\'spectral_count\'].notna()]\n\n# Print or | |
| process the results\nprint(cases_with_both_data[[\'case_id\', \'sample_id\', \'file_id\', \'file_name\', \'data_category\', \'gene_id\', \'spectral_count\']])'} | |
| error: error running tool "PythonTool.run": Query failed with code 400: {"errors":[{"message":"Cannot query field \"cases\" on type \"Query\". Did you mean \"case\", \"allCases\", or | |
| \"uiCase\"?","extensions":{"code":"GRAPHQL_VALIDATION_FAILED","exception":{"stacktrace":["GraphQLError: Cannot query field \"cases\" on type \"Query\". Did you mean \"case\", \"allCases\", or | |
| \"uiCase\"?"," at Object.Field (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/rules/FieldsOnCorrectTypeRule.js:48:31)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:323:29)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/utilities/TypeInfo.js:370:25)"," at visit (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:243:26)"," at | |
| validate (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/validate.js:69:24)"," at validate | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:477:27)"," at processGraphQLRequest | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:267:32)"," at runMicrotasks (<anonymous>)"," at processTicksAndRejections | |
| (internal/process/task_queues.js:95:5)"," at processHTTPRequest (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/runHttpQuery.ts:437:24)"]}}}]} | |
| thought: The error indicates that the field "cases" is not valid in the query. I need to correct the query by using a valid field such as "case", "allCases", or "uiCase". | |
| tool: AdhocApi.ask_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'query': 'What is the correct field to use for querying case information in the API? Should I use "case", "allCases", or "uiCase"?'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23617, min_total_token_count=32768. Running model without cache.'} | |
| observation: The correct field to use for querying a single case in the PDC API is **`uiCase`**. | |
| Here's why: | |
| * **`case`**: This field doesn't seem to exist in the provided schema. | |
| * **`allCases`**: While this field likely exists (though not in the provided documentation), it would probably be used to retrieve a list of cases, not information about a specific case. | |
| * **`uiCase`**: This field specifically targets a single case based on provided parameters like `case_id`, `case_submitter_id`, and `source`. This makes it the appropriate choice for | |
| querying information about a particular case. | |
| **Example:** | |
| ```graphql | |
| query CaseSummaryDataQuery($case_id: String!, $case_submitter_id: String!, $source: String!){ | |
| uiCase (case_id: $case_id, case_submitter_id: $case_submitter_id, source: $source) { | |
| # ... fields you want to retrieve about the case | |
| } | |
| } | |
| ``` | |
| Let me know if you'd like me to draft a Python script to make this query using the `requests` library! | |
| thought: I will correct the query by using the "uiCase" field to retrieve case information and execute the query again. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport pandas as pd\nimport os\n\n# Function to send a GraphQL query to the PDC API\n\ndef pdc_graphql_query(query_text):\n api_url = | |
| "https://pdc.cancer.gov/graphql"\n response = requests.post(api_url, json={\'query\': query_text})\n\n if response.status_code == 200:\n return response.json()\n else:\n | |
| raise Exception(f"Query failed with code {response.status_code}: {response.text}")\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# GraphQL query to fetch | |
| protein abundance (spectral counts) and genomic data\nquery = """\n{\n uiCase {\n case_id\n samples {\n sample_id\n files {\n file_id\n file_name\n | |
| data_category\n }\n }\n geneSpectralCounts {\n gene_id\n spectral_counts {\n spectral_count\n }\n }\n }\n}\n"""\n\n# Execute the query\nresponse = | |
| pdc_graphql_query(query)\n\n# Extract data and create DataFrames\ncases_df = pd.json_normalize(response[\'data\'][\'uiCase\'])\n\n# Explode files and spectral counts to get individual rows | |
| per file and gene\ncases_df = cases_df.explode(\'samples\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'samples\'].apply(pd.Series)], axis=1).drop(\'samples\', | |
| axis=1)\ncases_df = cases_df.explode(\'files\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'files\'].apply(pd.Series)], axis=1).drop(\'files\', axis=1)\ncases_df = | |
| cases_df.explode(\'geneSpectralCounts\', ignore_index=True)\ncases_df = pd.concat([cases_df, cases_df[\'geneSpectralCounts\'].apply(pd.Series)], axis=1).drop(\'geneSpectralCounts\', | |
| axis=1)\n\n# Filter for cases with both genomic and proteomic data\ncases_with_both_data = cases_df[cases_df[\'data_category\'].notna() & cases_df[\'spectral_count\'].notna()]\n\n# Print or | |
| process the results\nprint(cases_with_both_data[[\'case_id\', \'sample_id\', \'file_id\', \'file_name\', \'data_category\', \'gene_id\', \'spectral_count\']])'} | |
| error: error running tool "PythonTool.run": Query failed with code 400: {"errors":[{"message":"Cannot query field \"samples\" on type \"UICase\". Did you mean | |
| \"sample_id\"?","extensions":{"code":"GRAPHQL_VALIDATION_FAILED","exception":{"stacktrace":["GraphQLError: Cannot query field \"samples\" on type \"UICase\". Did you mean \"sample_id\"?"," | |
| at Object.Field (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/rules/FieldsOnCorrectTypeRule.js:48:31)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:323:29)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/utilities/TypeInfo.js:370:25)"," at visit (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:243:26)"," at | |
| validate (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/validate.js:69:24)"," at validate | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:477:27)"," at processGraphQLRequest | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:267:32)"," at runMicrotasks (<anonymous>)"," at processTicksAndRejections | |
| (internal/process/task_queues.js:95:5)"," at processHTTPRequest (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/runHttpQuery.ts:437:24)"]}}},{"message":"Cannot query | |
| field \"geneSpectralCounts\" on type \"UICase\".","extensions":{"code":"GRAPHQL_VALIDATION_FAILED","exception":{"stacktrace":["GraphQLError: Cannot query field \"geneSpectralCounts\" on type | |
| \"UICase\"."," at Object.Field (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/rules/FieldsOnCorrectTypeRule.js:48:31)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:323:29)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/utilities/TypeInfo.js:370:25)"," at visit (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:243:26)"," at | |
| validate (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/validate.js:69:24)"," at validate | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:477:27)"," at processGraphQLRequest | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:267:32)"," at runMicrotasks (<anonymous>)"," at processTicksAndRejections | |
| (internal/process/task_queues.js:95:5)"," at processHTTPRequest (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/runHttpQuery.ts:437:24)"]}}}]} | |
| thought: The error indicates that the fields "samples" and "geneSpectralCounts" are not valid for the "UICase" type. I need to ask the API for the correct fields to query for sample and | |
| spectral count information. | |
| tool: AdhocApi.ask_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'query': 'What are the correct fields to query for sample and spectral count information in the UICase type?'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23617, min_total_token_count=32768. Running model without cache.'} | |
| observation: The `UICase` type itself doesn't directly contain sample and spectral count information. You'll need to traverse through related fields to access that data. | |
| Here's a breakdown: | |
| 1. **Samples:** The `UICase` type has a field called `samples` which returns a list of `UISample` objects. | |
| 2. **Spectral Counts:** Spectral count information is associated with genes, not directly with samples or cases. To find spectral counts, you would use a different query type like | |
| `uiGeneSpectralCount`. | |
| You can find more information on the specific fields within the `UISample` type by referring to the provided schema. Look for `query SampleDataQuery` to see how to query for sample | |
| information. | |
| thought: I will draft a query using the correct fields to access sample and spectral count information through the UICase type and related queries. | |
| tool: AdhocApi.use_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'goal': 'Draft a query to access sample information through the UICase type and spectral count information using the uiGeneSpectralCount query.'} | |
| INFO {'api': 'Proteomic Data Commons', 'goal': 'Draft a query to access sample information through the UICase type and spectral count information using the uiGeneSpectralCount query.'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23690, min_total_token_count=32768. Running model without cache.'} | |
| INFO {'draft_code': 'import requests\nimport json\n\ndef get_case_and_spectral_counts(case_id, gene_name):\n """\n Gets case information and spectral counts for a given case ID and gene name.\n\n Args:\n case_id: The case ID to query.\n gene_name: The gene name to query.\n\n Returns:\n A dictionary containing the case information and spectral counts,\n or None if the query fails.\n """\n\n url = \'https://pdc.cancer.gov/graphql\'\n\n query = """\n query CaseAndSpectralCounts($case_id: String!, $gene_name: String!) {\n uiCase(case_id: $case_id) {\n case_id\n case_submitter_id\n project_name\n disease_type\n primary_site\n samples {\n sample_id\n sample_type\n }\n }\n uiGeneSpectralCount(gene_name: $gene_name) {\n gene_id\n gene_name\n spectral_counts {\n project_submitter_id\n plex\n spectral_count\n }\n }\n }\n """\n\n variables = {\n \'case_id\': case_id,\n \'gene_name\': gene_name,\n }\n\n response = requests.post(url, json={\'query\': query, \'variables\': variables})\n\n if response.status_code == 200:\n return json.loads(response.text)[\'data\']\n else:\n print(f"Query failed with code {response.status_code}: {response.text}")\n return None\n\n# Example usage:\ncase_id = "your_case_id" # Replace with the actual case ID\ngene_name = "EGFR" # Replace with the actual gene name\n\ndata = get_case_and_spectral_counts(case_id, gene_name)\n\nif data:\n print(json.dumps(data, indent=2))'} | |
| INFO {'fixed_code': 'import requests\nimport json\nimport os\n\ndef get_case_and_spectral_counts(case_id, gene_name):\n """\n Gets case information and spectral counts for a given case ID and gene name.\n\n Args:\n case_id: The case ID to query.\n gene_name: The gene name to query.\n\n Returns:\n A dictionary containing the case information and spectral counts,\n or None if the query fails.\n """\n\n url = \'https://pdc.cancer.gov/graphql\'\n\n query = """\n query CaseAndSpectralCounts($case_id: String!, $gene_name: String!) {\n uiCase(case_id: $case_id) {\n case_id\n case_submitter_id\n project_name\n disease_type\n primary_site\n samples {\n sample_id\n sample_type\n }\n }\n uiGeneSpectralCount(gene_name: $gene_name) {\n gene_id\n gene_name\n spectral_counts {\n project_submitter_id\n plex\n spectral_count\n }\n }\n }\n """\n\n variables = {\n \'case_id\': case_id,\n \'gene_name\': gene_name,\n }\n\n response = requests.post(url, json={\'query\': query, \'variables\': variables})\n\n if response.status_code == 200:\n return json.loads(response.text)[\'data\']\n else:\n print(f"Query failed with code {response.status_code}: {response.text}")\n return None\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Example usage:\ncase_id = "your_case_id" # Replace with the actual case ID\ngene_name = "EGFR" # Replace with the actual gene name\n\ndata = get_case_and_spectral_counts(case_id, gene_name)\n\nif data:\n print(json.dumps(data, indent=2))'} | |
| INFO {'info': 'directly returning code'} | |
| observation: import requests | |
| import json | |
| import os | |
| def get_case_and_spectral_counts(case_id, gene_name): | |
| """ | |
| Gets case information and spectral counts for a given case ID and gene name. | |
| Args: | |
| case_id: The case ID to query. | |
| gene_name: The gene name to query. | |
| Returns: | |
| A dictionary containing the case information and spectral counts, | |
| or None if the query fails. | |
| """ | |
| url = 'https://pdc.cancer.gov/graphql' | |
| query = """ | |
| query CaseAndSpectralCounts($case_id: String!, $gene_name: String!) { | |
| uiCase(case_id: $case_id) { | |
| case_id | |
| case_submitter_id | |
| project_name | |
| disease_type | |
| primary_site | |
| samples { | |
| sample_id | |
| sample_type | |
| } | |
| } | |
| uiGeneSpectralCount(gene_name: $gene_name) { | |
| gene_id | |
| gene_name | |
| spectral_counts { | |
| project_submitter_id | |
| plex | |
| spectral_count | |
| } | |
| } | |
| } | |
| """ | |
| variables = { | |
| 'case_id': case_id, | |
| 'gene_name': gene_name, | |
| } | |
| response = requests.post(url, json={'query': query, 'variables': variables}) | |
| if response.status_code == 200: | |
| return json.loads(response.text)['data'] | |
| else: | |
| print(f"Query failed with code {response.status_code}: {response.text}") | |
| return None | |
| # Ensure the data directory exists | |
| os.makedirs('./data/', exist_ok=True) | |
| # Example usage: | |
| case_id = "your_case_id" # Replace with the actual case ID | |
| gene_name = "EGFR" # Replace with the actual gene name | |
| data = get_case_and_spectral_counts(case_id, gene_name) | |
| if data: | |
| print(json.dumps(data, indent=2)) | |
| thought: I have drafted a query to access sample information through the UICase type and spectral count information using the uiGeneSpectralCount query. Now, I will run the code to get the | |
| results. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport json\nimport os\n\ndef get_case_and_spectral_counts(case_id, gene_name):\n """\n Gets case information and spectral counts for a given case | |
| ID and gene name.\n\n Args:\n case_id: The case ID to query.\n gene_name: The gene name to query.\n\n Returns:\n A dictionary containing the case information and | |
| spectral counts,\n or None if the query fails.\n """\n\n url = \'https://pdc.cancer.gov/graphql\'\n\n query = """\n query CaseAndSpectralCounts($case_id: String!, | |
| $gene_name: String!) {\n uiCase(case_id: $case_id) {\n case_id\n case_submitter_id\n project_name\n disease_type\n primary_site\n samples {\n | |
| sample_id\n sample_type\n }\n }\n uiGeneSpectralCount(gene_name: $gene_name) {\n gene_id\n gene_name\n spectral_counts {\n | |
| project_submitter_id\n plex\n spectral_count\n }\n }\n }\n """\n\n variables = {\n \'case_id\': case_id,\n \'gene_name\': gene_name,\n | |
| }\n\n response = requests.post(url, json={\'query\': query, \'variables\': variables})\n\n if response.status_code == 200:\n return json.loads(response.text)[\'data\']\n | |
| else:\n print(f"Query failed with code {response.status_code}: {response.text}")\n return None\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', | |
| exist_ok=True)\n\n# Example usage:\ncase_id = "your_case_id" # Replace with the actual case ID\ngene_name = "EGFR" # Replace with the actual gene name\n\ndata = | |
| get_case_and_spectral_counts(case_id, gene_name)\n\nif data:\n print(json.dumps(data, indent=2))'} | |
| observation: Query failed with code 400: {"errors":[{"message":"Cannot query field \"samples\" on type \"UICase\". Did you mean | |
| \"sample_id\"?","extensions":{"code":"GRAPHQL_VALIDATION_FAILED","exception":{"stacktrace":["GraphQLError: Cannot query field \"samples\" on type \"UICase\". Did you mean \"sample_id\"?"," | |
| at Object.Field (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/rules/FieldsOnCorrectTypeRule.js:48:31)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:323:29)"," at Object.enter | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/utilities/TypeInfo.js:370:25)"," at visit (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/language/visitor.js:243:26)"," at | |
| validate (/home/ec2-user/PDC_Build/graphql/node_modules/graphql/validation/validate.js:69:24)"," at validate | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:477:27)"," at processGraphQLRequest | |
| (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/requestPipeline.ts:267:32)"," at runMicrotasks (<anonymous>)"," at processTicksAndRejections | |
| (internal/process/task_queues.js:95:5)"," at processHTTPRequest (/home/ec2-user/PDC_Build/graphql/node_modules/apollo-server-core/src/runHttpQuery.ts:437:24)"]}}}]} | |
| thought: The error indicates that the field "samples" is not valid for the "UICase" type. I need to correct the query by using a valid field such as "sample_id". | |
| tool: AdhocApi.ask_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'query': 'What are the valid fields for querying sample information in the UICase type?'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23617, min_total_token_count=32768. Running model without cache.'} | |
| observation: The `UICase` type in this GraphQL schema does not directly return sample information as a field. However, it contains a field called `samples` which returns a list of `Sample` | |
| objects. | |
| To get sample information within the `UICase` type, you would query the fields within the `samples` list. Here's how the structure looks within the schema: | |
| ```graphql | |
| query CaseSummaryDataQuery($case_id: String!, $case_submitter_id: String!, $source: String!){ | |
| uiCase (case_id: $case_id, case_submitter_id: $case_submitter_id, source: $source) { | |
| ... other UICase fields ... | |
| samples{ | |
| sample_id | |
| gdc_sample_id | |
| gdc_project_id | |
| sample_submitter_id | |
| ... other Sample fields ... | |
| } | |
| ... other UICase fields ... | |
| } | |
| } | |
| ``` | |
| You can request any of the fields listed within the `samples` curly braces as they represent the fields within the `Sample` object. Refer to the complete schema to see all available `Sample` | |
| fields. | |
| thought: I will correct the query by using the valid fields within the samples list in the UICase type to access sample information. | |
| tool: AdhocApi.use_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'goal': 'Draft a query to access sample information using the valid fields within the samples list in the UICase type.'} | |
| INFO {'api': 'Proteomic Data Commons', 'goal': 'Draft a query to access sample information using the valid fields within the samples list in the UICase type.'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23690, min_total_token_count=32768. Running model without cache.'} | |
| INFO {'draft_code': 'import requests\nimport json\nimport pandas as pd\nimport os\n\ndef get_case_sample_info(case_submitter_id: str) -> pd.DataFrame:\n """\n This function takes a case_submitter_id and returns a pandas DataFrame containing\n information about the samples associated with that case.\n\n Args:\n case_submitter_id: The submitter ID of the case.\n\n Returns:\n A pandas DataFrame containing sample information.\n """\n\n url = \'https://pdc.cancer.gov/graphql\'\n query = """\n {\n uiCase(case_submitter_id: "%s") {\n samples {\n sample_id\n gdc_sample_id\n gdc_project_id\n sample_submitter_id\n sample_type\n status\n pool\n sample_is_ref\n biospecimen_anatomic_site\n biospecimen_laterality\n composition\n current_weight\n days_to_collection\n days_to_sample_procurement\n diagnosis_pathologically_confirmed\n freezing_method\n initial_weight\n intermediate_dimension\n longest_dimension\n method_of_sample_procurement\n pathology_report_uuid\n preservation_method\n sample_type_id\n shortest_dimension\n time_between_clamping_and_freezing\n time_between_excision_and_freezing\n tissue_type\n tumor_code\n tumor_code_id\n tumor_descriptor\n annotation\n }\n }\n }\n """ % case_submitter_id\n\n response = requests.post(url, json={\'query\': query})\n\n if response.status_code == 200:\n data = json.loads(response.text)\n if \'data\' in data:\n df = pd.json_normalize(data[\'data\'][\'uiCase\'], record_path=[\'samples\'])\n return df\n else:\n print(f"Error: Response does not contain \'data\' key: {data}")\n return pd.DataFrame()\n else:\n print(f"Error: Request failed with status code {response.status_code}")\n return pd.DataFrame()\n\n# Example usage\ncase_submitter_id = "C3L-00001"\nsample_df = get_case_sample_info(case_submitter_id)\n\nif not sample_df.empty:\n print(sample_df)'} | |
