Tutorial 1: CPTAC Data Introduction

The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. CPTAC generates comprehensive proteomics and genomics data from clinical cohorts, typically with ~100 samples per tumor type. The graphic below summarizes the structure of each CPTAC dataset. For more information, visit the NIH website.

This Python package makes accessing CPTAC data easy with Python code and Jupyter notebooks. The package contains several tutorials which demonstrate data access and usage. This first tutorial serves as an introduction to the data to help users become familiar with what is included and how it is presented.

Data Overview

Our package provides data access in a Python programming environment. If you have not installed Python or have not installed the package, see our installation documentation here.

Once we have the package installed and we're in our Python environment, we begin by importing the package with a standard Python import statement:

import cptac

cptac data is broken down into datasets by cancer type. To view the available cancers and their abbreviations, call the cptac.get_cancer_info() function.

cptac.get_cancer_info()

{'brca': 'Breast invasive carcinoma',
 'ccrcc': 'Clear cell renal cell carcinoma',
 'coad': 'Colon adenocarcinoma',
 'gbm': 'Glioblastoma multiforme',
 'hnscc': 'Head and Neck squamous cell carcinoma',
 'lscc': 'Lung squamous cell carcinoma',
 'luad': 'Lung adenocarcinoma',
 'ov': 'Ovarian serous cystadenocarcinoma',
 'pda': 'Pancreatic ductal adenocarcinoma',
 'pdac': 'Pancreatic ductal adenocarcinoma',
 'ucec': 'Uterine Corpus Endometrial Carcinoma'}

Now to understand the data types that are present call the cptac.get_cancer_options() function. You will note that sometimes a datatype has multiple sources. These represent different computational pipelines run by different bioinformatics groups. For example, the transcriptomics data has both bcm and broad as sources, designating that there are different versions of transcriptomics data create by the groups at Baylor College of Medicine and The Broad. As shown down below, wherever you request a dataframe, you need to specify a source.

cptac.get_source_options()

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

Exploring the data

cptac allows you to load the dataset into a Python variable, and you can use that variable to access and work with the data. To load a particular dataset into a variable, type the name you want to give the variable, followed by =, and then type cptac. and the name of the dataset in UpperCamelCase followed by two parentheses, e.g. cptac.Ucec() or cptac.Ccrcc():

en = cptac.Ucec()

To see what data is available, use the en.list_data_sources() function. This displays the different types of data included in the dataset for this particular cancer type, each stored in a pandas dataframe.

en.list_data_sources()

	Data type	Available sources
0	CNV	[bcm, washu]
1	circular_RNA	[bcm]
2	miRNA	[bcm, washu]
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	phosphoproteomics	[umich]
12	cibersort	[washu]
13	hla_typing	[washu]
14	tumor_purity	[washu]
15	xcell	[washu]

Molecular Omics

Data can be accessed through the get_dataframe function, or through one of several helper "get" functions. For example, we can look at the proteomics data by using en.get_proteomics(). This returns a pandas dataframe containing the proteomic data. Each column in the proteomics dataframe is the quantitiative measurement for a particular protein. Each row in the proteomics dataframe is a sample of either a tumor or non-tumor from a cancer patient.

# These two methods of getting proteomics data are functionally equivalent:
# get_dataframe needs at least two arguments: datatype, and source
proteomics = en.get_dataframe('proteomics', 'umich')
# there is also a get function for each datatype, this is the same as get_dataframe 
# but does not need the datatype argument. This function call below is equivalent to 
# the one above
proteomics = en.get_proteomics('umich')

samples = proteomics.index
proteins = proteomics.columns
print("Samples:",samples[0:20].tolist()) #the first twenty samples
print("Proteins:",proteins[0:20].tolist()) #the first twenty proteins

