def new_TE_distribution():
fields, filtered = filtered_low_counts(0)
TEs = {'ingolia_1': np.log2(filtered[:, fields['ingolia:RPF_1']]) - np.log2(filtered[:, fields['ingolia:mRNA_1']]),
'ingolia_2': np.log2(filtered[:, fields['ingolia:RPF_2']]) - np.log2(filtered[:, fields['ingolia:mRNA_2']]),
'ingolia_both': np.log2(filtered[:, fields['ingolia:RPF_1']] + filtered[:, fields['ingolia:RPF_2']]) - np.log2(filtered[:, fields['ingolia:mRNA_1']] + filtered[:, fields['ingolia:mRNA_2']]),
'weinberg_RiboZero': np.log2(filtered[:, fields['weinberg:RPF']]) - np.log2(filtered[:, fields['weinberg:RiboZero']]),
'weinberg_Dynabeads': np.log2(filtered[:, fields['weinberg:RPF']]) - np.log2(filtered[:, fields['weinberg:Dynabeads']]),
'weinberg_Unselected': np.log2(filtered[:, fields['weinberg:RPF']]) - np.log2(filtered[:, fields['weinberg:Unselected']]),
'artificial': np.log2(filtered[:, fields['weinberg:RPF']]) - np.log2(filtered[:, fields['weinberg:Dynabeads']] / np.asarray(filtered[:, fields['CDS_length']], dtype=float)),
'artificial2': np.log2(filtered[:, fields['weinberg:RPF']]) - np.log2(filtered[:, fields['weinberg:Dynabeads']] * np.asarray(filtered[:, fields['CDS_length']], dtype=float)),
}
for name in ['weinberg_RiboZero', 'weinberg_Dynabeads', 'weinberg_Unselected', 'ingolia_both', 'artificial', 'artificial2']:
plt.hist(TEs[name] - np.mean(TEs[name]), histtype='step', bins=100, range=(-4, 4), label=name)
plt.legend()
plt.xlabel('log2(RPF RPKM / mRNA RPKM')
plt.ylabel('Number of genes')
explore_UTRs.scatter_with_hists_colors(TEs['weinberg_RiboZero'] - np.mean(TEs['weinberg_RiboZero']),
TEs['weinberg_Unselected'] - np.mean(TEs['weinberg_Unselected']),
'weinberg_Ribozero',
'weinberg_Unselected',
'Joint distribution of TEs',
)
print scipy.stats.pearsonr(TEs['weinberg_RiboZero'], TEs['weinberg_Unselected'])
print scipy.stats.spearmanr(TEs['weinberg_RiboZero'], TEs['weinberg_Unselected'])
return TEs
def mRNA_RPKM_length_bias():
gtf_fn = '/home/jah/projects/arlen/data/organisms/saccharomyces_cerevisiae/EF4/transcriptome/genes.gtf'
genome_dir = '/home/jah/projects/arlen/data/organisms/saccharomyces_cerevisiae/EF4/genome'
coding_sequence_fetcher = gtf.make_coding_sequence_fetcher(gtf_fn, genome_dir)
lengths = np.asarray([len(coding_sequence_fetcher(name)) for name in gene_names])
explore_UTRs.scatter_with_hists_colors(lengths,
#np.log2(arrays['weinberg']['Dynabeads']) - np.log2(arrays['weinberg']['Unselected']),
np.log2(arrays['weinberg']['RPF']) - np.log2(arrays['ingolia']['RPF']),
'coding sequence length',
'log2(Ingolia mRNA RPKM / Weinberg mRNA RPKM)',
'',
)
plt.ylim(-7, 7)
plt.gcf().set_size_inches(12, 8)