| INFO {'fixed_code': 'import requests\nimport json\nimport pandas as pd\nimport os\n\ndef get_case_sample_info(case_submitter_id: str) -> pd.DataFrame:\n """\n This function takes a case_submitter_id and returns a pandas DataFrame containing\n information about the samples associated with that case.\n\n Args:\n case_submitter_id: The submitter ID of the case.\n\n Returns:\n A pandas DataFrame containing sample information.\n """\n\n url = \'https://pdc.cancer.gov/graphql\'\n query = """\n {\n uiCase(case_submitter_id: "%s") {\n samples {\n sample_id\n gdc_sample_id\n gdc_project_id\n sample_submitter_id\n sample_type\n status\n pool\n sample_is_ref\n biospecimen_anatomic_site\n biospecimen_laterality\n composition\n current_weight\n days_to_collection\n days_to_sample_procurement\n diagnosis_pathologically_confirmed\n freezing_method\n initial_weight\n intermediate_dimension\n longest_dimension\n method_of_sample_procurement\n pathology_report_uuid\n preservation_method\n sample_type_id\n shortest_dimension\n time_between_clamping_and_freezing\n time_between_excision_and_freezing\n tissue_type\n tumor_code\n tumor_code_id\n tumor_descriptor\n annotation\n }\n }\n }\n """ % case_submitter_id\n\n response = requests.post(url, json={\'query\': query})\n\n if response.status_code == 200:\n data = json.loads(response.text)\n if \'data\' in data:\n df = pd.json_normalize(data[\'data\'][\'uiCase\'], record_path=[\'samples\'])\n return df\n else:\n print(f"Error: Response does not contain \'data\' key: {data}")\n return pd.DataFrame()\n else:\n print(f"Error: Request failed with status code {response.status_code}")\n return pd.DataFrame()\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Example usage\ncase_submitter_id = "C3L-00001"\nsample_df = get_case_sample_info(case_submitter_id)\n\nif not sample_df.empty:\n print(sample_df)'} | |
| INFO {'info': 'directly returning code'} | |
| observation: import requests | |
| import json | |
| import pandas as pd | |
| import os | |
| def get_case_sample_info(case_submitter_id: str) -> pd.DataFrame: | |
| """ | |
| This function takes a case_submitter_id and returns a pandas DataFrame containing | |
| information about the samples associated with that case. | |
| Args: | |
| case_submitter_id: The submitter ID of the case. | |
| Returns: | |
| A pandas DataFrame containing sample information. | |
| """ | |
| url = 'https://pdc.cancer.gov/graphql' | |
| query = """ | |
| { | |
| uiCase(case_submitter_id: "%s") { | |
| samples { | |
| sample_id | |
| gdc_sample_id | |
| gdc_project_id | |
| sample_submitter_id | |
| sample_type | |
| status | |
| pool | |
| sample_is_ref | |
| biospecimen_anatomic_site | |
| biospecimen_laterality | |
| composition | |
| current_weight | |
| days_to_collection | |
| days_to_sample_procurement | |
| diagnosis_pathologically_confirmed | |
| freezing_method | |
| initial_weight | |
| intermediate_dimension | |
| longest_dimension | |
| method_of_sample_procurement | |
| pathology_report_uuid | |
| preservation_method | |
| sample_type_id | |
| shortest_dimension | |
| time_between_clamping_and_freezing | |
| time_between_excision_and_freezing | |
| tissue_type | |
| tumor_code | |
| tumor_code_id | |
| tumor_descriptor | |
| annotation | |
| } | |
| } | |
| } | |
| """ % case_submitter_id | |
| response = requests.post(url, json={'query': query}) | |
| if response.status_code == 200: | |
| data = json.loads(response.text) | |
| if 'data' in data: | |
| df = pd.json_normalize(data['data']['uiCase'], record_path=['samples']) | |
| return df | |
| else: | |
| print(f"Error: Response does not contain 'data' key: {data}") | |
| return pd.DataFrame() | |
| else: | |
| print(f"Error: Request failed with status code {response.status_code}") | |
| return pd.DataFrame() | |
| # Ensure the data directory exists | |
| os.makedirs('./data/', exist_ok=True) | |
| # Example usage | |
| case_submitter_id = "C3L-00001" | |
| sample_df = get_case_sample_info(case_submitter_id) | |
| if not sample_df.empty: | |
| print(sample_df) | |
| thought: I have drafted a query to access sample information using the valid fields within the samples list in the UICase type. Now, I will run the code to get the results. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport json\nimport pandas as pd\nimport os\n\ndef get_case_sample_info(case_submitter_id: str) -> pd.DataFrame:\n """\n This function takes a | |
