def main(_high_temporal_resolution=False, _pair_distance_range=None):
if _pair_distance_range is None:
_pair_distance_range = PAIR_DISTANCE_RANGE
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=_high_temporal_resolution,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_experiments = ['SN16']
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_pair_distance_range(_tuples, _pair_distance_range)
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
for _cell_id, _offset_y in product(['left_cell', 'right_cell'],
[0, OFFSET_Y]):
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': _offset_y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = compute.windows(
_arguments,
_keys=['experiment', 'series_id', 'group', 'cell_id', 'offset_y'])
_fiber_densities = compute.fiber_densities(_windows_to_compute)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_no_offset_derivatives_array = []
_offset_derivatives_array = []
for _tuple in tqdm(_tuples, desc='Main loop'):
_experiment, _series_id, _group = _tuple
_no_offset_left_cell_fiber_densities = _experiments_fiber_densities[(
_experiment, _series_id, _group, 'left_cell', 0)]
_no_offset_right_cell_fiber_densities = _experiments_fiber_densities[(
_experiment, _series_id, _group, 'right_cell', 0)]
_offset_left_cell_fiber_densities = _experiments_fiber_densities[(
_experiment, _series_id, _group, 'left_cell', OFFSET_Y)]
_offset_right_cell_fiber_densities = _experiments_fiber_densities[(
_experiment, _series_id, _group, 'right_cell', OFFSET_Y)]
_properties = \
load.group_properties(_experiment, _series_id, _group)
_no_offset_left_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['left_cell'],
_no_offset_left_cell_fiber_densities)
_no_offset_right_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['right_cell'],
_no_offset_right_cell_fiber_densities)
_offset_left_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['left_cell'],
_offset_left_cell_fiber_densities)
_offset_right_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['right_cell'],
_offset_right_cell_fiber_densities)
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
for _cell_pair in [(_no_offset_left_cell_fiber_densities,
_offset_left_cell_fiber_densities),
(_no_offset_right_cell_fiber_densities,
_offset_right_cell_fiber_densities)]:
for _time_frame in range(
1, min(len(_cell_pair[0]), len(_cell_pair[1]))):
_no_offset_cell_fiber_density_previous = _cell_pair[0][
_time_frame - 1]
_no_offset_cell_fiber_density = _cell_pair[0][_time_frame]
_offset_cell_fiber_density_previous = _cell_pair[1][_time_frame
- 1]
_offset_cell_fiber_density = _cell_pair[1][_time_frame]
if any([
_no_offset_cell_fiber_density_previous[1],
_no_offset_cell_fiber_density[1],
_offset_cell_fiber_density_previous[1],
_offset_cell_fiber_density[1]
]):
continue
_no_offset_cell_fiber_density_previous_z_score = compute_lib.z_score(
_x=_no_offset_cell_fiber_density_previous[0],
_average=_normalization['average'],
_std=_normalization['std'])
_no_offset_cell_fiber_density_z_score = compute_lib.z_score(
_x=_no_offset_cell_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std'])
_offset_cell_fiber_density_previous_z_score = compute_lib.z_score(
_x=_offset_cell_fiber_density_previous[0],
_average=_normalization['average'],
_std=_normalization['std'])
_offset_cell_fiber_density_z_score = compute_lib.z_score(
_x=_offset_cell_fiber_density[0],
_average=_normalization['average'],
_std=_normalization['std'])
_no_offset_derivative = _no_offset_cell_fiber_density_z_score - _no_offset_cell_fiber_density_previous_z_score
_offset_derivative = _offset_cell_fiber_density_z_score - _offset_cell_fiber_density_previous_z_score
_no_offset_derivatives_array.append(_no_offset_derivative)
_offset_derivatives_array.append(_offset_derivative)
print('Total points:', len(_no_offset_derivatives_array))
_offset_minus_no_offset = \
np.array(_offset_derivatives_array) - np.array(_no_offset_derivatives_array)
print('Wilcoxon of offset minus no offset around the zero:')
print(wilcoxon(_offset_minus_no_offset))
print('Higher offset amount:',
(_offset_minus_no_offset > 0).sum() / len(_offset_minus_no_offset))
# plot
_fig = go.Figure(
data=go.Scatter(x=_no_offset_derivatives_array,
y=_offset_derivatives_array,
mode='markers',
marker={
'size': 5,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'No offset derivative',
# 'zeroline': False,
# 'range': [-1.1, 1.2],
# 'tickmode': 'array',
# 'tickvals': [-1, -0.5, 0, 0.5, 1]
},
'yaxis': {
'title': 'Offset derivative',
# 'zeroline': False,
# 'range': [-1.1, 1.2],
# 'tickmode': 'array',
# 'tickvals': [-1, -0.5, 0, 0.5, 1]
},
'shapes': [
# {
# 'type': 'line',
# 'x0': -1,
# 'y0': -1,
# 'x1': -1,
# 'y1': 1,
# 'line': {
# 'color': 'black',
# 'width': 2
# }
# },
# {
# 'type': 'line',
# 'x0': -1,
# 'y0': -1,
# 'x1': 1,
# 'y1': -1,
# 'line': {
# 'color': 'black',
# 'width': 2
# }
# },
{
'type': 'line',
'x0': -5,
'y0': -5,
'x1': 5,
'y1': 5,
'line': {
'color': 'red',
'width': 2
}
}
]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot')
def main(_band=True, _high_temporal_resolution=False):
_y_arrays = [[] for _i in PAIR_DISTANCE_RANGES]
_names_array = []
for _distances_index, _distances_range in enumerate(PAIR_DISTANCE_RANGES):
print('Pair distance range:', str(_distances_range))
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=_high_temporal_resolution,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_pair_distance_range(_tuples, _distances_range)
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples, _band=_band)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = compute.windows(
_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_tuples_by_experiment = organize.by_experiment(_tuples)
_higher_same_counter = 0
_valid_tuples = []
for _experiment in _tuples_by_experiment:
print('Experiment:', _experiment)
_experiment_tuples = _tuples_by_experiment[_experiment]
for _same_index in tqdm(range(len(_experiment_tuples)),
desc='Main loop'):
_same_tuple = _experiment_tuples[_same_index]
_same_experiment, _same_series, _same_group = _same_tuple
_same_left_cell_fiber_densities = \
_experiments_fiber_densities[
(_same_experiment, _same_series, _same_group, 'left_cell')
]
_same_right_cell_fiber_densities = \
_experiments_fiber_densities[
(_same_experiment, _same_series, _same_group, 'right_cell')
]
_same_properties = \
load.group_properties(_same_experiment, _same_series, _same_group)
_same_left_cell_fiber_densities = compute.remove_blacklist(
_same_experiment, _same_series,
_same_properties['cells_ids']['left_cell'],
_same_left_cell_fiber_densities)
_same_right_cell_fiber_densities = compute.remove_blacklist(
_same_experiment, _same_series,
_same_properties['cells_ids']['right_cell'],
_same_right_cell_fiber_densities)
_same_left_cell_fiber_densities_filtered, _same_right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_same_left_cell_fiber_densities, _same_right_cell_fiber_densities
)
# ignore small arrays
if len(_same_left_cell_fiber_densities_filtered
) < compute.minimum_time_frames_for_correlation(
_same_experiment):
continue
