Molecular Data#

The module molecular_data provides functions related to Molecular Data section of cBioPortal Web Public API.

pybioportal.molecular_data.fetch_all_molecular_data_in_molecular_profile(molecular_profile_id, entrez_gene_ids=None, sample_ids=None, sample_list_id=None, projection='SUMMARY')#

Fetch molecular data in a molecular profile for a list of genes.

Parameters:

molecular_profile_id (str) – Molecular Profile ID (e.g., “acc_tcga_rna_seq_v2_mrna”).
entrez_gene_ids (list of str) – List of Entrez Gene IDs (e.g., [“672”,”675”]).
sample_ids (list of str) – List of Sample IDs (e.g., [“TCGA-AR-A1AR-01”,”TCGA-BH-A1EO-01”]).
sample_list_id (str) – Sample List ID (e.g., “brca_tcga_all”).
projection (str) –
Level of detail of the response.

Possible values:
- ”DETAILED”: Detailed information.
- ”ID”: Information with only IDs.
- ”META”: Metadata information.
- ”SUMMARY”: Summary information (default).

Returns:

A DataFrame containing molecular data for the specified genes.

Return type:

pandas.DataFrame

pybioportal.molecular_data.fetch_molecular_data(entrez_gene_ids=None, molecular_profile_ids=None, sample_molecular_identifiers=None, projection='SUMMARY')#

Fetch molecular data.

Parameters:

entrez_gene_ids (list of str) – List of Entrez Gene IDs (e.g., [“672”, “675”]).
molecular_profile_ids (list of str) – List of MolecularProfile IDs (e.g., [“brca_tcga_mrna”, “acc_tcga_rna_seq_v2_mrna”]).
sample_molecular_identifiers – List of Molecular Profile ID and Sample ID pairs.
projection (str) –
Level of detail of the response.

Possible values:
- ”DETAILED”: Detailed information.
- ”ID”: Information with only IDs.
- ”META”: Metadata information.
- ”SUMMARY”: Summary information (default).

Returns:

A DataFrame containing molecular data.

Return type:

pandas.DataFrame

pybioportal.molecular_data.get_all_molecular_data_in_molecular_profile(molecular_profile_id, sample_list_id, entrez_gene_id, projection='SUMMARY')#

Get all molecular data in a molecular profile for a specific gene.

Parameters:

molecular_profile_id (str) – Molecular Profile ID (e.g., “acc_tcga_rna_seq_v2_mrna”).
sample_list_id (str) – Sample List ID (e.g., “acc_tcga_all”).
entrez_gene_id (str) – Entrez Gene ID (e.g., “1”).
projection (str) –
Level of detail of the response.

Possible values:
- ”DETAILED”: Detailed information.
- ”ID”: Information with only IDs.
- ”META”: Metadata information.
- ”SUMMARY”: Summary information (default).

Returns:

A DataFrame containing molecular data for the specified gene.

Return type:

pandas.DataFrame

Examples#

from pybioportal import molecular_data as md

df1a = md.fetch_molecular_data(entrez_gene_ids=["672","675"],
                               molecular_profile_ids=["brca_tcga_mrna", "acc_tcga_rna_seq_v2_mrna"])
df1a