| case_submitter_id and returns a pandas DataFrame containing\n information about the samples associated with that case.\n\n Args:\n case_submitter_id: The submitter ID of the | |
| case.\n\n Returns:\n A pandas DataFrame containing sample information.\n """\n\n url = \'https://pdc.cancer.gov/graphql\'\n query = """\n {\n | |
| uiCase(case_submitter_id: "%s") {\n samples {\n sample_id\n gdc_sample_id\n gdc_project_id\n sample_submitter_id\n sample_type\n | |
| status\n pool\n sample_is_ref\n biospecimen_anatomic_site\n biospecimen_laterality\n composition\n current_weight\n | |
| days_to_collection\n days_to_sample_procurement\n diagnosis_pathologically_confirmed\n freezing_method\n initial_weight\n intermediate_dimension\n | |
| longest_dimension\n method_of_sample_procurement\n pathology_report_uuid\n preservation_method\n sample_type_id\n shortest_dimension\n | |
| time_between_clamping_and_freezing\n time_between_excision_and_freezing\n tissue_type\n tumor_code\n tumor_code_id\n tumor_descriptor\n | |
| annotation\n }\n }\n }\n """ % case_submitter_id\n\n response = requests.post(url, json={\'query\': query})\n\n if response.status_code == 200:\n data = | |
| json.loads(response.text)\n if \'data\' in data:\n df = pd.json_normalize(data[\'data\'][\'uiCase\'], record_path=[\'samples\'])\n return df\n else:\n | |
| print(f"Error: Response does not contain \'data\' key: {data}")\n return pd.DataFrame()\n else:\n print(f"Error: Request failed with status code | |
| {response.status_code}")\n return pd.DataFrame()\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Example usage\ncase_submitter_id = | |
| "C3L-00001"\nsample_df = get_case_sample_info(case_submitter_id)\n\nif not sample_df.empty:\n print(sample_df)'} | |
| observation: Error: Request failed with status code 400 | |
| thought: The error indicates that the request failed with status code 400, which suggests there might be an issue with the query or the case_submitter_id. I need to verify the query structure | |
| and the case_submitter_id. | |
| tool: AdhocApi.ask_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'query': 'What is the correct structure for querying sample information using the uiCase type, and what is a valid case_submitter_id?'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23617, min_total_token_count=32768. Running model without cache.'} | |
| observation: You can query sample information through the `uiCaseSummary` type, not `uiCase`. The `uiCaseSummary` provides detailed information about a case, including associated samples. | |
| You can then access the `samples` field within the `uiCaseSummary` results to retrieve sample-specific data. | |
| Here's how the structure looks: | |
| ```graphql | |
| query { | |
| uiCaseSummary(case_id: "YOUR_CASE_ID", source: "YOUR_SOURCE") { | |
| case_id | |
| samples { | |
| sample_id | |
| sample_submitter_id | |
| sample_type | |
| # ... other sample fields you need | |
| } | |
| # ... other case fields you may need | |
| } | |
| } | |
| ``` | |
| Replace placeholders accordingly: | |
| - **YOUR_CASE_ID:** A valid case ID. Example: `"7db8a437-a484-4440-8d01-581978607a9d"` | |
| - **YOUR_SOURCE:** The data source. Example: `"PDC"` or `"legacy"` | |
| To find a valid `case_submitter_id`, you can use the `FilteredCasesDataPaginated` query with the appropriate filters or use the `CaseSearchQuery`. | |
| For example, you can search for cases related to a specific disease type: | |
| ```graphql | |
| query { | |
| getPaginatedUICase( | |
| offset: 0 | |
| limit: 10 | |
| disease_type: "YOUR_DISEASE_TYPE" | |
| source: "PDC" | |
| ) { | |
| uiCases { | |
| case_id | |
| case_submitter_id | |
| # ... other fields | |
| } | |
| # ... pagination information | |
| } | |
| } | |
| ``` | |
| Replace `"YOUR_DISEASE_TYPE"` with the desired disease type. This will give you a list of up to 10 case IDs and their `case_submitter_id` values, which you can then use in the `uiCaseSummary` | |
| query. | |
| thought: I will correct the query by using the uiCaseSummary type to access sample information and ensure I use a valid case_id and source. | |