Samples: ['C3L-00006', 'C3L-00008', 'C3L-00032', 'C3L-00084', 'C3L-00090', 'C3L-00098', 'C3L-00136', 'C3L-00137', 'C3L-00139', 'C3L-00143', 'C3L-00145', 'C3L-00156', 'C3L-00157', 'C3L-00161', 'C3L-00356', 'C3L-00358', 'C3L-00361', 'C3L-00362', 'C3L-00413', 'C3L-00449']
Proteins: [('ARF5', 'ENSP00000000233.5'), ('M6PR', 'ENSP00000000412.3'), ('ESRRA', 'ENSP00000000442.6'), ('FKBP4', 'ENSP00000001008.4'), ('NDUFAF7', 'ENSP00000002125.4'), ('FUCA2', 'ENSP00000002165.5'), ('DBNDD1', 'ENSP00000002501.6'), ('SEMA3F', 'ENSP00000002829.3'), ('CFTR', 'ENSP00000003084.6'), ('CYP51A1', 'ENSP00000003100.8'), ('USP28', 'ENSP00000003302.4'), ('SLC7A2', 'ENSP00000004531.10'), ('HSPB6', 'ENSP00000004982.3'), ('PDK4', 'ENSP00000005178.5'), ('USH1C', 'ENSP00000005226.7'), ('RALA', 'ENSP00000005257.2'), ('BAIAP2L1', 'ENSP00000005260.8'), ('TMEM132A', 'ENSP00000005286.4'), ('DVL2', 'ENSP00000005340.4'), ('RPAP3', 'ENSP00000005386.3')]

Dataframe values

Values in the dataframe are protein abundance values. Values that read "NaN" mean that particular sample from that patient had no data for that particular protein. In CPTAC proteomics data, a TMT-reference channel strategy was used. A detailed description of this strategy can be found at Nature Protocols and also at PubMed Central. This strategy ratios each sample's abundance to a pooled reference. The ratio is then log transformed. Therefore positive values indicate a measurement higher than the pooled reference; negative values are lower than the pooled reference.

proteomics.head()

Name	ARF5	M6PR	ESRRA	FKBP4	NDUFAF7	FUCA2	DBNDD1	SEMA3F	CFTR	CYP51A1	...	SCRIB	WIZ	BPIFB4	LDB1	WIZ	TSGA10	RFX7	SWSAP1	MSANTD2	SVIL
Database_ID	ENSP00000000233.5	ENSP00000000412.3	ENSP00000000442.6	ENSP00000001008.4	ENSP00000002125.4	ENSP00000002165.5	ENSP00000002501.6	ENSP00000002829.3	ENSP00000003084.6	ENSP00000003100.8	...	ENSP00000501177.1	ENSP00000501256.3	ENSP00000501266.1	ENSP00000501277.1	ENSP00000501300.1	ENSP00000501312.1	ENSP00000501317.1	ENSP00000501355.1	ENSP00000501466.1	ENSP00000501521.1
Patient_ID
C3L-00006	-0.056513	0.016557	0.002569	0.389819	0.603610	-0.332543	-0.790426	NaN	0.822732	0.039134	...	0.161720	-0.884807	NaN	0.268247	0.125392	-0.880833	0.108554	0.107413	-0.085833	NaN
C3L-00008	0.549959	-0.206129	0.905784	-0.303631	0.018767	0.503513	0.950955	0.080142	NaN	-0.063213	...	NaN	0.054284	NaN	-0.106450	0.380557	-0.756099	0.264611	0.044423	-0.248319	-1.206596
C3L-00032	0.088681	-0.154447	-0.190515	0.170753	0.196356	0.544194	-0.179078	NaN	NaN	0.377405	...	-1.086905	0.055991	NaN	-0.021986	-0.229645	1.923986	NaN	-0.176694	-0.332384	-1.330653
C3L-00084	-0.846555	0.027740	NaN	0.178700	0.264054	-0.183548	0.077215	-0.247164	0.152277	-0.279549	...	-0.125796	0.944212	NaN	0.917409	0.026862	-0.885976	-0.006510	-0.014162	0.365158	NaN
C3L-00090	0.539019	0.956619	-0.039516	0.323656	0.064605	0.173433	-0.524325	-0.038590	-0.311486	0.309905	...	0.853362	-0.716947	NaN	-0.286277	-0.046076	0.089645	-0.444506	-0.072531	-0.463495	NaN

5 rows × 12662 columns

As seen in en.list_data_sources(), other omics data are also available (e.g. transcriptomics, copy number variation, phoshoproteomics).