	uniqueSampleKey	uniquePatientKey	entrezGeneId	molecularProfileId	sampleId	patientId	studyId	value
0	VENHQS1PUi1BNUoxLTAxOmFjY190Y2dh	VENHQS1PUi1BNUoxOmFjY190Y2dh	672	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J1-01	TCGA-OR-A5J1	acc_tcga	40.345800
1	VENHQS1PUi1BNUoxLTAxOmFjY190Y2dh	VENHQS1PUi1BNUoxOmFjY190Y2dh	675	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J1-01	TCGA-OR-A5J1	acc_tcga	15.850100
2	VENHQS1PUi1BNUoyLTAxOmFjY190Y2dh	VENHQS1PUi1BNUoyOmFjY190Y2dh	672	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J2-01	TCGA-OR-A5J2	acc_tcga	177.560000
3	VENHQS1PUi1BNUoyLTAxOmFjY190Y2dh	VENHQS1PUi1BNUoyOmFjY190Y2dh	675	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J2-01	TCGA-OR-A5J2	acc_tcga	29.392000
4	VENHQS1PUi1BNUozLTAxOmFjY190Y2dh	VENHQS1PUi1BNUozOmFjY190Y2dh	672	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J3-01	TCGA-OR-A5J3	acc_tcga	47.992900
...	...	...	...	...	...	...	...	...
1211	VENHQS1FMi1BMTVBLTA2OmJyY2FfdGNnYQ	VENHQS1FMi1BMTVBOmJyY2FfdGNnYQ	675	brca_tcga_mrna	TCGA-E2-A15A-06	TCGA-E2-A15A	brca_tcga	-1.171750
1212	VENHQS1FMi1BMTVLLTA2OmJyY2FfdGNnYQ	VENHQS1FMi1BMTVLOmJyY2FfdGNnYQ	672	brca_tcga_mrna	TCGA-E2-A15K-06	TCGA-E2-A15K	brca_tcga	-0.918167
1213	VENHQS1FMi1BMTVLLTA2OmJyY2FfdGNnYQ	VENHQS1FMi1BMTVLOmJyY2FfdGNnYQ	675	brca_tcga_mrna	TCGA-E2-A15K-06	TCGA-E2-A15K	brca_tcga	-0.193000
1214	VENHQS1FMi1BMTVFLTA2OmJyY2FfdGNnYQ	VENHQS1FMi1BMTVFOmJyY2FfdGNnYQ	672	brca_tcga_mrna	TCGA-E2-A15E-06	TCGA-E2-A15E	brca_tcga	-1.898417
1215	VENHQS1FMi1BMTVFLTA2OmJyY2FfdGNnYQ	VENHQS1FMi1BMTVFOmJyY2FfdGNnYQ	675	brca_tcga_mrna	TCGA-E2-A15E-06	TCGA-E2-A15E	brca_tcga	-2.228000

1216 rows × 8 columns

df1b = md.fetch_molecular_data(entrez_gene_ids=["672","675"],
                               sample_molecular_identifiers=[
                                         {"molecular_profile_id": "brca_tcga_mrna",
                                          "sample_ids": ["TCGA-AR-A1AR-01","TCGA-BH-A1EO-01"]},
                                         {"molecular_profile_id": "acc_tcga_rna_seq_v2_mrna",
                                          "sample_ids": ["TCGA-OR-A5J1-01","TCGA-OR-A5J2"]}
                                         ])
df1b

	uniqueSampleKey	uniquePatientKey	entrezGeneId	molecularProfileId	sampleId	patientId	studyId	value
0	VENHQS1BUi1BMUFSLTAxOmJyY2FfdGNnYQ	VENHQS1BUi1BMUFSOmJyY2FfdGNnYQ	672	brca_tcga_mrna	TCGA-AR-A1AR-01	TCGA-AR-A1AR	brca_tcga	-1.224333
1	VENHQS1BUi1BMUFSLTAxOmJyY2FfdGNnYQ	VENHQS1BUi1BMUFSOmJyY2FfdGNnYQ	675	brca_tcga_mrna	TCGA-AR-A1AR-01	TCGA-AR-A1AR	brca_tcga	0.027250
2	VENHQS1CSC1BMUVPLTAxOmJyY2FfdGNnYQ	VENHQS1CSC1BMUVPOmJyY2FfdGNnYQ	672	brca_tcga_mrna	TCGA-BH-A1EO-01	TCGA-BH-A1EO	brca_tcga	-1.739417
3	VENHQS1CSC1BMUVPLTAxOmJyY2FfdGNnYQ	VENHQS1CSC1BMUVPOmJyY2FfdGNnYQ	675	brca_tcga_mrna	TCGA-BH-A1EO-01	TCGA-BH-A1EO	brca_tcga	-1.380500
4	VENHQS1PUi1BNUoxLTAxOmFjY190Y2dh	VENHQS1PUi1BNUoxOmFjY190Y2dh	672	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J1-01	TCGA-OR-A5J1	acc_tcga	40.345800
5	VENHQS1PUi1BNUoxLTAxOmFjY190Y2dh	VENHQS1PUi1BNUoxOmFjY190Y2dh	675	acc_tcga_rna_seq_v2_mrna	TCGA-OR-A5J1-01	TCGA-OR-A5J1	acc_tcga	15.850100

