/
ctf.py
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/
ctf.py
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import biom
import skbio
from pandas import concat
from pandas import DataFrame
from skbio import OrdinationResults, DistanceMatrix
from gemelli.factorization import TensorFactorization
from gemelli.preprocessing import build, tensor_rclr
from gemelli._defaults import (DEFAULT_COMP, DEFAULT_MSC,
DEFAULT_MFC,
DEFAULT_TENSALS_MAXITER,
DEFAULT_FMETA as DEFFM)
def ctf(table: biom.Table,
sample_metadata: DataFrame,
individual_id_column: str,
state_column: str,
n_components: int = DEFAULT_COMP,
min_sample_count: int = DEFAULT_MSC,
min_feature_count: int = DEFAULT_MFC,
max_iterations_als: int = DEFAULT_TENSALS_MAXITER,
max_iterations_rptm: int = DEFAULT_TENSALS_MAXITER,
n_initializations: int = DEFAULT_TENSALS_MAXITER,
feature_metadata: DataFrame = DEFFM) -> (OrdinationResults,
OrdinationResults,
DistanceMatrix,
DataFrame,
DataFrame):
# run CTF helper and parse output for QIIME
state_ordn, ord_res, dists, straj, ftraj = ctf_helper(table,
sample_metadata,
individual_id_column,
[state_column],
n_components,
min_sample_count,
min_feature_count,
max_iterations_als,
max_iterations_rptm,
n_initializations,
feature_metadata)
# save only first state (QIIME can't handle a list yet)
dists = list(dists.values())[0]
straj = list(straj.values())[0]
ftraj = list(ftraj.values())[0]
state_ordn = list(state_ordn.values())[0]
return ord_res, state_ordn, dists, straj, ftraj
def ctf_helper(table: biom.Table,
sample_metadata: DataFrame,
individual_id_column: str,
state_columns: list,
n_components: int = DEFAULT_COMP,
min_sample_count: int = DEFAULT_MSC,
min_feature_count: int = DEFAULT_MFC,
max_iterations_als: int = DEFAULT_TENSALS_MAXITER,
max_iterations_rptm: int = DEFAULT_TENSALS_MAXITER,
n_initializations: int = DEFAULT_TENSALS_MAXITER,
feature_metadata: DataFrame = DEFFM) -> (dict,
OrdinationResults,
dict,
tuple):
""" Runs Compositional Tensor Factorization CTF.
"""
# validate the metadata using q2 as a wrapper
if sample_metadata is not None and not isinstance(sample_metadata,
DataFrame):
sample_metadata = sample_metadata.to_dataframe()
keep_cols = state_columns + [individual_id_column]
all_sample_metadata = sample_metadata.drop(keep_cols, axis=1)
sample_metadata = sample_metadata[keep_cols]
# validate the metadata using q2 as a wrapper
if feature_metadata is not None and not isinstance(feature_metadata,
DataFrame):
feature_metadata = feature_metadata.to_dataframe()
# match the data (borrowed in part from gneiss.util.match)
subtablefids = table.ids('observation')
subtablesids = table.ids('sample')
if len(subtablesids) != len(set(subtablesids)):
raise ValueError('Data-table contains duplicate sample IDs')
if len(subtablefids) != len(set(subtablefids)):
raise ValueError('Data-table contains duplicate feature IDs')
submetadataids = set(sample_metadata.index)
subtablesids = set(subtablesids)
subtablefids = set(subtablefids)
if feature_metadata is not None:
submetadatafeat = set(feature_metadata.index)
fidx = subtablefids & submetadatafeat
if len(fidx) == 0:
raise ValueError(("No more features left. Check to make "
"sure that the sample names between "
"`feature-metadata` and `table` are "
"consistent"))
feature_metadata = feature_metadata.reindex(fidx)
sidx = subtablesids & submetadataids
if len(sidx) == 0:
raise ValueError(("No more features left. Check to make sure that "