The transcriptomics looks almost identical to the proteomics data, available in a pandas dataframe with the same convention. Each set of samples is consistent, meaning samples found in the endometrial proteomics data will be the same samples in all other endometrial dataframes.

transcriptomics = en.get_transcriptomics('bcm')
transcriptomics.head()

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)

Name	A1BG	A1BG-AS1	A1CF	A2M	A2M-AS1	A2ML1	A2ML1-AS1	A2ML1-AS2	A2MP1	A3GALT2	...	ZXDB	ZXDC	ZYG11A	ZYG11AP1	ZYG11B	ZYX	ZYXP1	ZZEF1	hsa-mir-1253	hsa-mir-423
Database_ID	ENSG00000121410.12	ENSG00000268895.6	ENSG00000148584.15	ENSG00000175899.15	ENSG00000245105.4	ENSG00000166535.20	ENSG00000256661.1	ENSG00000256904.1	ENSG00000256069.7	ENSG00000184389.9	...	ENSG00000198455.4	ENSG00000070476.15	ENSG00000203995.10	ENSG00000232242.2	ENSG00000162378.13	ENSG00000159840.16	ENSG00000274572.1	ENSG00000074755.15	ENSG00000272920.1	ENSG00000266919.3
Patient_ID
C3L-00006	2.54	5.11	3.60	13.75	6.45	7.08	1.80	0.00	2.60	1.16	...	10.17	10.61	5.54	0.0	11.85	10.60	0.0	11.87	0.0	0.0
C3L-00008	4.40	4.63	5.49	13.89	6.61	6.97	0.00	2.74	3.25	0.00	...	9.79	10.48	7.79	0.0	12.28	11.28	0.0	11.93	0.0	0.0
C3L-00032	4.83	7.26	3.73	14.48	6.91	9.56	0.98	0.00	3.26	0.00	...	9.43	9.97	6.48	0.0	11.72	10.37	0.0	11.70	0.0	0.0
C3L-00084	4.73	6.01	5.37	15.17	7.93	3.86	0.00	0.00	3.73	1.15	...	9.23	10.37	7.47	0.0	11.86	10.13	0.0	11.19	0.0	0.0
C3L-00090	4.14	6.24	5.69	13.87	6.79	4.32	0.00	0.00	3.23	0.00	...	9.69	9.64	7.60	0.0	11.98	10.31	0.0	11.45	0.0	0.0

5 rows × 59286 columns

Clinical Data

The clinical dataframe lists clinical information for the patient associated with each sample (e.g. age, race, diabetes status, tumor size).

clinical = en.get_clinical('mssm')
clinical.head()

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
C3L-00006	UCEC	Yes	Tumor_and_Normal	NaN	NaN	64	Female	White	Not Hispanic or Latino	White	...	NaN	NaN	NaN	NaN	NaN	NaN	0	737.0	737.0	0.0
C3L-00008	UCEC	Yes	Tumor	NaN	NaN	58	Female	White	Not Hispanic or Latino	White	...	NaN	NaN	NaN	NaN	NaN	NaN	0	898.0	898.0	0.0
C3L-00032	UCEC	Yes	Tumor	NaN	NaN	50	Female	White	Not Hispanic or Latino	White	...	NaN	NaN	NaN	NaN	NaN	NaN	0	1710.0	1710.0	0.0
C3L-00084	UCEC	Yes	Tumor	NaN	NaN	74	Female	White	Not Hispanic or Latino	White	...	NaN	NaN	NaN	NaN	NaN	NaN	0	335.0	335.0	0.0
C3L-00090	UCEC	Yes	Tumor	NaN	NaN	75	Female	White	Not Hispanic or Latino	White	...	Yes	No	NaN	NaN	50.0	56.0	1	1281.0	1287.0	1.0

5 rows × 124 columns

In addition to donating a tumor sample, some patients also had a normal sample taken for control and comparison. We can identify these samples by looking for samples marked "Tumor_and_Normal" in the "type_of_analyzed_samples" column. For example, patient C3L-00006 provided both a tumor sample and a normal sample. Since the data in these cases is for both tumor and normal samples, where a column would have identical values for both samples from the same patient (e.g., patient age and gender), the information is recorded only for the tumor sample.

clinical.loc[["C3L-00006","C3L-00361","C3L-01246"]]