_same_correlation = compute_lib.correlation(
compute_lib.derivative(
_same_left_cell_fiber_densities_filtered,
_n=DERIVATIVE),
compute_lib.derivative(
_same_right_cell_fiber_densities_filtered,
_n=DERIVATIVE))
for _different_index in range(len(_experiment_tuples)):
if _same_index != _different_index:
_different_tuple = _experiment_tuples[_different_index]
_different_experiment, _different_series, _different_group = \
_different_tuple
for _same_cell_id, _different_cell_id in product(
['left_cell', 'right_cell'],
['left_cell', 'right_cell']):
_same_fiber_densities = _experiments_fiber_densities[
(_same_experiment, _same_series, _same_group,
_same_cell_id)]
_different_fiber_densities = _experiments_fiber_densities[
(_different_experiment, _different_series,
_different_group, _different_cell_id)]
_different_properties = load.group_properties(
_different_experiment, _different_series,
_different_group)
_same_fiber_densities = compute.remove_blacklist(
_same_experiment, _same_series,
_same_properties['cells_ids'][_same_cell_id],
_same_fiber_densities)
_different_fiber_densities = compute.remove_blacklist(
_different_experiment, _different_series,
_different_properties['cells_ids']
[_different_cell_id],
_different_fiber_densities)
_same_fiber_densities_filtered, _different_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_same_fiber_densities, _different_fiber_densities
)
# ignore small arrays
if len(
_same_fiber_densities_filtered
) < compute.minimum_time_frames_for_correlation(
_different_experiment):
continue
_different_correlation = compute_lib.correlation(
compute_lib.derivative(
_same_fiber_densities_filtered,
_n=DERIVATIVE),
compute_lib.derivative(
_different_fiber_densities_filtered,
_n=DERIVATIVE))
_point_distance = compute_lib.distance_from_a_point_to_a_line(
_line=[-1, -1, 1, 1],
_point=[
_same_correlation, _different_correlation
])
if _same_correlation > _different_correlation:
_y_arrays[_distances_index].append(
_point_distance)
_higher_same_counter += 1
else:
_y_arrays[_distances_index].append(
-_point_distance)
if _same_tuple not in _valid_tuples:
_valid_tuples.append(_same_tuple)
print('Total tuples:', len(_valid_tuples))
print('Total points:', len(_y_arrays[_distances_index]))
print('Wilcoxon around the zero:')
print(wilcoxon(_y_arrays[_distances_index]))
print('Higher same amount:',
_higher_same_counter / len(_y_arrays[_distances_index]))
_names_array.append(
str(_distances_range[0]) + '-' + str(_distances_range[1]))
# plot
_colors_array = config.colors(3)
_fig = go.Figure(data=[
go.Box(y=_y,
name=_name,
boxpoints=False,
line={'width': 1},
marker={
'size': 10,
'color': _color
},
showlegend=False)
for _y, _name, _color in zip(_y_arrays, _names_array, _colors_array)
],
layout={
'xaxis': {
'title': 'Pair distance (cell diameter)',
'zeroline': False,
'type': 'category'
},
'yaxis': {
'title': 'Same minus different correlation',
'range': [-1, 1.1],
'zeroline': False,
'tickmode': 'array',
'tickvals': [-1, -0.5, 0, 0.5, 1]
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_band_' + str(_band) + '_high_temporal_res_' +
str(_high_temporal_resolution))
def main(_band=None,
_high_temporal_resolution=True,
_tuples_to_mark=None,
_tuples_to_plot=None,
_plots=None):
if _plots is None:
_plots = ['whiteness', 'granger']
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=_high_temporal_resolution,
_is_bleb=False,
_is_dead_dead=False,
_is_live_dead=False,
_is_bead=False,
_is_metastasis=False)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_time_frames_amount(_tuples,
_time_frames=MINIMUM_TIME_FRAMES)
_tuples = filtering.by_pair_distance_range(
_tuples, _distance_range=PAIR_DISTANCE_RANGE)
_tuples = filtering.by_real_pairs(_tuples)
_tuples = filtering.by_band(_tuples, _band=_band)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': OFFSET_Y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = compute.windows(
_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_n_pairs = 0
_n_pairs_with_band = 0
_whiteness_p_values = []
_n_passed_whiteness_with_band = 0
_granger_causality_p_values = []
_n_passed_granger_causality_with_band = 0
_correlations = []
_time_lag_correlations = []
_end_fiber_densities = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_left_cell_fiber_densities = \
_experiments_fiber_densities[(_experiment, _series_id, _group, 'left_cell')]
_right_cell_fiber_densities = \
_experiments_fiber_densities[(_experiment, _series_id, _group, 'right_cell')]
_properties = load.group_properties(_experiment, _series_id, _group)
_left_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['left_cell'],
_left_cell_fiber_densities)
_right_cell_fiber_densities = compute.remove_blacklist(
_experiment, _series_id, _properties['cells_ids']['right_cell'],
_right_cell_fiber_densities)
_left_cell_fiber_densities_filtered, _right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_left_cell_fiber_densities, _right_cell_fiber_densities)
# ignore small arrays
if len(_left_cell_fiber_densities_filtered) < MINIMUM_TIME_FRAMES:
continue
_n_pairs += 1
if _properties['band']:
_n_pairs_with_band += 1
_start_time_frame = 0
for _left in _left_cell_fiber_densities:
if _left[0] == _left_cell_fiber_densities_filtered[0]:
break
_start_time_frame += 1
# stationary test
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=InterpolationWarning)
# find derivative for stationary
for _derivative in range(10):
_left_cell_fiber_densities_derivative = \
compute_lib.derivative(_left_cell_fiber_densities_filtered, _n=_derivative)
_right_cell_fiber_densities_derivative = \
compute_lib.derivative(_right_cell_fiber_densities_filtered, _n=_derivative)
if ADF_TEST:
_, _left_cell_adf_p_value, _, _, _, _ = adfuller(
_left_cell_fiber_densities_derivative)
_, _right_cell_adf_p_value, _, _, _, _ = adfuller(
_right_cell_fiber_densities_derivative)
if _left_cell_adf_p_value > 0.05 or _right_cell_adf_p_value > 0.05:
continue
if KPSS_TEST:
_, _left_cell_kpss_p_value, _, _ = kpss(
_left_cell_fiber_densities_derivative, nlags='legacy')
_, _right_cell_kpss_p_value, _, _ = kpss(
_right_cell_fiber_densities_derivative, nlags='legacy')
if _left_cell_kpss_p_value < 0.05 or _right_cell_kpss_p_value < 0.05:
continue
# stationary
break
# causality
try:
_x = pd.DataFrame(data=[[_left_value, _right_value]
for _left_value, _right_value in zip(
_left_cell_fiber_densities_derivative,
_right_cell_fiber_densities_derivative)
],
columns=['left', 'right'])
# var model to retrieve lag
_var_model = VAR(_x)
_lag_order_results = _var_model.select_order()
_estimators_lags = [
_lag_order_results.aic, _lag_order_results.bic,
_lag_order_results.fpe, _lag_order_results.hqic
]
_min_estimator_lag = min(_estimators_lags)
# found a lag
if 0 < _min_estimator_lag <= MAXIMUM_LAG:
_var_model_results = _var_model.fit(maxlags=_min_estimator_lag,
ic=None)
_whiteness = _var_model_results.test_whiteness(
nlags=_min_estimator_lag + 1)
_whiteness_p_values.append(_whiteness.pvalue)
if _tuples_to_mark is not None and _tuple in _tuples_to_mark and _whiteness.pvalue > 0.05:
print(_tuple, 'marked whiteness p-value:',
