Skip to content
Snippets Groups Projects
Commit 0372a1b8 authored by Blaise Li's avatar Blaise Li
Browse files

Skipping size factor graph for small data.

parent 49776bd1
No related branches found
No related tags found
No related merge requests found
......@@ -1617,7 +1617,8 @@ rule test_size_factor:
# The filter amounts to counts_data.mean(axis=1) > 4
#np.log10(counts_data[counts_data.sum(axis=1) > 4 * len(counts_data.columns)] + 1).plot.kde()
#np.log10(counts_data[counts_data.prod(axis=1) > 0]).plot.kde()
assert len(counts_data) > 1, "Counts data with only one row cannot have its distribution estimated using KDE."
# assert len(counts_data) > 1, "Counts data with only one row cannot have its distribution estimated using KDE."
if len(counts_data) > 1:
pp = PdfPages(output.norm_counts_distrib_plot)
for normalizer in params.size_factor_types:
if normalizer == "median_ratio_to_pseudo_ref":
......@@ -1677,6 +1678,9 @@ rule test_size_factor:
# "The data matrix has %d lines and %d columns.\n" % (len(data), len(data.columns))])
# warnings.warn(msg + "\nSkipping %s_%s" % (wildcards.orientation, wildcards.biotype))
pp.close()
else:
# Make the file empty
open(output.norm_counts_distrib_plot, "w").close()
# TODO: Deal with 0-counts cases:
......
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment