def main():
parser = argparse.ArgumentParser()
parser.add_argument('--cancerType', dest='type',\
help='Cancer type to be collected')
parser.add_argument('--getData',dest='get', action='store_true',\
default=False,help='Set flag to get all data')
opts = parser.parse_args()
if opts.get:
for ds in ['brca', 'ccrcc', 'colon', 'ovarian', 'endometrial', 'luad']:
cptac.download(dataset=ds)
if opts.type.lower() == 'brca':
dat = cptac.Brca()
elif opts.type.lower() == 'ccrcc':
dat = cptac.Ccrcc()
elif opts.type.lower() == 'coad':
dat = cptac.Colon()
elif opts.type.lower() == 'ovca':
dat = cptac.Ovarian()
elif opts.type.lower() == 'luad':
dat = cptac.Luad()
elif opts.type.lower() == 'endometrial':
dat = cptac.Endometrial()
else:
exit()
df = dat.get_phosphoproteomics()
pdf = dat.get_proteomics()
# df.columns = [' '.join(col).strip() for col in df.columns.values]
df.to_csv(path_or_buf="phos_file.tsv", sep='\t')
pdf.to_csv(path_or_buf='prot_file.tsv', sep='\t')
def getDataForCancer(ctype):
if ctype.lower() == 'brca':
dat = cptac.Brca()
elif ctype.lower() == 'ccrcc':
dat = cptac.Ccrcc()
elif ctype.lower() == 'coad':
dat = cptac.Colon()
elif ctype.lower() == 'ovca':
dat = cptac.Ovarian()
elif ctype.lower() == 'luad':
dat = cptac.Luad()
elif ctype.lower() == 'endometrial':
dat = cptac.Endometrial()
else:
exit()
return dat
def cptacData():
'''
We need to collect and load CPTAC data
'''
print("Loading cptac datasets")
#we need to make sure all datasets are downloaded
##here are the cancers that are available without login information
allcans = ['brca', 'ccrcc', 'colon', 'ovarian', 'luad',\
#'hnscc','gbm','lscc',\
'endometrial']
print("Downloading cptac data")
for ct in allcans:
cptac.download(dataset=ct)
#then we load them into a dictionary
fdict = {'brca':cptac.Brca(), 'ccrcc':cptac.Ccrcc(),\
'colon':cptac.Colon(), 'ovarian':cptac.Ovarian(),\
#'hnscc':cptac.Hnscc(),'gbm':cptac.Gbm(), 'lscc':cptac.Lscc(),\
'endometrial':cptac.Endometrial(), 'luad':cptac.Luad()}
return fdict
#!/usr/bin/env python
# coding: utf-8
### Creation of BoxPlots for ESR1 Protien Expression ###
import cptac # import cptac to download cptac protein and clinical data
import pandas as pd # import pandas for
import matplotlib.pyplot as plt # import matplotlib for boxplot
import seaborn as sns # import seaborn for boxplot
from scipy import stats
from statannot import add_stat_annotation
cptac.download(dataset="Brca") # download breast cancer dataset
br = cptac.Brca() # save data in br variable
protein_data = br.get_proteomics() # save proteomic data
protein_data = protein_data.droplevel(1, axis=1) # remove multi index
clinical_data = br.get_clinical() # save clinical data
esr1 = protein_data["ESR1"] # save ESR1 protein expression column
clinical_data["ER.IHC.Score"] = clinical_data["ER.IHC.Score"].fillna(
"Not reported") # fill in null values
er_mask = clinical_data[
"ER.IHC.Score"] == "3+" # 3+ is ER-positive, create mask of ER-positive patients
patients = esr1[er_mask] # apply mask to protein expression data
ages = clinical_data["Age.in.Month"][er_mask] / 12 # calculate ages in years
er_positive_patients = pd.DataFrame(patients,
columns=["ESR1"]) # create new dataframe
er_positive_patients["Age"] = ages # apply ages to new column in dataframe
category = [] # set categories list
import cptac
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
#Alyssa's code
cptac.download(dataset="Brca")
br = cptac.Brca()
protein_data = br.get_proteomics()
#Remove the "multi" part of the dataframe (the dataframes are MultIndex pandas dataframes)
protein_data = protein_data.droplevel(1, axis=1)
rna_data = br.get_transcriptomics()
clinical_data = br.get_clinical()
clinical_data["Age_in_years"] = clinical_data["Age.in.Month"]/12
# clinical_data.to_csv("~/Desktop/qbioresearch/qbio_data_analysis_alyssa/data/clinical_data.csv")
# protein_data.to_csv("~/Desktop/qbioresearch/qbio_data_analysis_alyssa/data/protein_data.csv")
# rna_data.to_csv("~/Desktop/qbioresearch/qbio_data_analysis_alyssa/data/rna_data.csv")
# clinical_data_readin = pd.read_csv("~/Desktop/qbioresearch/qbio_data_analysis_alyssa/data/clinical_data.csv", index_col=1) #The index_col=0 creates the index (rownames) as the first column.
#Check that the patients are in the same order in rna_data and protein_data.
#We are looking at the gene expression and protein information of EACH patient, so the data needs to be in pairs.
# To view available datasets, enter 'cptac.list_data()'.
cptac.list_datasets()
cptac.download(dataset = "endometrial")
cptac.download(dataset = 'colon')
cptac.download(dataset = 'ovarian')
cptac.download(dataset = 'RenalCcrcc')
#cptac.download(dataset ='luad')
#cptac.download(dataset ='brca')
downloadCptac()
endometrialData = cptac.Endometrial()
colorectalData = cptac.Colon()
ovarianData = cptac.Ovarian()
renalData = cptac.RenalCcrcc()
lungData = cptac.Luad()
breastData = cptac.Brca()
def listDataForEachCancer():
print("endometrial")
endometrialData.list_data()
print("\n\ncolorectal")
colorectalData.list_data()
print("\n\novarian")
ovarianData.list_data()
print("\n\nrenal")
renalData.list_data()
listDataForEachCancer()
#################################################################
# Correlation: Proteomics vs Transcriptom in Endometrial Cancer #