import errno
from fbpca import pca
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import os
import sys
np.random.seed(0)
def dispersion(X):
mean = X.mean(0)
dispersion = np.zeros(mean.shape)
nonzero_idx = np.nonzero(mean > 1e-10)[1]
X_nonzero = X[:, nonzero_idx]
nonzero_mean = X_nonzero.mean(0)
nonzero_var = (X_nonzero.multiply(X_nonzero)).mean(0)
temp = (nonzero_var / nonzero_mean)
dispersion[mean > 1e-10] = temp.A1
dispersion[mean <= 1e-10] = float('-inf')
return dispersion
def reduce_dimensionality(X, dim_red_k=100):
k = min((dim_red_k, X.shape[0], X.shape[1]))
U, s, Vt = pca(X, k=k) # Automatically centers.
return U[:, range(k)] * s[range(k)]
def visualize_cluster(coords, cluster, cluster_labels,
cluster_name=None, size=1, viz_prefix='vc',
image_suffix='.svg'):
if not cluster_name:
cluster_name = cluster
labels = [ 1 if c_i == cluster else 0
for c_i in cluster_labels ]
c_idx = [ i for i in range(len(labels)) if labels[i] == 1 ]
nc_idx = [ i for i in range(len(labels)) if labels[i] == 0 ]
colors = np.array([ '#cccccc', '#377eb8' ])
image_fname = '{}_cluster{}{}'.format(
viz_prefix, cluster, image_suffix
)
plt.figure()
plt.scatter(coords[nc_idx, 0], coords[nc_idx, 1],
c=colors[0], s=size)
plt.scatter(coords[c_idx, 0], coords[c_idx, 1],
c=colors[1], s=size)
plt.title(str(cluster_name))
plt.savefig(image_fname, dpi=500)
def visualize_expr(X, coords, genes, viz_gene, image_suffix='.svg',
new_fig=True, size=1, viz_prefix='ve'):
genes = [ gene.upper() for gene in genes ]
viz_gene = viz_gene.upper()
if not viz_gene.upper() in genes:
sys.stderr.write('Warning: Could not find gene {}\n'.format(viz_gene))
return
image_fname = '{}_{}{}'.format(
viz_prefix, viz_gene, image_suffix
)
# Color based on percentiles.
x_gene = X[:, list(genes).index(viz_gene)].toarray()
colors = np.zeros(x_gene.shape)
n_tiles = 100
prev_percentile = min(x_gene)
for i in range(n_tiles):
q = (i+1) / float(n_tiles) * 100.
percentile = np.percentile(x_gene, q)
idx = np.logical_and(prev_percentile <= x_gene,
x_gene <= percentile)
colors[idx] = i
prev_percentile = percentile
colors = colors.flatten()
if new_fig:
plt.figure()
plt.title(viz_gene)
plt.scatter(coords[:, 0], coords[:, 1],
c=colors, cmap=cm.get_cmap('Reds'), s=size)
plt.savefig(image_fname, dpi=500)
def visualize_dropout(X, coords, image_suffix='.svg',
new_fig=True, size=1, viz_prefix='dropout'):
image_fname = '{}{}'.format(
viz_prefix, image_suffix
)
# Color based on percentiles.
x_gene = np.array(np.sum(X != 0, axis=1))
colors = np.zeros(x_gene.shape)
n_tiles = 100
prev_percentile = min(x_gene)
for i in range(n_tiles):
q = (i+1) / float(n_tiles) * 100.
percentile = np.percentile(x_gene, q)
idx = np.logical_and(prev_percentile <= x_gene,
x_gene <= percentile)
colors[idx] = i
prev_percentile = percentile
colors = colors.flatten()
if new_fig:
plt.figure()
plt.title(viz_prefix)
plt.scatter(coords[:, 0], coords[:, 1],
c=colors, cmap=cm.get_cmap('Reds'), s=size)
plt.savefig(image_fname, dpi=500)
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def handle_zeros_in_scale(scale, copy=True):
''' Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
Adapted from sklearn.preprocessing.data'''
# if we are fitting on 1D arrays, scale might be a scalar
if np.isscalar(scale):
if scale == .0:
scale = 1.
return scale
elif isinstance(scale, np.ndarray):
if copy:
# New array to avoid side-effects
scale = scale.copy()
scale[scale == 0.0] = 1.0
return scale
