DSuveges
commented
2 years ago To get the enriched cell dataset, at first we have to create a new cell description file following these steps:
import requests
import pandas as pd
def uberon_lookup(label: str)-> str:
'''Retrieving uberon identifier of a label from OLS, assuming perfect match.'''
if not label:
return None
label = label.lower()
url = f'https://www.ebi.ac.uk/ols/api/search?q={label}&queryFields=label&ontology=uberon&exact=true'
# Parsing:
try:
data = requests.get(url).json()
uberon_id = data['response']['docs'][0]['short_form']
return uberon_id
except IndexError:
return None
except KeyError:
return None
except ConnectionError:
return None
# Loading cell description from project score:
crispr_cell_description = (
pd.read_csv('crispr_cell_lines.tsv', sep='\t')
.rename(columns={
'Name': 'name',
'Tissue': 'tissue',
'Cancer Type': 'diseaseFromSource'
})
)
print(f'Number of cell lines in the crispr cell lines: {len(crispr_cell_description)}')
print(f'Number of cell lines with no tissue: {len(crispr_cell_description.loc[crispr_cell_description.tissue.isna()])}')
# Extract unique list of tissues and map to uberon:
annotated_tissues = (
crispr_cell_description
[['tissue']]
.drop_duplicates()
.assign(tissueId = lambda df: df.tissue.apply(uberon_lookup))
)
print(f'Number of unique tissues: {len(annotated_tissues)}')
print(f'Number of tissues with no uberon mapping: {len(annotated_tissues.loc[annotated_tissues.tissueId.isna()])}')
# Fetching cell model data from Sanger:
cell_models =(
pd.read_csv('https://cog.sanger.ac.uk/cmp/download/model_list_20210719.csv')
[['model_id', 'model_name']]
.rename(columns={
'model_id': 'id',
'model_name': 'name'
})
.drop_duplicates()
)
print(f'Number of cell models: {len(cell_models)}')
# Finalising and saving data:
crispr_cell_description = (
crispr_cell_description
# Merging uberon annotation and cell line identifiers with cell lines:
.merge(annotated_tissues, on='tissue', how='left')
# Merging cell line identifiers with cell lines:
.merge(cell_models, on='name', how='left')
)
print(f'Number of cell lines at the end: {len(crispr_cell_description)}')
print(f'Number of cell lines with no identifier: {len(crispr_cell_description.loc[crispr_cell_description.id.isna()])}')
print(f'Number of cell lines with no uberon: {len(crispr_cell_description.loc[crispr_cell_description.tissueId.isna()])}')
# Saving enriched data file:
(
crispr_cell_description
.to_csv('crispr_cell_lines_enriched_2021-10-22.tsv', sep='\t', index=False)
)What the output shows:
Number of cell lines in the crispr cell lines: 336
Number of cell lines with no tissue: 0
Number of unique tissues: 19
Number of tissues with no uberon mapping: 7
Number of cell models: 2007
Number of cell lines at the end: 336
Number of cell lines with no identifier: 0
Number of cell lines with no uberon: 69The file now is a tsv with the followign header:
| name | tissue | diseaseFromSource | tissueId | id |
|---|---|---|---|---|
| A375 | Skin | Melanoma | SIDM00795 | |
| HCT-15 | Large Intestine | Colorectal Carcinoma | UBERON_0000059 | SIDM00789 |
| HT-29 | Large Intestine | Colorectal Carcinoma | UBERON_0000059 | SIDM00136 |
| HCC-78 | Lung | Lung Adenocarcinoma | UBERON_0002048 | SIDM01068 |
| SW620 | Large Intestine | Colorectal Carcinoma | UBERON_0000059 | SIDM00841 |
The new file has been uploaded to the usual google bucket: gs://otar000-evidence_input/CRISPR/data_files
ireneisdoomed
The disease/target json schema is being updated to capture cell line information better (
diseaseCellLinesfield). To reflect this advancement, the parser of the project score data needs to be updated too.For details of the json schema change see #1825