#!/usr/bin/env python
import pandas as pd
import numpy as np
import sklearn.decomposition
import dynclipy
import time
checkpoints = {}
#####################################
### LOAD DATA ###
#####################################
task = dynclipy.main(definition_location = "/tests/python/definition.yml")
expression = task["expression"]
params = task["parameters"]
cell_ids = expression.index
if "start_id" in task["priors"]:
start_id = task["priors"]["start_id"]
else:
start_id = None
checkpoints["method_afterpreproc"] = time.time()
#####################################
### INFER TRAJECTORY ###
#####################################
# do PCA
#!/usr/local/bin/python
import dynclipy
task = dynclipy.main()
import pandas as pd
import numpy as np
import json
from pymatcher import matcher
import time
checkpoints = {}
# ____________________________________________________________________________
# Load data ####
expression = task["expression"]
parameters = task["parameters"]
# ____________________________________________________________________________
# Infer trajectory ####
m = matcher.MATCHER([expression.values])
m.infer(quantiles=parameters["quantiles"], method=parameters["method"])
checkpoints["method_aftermethod"] = time.time()
# ____________________________________________________________________________
# Save output ####
dataset = dynclipy.wrap_data(cell_ids=expression.index)
# pseudotime
#!/usr/bin/env python
import dynclipy
dataset = dynclipy.main()
import pandas as pd
import sklearn.decomposition
# infer trajectory
pca = sklearn.decomposition.PCA()
dimred = pca.fit_transform(dataset['expression'])
pseudotime = pd.Series(dimred[:, dataset['parameters']['component'] - 1],
index=dataset['expression'].index)
# build trajectory
trajectory = dynclipy.wrap_data(cell_ids=dataset['expression'].index)
trajectory.add_linear_trajectory(pseudotime=pseudotime)
# save output
trajectory.write_output(dataset['output'])