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NSAF.py
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NSAF.py
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class NSAF():
import pandas as pd
import mysql.connector
import math
from tqdm import tqdm
def __init__(self, db, tissue, percentage, disease_status):
"""Start building the atlas. Provide the database connection, level of the atlas (tissue_name or cell_type), the percentage of filtering and the disease_status which is desired (Healthy, None, Cancer,...)"""
self.db = db
self.percentage = percentage
self.conn = self.mysql.connector.connect(user='root', password='password', host='127.0.0.1', port='3306',database=self.db, auth_plugin='mysql_native_password')
mycursor = self.conn.cursor()
if tissue in ('tissue_name', 'cell_type'):
self.tissue = tissue
else:
print('tissue type not defined')
raise ValueError
self.disease_status = disease_status
def check_connection(self):
"""check if the connection to the mysql database is active """
if self.conn.is_connected():
print("connection succesfull")
else:
print("no connection")
def get_tissue_assay_quantification(self):
""" Build the table containing assay - peptide - quantification - tissue information"""
assayprojectsql = "SELECT * FROM assay where assay_id in (SELECT assay_id FROM tissue_to_assay);"
assayprojectData = self.pd.read_sql_query(assayprojectsql, self.conn)
assaysql = "SELECT assay_id, peptide_id, quantification FROM peptide_to_assay"
assayData = self.pd.read_sql_query(assaysql, self.conn)
assaytissuesql = "SELECT assay_id, tissue_id FROM tissue_to_assay"
assaytissueData = self.pd.read_sql_query(assaytissuesql, self.conn)
tissuesql = "SELECT tissue_id, tissue_name,cell_type, disease_status FROM tissue"
tissueData = self.pd.read_sql_query(tissuesql, self.conn)
tissue_assay = self.pd.merge(assaytissueData, tissueData, on='tissue_id', how='left')
tissue_assay = self.pd.merge(assayData, tissue_assay, on='assay_id', how='left')
return tissue_assay
def get_sequence_data(self):
""" select all the proteins from the database and their length """
seqsql = "SELECT uniprot_id, length FROM protein WHERE length IS NOT NULL"
seqData = self.pd.read_sql_query(seqsql, self.conn)
seqData['length'] = self.pd.to_numeric(seqData['length'], errors='coerce')
return seqData
def get_proteins(self):
"Get table containing the proteins and their quantification, cleaned for proteotypic data"
pepsql = "SELECT peptide_to_protein.peptide_id, peptide_to_protein.uniprot_id FROM peptide_to_protein"
pepData = self.pd.read_sql_query(pepsql, self.conn)
proteotypicData = pepData.groupby("peptide_id").filter(lambda x: len(x) == 1)
proteins = proteotypicData.groupby("uniprot_id").filter(lambda x: len(x) > 2)
non_human_proteins = ['TRYP_PIG', 'TRY2_BOVIN','TRY1_BOVIN','SSPA_STAAU','SRPP_HEVBR','REF_HEVBR', 'ADH1_YEAST', 'ALBU_BOVIN', 'CAS1_BOVIN', 'CAS2_BOVIN', 'CASK_BOVIN', 'CASB_BOVIN', 'OVAL_CHICK', 'ALDOA_RABIT', 'BGAL_ECOLI', 'CAH2_BOVIN', 'CTRA_BOVIN', 'CTRB_BOVIN', 'CYC_HORSE', 'DHE3_BOVIN', 'GAG_SCVLA', 'GFP_AEQVI', 'K1C15_SHEEP', 'K1M1_SHEEP', 'K2M2_SHEEP', 'K2M3_SHEEP', 'KRA3A_SHEEP', 'KRA3_SHEEP', 'KRA61_SHEEP', 'LALBA_BOVIN', 'LYSC_CHICK', 'LYSC_LYSEN', 'MYG_HORSE', 'K1M2_SHEEP', 'K2M1_SHEEP']
proteins = proteins[~proteins['uniprot_id'].isin(non_human_proteins)]
return proteins
def merge_protein_and_tissue_assay(self):
"merge the information from the proteins with the assay and tissue information"
tissue_assay = self.get_tissue_assay_quantification()
proteins = self.get_proteins()
protData = self.pd.merge(tissue_assay, proteins, on = 'peptide_id').sort_values(['assay_id','uniprot_id'])
if self.tissue == 'tissue_name':
del protData['cell_type']
if self.tissue == 'cell_type':
del protData['tissue_name']
del protData['peptide_id']
del protData['tissue_id']
del protData['disease_status']
return protData
def filter_protein_data(self):
"""filter the proteins per tissue based on the percentage given in the initialisation"""
print('started protein filtering')
protData = self.merge_protein_and_tissue_assay()
assays = protData[self.tissue].unique()
reduction = []
DataFrameDict = {elem : self.pd.DataFrame for elem in assays}
for key in self.tqdm(DataFrameDict.keys()):
DataFrameDict[key] = protData[:][protData[self.tissue] == key]
perc = self.math.floor(self.percentage * len(self.pd.unique(DataFrameDict[key]['assay_id'])))
before= DataFrameDict[key]['uniprot_id'].nunique()
DataFrameDict[key] = DataFrameDict[key].groupby('uniprot_id').filter(lambda x : len(x)>perc)
after= DataFrameDict[key]['uniprot_id'].nunique()
reduction.append(before-after)
#print('{} is done'.format(key))
filteredData = self.pd.DataFrame()
for key in DataFrameDict.keys():
filteredData = filteredData.append(DataFrameDict[key])
del filteredData[self.tissue]
filteredData = filteredData
reduction = sum(reduction)
print('finished protein filtering')
print('first there were {} proteins and now there are {}'.format(before, after))
return filteredData
def calculate_NSAF(self):
"""calculate the NSAF from the filtered protein data"""
seqData = self.get_sequence_data()
filteredData = self.filter_protein_data()
assays = filteredData['assay_id'].unique()
print('started NSAF calculations')
DataFrameDict3 = {elem : self.pd.DataFrame for elem in assays}
counter = 0
for key in self.tqdm(DataFrameDict3.keys()):
DataFrameDict3[key] = filteredData[:][filteredData['assay_id'] == key]
sumSaf = 0
df = DataFrameDict3[key]
df = df.drop(columns=['assay_id'])
# calculate sum of spectral counts for each protein
df_seq = self.pd.merge(df.groupby('uniprot_id').sum().reset_index(), seqData, on = 'uniprot_id')
df_seq.insert(loc = 2, column = 'SAF', value = 0)
df_seq.insert(loc = 3, column = 'NSAF', value = 0)
# calculate SAF score for each protein by dividing sum of spectral counts by protein length
df_seq['SAF'] = df_seq['quantification']/df_seq['length']
# calculate sum of SAF scores in assay
sumSaf = df_seq['SAF'].sum()
# Calculate NSAF score by normalizing each SAF score
df_seq['NSAF'] = df_seq['SAF']/ sumSaf
del df_seq['length']
del df_seq['quantification']
del df_seq['SAF']
df_seq.insert(loc = 0, column = 'assay_id', value = key)
DataFrameDict3[key] = df_seq
proteinData = self.pd.DataFrame()
for key in self.tqdm(DataFrameDict3.keys()):
proteinData = proteinData.append(DataFrameDict3[key])
self.proteinData = proteinData
print('finished NSAF calculations')
return self.proteinData
class Atlas():
import pandas as pd
import mysql.connector
def __init__(self, db, tissue, disease_status, df_nsaf):
"""This function merges the NSAF data with the tissue data from the MySQL database. Provide the database connection, level of the atlas (tissue_name or cell_type), the disease_status which is desired (Healthy, None, Cancer,...) and the NSAF file"""
self.db = db
self.conn = self.mysql.connector.connect(user='root', password='password', host='127.0.0.1', port='3306',database=self.db, auth_plugin='mysql_native_password')
mycursor = self.conn.cursor()
if tissue in ('tissue_name', 'cell_type'):
self.tissue = tissue
else:
print('tissue type not defined')
raise ValueError
self.disease_status = disease_status
self.nsaf = df_nsaf
def get_tissue(self):
""" get all the tissue information with the assay ids and select based on the disease status"""
tissuesql = """SELECT tissue_to_assay.assay_id, tissue.cell_type, tissue.tissue_name, tissue.disease_status, tissue.organ_id, tissue.fluid FROM tissue_to_assay JOIN tissue ON tissue_to_assay.tissue_id = tissue.tissue_id"""
tissueData = self.pd.read_sql_query(tissuesql,self.conn)
if self.disease_status != None:
tissueData = tissueData[tissueData['disease_status']==self.disease_status]
return tissueData
def create_atlas(self):
""" create the atlas using the NSAF proteome and the assay-tissue values"""
tissueData = self.get_tissue()
atlas = self.pd.merge(self.nsaf, tissueData, on='assay_id')
return atlas
class Predictor():
import pandas as pd
import mysql.connector
def __init__(self, db, tissue, disease_status, df_nsaf):
"""Build the atlases used for training the classifier. Provide the database connection, the level of the atlas (tissue_name or cell_type), the disease_status and the NSAF file"""
self.db = db
self.conn = self.mysql.connector.connect(user='root', password='password', host='127.0.0.1', port='3306',database=self.db, auth_plugin='mysql_native_password')
mycursor = self.conn.cursor()
if tissue in ('tissue_name', 'cell_type'):
self.tissue = tissue
else:
print('tissue type not defined')
raise ValueError
self.disease_status = disease_status
self.nsaf = df_nsaf
def get_tissue(self):
"""get the tissue and assay information from the database"""
tissuesql = """SELECT tissue_to_assay.assay_id, tissue.cell_type, tissue.tissue_name, tissue.disease_status, tissue.fluid FROM tissue_to_assay JOIN tissue ON tissue_to_assay.tissue_id = tissue.tissue_id"""
tissueData = self.pd.read_sql_query(tissuesql,self.conn)
if self.disease_status != None:
tissueData = tissueData[tissueData['disease_status']==self.disease_status]
if self.tissue == 'tissue':
tissueData = tissueData.drop(['cell_type', 'disease_status', 'fluid'], axis=1)
elif self.tissue == 'cell_type':
tissueData = tissueData.drop(['tissue_name', 'disease_status', 'fluid'], axis=1)
return tissueData
def get_assay_atlas(self):
"""merge the NSAF values with the tissue data on assay to get the final predictor atlas"""
tissueData = self.get_tissue()
assay_atlas = self.pd.pivot_table(self.nsaf, values = 'NSAF', index = 'assay_id', columns = 'uniprot_id').fillna(0).reset_index()
atlas = self.pd.merge(assay_atlas, tissueData, on='assay_id')
atlas = atlas.drop(columns=['assay_id'])
return atlas