Tutorial 6: CPTAC basics review¶

First, we'll import the package.

In [1]:

import cptac

We can list which cancers we have data for.

In [2]:

cptac.get_cancer_options()

Out[2]:

		Datatype
Cancer	Source
all_cancers	harmonized	[somatic_mutation, ancestry_prediction]
	mssm	[clinical]
	washu	[tumor_purity, hla_typing]
brca	bcm	[miRNA, phosphoproteomics, CNV, proteomics, tr...
	broad	[transcriptomics]
	umich	[proteomics, acetylproteomics, phosphoproteomics]
	washu	[CNV, cibersort, xcell, transcriptomics]
ccrcc	bcm	[miRNA, phosphoproteomics, CNV, circular_RNA, ...
	broad	[transcriptomics]
	umich	[proteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...
coad	bcm	[miRNA, phosphoproteomics, CNV, proteomics, tr...
	broad	[transcriptomics]
	umich	[proteomics, phosphoproteomics]
	washu	[CNV, transcriptomics, xcell, somatic_mutation...
gbm	bcm	[miRNA, phosphoproteomics, CNV, circular_RNA, ...
	broad	[transcriptomics]
	umich	[proteomics, acetylproteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...
hnscc	bcm	[miRNA, phosphoproteomics, circular_RNA, prote...
	broad	[transcriptomics]
	umich	[proteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...
lscc	bcm	[miRNA, phosphoproteomics, CNV, circular_RNA, ...
	broad	[transcriptomics]
	umich	[proteomics, acetylproteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...
luad	bcm	[miRNA, phosphoproteomics, CNV, circular_RNA, ...
	broad	[transcriptomics]
	umich	[proteomics, acetylproteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...
ov	bcm	[CNV, proteomics, transcriptomics, phosphoprot...
	broad	[transcriptomics]
	umich	[proteomics]
	washu	[CNV, transcriptomics, xcell, somatic_mutation...
pdac	bcm	[miRNA, phosphoproteomics, CNV, circular_RNA, ...
	broad	[transcriptomics]
	umich	[proteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...
ucec	bcm	[miRNA, phosphoproteomics, CNV, circular_RNA, ...
	broad	[transcriptomics]
	umich	[proteomics, acetylproteomics, phosphoproteomics]
	washu	[total_miRNA, CNV, mature_miRNA, precursor_miR...

Load the BRCA dataset¶

In [3]:

br = cptac.Brca()

We can list which data types are available from which sources.

In [4]:

br.list_data_sources()

Out[4]:

	Data type	Available sources
0	CNV	[bcm, washu]
1	miRNA	[bcm]
2	phosphoproteomics	[bcm, umich]
3	proteomics	[bcm, umich]
4	transcriptomics	[bcm, broad, washu]
5	ancestry_prediction	[harmonized]
6	somatic_mutation	[harmonized, washu]
7	clinical	[mssm]
8	follow-up	[mssm]
9	medical_history	[mssm]
10	acetylproteomics	[umich]
11	cibersort	[washu]
12	hla_typing	[washu]
13	tumor_purity	[washu]
14	xcell	[washu]

Download¶

Each file will be automatically downloaded when requested, but authentication through your Box account is required to download pancan data.

See the end of this tutorial for how to download files on a remote computer that doesn't have a web browser for logging into Box.

Let's get some data tables.

In [5]:

br.get_clinical(source="mssm")
#Note: the syntax above is optional, calling the function as br.get_clinical('mssm') works just as well

Out[5]:

Name	tumor_code	discovery_study	type_of_analyzed_samples	confirmatory_study	type_of_analyzed_samples	age	sex	race	ethnicity	ethnicity_race_ancestry_identified	...	additional_treatment_pharmaceutical_therapy_for_new_tumor	additional_treatment_immuno_for_new_tumor	number_of_days_from_date_of_initial_pathologic_diagnosis_to_date_of_additional_surgery_for_new_tumor_event_loco-regional	number_of_days_from_date_of_initial_pathologic_diagnosis_to_date_of_additional_surgery_for_new_tumor_event_metastasis	Recurrence-free survival, days	Recurrence-free survival from collection, days	Recurrence status (1, yes; 0, no)	Overall survival, days	Overall survival from collection, days	Survival status (1, dead; 0, alive)
Patient_ID
01BR001	BR	Yes	NaN	NaN	NaN	55	Female	Black or African American	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	421.0	NaN	0.0
01BR008	BR	Yes	NaN	NaN	NaN	48	Female	Black or African American	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN
01BR009	BR	Yes	NaN	NaN	NaN	64	Female	Black or African American	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN
01BR010	BR	Yes	NaN	NaN	NaN	65	Female	Black or African American	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN
01BR015	BR	Yes	NaN	NaN	NaN	35	Female	White	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	347.0	NaN	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21BR010	BR	Yes	NaN	NaN	NaN	71	Female	White	Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	327.0	NaN	0.0
22BR003	BR	Yes	NaN	NaN	NaN	30	Female	White	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN
22BR005	BR	Yes	NaN	NaN	NaN	46	Female	White	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	348.0	NaN	0.0
22BR006	BR	Yes	NaN	NaN	NaN	55	Female	Black or African American	Not Hispanic or Latino	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	282.0	NaN	1.0
604	BR	Yes	NaN	NaN	NaN	41	Female	Black or African American	Unknown	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	NaN	NaN

