def main(args):
"""Runs eveything"""
# Importing data
dat = wideToDesign(args.input, args.design, args.uniqID, logger=logger)
# Cleaning from missing data
dat.dropMissing()
# Getting labels to drop from arguments
x = True
y = True
if "x" in args.labels:
x = False
if "y" in args.labels:
y = False
print("x =", x)
print("y =", y)
#Plotting with dendogram Hierarchical cluster heatmap (HCH)
logger.info("Plotting heatmaps")
if args.dendogram == True:
fh = hm.plotHCHeatmap(dat.wide,
hcheatmap=True,
cmap=palette.mpl_colormap,
xlbls=x,
ylbls=y)
fh.savefig(args.fig, format="pdf")
#Plotting without a dendogram single heatmap
else:
# Creating figure Handler object
fh = figureHandler(proj='2d', figsize=(14, 14))
# Creating plot
hm.plotHeatmap(dat.wide,
fh.ax[0],
cmap=palette.mpl_colormap,
xlbls=x,
ylbls=y)
# formating axis
fh.formatAxis(xTitle="sampleID")
# Saving figure
fh.export(out=args.fig, dpi=300)
# Finishing script
logger.info("Script Complete!")
def nontechnical_analysis(args, df, mask, C, clustering):
# Re-order things more palatably for the user,
# based on the results of the technical analysis.
# Get the map from the name to the original row index.
all_row_names = df.index.values
row_index_map = {s: i for i, s in enumerate(all_row_names)}
# If some variables are uninformative for clustering,
# the correlation matrix and the cluster vector will have smaller
# dimensions than the number of rows in the original data frame.
remaining_row_names = df[mask].index.values
# Count the variables included in the clustering.
p = clustering.shape[0]
# Count the clusters.
k = clustering.max() + 1
# To sort the modules and to sort the variables within the modules,
# we want to use absolute values of correlations.
C_abs = np.abs(C)
# For each cluster, get its indices and its submatrix of C_abs.
selections = []
submatrices = []
degrees = np.zeros(p, dtype=float)
for i in range(k):
selection = np.flatnonzero(clustering == i)
selections.append(selection)
submatrix = C_abs[np.ix_(selection, selection)]
submatrices.append(submatrix)
if selection.size > 1:
denom = selection.size - 1
degrees[selection] = (submatrix.sum(axis=0) - 1) / denom
# Modules should be reordered according to decreasing "average degree".
cluster_sizes = []
average_degrees = []
for selection in selections:
cluster_sizes.append(selection.size)
average_degrees.append(degrees[selection].mean())
module_to_cluster = np.argsort(average_degrees)[::-1]
cluster_to_module = {v: k for k, v in enumerate(module_to_cluster)}
triples = [(
cluster_to_module[clustering[i]],
-degrees[i],
i,
) for i in range(p)]
_a, _b, new_to_old_idx = zip(*sorted(triples))
# Make a csv file if requested.
header = ('Gene', 'Module', 'Entry Index', 'Average Degree', 'Degree')
with open(args.out, 'wb') as fout:
writer = csv.writer(
fout, 'excel-tab') #problematic; need to switch to tsv file!
writer.writerow(header)
for old_i in new_to_old_idx:
name = remaining_row_names[old_i]
cluster = clustering[old_i]
row = (
name,
cluster_to_module[cluster] + 1,
row_index_map[name] + 1,
average_degrees[cluster],
degrees[old_i],
)
writer.writerow(row)
#Create Output
fh1 = figureHandler(proj="2d")
fh2 = figureHandler(proj="2d")
fh3 = figureHandler(proj="2d")
# Prepare to create the sorted heatmaps. (fh2)
C_sorted = C[np.ix_(new_to_old_idx, new_to_old_idx)]
clustering_new = clustering[np.ix_(new_to_old_idx)]
# Draw the third heatmap (smoothed).
# Make a smoothed correlation array. (fh3)
S = expansion(clustering_new)
block_mask = S.dot(S.T)
denom = np.outer(S.sum(axis=0), S.sum(axis=0))
small = S.T.dot(C_sorted).dot(S) / denom
C_all_smoothed = S.dot(small).dot(S.T)
C_smoothed = (C_all_smoothed * (1 - block_mask) + C_sorted * block_mask)
# Getting list of names for heatmaps 2 and 3
hpnames = [remaining_row_names[old_i] for old_i in new_to_old_idx]
# Plot using something like http://stackoverflow.com/questions/15988413/
# Drawing heatmaps
# Draw first heatmap [C]
hm.plotHeatmap(C,
fh1.ax[0],
cmap=palette.mpl_colormap,
xlbls=remaining_row_names,
ylbls=remaining_row_names)
fh1.formatAxis(xTitle="sampleID", figTitle="Correlations")
# Draw second heatmap [C_sorted](reordered according to the clustering).
hm.plotHeatmap(C_sorted,
fh2.ax[0],
cmap=palette.mpl_colormap,
xlbls=hpnames,
ylbls=hpnames)
fh2.formatAxis(xTitle="sampleID", figTitle="Re-Ordered correlations")
# Draw the heatmap [C_smoothed](smoothed version of C_sorted)
hm.plotHeatmap(C_smoothed,
fh3.ax[0],
cmap=palette.mpl_colormap,
xlbls=hpnames,
ylbls=hpnames)
fh3.formatAxis(xTitle="sampleID", figTitle="Smoothed correlations")
#Create output from maps
with PdfPages(args.figure) as pdf:
fh1.addToPdf(pdf)
fh2.addToPdf(pdf)
fh3.addToPdf(pdf)