def gene_specificity(self, gene, log=True):
gene_i = self.name_or_index(gene)
if gene_i == None:
spec = 0
else:
gexpr = self.expr[gene_i,:]
if log:
gexpr = [math.log(e+1) for e in gexpr]
if sum(gexpr) == 0:
spec = 0
else:
gexpr = stats.normalize(gexpr)
min_jsd = 1.0
for j in range(len(self.experiments)):
q_j = [0]*len(self.experiments)
q_j[j] = 1.0
min_jsd = min(min_jsd, math.sqrt(stats.jsd(gexpr, q_j)))
spec = 1.0 - min_jsd
return spec
def gene_specificity(self, gene, log=True):
gene_i = self.name_or_index(gene)
if gene_i == None:
spec = 0
else:
gexpr = self.expr[gene_i, :]
if log:
gexpr = [math.log(e + 1) for e in gexpr]
if sum(gexpr) == 0:
spec = 0
else:
gexpr = stats.normalize(gexpr)
min_jsd = 1.0
for j in range(len(self.experiments)):
q_j = [0] * len(self.experiments)
q_j[j] = 1.0
min_jsd = min(min_jsd, math.sqrt(stats.jsd(gexpr, q_j)))
spec = 1.0 - min_jsd
return spec
def genes_jsd(self, gene1, gene2, log=True):
gene1_i = self.name_or_index(gene1)
gene2_i = self.name_or_index(gene2)
gexpr1 = self.expr[gene1_i,:]
if log:
gexpr1 = [math.log(e+1) for e in gexpr1]
gexpr1 = stats.normalize(gexpr1)
gexpr2 = self.expr[gene2_i,:]
if log:
gexpr2 = [math.log(e+1) for e in gexpr2]
gexpr2 = stats.normalize(gexpr2)
return stats.jsd(gexpr1, gexpr2)
def genes_jsd(self, gene1, gene2, log=True):
gene1_i = self.name_or_index(gene1)
gene2_i = self.name_or_index(gene2)
gexpr1 = self.expr[gene1_i, :]
if log:
gexpr1 = [math.log(e + 1) for e in gexpr1]
gexpr1 = stats.normalize(gexpr1)
gexpr2 = self.expr[gene2_i, :]
if log:
gexpr2 = [math.log(e + 1) for e in gexpr2]
gexpr2 = stats.normalize(gexpr2)
return stats.jsd(gexpr1, gexpr2)