"the sample names between `sample-metadata` and"
" `table` are consistent"))
if feature_metadata is not None:
table.filter(list(fidx), axis='observation', inplace=True)
table.filter(list(sidx), axis='sample', inplace=True)
sample_metadata = sample_metadata.reindex(sidx)
# filter and import table
for axis, min_sum in zip(['sample',
'observation'],
[min_sample_count,
min_feature_count]):
table = table.filter(table.ids(axis)[table.sum(axis) >= min_sum],
axis=axis, inplace=True)
# table to dataframe
table = DataFrame(table.matrix_data.toarray(),
table.ids('observation'),
table.ids('sample'))
# tensor building
tensor = build()
tensor.construct(table, sample_metadata,
individual_id_column, state_columns)
# factorize
TF = TensorFactorization(
n_components=n_components,
max_als_iterations=max_iterations_als,
max_rtpm_iterations=max_iterations_rptm,
n_initializations=n_initializations).fit(tensor_rclr(tensor.counts))
# label tensor loadings
TF.label(tensor, taxonomy=feature_metadata)
# if the n_components is two add PC3 of zeros
# this is referenced as in issue in
# <https://github.com/biocore/emperor/commit
# /a93f029548c421cb0ba365b4294f7a5a6b0209ce>
if n_components == 2:
TF.subjects.loc[:, 'PC3'] = [0] * len(TF.subjects.index)
TF.features.loc[:, 'PC3'] = [0] * len(TF.features.index)
TF.proportion_explained['PC3'] = 0
TF.eigvals['PC3'] = 0
# save ordination results
short_method_name = 'CTF_Biplot'
long_method_name = 'Compositional Tensor Factorization Biplot'
# only keep PC -- other tools merge metadata
keep_PC = [col for col in TF.features.columns if 'PC' in col]
subj_ordin = OrdinationResults(
short_method_name,
long_method_name,
TF.eigvals,
samples=TF.subjects[keep_PC].dropna(axis=0),
features=TF.features[keep_PC].dropna(axis=0),
proportion_explained=TF.proportion_explained)
# save distance matrix for each condition
distances = {}
state_ordn = {}
subject_trajectories = {}
feature_trajectories = {}
for condition, cond, dist, straj, ftraj in zip(tensor.conditions,
TF.conditions,
TF.subject_distances,
TF.subject_trajectory,
TF.feature_trajectory):
# match distances to metadata
ids = straj.index
ind_dict = dict((ind, ind_i) for ind_i, ind in enumerate(ids))
inter = set(ind_dict).intersection(sample_metadata.index)
indices = sorted([ind_dict[ind] for ind in inter])
dist = dist[indices, :][:, indices]
distances[condition] = skbio.stats.distance.DistanceMatrix(
dist, ids=ids[indices])
# fix conditions
if n_components == 2:
cond['PC3'] = [0] * len(cond.index)
cond = OrdinationResults(short_method_name,
long_method_name,
TF.eigvals,
samples=cond[keep_PC].dropna(axis=0),
features=TF.features[keep_PC].dropna(axis=0),
proportion_explained=TF.proportion_explained)
state_ordn[condition] = cond
# add the sample metadata before returning output
# addtionally only keep metadata with trajectory
# output available.
pre_merge_cols = list(straj.columns)
straj = concat([straj.reindex(all_sample_metadata.index),
all_sample_metadata],
axis=1, sort=True)
straj = straj.dropna(subset=pre_merge_cols)
# ensure index name for q2
straj.index.name = "#SampleID"
# save traj.
keep_PC_traj = [col for col in straj.columns
if 'PC' in col]
straj[keep_PC_traj] -= straj[keep_PC_traj].mean()
ftraj[keep_PC_traj] -= ftraj[keep_PC_traj].mean()
subject_trajectories[condition] = straj
ftraj.index = ftraj.index.astype(str)
feature_trajectories[condition] = ftraj
return (state_ordn, subj_ordin, distances,
subject_trajectories, feature_trajectories)