134 rows × 124 columns

In [6]:

br.get_somatic_mutation('harmonized')

cptac warning: Your version of cptac (1.5.1) is out-of-date. Latest is 1.5.0. Please run 'pip install --upgrade cptac' to update it. (C:\Users\sabme\anaconda3\lib\threading.py, line 910)

Out[6]:

Name	Gene	Mutation	Location	Entrez_Gene_Id	NCBI_Build	Chromosome	Start_Position	End_Position	Strand	Variant_Type	...	HGNC_UniProt_ID(supplied_by_UniProt)	HGNC_Ensembl_ID(supplied_by_Ensembl)	HGNC_UCSC_ID(supplied_by_UCSC)	Oreganno_Build	Simple_Uniprot_alt_uniprot_accessions	dbSNP_TOPMED	HGNC_Entrez_Gene_ID(supplied_by_NCBI)	COHORT	getz	washu
Patient_ID
01BR001	CCDC136	Intron	NaN	64753.0	hg38	chr7	128815741	128815741	+	SNP	...	Q96JN2	ENSG00000128596	uc003vnv.3	NaN	A4D1K1\|A7MCY7\|A8MYA7\|Q6ZVK7\|Q9H8M3\|Q9UFE1	NaN	64753.0	BRCA	True	True
01BR001	MYBPC1	Splice_Site	NaN	4604.0	hg38	chr12	101661264	101661264	+	SNP	...	Q00872	ENSG00000196091	uc001tih.4	NaN	B4DKR5\|B7Z8G8\|B7ZL02\|B7ZL09\|B7ZL10\|E7ESM5\|E7EW...	NaN	4604.0	BRCA	True	True
01BR001	KRT77	Silent	p.G516G	374454.0	hg38	chr12	52691354	52691354	+	SNP	...	Q7Z794	ENSG00000189182	uc001saw.4	NaN	Q7RTS8	NaN	374454.0	BRCA	True	NaN
01BR001	TENM4	Missense_Mutation	p.E19K	26011.0	hg38	chr11	79069890	79069890	+	SNP	...	Q6N022	ENSG00000149256	uc001ozl.5	hg38	A6ND26\|Q7Z3C7\|Q96MS6\|Q9P2P4\|Q9Y4S2	NaN	26011.0	BRCA	True	True
01BR001	PPFIA1	Missense_Mutation	p.H795L	8500.0	hg38	chr11	70355707	70355707	+	SNP	...	Q13136	ENSG00000131626	uc001opo.4	NaN	A6NLE3\|Q13135\|Q14567\|Q8N4I2	NaN	8500.0	BRCA	True	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
604	EXPH5	Silent	p.Q1690Q	23086.0	hg38	chr11	108510437	108510437	+	SNP	...	Q8NEV8	ENSG00000110723	uc001pkk.3	NaN	Q2KHM1\|Q9Y4D6	0.99998407237512742,0.00001592762487257	23086.0	BRCA	True	True
604	IGSF9B	Missense_Mutation	p.V493M	22997.0	hg38	chr11	133931026	133931026	+	SNP	...	Q9UPX0	ENSG00000080854	uc031qfh.2	hg38	G5EA26	NaN	22997.0	BRCA	True	True
604	ANO2	Intron	NaN	57101.0	hg38	chr12	5827750	5827750	+	SNP	...	Q9NQ90	ENSG00000047617	uc058kbl.1	NaN	C4N787\|Q9H847	0.99998407237512742,.,0.00001592762487257	57101.0	BRCA	True	NaN
604	HFM1	Silent	p.G133G	164045.0	hg38	chr1	91394188	91394188	+	SNP	...	A2PYH4	ENSG00000162669	uc001doa.4	NaN	B1B0B6\|Q8N9Q0	NaN	164045.0	BRCA	True	True
604	SLC44A2	Frame_Shift_Del	p.E435fs	57153.0	hg38	chr19	10636388	10636407	+	DEL	...	Q8IWA5	ENSG00000129353	uc002mpf.4	NaN	B2RBB1\|B3KNH3\|B4DFJ0\|F2Q9D7\|Q658V1\|Q658Z2\|Q6PJ...	NaN	57153.0	BRCA	True	True

