def _load_go_v1_1(taxon_id):
p = inout.get_path(
'geisen', 'ncbi/gene2go/gene2go_taxon_{}.csv.gz'.format(
int(taxon_id)))
df = pd.read_csv(p)
p = inout.get_path(
'geisen', 'ncbi/gene2go/go_id_to_term.csv.gz')
df_label = pd.read_csv(p)
df = pd.merge(
df, df_label, left_on='GO_ID', right_on='GO_ID', how='left')
return df
def human_phenotype_genealacart(taxon_id=9606, add_absenece=True):
"""
Human phenotypes
Source: Human Phenotype Ontology through Genealacart
Input:
taxon_id int
add_absence bool; default is True; add genes for which there
is no phenotype entry
Output:
dataframe
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path(
'geisen',
'genealacart/genealacart_phenotype_ontology_kind.gz')
df = pd.read_csv(p)
df = df.set_index('gene_ncbi')
df = df.reset_index()
if add_absenece:
p = inout.get_path(
'geisen',
'genealacart/genealacart_phenotype_ontology_amount.gz')
df_c = pd.read_csv(p)
extra = np.setdiff1d(
np.array(df_c['gene_ncbi'].unique()),
np.array(df['gene_ncbi'].unique()))
df_p = pd.DataFrame(
index=extra,
columns=[
'human_phenotype_genealacart: human_phenotype_id',
'human_phenotype_genealacart: human_phenotype_name'
])
df_p.loc[
:,
'human_phenotype_genealacart: human_phenotype_name'] = \
'No known human phenotype'
df_p.index.name = 'gene_ncbi'
df_p = df_p.reset_index()
df = pd.concat([df, df_p], axis=0)
return df
def rosenfeld_2013(cols_to_use=None):
"""
Will load gene-linked patent data as described by
Rosenfeld et al. ; Note that most patents are filed
for sequences, rather than genes, and are ambiguous,
and including related genes, on purpose. See
publication of Rosenfeld et al. on that
socio-economical problem, and for specific
cutoff / mapping scheme applied by them (Note:
which sounds like a very reasonable compromise)
Note: original datset might have some excess
records that could not be unambiguously mapped
to gene_ncbi (Entrez gene IDs)
Output:
linkage table between patent and gene_ncbi
"""
p_folder = inout.get_path(
'geisen',
os.path.join('papers', 'rosenfeld_2013',
'rosenfeld_2013_patents_ncbi_gene.csv.gz'))
df = pd.read_csv(p_folder, usecols=['patent',
'gene_ncbi']) # skip index column
df = df.drop_duplicates()
return df
def allelepool_lek_2016_anticipation(taxon_id=9606):
"""
Anticipated variability in human populations (allele diversity)
Original source data: Lek et al. 2015
Input:
taxon_id int (safety check)
Output:
df ordinal numbers
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
n = 'lek2016_anticipation_ordnum_ncbi_gene.csv.gz'
p = inout.get_path('geisen', 'papers/lek_2016/{}'.format(n))
df = pd.read_csv(p)
def add_label(x):
if not x.startswith('gene_ncbi'):
x = 'Population variability Lek ' + x
return x
df.columns = [add_label(x) for x in df.columns]
return df
def challenged_proteins():
p = inout.get_path('publications', 'ezkurdia2014/ddu309supp_tables1.xlsx')
df_all = pd.read_excel(p, sheet_name='All G12 genes')
df_tagged = pd.read_excel(p, sheet_name='Possible non-coding set')
gi = _get_gene_ncbi_2_ensembl()
if (df_tagged['ENSEMBL'].isin(df_all['ENSEMBL'])).all():
ge = sorted(gi[gi['gene_ensembl'].isin(
df_all['ENSEMBL'])]['gene_ncbi'])
df_suspicious = gi.copy()
df_suspicious = df_suspicious[df_suspicious['gene_ensembl'].isin(
df_all['ENSEMBL'])]
df_suspicious.loc[
:,
'ezkurdia_challenged'] = df_suspicious.loc[:, 'gene_ensembl'].isin(
df_tagged['ENSEMBL'])
dd = df_suspicious[['gene_ncbi', 'ezkurdia_challenged']
].set_index('gene_ncbi')
cl = dd.copy()
return cl, dd, ge
def protein_abundance_itzhak_2015(taxon_id=9606):
"""
Protein abundance in a human cell line
Original source data: Itzhak et al. 2015
Input:
taxon_id int (safety check)
Output:
df ordinal numbers
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path(
'geisen', 'papers/itzhak_2016/' +
'itzhak2016_protein_abundance_ordnum_ncbi_gene.csv.gz')
df = pd.read_csv(p)
def add_label(x):
if not x.startswith('gene_ncbi'):
x = 'Protein Itzhak ' + x
return x
df.columns = [add_label(x) for x in df.columns]
return df
def generif(taxon_id):
"""
Loads gene RIFs
Input:
taxon_id list of taxa, or int of taxon id, or 'all'
Output:
generifs dataframe
"""
p = inout.get_path(
'geisen',
'ncbi/generifs_basic.gz')
df = pd.read_csv(p, low_memory=False)
if isinstance(taxon_id, int):
taxon_id = [taxon_id]
if taxon_id != 'all':
f = df.loc[:, 'taxon_ncbi'].isin(taxon_id)
df = df.loc[f, :]
return df
def genbank_genomic_cds(taxon_id):
"""
Features for predicted coding sequences; Source is one of the
most complete genbank releases (manually selected one per species).
Features have been extracted for a manually selected set of species
(roughly corresponding to heavily studied species.)
For genbank_genomic_cds the features correspond to individual
nucleotides and individual codons.
Note that genomic CDS may not be defined for some species within
the original data source (genbank)
Input:
taxon_id int
Output:
df ordinal numbers
"""
p = inout.get_path(
'geisen',
'genbank/genomic_cds/genbank_genomic_cds_{}.csv'.format(taxon_id))
if os.path.exists(p):
df = pd.read_csv(p)
else:
print(p)
raise EnvironmentError(
'Did not find data for taxon {}'.format(taxon_id))
return df
def genome_rnai(taxon_id):
"""
Will load genome_rnai for datasets mappable to a
Pubmed ID
Input:
taxon_id e.g.: 9606 for human
Output:
df Genome RNAi table
"""
if taxon_id == 9606:
pheno_set = 'GenomeRNAi_v17_Homo_sapiens.txt'
else:
raise EnvironmentError('This function presenlty only supports human.')
# import genome RNAi dataset
p = inout.get_path('genome_rnai', pheno_set)
agg = []
is_valid_pubmed = False
pubmed_id = 'not_set'
with open(p, 'r') as rea:
for line in rea:
line = line.strip('\n')
if line.startswith('#Pubmed ID='):
pubmed_id = line[len('#Pubmed ID='):]
is_valid_pubmed = len(pubmed_id) > 1
elif line.startswith('#Screen Title'):
pubmed_id = 'invalid'
elif not line.startswith('#'):
if not line.startswith('//'):
if is_valid_pubmed:
h = line.strip('\n').split('\t')
h = h + [pubmed_id]
agg.append(h)
co = [
'Stable ID', 'Entrez ID', 'Gene ID', 'Gene Symbol', 'Reagent ID',
'Score', 'Phenotype', 'Conditions', 'Follow Up', 'Comment', 'pubmed_id'
]
df = pd.DataFrame(data=agg, columns=co)
f = df['Phenotype'] == 'Inconclusive'
df = df.loc[~f, :]
df = df.rename(
columns={
'Entrez ID': 'gene_ncbi',
'Gene ID': 'gene_id_ambiguous',
'Gene Symbol': 'gene_symbol_ambiguous',
'Stable ID': 'genome_rnai_id',
'Phenotype': 'phenotype',
'Reagent ID': 'reagent_id'
})
df = df.reset_index(drop=True)
return df
def compartment_itzhak_2016_cytoplasmic(taxon_id=9606):
"""
Cytoplasmic location (note complements with non-cytoplasmic
localization to 100); Note that the amount of recoreds is higher
than the amount of records for other predictions / features
of the original underlying publication (the finely resolved
localization)
Input:
taxon_id int safety
Ouput:
df nominal data
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
def _load_cytoplasmic_v1():
df = compartment_itzhak_2016_global_scores(taxon_id)
col_to_use = ['gene_ncbi', 'Localization Itzhak Cytosolic Pool']
df = df.loc[:, col_to_use]
return df
p = inout.get_path(
'geisen', 'papers/itzhak_2016/' +
'itzhak2016_localization_cytoplasm_ncbi_gene.csv.gz')
if os.path.exists(p): # geisen v1_1 or higher
df = pd.read_csv(p)
else:
df = _load_cytoplasmic_v1(taxon_id)
return df
def compartment_itzhak_2016_global_scores(taxon_id=9606):
"""
Prediction accuracy, and broad classification by nucleo-cyplasmic
localization
Input:
taxon_id int (safety check)
Output:
df ordinal numbers
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path(
'geisen', 'papers/itzhak_2016/' +
'itzhak2016_localization_stats_ordnum_ncbi_gene.csv.gz')
df = pd.read_csv(p)
def add_global(x):
if not x.startswith('gene_ncbi'):
x = 'Localization Itzhak ' + x
return x
df.columns = [add_global(x) for x in df.columns]
return df
def transcript_abundance_gerstein(taxon_id=6239):
"""
Abundance of transcripts under mixed conditoins
Source: modENCODE through gesrtein lab
NaN if original value below 1 (0 on log scale) or below detection threshold
Note that their meta-annotation is horrible, and misleading, and does
not allow to separate treatments from tissues etc.
While modEncode was helpful, and relayed request to Gerstein lab,
the Gerstein lab never replied (neither to modEncode help, nor me),
although this highl-level dataset is shown as part of modEncode
Input:
taxon_id int (saftey check)
Output:
df ordinal numbers,
and NaN (below reliable measurement threshold)
"""
if taxon_id != 6239:
raise EnvironmentError('Only supports taxon 6239, C. elegans')
p = inout.get_path('geisen',
'gerstein/gerstein_expression__ncbi_gene.csv.gz')
df = pd.read_csv(p)
return df
def genbank_validated_rna(taxon_id):
"""
Features for RNA with high support by genbank curators;
Source is one of the most complete genbank releases
(manually selected one per species). Features have been extracted
for a manually selected set of species
(roughly corresponding to heavily studied species.)
For genbank_validated_rna the features correspond to
individual nucleotides, codons, and cdon bias.
Note that validated RNA may not be defined for some species within
the original data source (genbank)
Input:
taxon_id int
Output:
df ordinal numbers
"""
p = inout.get_path(
'geisen',
'genbank/validated_rna/genbank_validated_rna_{}.csv'.format(taxon_id))
if os.path.exists(p):
df = pd.read_csv(p)
else:
raise EnvironmentError(
'Did not find data for taxon {}'.format(taxon_id))
return df
def omim_genealacart(taxon_id=9606, add_absenece=True):
"""
Mendelian diseases
Source: OMIM through Genealacart
Note that the source OMIM database, which is quite messy, would
in principle allow a higher level of stratication then Genealacart
Input:
taxon_id int
add_absence bool; default is True; add genes for which there
is no disease entry
Output:
dataframe
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path(
'geisen',
'genealacart/genealacart_omim_kind.gz')
df = pd.read_csv(p)
df = df.set_index('gene_ncbi')
df.columns = ['omim_disease__{}'.format(x) for x in df.columns]
df = df.rename(columns={'omim_disease__disease_name': 'omim_disease'})
df = df.reset_index()
if add_absenece:
p = inout.get_path(
'geisen',
'genealacart/genealacart_omim_amount.gz')
df_c = pd.read_csv(p)
extra = np.setdiff1d(
np.array(df_c['gene_ncbi'].unique()),
np.array(df['gene_ncbi'].unique()))
df_p = pd.DataFrame(index=extra, columns=['omim_disease'])
df_p.loc[:, 'omim_disease'] = 'No entry in OMIM'
df_p.index.name = 'gene_ncbi'
df_p = df_p.reset_index()
df = pd.concat([df, df_p], axis=0)
return df
def load_all_taxa(usecols=usecols):
p = inout.get_path('geisen', 'ncbi/gene_info/gene_info_full.gz')
if os.path.exists(p):
df = pd.read_csv(p, usecols=usecols)
else:
df = None # Extra Safety
raise EnvironmentError('Did not find gene info for all taxa')
return df
def genes_2_drugs_and_status(taxon_id, target_class):
"""
Loads drugbank IDs, and status for individual genes of
taxon_id
Input:
taxon_id int taxon ID
target_class str 'pharmacologically_active' or 'all'
"""
# Define code to filter drugbank
dictionary_of_taxa = {9606: 'Human'}
# neglect separation by drug class (e.g: small molecule)
considered_status = [
'approved', 'experimental', 'illicit', 'investigational',
'nutrazeutical', 'withdrawn'
]
agg = []
for status in considered_status:
p = inout.get_path(
'drugbank',
'protein_identifiers/drug_target_identifiers/{}/{}.csv'.format(
status, target_class))
df = pd.read_csv(p)
f = df['Species'] == dictionary_of_taxa[taxon_id]
df = df.loc[f, ['UniProt ID', 'Drug IDs']]
df.loc[:, 'status'] = status
agg.append(df)
df = pd.concat(agg, axis=0)
df = utils.split_text_to_multiple_rows(df, 'Drug IDs', ';')
df = df.rename(columns={
'UniProt ID': 'protein_uniprot',
'Drug IDs': 'drug_drugbank'
}).drop_duplicates()
p_mapper = mapper._get_geisen_path('uniprot/uniprot_id_mapper.h5')
if not os.path.exists(p_mapper):
raise EnvironmentError(
'uniprot_protein_2_gene_ncbi() requires uniprot_id_mapper')
ma = pd.read_hdf(p_mapper,
'table',
columns=['protein_uniprot', 'gene_ncbi'],
where='taxon_ncbi={}'.format(taxon_id))
df = pd.merge(df, ma)[['gene_ncbi', 'drug_drugbank',
'status']].drop_duplicates()
df = df.sort_values(['gene_ncbi', 'status',
'drug_drugbank']).reset_index(drop=True)
return df
def disease_genealacart(taxon_id=9606, add_absenece=True):
"""
Unified diseases
Source: Genealacart
Input:
taxon_id int
add_absence bool; default is True; add genes for which there
is no disease entry
Output:
dataframe
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path(
'geisen',
'genealacart/genealacart_diseases_kind.gz')
df = pd.read_csv(p)
df = df.set_index('gene_ncbi')
df.columns = ['unified_disease__{}'.format(x) for x in df.columns]
df = df.rename(
columns={'unified_disease__disease_name': 'unified_disease'})
df = df.reset_index()
if add_absenece:
p = inout.get_path(
'geisen',
'genealacart/genealacart_diseases_amount.gz')
df_c = pd.read_csv(p)
extra = np.setdiff1d(
np.array(df_c['gene_ncbi'].unique()),
np.array(df['gene_ncbi'].unique()))
df_p = pd.DataFrame(index=extra, columns=['unified_disease'])
df_p.loc[:, 'unified_disease'] = 'No known disease'
df_p.index.name = 'gene_ncbi'
df_p = df_p.reset_index()
df = pd.concat([df, df_p], axis=0)
return df
def load_file(code):
n = 'hart_2015_hart2015_{}_ordnum_ordnum_gene_ncbi.csv.gz'.format(code)
p = inout.get_path('geisen', 'papers/hart_2015/{}'.format(n))
if os.path.exists(p):
df = pd.read_csv(p)
else:
print(p)
raise EnvironmentError('Did not find data for {}'.format(code))
return df
def load_taxon(taxon_id, usecols):
p = inout.get_path(
'geisen', 'ncbi/gene_info/gene_info_taxon_{}.gz'.format(taxon_id))
if os.path.exists(p):
df = pd.read_csv(p, usecols=usecols)
else:
df = None # Extra Safety
raise EnvironmentError(
'Did not find gene info for taxon {}'.format(taxon_id))
return df
def _get_geisen_path(ext):
"""
support function to get to geisen files;
Note that this function is built to later allow updates of mapping
functions through considering updates in Geisen
Input:
ext extensin within geisen folder
"""
p = inout.get_path('geisen', ext)
return p
def external_links():
p = inout.get_path('drugbank', 'external_drug_links/drug_links.csv')
df = pd.read_csv(p)
df = df.rename(columns={
'DrugBank ID': 'drug_drugbank'
}).set_index('drug_drugbank', verify_integrity=True)
if df['Name'].str.lower().value_counts().max() > 1:
raise ValueError('Drug names are not unambigous')
return df
def gene2pubmed(taxon_id=None, usecols=None, paper_kind=None, ref_genes=None):
"""
Loads gene2pubmed from NIH; Will only return
non-duplicated data in casse that columns are
specified.
Input:
taxon_id int, or 'all'
usecols optional, list of columns to be loaded
paper_kind optional; filter for articles, e.g:
'research' to filter for papers in
Medline, where meta data suggests
that it is a research paper
ref_genes optional; filter for genes in ref_genes
Output:
gene2pubmed df
"""
p = inout.get_path('geisen', 'ncbi/gene2pubmed.h5')
if os.path.exists(p) is False:
raise EnvironmentError('Did not find gene2pubmed')
def load_all_taxa():
df = pd.read_hdf(p, 'table')
return df
def load_taxon(usecols):
q = 'taxon_ncbi=={}'.format(taxon_id)
df = pd.read_hdf(p, 'table', where=q)
return df
# Implement input specific behavior of gene_info
if taxon_id == 'all':
df = load_all_taxa()
elif isinstance(taxon_id, int):
df = load_taxon(taxon_id)
else:
raise EnvironmentError('Did not recognize format of taxon_id')
if usecols is not None:
df = df.loc[:, usecols]
df = df.drop_duplicates()
if paper_kind is not None:
df = standardizer.filter_by_paper_kind(df, paper_kind)
if ref_genes is not None:
df = df[df['gene_ncbi'].isin(ref_genes)]
return df
def dais(subset, allowed_dais='all', allowed_wos='all'):
"""
Loads all disambiguated author data for Web Of Science,
together with authorship information; Will iterate through
batched data; Thus filtering might be used to reduce memory
footprint (e.g.: if wos IDs are arleady known)
Input:
subset wos_dais that should be loaded; options:
'gene-linked' and 'all'
allowed_dais list of dais that should be loaded
allowed_wos list of wos IDs that should be loaded
Output:
df_wos_dais
"""
p = inout.get_path('rbusa', 'disambiguation/wos_dais')
if subset == 'gene-linked':
mask = os.path.join(p, 'wos_dais_gene_mapped_batch_*.csv.gz')
elif subset == 'all':
mask = os.path.join(p, 'wos_dais_all_batch*.csv.gz')
else:
raise EnvironmentError('subset not specified')
agg = []
for fi in glob.glob(mask):
df = pd.read_csv(fi)
if allowed_dais is not 'all':
f = df.loc[:, 'DAIS'].isin(allowed_dais)
df = df.loc[f, :]
if allowed_wos is not 'all':
f = df.loc[:, 'WOS'].isin(allowed_wos)
df = df.loc[f, :]
agg.append(df)
df = pd.concat(agg)
df = df.rename(columns={'WOS': 'wos_id', 'DAIS': 'dais_id'})
df.loc[:, 'dais_id'] = df.loc[:, 'dais_id'].astype(int)
df.loc[:, 'wos_id'] = df.loc[:, 'wos_id'].astype(str)
target_amount_of_numbes = 15
df.loc[:, 'wos_id'] = df.loc[:, 'wos_id'].apply(
lambda x: x.zfill(target_amount_of_numbes))
df.loc[:, 'wos_id'] = df.loc[:, 'wos_id'].astype(str)
return df
def _load_batches(path_pattern, dataset):
p_scheme = inout.get_path('geisen_manual', os.path.join(path_pattern))
agg = []
for p in glob.glob(p_scheme):
p = os.path.join(p, '{}.txt'.format(dataset))
agg.append(pd.read_table(p))
df = pd.concat(agg)
df = df.drop_duplicates()
# place symbols lower-case as genealacart does not appear
# to distinguish internally
n = ['InputTerm', 'Symbol']
for t in n:
df.loc[:, t] = df.loc[:, t].str.lower()
return df
def transcript_abundance_gex_mantalek_170222(mask):
"""
Tissue specific gene expression
Source: EBML-EBI Expression Atlas (https://www.ebi.ac.uk/gxa/)
Selected datasets manually downloaded by M. Antalek on
170222 using cutoff of 0
Unlike many other gene expression datasets, the returned gene
expression values are not log-transformed
Input:
mask int or str; e.g: taxon_id, or taxon_id-condition
(where condition is sample specific, e.g.: 10116-female)
Output:
df ordinal numbers
"""
p = inout.get_path(
'geisen', 'gxa/matt_antalek_170222/*-{}[-_]*_gene.csv.gz'.format(mask))
g = glob.glob(p)
if len(g) == 0:
raise EnvironmentError(
'Did not find any dataset matching the mask {}'.format(mask))
df = pd.read_csv(g[0])
if len(g) > 1:
for gn in g:
df_n = pd.read_csv(gn)
ref_c = 'gene_ncbi'
if sorted(df[ref_c].values) == sorted(df_n[ref_c].values):
df = pd.merge(df,
df_n,
left_on=ref_c,
right_on=ref_c,
how='outer')
if any(df.isnull().sum()):
raise EnvironmentError(
'Some measurements are lacking in one of the datasets.')
return df
def taxon_name(taxon_id):
"""
Obtains name of taxon
Note that the original reference data would also
contain synonymous names, and the class of the name
(e.g.: whether it is a scientific name)
Input:
taxon_id int; optionally set to 'all' to load
table with full taxonomy information
Output:
taxon_name str
"""
p = inout.get_path('geisen', 'ncbi/taxon_names.h5')
if taxon_id == 'all':
df = pd.read_hdf(p, 'table')
df = df.set_index('taxon_ncbi', verify_integrity=True)
output = df
else:
q = 'taxon_ncbi=={}'.format(taxon_id)
df = pd.read_hdf(p, 'table', where=q)
v = df['taxon_name'].values
if len(v) == 0:
print('Could not find name of taxon {}'.format(taxon_id))
name = 'not found taxon; id is {}'.format(taxon_id)
elif len(v) > 1:
raise EnvironmentError(
'Found multipole records for taxon {}'.format(taxon_id))
elif len(v) == 1:
name = str(v[0])
else:
raise EnvironmentError(
'Some error in code. This condition should never be' +
'triggered. Please investigate code of this function.')
output = name
return output
def transcript_halflife_tani_2012_assuming_48h_for_stable(taxon_id=9606):
"""
Transcript halflife in HeLa cells, as measured by Tani et al.
Note that the orgiginal data is on RNA, and different RNA of
same gene can get pooled (here: median). Thus this function uses
an educated guess for > 24h; more specifically, 48h hours will be used
so that, if one transcript is >24 and there is only one <24h,
the presence of a long lived transcript will be shown in the
aggregated readout of both RNA species of the same gene
Input:
taxon_id int (saftey check)
Output:
df ordnum (having given categorial value >24
the auxiliary value 48) - see above
"""
hours_to_assume_for_over_24 = 48 # manually selected by Thomas Stoeger
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path('publications',
'tani2012/Tani_Supp_Tables_revised2.xls')
df = pd.read_excel(p, sheet_name='Table S1', skiprows=3)
f = df['t1/2 (h)'] != 'N.D.'
df = df.loc[f, ['RepName', 't1/2 (h)']].rename(columns={
'RepName': 'rna_ncbi',
't1/2 (h)': 'rna_halflife_h'
})
df['rna_ncbi'] = df['rna_ncbi'].str.strip(',')
df = utils.split_text_to_multiple_rows(df, 'rna_ncbi', ',')
df['rna_halflife_h'] = df['rna_halflife_h'].replace(
'>24', hours_to_assume_for_over_24).astype(float)
df = df.groupby('rna_ncbi').agg(np.median)
df = mapper.rna_ncbi_2_gene_ncbi(df, 'median')
df = df.reset_index()
return df
def transcript_detection_uhlen_2015_tissues(taxon_id=9606):
"""
Fraction of tissue samples with expression >= 1FPKM
Source: Uhlen et al. 2015
Input:
taxon_id int (saftey check)
Output:
df ordinal numbers
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
n = 'uhlen_2015_detected_in_tissuess_ncbi_gene.csv.gz'
p = inout.get_path('geisen', 'papers/uhlen_2015/{}'.format(n))
df = pd.read_csv(p)
return df
def transcription_factors_genealacart_promoters(taxon_id=9606):
"""
Occurences of transcription factors in promoter regions
Source: Genealacart
Input:
taxon_id int (saftey check)
Output:
df ordinal numbers
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
n = 'genealacart_promoters_tfs_by_gene.gz'
p = inout.get_path('geisen', 'genealacart/{}'.format(n))
df = pd.read_csv(p)
return df
def rvis(taxon_id=9606):
"""
RVIS mutagenesis intolerance
source: RVIS through genealacart
Input:
taxon_id int (safety)
Output:
df ordinal numbers
"""
if taxon_id != 9606:
raise EnvironmentError('Only supports taxon 9606, H**o sapiens')
p = inout.get_path('geisen', 'genealacart/genealacart_intolerance_rvis.gz')
df = pd.read_csv(p)
return df
