Skip to content

Latest commit

 

History

History
199 lines (182 loc) · 5.15 KB

lister.md

File metadata and controls

199 lines (182 loc) · 5.15 KB
Raw

Lister

Lister is a class that creates a Lister object, which grants access to all of the initial data that we will use for the project.

Lister uses pandas to manipulate our data so that we can more easily call it other processes.

Description

The data to be initialized includes the following:

  • Master_Accession_File.csv (include project name)
  • common_names.csv (PAML) (rename)
  • taxon_ids.txt

Add some examples of these files as template

Usage

Using docstrings for help:

from manager.lister import Lister
help(Lister)

Using this class is simple:

from lister import Lister
from pprint import pprint
x = Lister(acc_file = 'Master_Accession_File.csv', paml_file = 'common_names.csv', taxon_file = 'taxon_ids.txt')

Parameters

from lister import Lister
x = Lister(acc_file, paml_file, taxon_file, go_list = None, hgnc_file = False)
  • acc_file ('Master_Accession_file.csv') - Contains accession numbers for a group of genes ranked by tier. Each gene has a group of orthlogs used in our phylogenetic anlysis.
  • paml_file ('commonnames.csv') - Contains a list of shortened organism names used in the MSA files. This is done to compy with PAML.
  • taxon_file ('taxon_ids.txt') - Contains an ordered list of taxon ids
  • go_list ([[gene.1, org.1], ... , [gene.n, org.n]]) - A nested list that can be used to get information about specific gene/org pairs.
  • hgnc_file - For future implementation. Used as a file handle to parse an HGNC .csv file.

Returned Variables

  • x.gene_count
  • x.org_count
  • x.paml_org_list
  • x.taxon_ids

Lists

List that contain header info(x.header):

pprint(x.header)
['Tier',
 'Gene',
 'Homo_sapiens',
 'Macaca_mulatta',
 'Mus_musculus',
 'Rattus_norvegicus',
...
 'Trichechus_manatus_latirostris',
 'Tupaia_chinensis',
 'Tursiops_truncatus']

List of Accessions (x.acc_list):

pprint(x.acc_list)
['NM_000680.3',
 'NM_000679.3',
 'NM_000678.3',
...
 'xm_004368425.2',
 'XM_006155397.2',
 'XM_004317686.1']

List of Genes (x.gene_list):

pprint(x.gene_list)
['ADRA1A',
 'ADRA1B',
 'CHRM2',
 ...
 'SSTR2',
 'TSHR',
 'VIPR1']

List of Organisms (x.org_list):

pprint(x.org_list)
['Homo_sapiens',
 'Macaca_mulatta',
 'Mus_musculus',
 'Rattus_norveg',
...
'Trichechus_manatus_latirostris',
 'Tupaia_chinensis',
 'Tursiops_truncatus']

Dictionaries

To use a dictionary of accessions.

>>> pprint(x.acc_dict)
{'NM_000115.3': ['EDNRB', 'Homo_sapiens'],
 'NM_000145.3': ['FSHR', 'Homo_sapiens'],
 'NM_000164.3': ['GIPR', 'Homo_sapiens'],
...
 'NM_001001620.1': ['CCR3', 'Sus_scrofa'],
 'NM_001002911.3': ['GPR139', 'Homo_sapiens'],
 'NM_001002944.1': ['ADORA2B', 'Canis_lupus_familiaris']}

Dictionary of Genes is a nested dictionary. (x.gene_dict, x.tier_dict):

pprint(x.gene_dict['HTR1A'])
{'Ailuropoda_melanoleuca': 'XM_002926305.1',
 'Bos_taurus': 'XM_600535.5',
 'Callithrix_jacchus': 'XM_008992005.2',
 ...
 'Tier': '1',
 'Trichechus_manatus_latirostris': 'xm_004374552.2',
 'Tupaia_chinensis': 'xm_006156821.1',
 'Tursiops_truncatus': 'xm_004325159.1'}

pprint(x.tier_dict['HTR1A'])
'1'

Dictionary of Organisms is a nested dictionary (x.org_dict)

homosapiens_query = x.org_dict['Homo_sapiens'].values()
homosapiens_gene_list = x.org_dict['Homo_sapiens'].keys()
>>> pprint(list(homosapiens_query))
['NM_000680.3',
 'NM_000679.3',
 'NM_000678.3',
...
 'XM_011517263.2',
 'NM_000369.2',
 'NM_004624.3']

Pandas Dataframes

Dataframe that uses Gene as an index (x.df):

pprint(x.df.T.HTR1A)
['Tier                                            1
Homo_sapiens                          NM_000524.3
Macaca_mulatta                     NM_001198700.1
Mus_musculus                          NM_008308.4
...
Tupaia_chinensis                   xm_006156821.1
Tursiops_truncatus                 xm_004325159.1
Name: HTR1A, dtype: object']

Pivot Table MultiIndexed with pandas(x.pt):

# #### Format the main pivot table #### #
self.pt = pd.pivot_table(pd.read_csv(self.__filename_path), index=['Tier', 'Gene'], aggfunc='first')
array = self.pt.axes[1].tolist() # Organism list
self.pt.columns = pd.Index(array, name='Organism')

If your data has tiers or is divided into groups (x.get_tier_frame, x.tier_frame_dict):

Tiers = x.get_tier_frame('1')
Tiers.keys()
dict_keys(['1'])
Tiers = x.tier_frame_dict()
Tiers.keys()
dict_keys(['1', '2', '3', 'None'])

Methods

Lookup Accessions (x.get_accession(gene, org), x.get_accesions(go_list=None))

x.get_accession('HTR1A', 'Homo_sapiens')
'NM_000524.3'

go_list = [['HTR1A', 'Homo_sapiens'], ['HTR1A', 'Macaca_mulatta']]

x.get_accessions(go_list = go_list)
['NM_000524.3', 'NM_001198700.1']

Lookup a list of Accession for alignment(x.get_accession_alignment(gene)):

pprint(x.get_accession_alignment('HTR1A'))
['NM_000524.3',
 'NM_001198700.1',
...
 'xm_006156821.1',
 'xm_004325159.1']

Get the master lists from a new dataframe(self.get_master_list(df)):

from Manager.lister import Lister
import os
from pathlib import Path
y = Lister()
y.get_master_lists(csv_file='MAFV3.1.csv')