def _create_ordination_results(self):
eigvals = [0.51236726, 0.30071909, 0.26791207, 0.20898868]
proportion_explained = [
0.2675738328, 0.157044696, 0.1399118638, 0.1091402725
]
sample_ids = [
'1.SKM4.640180', '1.SKB8.640193', '1.SKD8.640184', '1.SKM9.640192',
'1.SKB7.640196'
]
axis_labels = ['PC1', 'PC2', 'PC3', 'PC4']
samples = [[-2.584, 1.739, 3.828, -1.944],
[-2.710, -1.859, -8.648, 1.180],
[2.350, 9.625, -3.457,
-3.208], [2.614, -1.114, 1.476, 2.908],
[2.850, -1.925, 6.232, 1.381]]
ord_res = OrdinationResults(
short_method_name='PCoA',
long_method_name='Principal Coordinate Analysis',
eigvals=pd.Series(eigvals, index=axis_labels),
samples=pd.DataFrame(np.asarray(samples),
index=sample_ids,
columns=axis_labels),
proportion_explained=pd.Series(proportion_explained,
index=axis_labels))
fd, fp = mkstemp(suffix='.txt', dir=self.out_dir)
close(fd)
ord_res.write(fp)
return fp
def rpca(in_biom: str, output_dir: str,
min_sample_depth: int, rank: int) -> None:
""" Runs RPCA with an rclr preprocessing step"""
# import table
table = load_table(in_biom)
# filter sample to min depth
def sample_filter(val, id_, md): return sum(val) > min_sample_depth
table = table.filter(sample_filter, axis='sample')
table = table.to_dataframe().T.drop_duplicates()
# rclr for saving the transformed OTU table (RSC edited)
tablefit = rclr().fit_transform(table.copy())
U,s,V = OptSpace().fit_transform(tablefit)
tablefit = np.dot(np.dot(U, s), V.T)
tablefit = pd.DataFrame(tablefit.T, index=table.columns, columns=table.index)
with open(os.path.join(output_dir, 'rclr_OTUtable.txt'), 'w'):
tablefit.to_csv(os.path.join(output_dir, 'rclr_OTUtable.txt'), sep='\t', index_label='OTU_ID')
# rclr preprocessing and OptSpace (RPCA)
opt = OptSpace(rank=rank).fit(rclr().fit_transform(table.copy()))
rename_cols = {i - 1: 'PC' + str(i) for i in range(1, rank + 1)}
# Feature Loadings
feature_loading = pd.DataFrame(opt.feature_weights, index=table.columns)
feature_loading = feature_loading.rename(columns=rename_cols)
feature_loading.sort_values('PC1', inplace=True, ascending=True)
# Sample Loadings
sample_loading = pd.DataFrame(opt.sample_weights, index=table.index)
sample_loading = sample_loading.rename(columns=rename_cols)
proportion_explained = pd.Series(opt.explained_variance_ratio,
index=list(rename_cols.values()))
eigvals = pd.Series(opt.eigenvalues,
index=list(rename_cols.values()))
# save ordination results
ord_res = OrdinationResults(
'PCoA',
'Principal Coordinate Analysis',
eigvals.copy(),
sample_loading.copy(),
features=feature_loading.copy(),
proportion_explained=proportion_explained.copy())
# write files to output folder
ord_res.write(os.path.join(output_dir, 'RPCA_Ordination.txt'))
# save distance matrix
dist_res = skbio.stats.distance.DistanceMatrix(
opt.distance, ids=sample_loading.index)
dist_res.write(os.path.join(output_dir, 'RPCA_distance.txt'))
return
def _1(data: skbio.OrdinationResults) -> OrdinationFormat:
ff = OrdinationFormat()
data.write(str(ff), format='ordination')
return ff
def _1(data: skbio.OrdinationResults) -> OrdinationFormat:
ff = OrdinationFormat()
data.write(str(ff), format='ordination')
return ff