df2 = md.get_all_molecular_data_in_molecular_profile(molecular_profile_id="brca_tcga_rppa",
                                                     sample_list_id="brca_tcga_all",
                                                     entrez_gene_id="675")
df2

	uniqueSampleKey	uniquePatientKey	entrezGeneId	molecularProfileId	sampleId	patientId	studyId	value
0	VENHQS0zQy1BQUxJLTAxOmJyY2FfdGNnYQ	VENHQS0zQy1BQUxJOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-3C-AALI-01	TCGA-3C-AALI	brca_tcga	-0.181730
1	VENHQS0zQy1BQUxLLTAxOmJyY2FfdGNnYQ	VENHQS0zQy1BQUxLOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-3C-AALK-01	TCGA-3C-AALK	brca_tcga	-0.366790
2	VENHQS00SC1BQUFLLTAxOmJyY2FfdGNnYQ	VENHQS00SC1BQUFLOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-4H-AAAK-01	TCGA-4H-AAAK	brca_tcga	-0.108050
3	VENHQS01TC1BQVQxLTAxOmJyY2FfdGNnYQ	VENHQS01TC1BQVQxOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-5L-AAT1-01	TCGA-5L-AAT1	brca_tcga	-0.785290
4	VENHQS01VC1BOVFBLTAxOmJyY2FfdGNnYQ	VENHQS01VC1BOVFBOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-5T-A9QA-01	TCGA-5T-A9QA	brca_tcga	-0.469280
...	...	...	...	...	...	...	...	...
887	VENHQS1XOC1BODZHLTAxOmJyY2FfdGNnYQ	VENHQS1XOC1BODZHOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-W8-A86G-01	TCGA-W8-A86G	brca_tcga	-0.083261
888	VENHQS1XVC1BQjQxLTAxOmJyY2FfdGNnYQ	VENHQS1XVC1BQjQxOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-WT-AB41-01	TCGA-WT-AB41	brca_tcga	-0.386950
889	VENHQS1YWC1BODk5LTAxOmJyY2FfdGNnYQ	VENHQS1YWC1BODk5OmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-XX-A899-01	TCGA-XX-A899	brca_tcga	-0.472410
890	VENHQS1aNy1BOFI1LTAxOmJyY2FfdGNnYQ	VENHQS1aNy1BOFI1OmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-Z7-A8R5-01	TCGA-Z7-A8R5	brca_tcga	-0.458130
891	VENHQS1aNy1BOFI2LTAxOmJyY2FfdGNnYQ	VENHQS1aNy1BOFI2OmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-Z7-A8R6-01	TCGA-Z7-A8R6	brca_tcga	-0.382420

892 rows × 8 columns

df3 = md.fetch_all_molecular_data_in_molecular_profile(molecular_profile_id = "brca_tcga_rppa",
                                                       entrez_gene_ids = ["672","675"],
                                                       sample_ids = ["TCGA-AR-A1AR-01","TCGA-BH-A1EO-01"])
df3

	uniqueSampleKey	uniquePatientKey	entrezGeneId	molecularProfileId	sampleId	patientId	studyId	value
0	VENHQS1CSC1BMUVPLTAxOmJyY2FfdGNnYQ	VENHQS1CSC1BMUVPOmJyY2FfdGNnYQ	675	brca_tcga_rppa	TCGA-BH-A1EO-01	TCGA-BH-A1EO	brca_tcga	-0.40763