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
C3L-00006	UCEC	Yes	Tumor_and_Normal	NaN	NaN	64	Female	White	Not Hispanic or Latino	White	...	NaN	NaN	NaN	NaN	NaN	NaN	0	737.0	737.0	0.0
C3L-00361	UCEC	Yes	Tumor_and_Normal	NaN	NaN	64	Female	White	Not Hispanic or Latino	WHITE	...	NaN	NaN	NaN	NaN	NaN	NaN	0	1707.0	1707.0	0.0
C3L-01246	UCEC	Yes	Tumor_and_Normal	NaN	NaN	62	Female	White	Unknown	Medical record does not state.	...	NaN	NaN	NaN	NaN	NaN	NaN	0	1538.0	1524.0	0.0

3 rows × 124 columns

Mutation data

Each cancer dataset contains mutation data for the cohort. The data consists of all somatic mutations found for each sample (meaning there will be many lines for each sample). Each row lists the specific gene that was mutated, the type of mutation, and the location of the mutation. This data is a direct import of a MAF file.

somatic_mutations = en.get_somatic_mutation('harmonized')
somatic_mutations.head()

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
C3L-00006	TEKT5	Silent	p.C178C	146279.0	hg38	chr16	10694340	10694340	+	SNP	...	Q96M29	ENSG00000153060	uc002czz.2	NaN	A1L3Z3	0.99969737512742099,0.00030262487257900	146279.0	UCEC	True	NaN
C3L-00006	AK9	Missense_Mutation	p.A1819V	221264.0	hg38	chr6	109494058	109494058	+	SNP	...	Q5TCS8	ENSG00000155085	NaN	NaN	A6NL75|B2RDJ0|B6ZDM7|Q3MIS4|Q5I0W8|Q6ZNF1|Q6ZV...	NaN	221264.0	UCEC	True	True
C3L-00006	FYN	Missense_Mutation	p.T15M	2534.0	hg38	chr6	111720008	111720008	+	SNP	...	P06241	ENSG00000010810	uc003pvk.3	NaN	B5BU57|E1P557|H0UI48|Q16248|Q5R3A6|Q5R3A7|Q8N5D7	NaN	2534.0	UCEC	True	True
C3L-00006	LAMA4	Missense_Mutation	p.T206I	3910.0	hg38	chr6	112191737	112191737	+	SNP	...	Q16363	ENSG00000112769	uc003pvv.4	hg38	Q14731|Q14735|Q15335|Q4LE44|Q5SZG8|Q9BTB8|Q9UE...	NaN	3910.0	UCEC	True	True
C3L-00006	MARCKS	Missense_Mutation	p.A129V	4082.0	hg38	chr6	113859966	113859966	+	SNP	...	P29966	ENSG00000277443	uc032xir.2	NaN	E1P560|Q2LA83|Q5TDB7	NaN	4082.0	UCEC	True	True

5 rows × 131 columns

Exporting dataframes

If you wish to export a dataframe to a file, simply call the dataframe's to_csv method, passing the path you wish to save the file to, and the value separator you want:

clinical = en.get_clinical('mssm')
clinical.to_csv(path_or_buf="clinical_dataframe.tsv", sep='\t')

Downloading data

The cptac package stores the data files for each dataset on a remote server. When you first install cptac, you will have no data files. Data files will be automatically downloaded the first time you try to use them. If you won't have internet access and need to download the files for a specific cancer beforehand, that can be done with the "get" functions:

br = cptac.Brca()
br.get_transcriptomics('broad')
#This downloads and displays the brca transcriptomic data from the "broad" source