| tool: AdhocApi.use_api | |
| tool_input: {'api': 'Proteomic Data Commons', 'goal': 'Draft a query using the uiCaseSummary type to access sample information with a valid case_id and source.'} | |
| INFO {'api': 'Proteomic Data Commons', 'goal': 'Draft a query using the uiCaseSummary type to access sample information with a valid case_id and source.'} | |
| INFO {'cache': 'No cached content found for "api_assistant_gdc_david_testing". pushing new instance.'} | |
| INFO {'cache': '400 Cached content is too small. total_token_count=23690, min_total_token_count=32768. Running model without cache.'} | |
| INFO {'draft_code': 'import requests\nimport json\nimport pandas as pd\nimport os\n\n# Replace with a valid case_id\ncase_id = "case_id"\n\n# Source (e.g., \'PDC\')\nsource = "PDC"\n\nquery = """\nquery FilteredStudiesData {\n uiCaseSummary(case_id: "%s", source: "%s") {\n samples {\n sample_id\n sample_submitter_id\n annotation\n }\n }\n}\n""" % (case_id, source)\n\nurl = \'https://pdc.cancer.gov/graphql\'\nr = requests.post(url, json={\'query\': query})\n\n# Convert the result to JSON\ndata = json.loads(r.text)\n\n# Access and process the data\nsamples_data = data[\'data\'][\'uiCaseSummary\'][\'samples\']\n\n# Further process or display the data as needed\nprint(json.dumps(samples_data, indent=4))'} | |
| INFO {'fixed_code': 'import requests\nimport json\nimport os\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Replace with a valid case_id\ncase_id = "case_id"\n\n# Source (e.g., \'PDC\')\nsource = "PDC"\n\nquery = """\nquery FilteredStudiesData {\n uiCaseSummary(case_id: "%s", source: "%s") {\n samples {\n sample_id\n sample_submitter_id\n annotation\n }\n }\n}\n""" % (case_id, source)\n\nurl = \'https://pdc.cancer.gov/graphql\'\nr = requests.post(url, json={\'query\': query})\n\n# Convert the result to JSON\ndata = json.loads(r.text)\n\n# Access and process the data\nsamples_data = data[\'data\'][\'uiCaseSummary\'][\'samples\']\n\n# Further process or display the data as needed\nprint(json.dumps(samples_data, indent=4))'} | |
| INFO {'info': 'directly returning code'} | |
| observation: import requests | |
| import json | |
| import os | |
| # Ensure the data directory exists | |
| os.makedirs('./data/', exist_ok=True) | |
| # Replace with a valid case_id | |
| case_id = "case_id" | |
| # Source (e.g., 'PDC') | |
| source = "PDC" | |
| query = """ | |
| query FilteredStudiesData { | |
| uiCaseSummary(case_id: "%s", source: "%s") { | |
| samples { | |
| sample_id | |
| sample_submitter_id | |
| annotation | |
| } | |
| } | |
| } | |
| """ % (case_id, source) | |
| url = 'https://pdc.cancer.gov/graphql' | |
| r = requests.post(url, json={'query': query}) | |
| # Convert the result to JSON | |
| data = json.loads(r.text) | |
| # Access and process the data | |
| samples_data = data['data']['uiCaseSummary']['samples'] | |
| # Further process or display the data as needed | |
| print(json.dumps(samples_data, indent=4)) | |
| thought: I have drafted a query using the uiCaseSummary type to access sample information with a valid case_id and source. Now, I will run the code to get the results. | |
| tool: PythonTool.run | |
| tool_input: {'code': 'import requests\nimport json\nimport os\n\n# Ensure the data directory exists\nos.makedirs(\'./data/\', exist_ok=True)\n\n# Replace with a valid case_id\ncase_id = | |
| "7db8a437-a484-4440-8d01-581978607a9d"\n\n# Source (e.g., \'PDC\')\nsource = "PDC"\n\nquery = """\nquery FilteredStudiesData {\n uiCaseSummary(case_id: "%s", source: "%s") {\n samples {\n | |
| sample_id\n sample_submitter_id\n annotation\n }\n }\n}\n""" % (case_id, source)\n\nurl = \'https://pdc.cancer.gov/graphql\'\nr = requests.post(url, json={\'query\': | |
| query})\n\n# Convert the result to JSON\ndata = json.loads(r.text)\n\n# Access and process the data\nsamples_data = data[\'data\'][\'uiCaseSummary\'][\'samples\']\n\n# Further process or | |
| display the data as needed\nprint(json.dumps(samples_data, indent=4))'} | |
| Error: Too many errors during task. Last error: error running tool "PythonTool.run": list indices must be integers or slices, not str |
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