29017 rows × 131 columns

In [7]:

br.get_proteomics(source="umich")

Out[7]:

Name	ARF5	M6PR	ESRRA	FKBP4	NDUFAF7	FUCA2	DBNDD1	SEMA3F	CFTR	CYP51A1	...	DDHD1	WIZ	GBF1	APOA5	WIZ	LDB1	WIZ	RFX7	SWSAP1	SVIL
Database_ID	ENSP00000000233.5	ENSP00000000412.3	ENSP00000000442.6	ENSP00000001008.4	ENSP00000002125.4	ENSP00000002165.5	ENSP00000002501.6	ENSP00000002829.3	ENSP00000003084.6	ENSP00000003100.8	...	ENSP00000500986.2	ENSP00000500993.1	ENSP00000501064.1	ENSP00000501141.1	ENSP00000501256.3	ENSP00000501277.1	ENSP00000501300.1	ENSP00000501317.1	ENSP00000501355.1	ENSP00000501521.1
Patient_ID
01BR001	0.012367	-0.945999	NaN	-0.478170	1.135840	-0.512706	0.750335	-0.274824	NaN	-0.278244	...	-0.649127	-0.580869	-0.226667	NaN	NaN	-0.676185	-0.068202	-0.078207	-0.328420	NaN
01BR008	-0.514386	0.462307	0.230124	-0.555968	0.491366	-0.656034	-1.220890	-0.369282	-1.036441	-0.059327	...	0.632221	NaN	0.032873	NaN	NaN	-0.015459	0.227424	0.325643	-0.606240	NaN
01BR009	-0.210782	-0.085055	0.380296	-0.389491	1.255391	-0.608007	-0.231318	0.092870	-1.505195	0.206595	...	0.450818	NaN	-0.341503	NaN	NaN	-0.220239	0.125092	0.365397	-0.167392	NaN
01BR010	0.105457	0.351335	-0.322798	-0.821610	0.241406	-0.500140	-0.137824	0.113791	NaN	0.498314	...	-0.423470	NaN	0.360900	NaN	NaN	-0.451556	-0.098897	0.208643	-0.729096	0.670307
01BR015	-0.509298	-0.874164	NaN	-0.113804	-0.131347	-0.412813	0.262210	0.042333	NaN	-0.657666	...	0.406016	-0.493869	-0.192847	NaN	NaN	0.083639	0.966976	-0.012664	0.081968	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21BR010	0.528298	-0.127929	-0.497360	-0.151022	0.082288	0.447267	0.151024	0.220194	0.516739	-0.230357	...	-0.172151	0.636608	0.267400	NaN	-0.09507	-0.017522	-0.220463	-0.067717	-0.311446	0.602422
22BR005	-0.549542	0.134236	NaN	0.580773	-0.080663	-0.056509	-0.148632	0.260986	NaN	-0.348578	...	0.791937	NaN	0.171712	NaN	NaN	0.083980	-0.200083	0.155198	NaN	0.456801
22BR006	0.336092	0.125742	NaN	-0.360510	0.086199	0.470607	-0.515990	-0.162247	1.003075	0.342987	...	-0.080755	NaN	0.174904	-0.353412	NaN	-0.013793	-0.253829	-0.117960	NaN	1.094966
CPT000814	-0.518995	0.262582	0.277980	0.137505	0.600041	-1.041230	0.513974	-0.012011	NaN	-0.411714	...	-2.011008	NaN	0.035445	NaN	NaN	-1.385942	0.620827	NaN	NaN	NaN
CPT001846	0.652513	-0.165268	0.809801	-1.108263	0.557576	0.007854	0.213734	-0.721577	-2.695651	0.296581	...	-0.057923	NaN	0.252335	NaN	NaN	0.040722	-0.006118	-0.251360	0.443203	NaN

125 rows × 12922 columns