Name	A1BG					A1BG-AS1					...	ZZEF1								hsa-mir-1253	hsa-mir-423
Transcript_ID	ENST00000263100.8	ENST00000595014.1	ENST00000596924.1	ENST00000598345.1	ENST00000600966.1	ENST00000593374.5	ENST00000593960.5	ENST00000594950.5	ENST00000595302.1	ENST00000599728.5	...	ENST00000572426.5	ENST00000572699.1	ENST00000572831.1	ENST00000573183.1	ENST00000573536.1	ENST00000573606.2	ENST00000574474.1	ENST00000575428.1	ENST00000609567.1	ENST00000586878.1
Database_ID	ENSG00000121410.12	ENSG00000121410.12	ENSG00000121410.12	ENSG00000121410.12	ENSG00000121410.12	ENSG00000268895.6	ENSG00000268895.6	ENSG00000268895.6	ENSG00000268895.6	ENSG00000268895.6	...	ENSG00000074755.15	ENSG00000074755.15	ENSG00000074755.15	ENSG00000074755.15	ENSG00000074755.15	ENSG00000074755.15	ENSG00000074755.15	ENSG00000074755.15	ENSG00000272920.1	ENSG00000266919.3
Patient_ID
01BR001	0.0	0.06	0.15	10.13	0.00	0.0	0.21	1.26	1.28	0.0	...	0.27	0.62	0.00	0.00	1.38	0.19	0.14	0.00	0.0	0.0
01BR008	0.0	0.09	0.06	3.12	0.11	0.0	0.00	2.98	1.94	0.0	...	0.05	0.12	0.00	0.00	0.74	0.27	0.03	0.00	0.0	0.0
01BR009	0.0	0.13	0.47	11.88	0.00	0.0	0.00	1.22	0.10	0.0	...	0.32	1.19	0.00	0.00	3.53	1.55	0.32	0.28	0.0	0.0
01BR010	0.0	0.16	0.25	12.34	0.00	0.0	0.41	2.05	0.79	0.0	...	0.30	1.66	0.00	0.00	1.60	1.21	0.10	0.00	0.0	0.0
01BR015	0.0	0.04	0.39	19.99	0.00	0.0	0.00	1.93	0.79	0.0	...	0.77	1.29	0.48	0.00	2.08	0.50	0.36	0.00	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21BR010	0.0	0.17	0.20	1.47	0.00	0.0	0.00	0.00	0.53	0.0	...	1.38	0.56	0.00	0.00	1.56	0.95	0.87	0.22	0.0	0.0
22BR005	0.0	0.03	0.21	2.33	0.00	0.0	0.00	0.00	0.56	0.0	...	0.19	0.54	0.00	0.43	1.44	0.72	0.32	0.00	0.0	0.0
22BR006	0.0	0.94	0.21	4.98	0.00	0.0	0.24	0.08	1.34	0.0	...	1.31	1.08	0.85	0.52	3.46	0.67	0.29	0.00	0.0	0.0
CPT000814	0.0	0.00	0.00	21.64	0.17	0.0	0.64	2.38	1.05	0.0	...	0.00	0.00	0.00	0.00	0.49	0.43	0.05	0.00	0.0	0.0
CPT001846	0.0	0.05	0.07	51.13	0.05	0.0	0.83	2.24	0.65	0.0	...	0.06	0.00	0.00	0.13	0.14	0.00	0.02	0.00	0.0	0.0

121 rows × 225997 columns

Getting help with a dataset or function

To view the documentation for a dataset, pass it to the Python help function, e.g. help(en). You can also view the documentation for just a specific function: help(en.join_omics_to_omics).

help(en.join_omics_to_omics)

Help on method join_omics_to_omics in module cptac.cancers.cancer:

join_omics_to_omics(df1_name: str, df2_name: str, df1_source: str = None, df2_source: str = None, genes1: str = None, genes2: str = None, how: str = 'outer', quiet: bool = False, tissue_type: str = 'both') -> pandas.core.frame.DataFrame method of cptac.cancers.ucec.Ucec instance
    Take specified column(s) from one omics dataframe, and join to specified columns(s) from another omics dataframe. Intersection (inner join) of indices is used.
    
    Parameters:
    df1_name (str): Name of first omics dataframe to select columns from.
    df2_name (str): Name of second omics dataframe to select columns from.
    df1_source (str): Name of source for the first omics dataframe.
    df2_source (str): Name of source for the second omics dataframe.
    genes1 (str, or list or array-like of str, optional): Gene(s) for column(s) to select from df1_name. str if one key, list or array-like of str if multiple.
        Default of None will select entire dataframe.
    genes2 (str, or list or array-like of str, optional): Gene(s) for Column(s) to select from df2_name. str if one key, list or array-like of str if multiple.
        Default of None will select entire dataframe.
    how (str, optional): How to perform the join, acceptable values are from ['outer', 'inner', 'left', 'right']. Defaults to 'outer'.
    quiet (bool, optional): Whether to warn when inserting NaNs. Defaults to False.
    tissue_type (str): Acceptable values in ["tumor","normal","both"]. Specifies the desired tissue type for the dataframe. Defaults to "both".
    
    Returns:
    pandas.DataFrame: The selected columns from the two omics dataframes, joined into one dataframe.