def dot_plot(
robjects,
r_runtime_error: Exception,
means_path: str,
pvalues_path: str,
output_path: str,
output_name: str,
rows: Optional[str] = None,
columns: Optional[str] = None,
) -> None:
pvalues_separator = _get_separator(os.path.splitext(pvalues_path)[-1])
means_separator = _get_separator(os.path.splitext(means_path)[-1])
output_extension = os.path.splitext(output_name)[-1].lower()
filename = os.path.join(output_path, output_name)
means_df = pd.read_csv(means_path, sep=means_separator)
n_rows, n_cols = means_df.shape
n_cols -= 11
n_rows, selected_rows = selected_items(rows, n_rows)
n_cols, selected_columns = selected_items(columns, n_cols)
this_file_dir = os.path.dirname(os.path.realpath(__file__))
robjects.r.source(
os.path.join(this_file_dir, 'R/plot_dot_by_column_name.R'))
available_names = list(robjects.globalenv.keys())
plot_function = 'dot_plot'
if plot_function in available_names:
function_name = plot_function
else:
raise MissingPlotterFunctionException()
plotter = robjects.r[function_name]
try:
plotter(selected_rows=selected_rows,
selected_columns=selected_columns,
filename=filename,
width=int(5 + max(3, n_cols * 0.8)),
height=int(5 + max(5, n_rows * 0.5)),
means_path=means_path,
pvalues_path=pvalues_path,
means_separator=means_separator,
pvalues_separator=pvalues_separator,
output_extension=output_extension)
except r_runtime_error as e:
raise RRuntimeException(e)
def generate_complex(user_complex: Optional[str], result_path: str, log_file: str):
output_path = _set_paths(output_dir, result_path)
log_path = '{}/{}'.format(output_path, log_file)
curated_complex = pd.read_csv(os.path.join(data_dir, 'sources/complex_curated.csv'))
if user_complex:
separator = _get_separator(os.path.splitext(user_complex)[-1])
user_complex = pd.read_csv(user_complex, sep=separator)
result = complex_generator(curated_complex, user_complex, log_path)
result.to_csv('{}/{}'.format(output_path, 'complex_generated.csv'), index=False)
def generate_genes(
user_gene: Optional[str],
fetch_uniprot: bool,
fetch_ensembl: bool,
result_path: str,
project_name: str,
) -> None:
output_path = _set_paths(result_path, project_name)
# TODO: Add logger
if fetch_ensembl:
print('fetching remote ensembl data ... ', end='')
source_url = 'http://www.ensembl.org/biomart/martservice?query={}'
query = '<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE Query><Query virtualSchemaName = "default" ' \
'formatter = "CSV" header = "1" uniqueRows = "1" count = "" datasetConfigVersion = "0.6" >' \
'<Dataset name = "hsapiens_gene_ensembl" interface = "default" >' \
'<Attribute name = "ensembl_gene_id" />' \
'<Attribute name = "ensembl_transcript_id" />' \
'<Attribute name = "external_gene_name" />' \
'<Attribute name = "hgnc_symbol" />' \
'<Attribute name = "uniprotswissprot" />' \
'</Dataset>' \
'</Query>'
url = source_url.format(urllib.parse.quote(query))
ensembl_db = pd.read_csv(url)
print('done')
else:
ensembl_db = utils.read_data_table_from_file(
os.path.join(data_dir, 'sources/ensembl.txt'))
print('read local ensembl file')
# additional data comes from given file or uniprot remote url
if fetch_uniprot:
try:
print('fetching remote uniprot file ... ', end='')
source_url = 'https://www.uniprot.org/uniprot/?query=*&format=tab&force=true' \
'&columns=id,entry%20name,reviewed,protein%20names,genes,organism,length' \
'&fil=organism:%22Homo%20sapiens%20(Human)%20[9606]%22%20AND%20reviewed:yes' \
'&compress=yes'
uniprot_db = pd.read_csv(source_url, sep='\t', compression='gzip')
print('done')
except Exception as e:
print('Error fetching remote UniProt data, fetching local data')
uniprot_db = pd.read_csv(os.path.join(data_dir,
'sources/uniprot.tab'),
sep='\t')
print('read local uniprot file')
else:
uniprot_db = utils.read_data_table_from_file(
os.path.join(data_dir, 'sources/uniprot.tab'))
print('read local uniprot file')
ensembl_columns = {
'Gene name': 'gene_name',
'Gene stable ID': 'ensembl',
'HGNC symbol': 'hgnc_symbol',
'UniProtKB/Swiss-Prot ID': 'uniprot'
}
uniprot_columns = {'Entry': 'uniprot', 'Gene names': 'gene_names'}
result_columns = ['gene_name', 'uniprot', 'hgnc_symbol', 'ensembl']
ensembl_db = ensembl_db[list(
ensembl_columns.keys())].rename(columns=ensembl_columns)
uniprot_db = uniprot_db[list(
uniprot_columns.keys())].rename(columns=uniprot_columns)
hla_genes = utils.read_data_table_from_file(
os.path.join(data_dir, 'sources/hla_curated.csv'))
if user_gene:
separator = _get_separator(os.path.splitext(user_gene)[-1])
user_gene = pd.read_csv(user_gene, sep=separator)
cpdb_genes = gene_generator(ensembl_db, uniprot_db, hla_genes, user_gene,
result_columns)
cpdb_genes[result_columns].to_csv('{}/{}'.format(output_path,
'gene_generated.csv'),
index=False)
def generate_proteins(user_protein: Optional[str], fetch_uniprot: bool,
result_path: str, log_file: str, project_name: str):
uniprot_columns = {
'Entry': 'uniprot',
'Entry name': 'protein_name',
}
# additional data comes from given file or uniprot remote url
if fetch_uniprot:
try:
source_url = 'https://www.uniprot.org/uniprot/?query=*&format=tab&force=true' \
'&columns=id,entry%20name,reviewed,protein%20names,genes,organism,length' \
'&fil=organism:%22Homo%20sapiens%20(Human)%20[9606]%22%20AND%20reviewed:yes' \
'&compress=yes'
uniprot_db = pd.read_csv(source_url, sep='\t', compression='gzip')
print('read remote uniprot file')
except Exception as e:
print('Error fetching remote UniProt data, fetching local data')
uniprot_db = pd.read_csv(os.path.join(data_dir,
'sources/uniprot.tab'),
sep='\t')
print('read local uniprot file')
else:
uniprot_db = pd.read_csv(os.path.join(data_dir, 'sources/uniprot.tab'),
sep='\t')
print('read local uniprot file')
default_values = {
'transmembrane': False,
'peripheral': False,
'secreted': False,
'secreted_desc': pd.np.nan,
'secreted_highlight': False,
'receptor': False,
'receptor_desc': pd.np.nan,
'integrin': False,
'other': False,
'other_desc': pd.np.nan,
'tags': 'To_add',
'tags_reason': pd.np.nan,
'tags_description': pd.np.nan,
'pfam': pd.np.nan,
}
default_types = {
'uniprot': str,
'protein_name': str,
'transmembrane': bool,
'peripheral': bool,
'secreted': bool,
'secreted_desc': str,
'secreted_highlight': bool,
'receptor': bool,
'receptor_desc': str,
'integrin': bool,
'other': bool,
'other_desc': str,
'tags': str,
'tags_reason': str,
'tags_description': str,
'pfam': str,
}
result_columns = list(default_types.keys())
output_path = _set_paths(result_path, project_name)
log_path = '{}/{}'.format(output_path, log_file)
uniprot_db = uniprot_db[list(
uniprot_columns.keys())].rename(columns=uniprot_columns)
curated_proteins = pd.read_csv(
os.path.join(data_dir, 'sources/protein_curated.csv'))
if user_protein:
separator = _get_separator(os.path.splitext(user_protein)[-1])
user_protein = pd.read_csv(user_protein, sep=separator)
result = protein_generator(uniprot_db, curated_proteins, user_protein,
default_values, default_types, result_columns,
log_path)
result[result_columns].to_csv('{}/{}'.format(output_path,
'protein_generated.csv'),
index=False)
def generate_interactions(
proteins: str,
genes: str,
complex: str,
user_interactions: Optional[str],
user_interactions_only: bool,
result_path: str,
fetch_imex: bool,
fetch_iuphar: bool,
project_name: str,
) -> None:
if user_interactions_only and not user_interactions:
raise Exception('You need to set --user-interactions parameter')
output_path = utils.set_paths(result_path, project_name)
downloads_path = utils.set_paths(
utils.set_paths(result_path, project_name), 'downloads')
proteins = utils.read_data_table_from_file(proteins)
genes = utils.read_data_table_from_file(genes)
complexes = utils.read_data_table_from_file(complex)
if not user_interactions_only:
raw_imex = get_imex.call(genes, downloads_path, fetch_imex)
interactions_to_remove = utils.read_data_table_from_file(
os.path.join(data_dir, 'sources/excluded_interaction.csv'))
interaction_curated = utils.read_data_table_from_file(
os.path.join(data_dir, 'sources/interaction_curated.csv'))
if user_interactions:
separator = _get_separator(os.path.splitext(user_interactions)[-1])
user_interactions = pd.read_csv(user_interactions, sep=separator)
user_interactions['partner_a'] = user_interactions['partner_a'].apply(
lambda x: str(x).strip())
user_interactions['partner_b'] = user_interactions['partner_b'].apply(
lambda x: str(x).strip())
user_interactions['annotation_strategy'] = 'user_curated'
if not 'protein_name_a' in user_interactions.columns:
user_interactions['protein_name_a'] = ''
if not 'protein_name_b' in user_interactions.columns:
user_interactions['protein_name_b'] = ''
result_columns = [
'partner_a', 'partner_b', 'protein_name_a', 'protein_name_b',
'annotation_strategy', 'source'
]
if not user_interactions_only:
print('Parsing IMEX file')
imex_interactions = parse_interactions_imex(raw_imex, proteins, genes)
print('Getting iuphar data')
raw_iuphar = get_iuphar.call(downloads_path, fetch_iuphar)
print('Generating iuphar interactions')
iuphar_interactions = parse_iuphar_guidetopharmacology.call(
raw_iuphar, genes, proteins)
print('Merging iuphar/imex')
merged_interactions = merge_iuphar_imex_interactions(
iuphar_interactions, imex_interactions)
print('Removing complex interactions')
no_complex_interactions = only_noncomplex_interactions(
merged_interactions, complexes)
print('Removing selected interactions')
clean_interactions = remove_interactions_in_file(
no_complex_interactions, interactions_to_remove)
print('Adding curated interaction')
interactions_with_curated = add_curated(clean_interactions,
interaction_curated)
result = tools_helper.normalize_interactions(
interactions_with_curated.append(user_interactions,
ignore_index=True,
sort=False), 'partner_a',
'partner_b').drop_duplicates(['partner_a', 'partner_b'],
keep='last')
else:
result = tools_helper.normalize_interactions(user_interactions, 'partner_a', 'partner_b') \
.drop_duplicates(['partner_a', 'partner_b'], keep='last')
result[result_columns].sort_values(['partner_a', 'partner_b']).to_csv(
'{}/interaction_input.csv'.format(output_path), index=False)
