def coexpression(self,gene_a,gene_b):
'''
Returns a coexpression z-score between two genes. This
is the pearson correlation coefficient of the two genes'
expression profiles across the accessions (experiments).
This value is pulled from the
Parameters
----------
gene_a : camoco.Locus
The first gene
gene_b : camoco.Locus
The second gene
Returns
-------
Coexpression Z-Score
'''
# Grab the indices in the original expression matrix
ids = np.array([self._expr_index[gene_a.id],self._expr_index[gene_b.id]])
# We need the number of genes
num_genes = self.num_genes()
index = PCCUP.coex_index(ids,num_genes)[0]
return self.coex.iloc[index]
def subnetwork(self,gene_list=None,sig_only=True,min_distance=100000,
filter_missing_gene_ids=True):
'''
Input: a gene list (passing None gives you all genes)
Output: a dataframe containing all edges EXCLUSIVELY between genes
within list
'''
if gene_list is None:
df = self.coex
else:
ids = np.array([self._expr_index[x.id] for x in gene_list])
if filter_missing_gene_ids:
# filter out the Nones
ids = np.array(list(filter(None,ids)))
num_genes = self.num_genes()
# Grab the coexpression indices for the genes
indices = PCCUP.coex_index(ids,num_genes)
df = self.coex.iloc[indices]
if min_distance:
df = df.loc[df.distance >= min_distance,:]
if sig_only:
df = df.loc[df.significant == 1,:]
return df.copy()