_whiteness.pvalue)
# no autocorrelation in the residuals
if _whiteness.pvalue > 0.05:
if _properties['band']:
_n_passed_whiteness_with_band += 1
# time lag = 0
_correlation = compute_lib.correlation(
_left_cell_fiber_densities_derivative,
_right_cell_fiber_densities_derivative)
# if _correlation < 0.5:
# continue
# granger causality
for _caused, _causing in zip(['left', 'right'],
['right', 'left']):
_granger = _var_model_results.test_causality(
caused=_caused, causing=_causing)
_granger_causality_p_values.append(_granger.pvalue)
# time lag = 0
_correlations.append(_correlation)
# time lag = min estimator
if _causing == 'left':
_left_fiber_densities_time_lag = \
_left_cell_fiber_densities_derivative[:-_min_estimator_lag]
_right_fiber_densities_time_lag = \
_right_cell_fiber_densities_derivative[_min_estimator_lag:]
else:
_left_fiber_densities_time_lag = \
_left_cell_fiber_densities_derivative[_min_estimator_lag:]
_right_fiber_densities_time_lag = \
_right_cell_fiber_densities_derivative[:-_min_estimator_lag]
_time_lag_correlation = compute_lib.correlation(
_left_fiber_densities_time_lag,
_right_fiber_densities_time_lag)
_time_lag_correlations.append(_time_lag_correlation)
# end fiber density
_time_frame = compute.density_time_frame(_experiment)
if len(_left_cell_fiber_densities_filtered
) > _time_frame:
_end_fiber_density = \
(_left_cell_fiber_densities_filtered[_time_frame] +
_right_cell_fiber_densities_filtered[_time_frame]) / 2
else:
_end_fiber_density = \
(_left_cell_fiber_densities_filtered[-1] +
_right_cell_fiber_densities_filtered[-1]) / 2
_normalization = load.normalization_series_file_data(
_experiment, _series_id)
_normalized_fiber_density = compute_lib.z_score(
_end_fiber_density, _normalization['average'],
_normalization['std'])
_end_fiber_densities.append(_normalized_fiber_density)
# marking
if _tuples_to_mark is not None and _tuple in _tuples_to_mark and _granger.pvalue < 0.05:
print(_tuple, 'causing:', _causing,
'marked granger p-value:', _granger.pvalue)
if _granger.pvalue < 0.05:
if _properties['band']:
_n_passed_granger_causality_with_band += 1
_normality = _var_model_results.test_normality()
_inst_granger = _var_model_results.test_inst_causality(
causing=_causing)
print(
_tuple,
_causing.capitalize() + ' causes ' + _caused +
'!',
'time-points: ' +
str(len(
_left_cell_fiber_densities_derivative)),
'stationary derivative: ' + str(_derivative),
'band:' + str(_properties['band']),
'p-value: ' + str(round(_granger.pvalue, 4)),
'lag: ' + str(_min_estimator_lag),
'normality p-value: ' +
str(round(_normality.pvalue, 4)),
'inst p-value: ' +
str(round(_inst_granger.pvalue, 4)),
sep='\t')
# lag = 0
print('Time lag = 0 correlation:', _correlation)
# rest of lags
for _lag in range(1, _min_estimator_lag + 1):
if _causing == 'left':
_left_fiber_densities_time_lag = _left_cell_fiber_densities_derivative[:
-_lag]
_right_fiber_densities_time_lag = _right_cell_fiber_densities_derivative[
_lag:]
else:
_left_fiber_densities_time_lag = _left_cell_fiber_densities_derivative[
_lag:]
_right_fiber_densities_time_lag = _right_cell_fiber_densities_derivative[:
-_lag]
_correlation = compute_lib.correlation(
_left_fiber_densities_time_lag,
_right_fiber_densities_time_lag)
print(
'Time lag = ' + str(_lag) +
' correlation:', _correlation)
# plots
if _tuples_to_plot is not None and _tuple in _tuples_to_plot:
_y_arrays = [
_left_cell_fiber_densities_derivative,
_right_cell_fiber_densities_derivative
]
_names_array = ['Left cell', 'Right cell']
_colors_array = config.colors(2)
