Skipping size factor graph for small data.

RNA_Seq_Cecere/RNA-seq.snakefile

@@ -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: