for i in range(1, len(imageset.images) - 1):
arr = peripheral_profiles[np.random.choice(
peripheral_profiles.shape[0], i, replace=True)]
rand_idx = randint(0, peripheral_profiles.shape[0] - 1)
mean_arr = np.mean(arr, axis=0)
arr_diff = mean_arr - peripheral_profiles[rand_idx, :]
mse = helpers.mean_absolute_deviation(arr_diff)
mads.append(mse)
total_mads.append(mads)
return total_mads
# Figure 2.D Mean Absolute Deviation of Arhgdia mRNA distribution for peripheral fraction descriptors of a randomly selected cell
# from a pooled average of up to ~40 cells for cultured and micropatterned cells.
constants.init_config(analysis_config_js_path=pathlib.Path(
global_root_dir, "src/analysis/stability/config_original.json"))
dataset_root_fp = pathlib.Path(
constants.analysis_config['DATASET_CONFIG_PATH'].format(
root_dir=global_root_dir)).parent
primary_fp = pathlib.Path(dataset_root_fp,
constants.dataset_config['PRIMARY_FILE_NAME'])
secondary_fp = pathlib.Path(dataset_root_fp,
constants.dataset_config['SECONDARY_FILE_NAME'])
analysis_repo = H5RepositoryWithCheckpoint(repo_path=primary_fp,
secondary_repo_path=secondary_fp)
total_mads = []
for gene in constants.analysis_config['MRNA_GENES']:
total_mads.append(compute_stability(gene, bootstrap=500))
tgt_image_name = constants.analysis_config['FIGURE_NAME_FORMAT']
["src/analysis/colocalization/config_nocodazole_pard3.json"]
]
# Figure 6C top Peripheral Colocalization Score (CS) for original data
# Figure 6C top Cytoplasmic Colocalization Score (CS) for original data
# Figure 6D Colocalization Score (CS) for original data (4 figures)
# Figure 7F top left Colocalization Score (CS) for arhgdia data
# Figure 7F top right Colocalization Score (CS) for nocodazole arhgdia data
# Figure 7F bottom left Colocalization Score (CS) for pard3 data
# Figure 7F bottom right Colocalization Score (CS) for nocodazole pard3 data
if __name__ == '__main__':
np.random.seed(int(round(time.time())))
for conf in configurations:
logger.info("Colocalization Score")
conf_full_path = pathlib.Path(global_root_dir, conf[0])
constants.init_config(analysis_config_js_path=conf_full_path)
repo = helpers.open_repo()
peripheral_flag = "periph" in conf[0]
stripes = constants.analysis_config["STRIPE_NUM"]
mrna_densities = compute_relative_densities(
repo, 'mrna', quadrants_num=8, peripheral_flag=peripheral_flag)
prot_densities = compute_relative_densities(
repo, 'protein', quadrants_num=8, peripheral_flag=peripheral_flag)
css, results = [], {}
for gene in constants.analysis_config['PROTEINS']:
cs, p, ranking = helpers.calculate_colocalization_score(
mrna_densities[gene],
prot_densities[gene],
constants.dataset_config['TIMEPOINTS_NUM_MRNA'],
constants.dataset_config['TIMEPOINTS_NUM_PROTEIN'],
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Credits: Benjamin Dartigues, Emmanuel Bouilhol, Hayssam Soueidan, Macha Nikolski
import pathlib
import warnings
from unittest import TestCase
import constants
import image_processing as ip
import path
from imageWithSpots import ImageWithSpots
from repository import H5RepositoryWithCheckpoint
constants.init_config(analysis_config_js_path=path.test_config_path)
class TestImageWithSpots(TestCase):
def setUp(self) -> None:
self.h5_sample_path = pathlib.Path(path.global_example_data,
"basic.h5")
self.repo = H5RepositoryWithCheckpoint(repo_path=self.h5_sample_path)
self.img = ImageWithSpots(repository=self.repo,
image_path="mrna/arhgdia/2h/1")
def tearDown(self) -> None:
self.repo.clear()
def test_get_spots(self):
self.assertEqual(self.img.get_spots().shape, (218, 3))
def compute_zline_distance(repo, molecule_list, timepoints, z_line_spacing):
all_median_profiles = []
for molecule in molecule_list:
for timepoint in timepoints:
image_set = ImageSet(repo, [f"{'mrna'}/{molecule}/{timepoint}/"])
if image_set.__sizeof__() < 5:
logger.warning("Image set is small for {}", molecule)
total_profile = image_set.compute_zline_distance(z_line_spacing)
all_median_profiles.append(np.median(total_profile, axis=0))
return all_median_profiles
# figure 8B mRNA enrichment between z-lines in muscle fibers
constants.init_config(analysis_config_js_path=pathlib.Path(
global_root_dir, "src/analysis/muscle/config_muscle.json"))
dataset_root_fp = pathlib.Path(
constants.analysis_config['DATASET_CONFIG_PATH'].format(
root_dir=global_root_dir)).parent
primary_fp = pathlib.Path(dataset_root_fp,
constants.dataset_config['PRIMARY_FILE_NAME'])
secondary_fp = pathlib.Path(dataset_root_fp,
constants.dataset_config['SECONDARY_FILE_NAME'])
analysis_repo = H5RepositoryWithCheckpoint(repo_path=primary_fp,
secondary_repo_path=secondary_fp)
z_line_spacing = constants.analysis_config['Z_LINE_SPACING']
molecule_list = ['actn2', 'gapdh']
timepoints = ['mature']
all_median_profiles_mature = compute_zline_distance(analysis_repo,
molecule_list, timepoints,
# Credits: Benjamin Dartigues, Emmanuel Bouilhol, Hayssam Soueidan, Macha Nikolski
import pathlib
from unittest import TestCase
import numpy as np
from loguru import logger
import constants
import helpers
import image_processing as ip
import path
from image3d import Image3d
from repository import H5RepositoryWithCheckpoint
constants.init_config(
analysis_config_js_path=path.test_config_path) # TODO this is annoying
class TestImage3d(TestCase):
def setUp(self) -> None:
self.h5_sample_path = pathlib.Path(path.global_example_data,
"basic.h5")
self.repo = H5RepositoryWithCheckpoint(repo_path=self.h5_sample_path)
self.img = Image3d(repository=self.repo,
image_path="mrna/arhgdia/2h/1")
def tearDown(self) -> None:
self.repo.clear()
def test_compute_cell_mask_slices(self):
cell_mask = helpers.unit_circle(15, 7)