_temporal_resolution = compute.temporal_resolution_in_minutes(
_experiment)
_fig = go.Figure(data=[
go.Scatter(x=np.arange(
start=_start_time_frame,
stop=_start_time_frame +
len(_left_cell_fiber_densities_derivative
),
step=1) * _temporal_resolution,
y=_y,
name=_name,
mode='lines',
line={
'color': _color,
'width': 1
})
for _y, _name, _color in zip(
_y_arrays, _names_array, _colors_array)
],
layout={
'xaxis': {
'title':
'Time (minutes)',
'zeroline': False
},
'yaxis': {
'title':
'Fiber density (z-score)'
+ '\'' * _derivative,
'zeroline':
False
},
'legend': {
'xanchor': 'left',
'x': 0.1,
'yanchor': 'top',
'bordercolor':
'black',
'borderwidth': 2,
'bgcolor': 'white'
},
})
_experiment, _series_id, _group = _tuple
save.to_html(
_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_' + _experiment + '_' +
str(_series_id) + '_' + _group)
# residuals
_y_arrays = \
[_var_model_results.resid.values[:, 0], _var_model_results.resid.values[:, 1]]
_fig = go.Figure(data=[
go.Scatter(x=np.arange(
start=_start_time_frame,
stop=_start_time_frame + len(_y),
step=1) * _temporal_resolution,
y=_y,
name=_name,
mode='lines',
line={
'color': _color,
'width': 1
})
for _y, _name, _color in zip(
_y_arrays, _names_array, _colors_array)
],
layout={
'xaxis': {
'title':
'Time (minutes)',
'zeroline': False
},
'yaxis': {
'title': 'Residual',
'zeroline': False
},
'legend': {
'xanchor': 'left',
'x': 0.1,
'yanchor': 'top',
'bordercolor':
'black',
'borderwidth': 2,
'bgcolor': 'white'
},
})
_experiment, _series_id, _group = _tuple
save.to_html(
_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_residuals_' + _experiment +
'_' + str(_series_id) + '_' + _group)
# not enough time points
except ValueError:
continue
print('Total pairs:', _n_pairs)
print('Total pairs with band:', _n_pairs_with_band)
print('Total pairs passed whiteness:',
(np.array(_whiteness_p_values) > 0.05).sum())
print('Total pairs passed whiteness with band:',
_n_passed_whiteness_with_band)
print('Total cells passed granger causality:',
(np.array(_granger_causality_p_values) < 0.05).sum())
print('Total cells passed granger causality with band:',
_n_passed_granger_causality_with_band)
# p-value correction
print('Corrections of GC p-value < 0.05:')
_granger_causality_p_values_corrected = multipletests(
pvals=_granger_causality_p_values, method='fdr_bh')
for _p_value, _p_value_corrected in zip(
_granger_causality_p_values,
_granger_causality_p_values_corrected[1]):
if _p_value < 0.05:
print('Original GC p-value:', _p_value, 'corrected:',
_p_value_corrected)
# plots
for _test_name, _y_title, _y_array in \
zip(
['whiteness', 'granger'],
['Whiteness p-value', 'Granger causality p-value'],
[_whiteness_p_values, _granger_causality_p_values]
):
if _test_name in _plots:
_fig = go.Figure(data=go.Box(y=_y_array,
boxpoints='all',
jitter=1,
pointpos=0,
line={'width': 1},
fillcolor='white',
marker={
'size': 10,
'color': '#ea8500'
},
opacity=0.7,
showlegend=False),
layout={
'xaxis': {
'zeroline': False
},
'yaxis': {
'title': _y_title,
'zeroline': False,
'range': [-0.1, 1.1],
'tickmode': 'array',
'tickvals': [0.05, 1]
},
'shapes': [{
'type': 'line',
'x0': -0.75,
'y0': 0.05,
'x1': 0.75,
'y1': 0.05,
'line': {
'color': 'red',
'width': 2,
'dash': 'dash'
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS,
save.get_module_name()),
_filename='plot_' + _test_name)
# granger versus correlation
print(
'GC vs. correlation pearson correlation:',
compute_lib.correlation(_granger_causality_p_values,
_correlations,
_with_p_value=True))
_fig = go.Figure(data=go.Scatter(x=_granger_causality_p_values,
y=_correlations,
mode='markers',
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Granger causality p-value',
'zeroline': False,
},
'yaxis': {
'title': 'Inner correlation',
'zeroline': False,
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_gc_vs_correlation')
# granger versus time lag correlation
print(
'GC vs. time lag correlation pearson correlation:',
compute_lib.correlation(_granger_causality_p_values,
_time_lag_correlations,
_with_p_value=True))
_fig = go.Figure(data=go.Scatter(x=_granger_causality_p_values,
y=_time_lag_correlations,
mode='markers',
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Granger causality p-value',
'zeroline': False,
},
'yaxis': {
'title': 'GC lag inner correlation',
'zeroline': False,
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_gc_vs_time_lag_correlation')
# granger versus end fiber density
print(
'GC vs. end fiber density pearson correlation:',
compute_lib.correlation(_granger_causality_p_values,
_end_fiber_densities,
_with_p_value=True))
_fig = go.Figure(data=go.Scatter(x=_granger_causality_p_values,
y=_end_fiber_densities,
mode='markers',
marker={
'size': 10,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Granger causality p-value',
'zeroline': False,
},
'yaxis': {
'title': 'End fiber density (z-score)',
'zeroline': False,
}
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_gc_vs_end_density')
def main(_real_cells=True,
_static=False,
_dead_dead=False,
_live_dead=False,
_dead=False,
_live=False,
_bead=False,
_metastasis=False,
_bleb=False,
_bleb_amount_um=None,
_band=True,
_offset_y=0.5,
_high_temporal_resolution=False):
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=_high_temporal_resolution,
_is_bleb=_bleb,
_is_dead_dead=_dead_dead,
_is_live_dead=_live_dead,
_is_bead=_bead,
_is_metastasis=_metastasis)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_pair_distance_range(_tuples, PAIR_DISTANCE_RANGE)
_tuples = filtering.by_real_pairs(_tuples, _real_pairs=_real_cells)
_tuples = filtering.by_fake_static_pairs(_tuples,
_fake_static_pairs=_static)
if _dead_dead is not False or _live_dead is not False:
_tuples = filtering.by_dead_live(_tuples, _dead=_dead, _live=_live)
_tuples = filtering.by_band(_tuples, _band=_band)
if _bleb:
_tuples = filtering.by_bleb_amount_um(_tuples,
_amount_um=_bleb_amount_um)
_tuples_matched = organize.by_matched_real_real_and_real_fake(_tuples)
print('Total matched pairs:', len(_tuples_matched))
_arguments = []
for _matched_tuple in _tuples_matched:
for _tuple in _matched_tuple:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(
_experiment, _series_id, _group, OFFSET_X)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': _offset_y,
'offset_z': OFFSET_Z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = compute.windows(
_arguments, _keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute,
_subtract_border=True)
_experiments_fiber_densities = {
_key:
[_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_real_real_correlations_array = []
_real_fake_correlations_array = []
_valid_real_real_tuples = []
for _tuple_matched in tqdm(_tuples_matched, desc='Main loop'):
_real_real_tuple, _real_fake_tuple = _tuple_matched
_real_real_experiment, _real_real_series, _real_real_group = _real_real_tuple
_, _, _real_fake_group = _real_fake_tuple
_real_real_left_cell_fiber_densities = \
_experiments_fiber_densities[
(_real_real_experiment, _real_real_series, _real_real_group, 'left_cell')
]
_real_real_right_cell_fiber_densities = \
_experiments_fiber_densities[
(_real_real_experiment, _real_real_series, _real_real_group, 'right_cell')
]
_real_fake_left_cell_fiber_densities = \
_experiments_fiber_densities[
(_real_real_experiment, _real_real_series, _real_fake_group, 'left_cell')
]
_real_fake_right_cell_fiber_densities = \
_experiments_fiber_densities[
(_real_real_experiment, _real_real_series, _real_fake_group, 'right_cell')
]
_properties = \
load.group_properties(_real_real_experiment, _real_real_series, _real_real_group)
_real_real_left_cell_fiber_densities = compute.remove_blacklist(
_real_real_experiment, _real_real_series,
_properties['cells_ids']['left_cell'],
_real_real_left_cell_fiber_densities)
_real_real_right_cell_fiber_densities = compute.remove_blacklist(
_real_real_experiment, _real_real_series,
_properties['cells_ids']['right_cell'],
_real_real_right_cell_fiber_densities)
_real_fake_left_cell_fiber_densities = compute.remove_blacklist(
_real_real_experiment, _real_real_series,
_properties['cells_ids']['left_cell'],
_real_fake_left_cell_fiber_densities)
_real_fake_right_cell_fiber_densities = compute.remove_blacklist(
_real_real_experiment, _real_real_series,
_properties['cells_ids']['right_cell'],
_real_fake_right_cell_fiber_densities)
_real_real_left_cell_fiber_densities_filtered, _real_real_right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_real_real_left_cell_fiber_densities, _real_real_right_cell_fiber_densities
)
# ignore small arrays
_minimum_time_frame_for_correlation = compute.minimum_time_frames_for_correlation(
_real_real_experiment)
if len(_real_real_left_cell_fiber_densities_filtered
) < _minimum_time_frame_for_correlation:
continue
_real_real_correlation = compute_lib.correlation(
compute_lib.derivative(
_real_real_left_cell_fiber_densities_filtered, _n=DERIVATIVE),
compute_lib.derivative(
_real_real_right_cell_fiber_densities_filtered, _n=DERIVATIVE))
_real_fake_left_cell_fiber_densities_filtered, _real_fake_right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_real_fake_left_cell_fiber_densities, _real_fake_right_cell_fiber_densities
)
# ignore small arrays
if len(_real_fake_left_cell_fiber_densities_filtered
) < _minimum_time_frame_for_correlation:
continue
_real_fake_correlation = compute_lib.correlation(
compute_lib.derivative(
_real_fake_left_cell_fiber_densities_filtered, _n=DERIVATIVE),
compute_lib.derivative(
_real_fake_right_cell_fiber_densities_filtered, _n=DERIVATIVE))
_real_real_correlations_array.append(_real_real_correlation)
_real_fake_correlations_array.append(_real_fake_correlation)
if _real_real_tuple not in _valid_real_real_tuples:
_valid_real_real_tuples.append(_real_real_tuple)
print('Total real-real pairs:', len(_valid_real_real_tuples))
_real_minus_fake = np.array(_real_real_correlations_array) - np.array(
_real_fake_correlations_array)
print('Wilcoxon of real-real minus real-fake around the zero:')
print(wilcoxon(_real_minus_fake))
print('Higher real-real amount:',
(_real_minus_fake > 0).sum() / len(_real_minus_fake))
# plot
_fig = go.Figure(data=go.Scatter(x=_real_real_correlations_array,
y=_real_fake_correlations_array,
mode='markers',
marker={
'size': 5,
'color': '#ea8500'
},
showlegend=False),
layout={
'xaxis': {
'title': 'Real-real correlation',
'zeroline': False,
'range': [-1.1, 1.2],
'tickmode': 'array',
'tickvals': [-1, -0.5, 0, 0.5, 1]
},
'yaxis': {
'title': 'Real-fake correlation',
'zeroline': False,
'range': [-1.1, 1.2],
'tickmode': 'array',
'tickvals': [-1, -0.5, 0, 0.5, 1]
},
'shapes': [{
'type': 'line',
'x0': -1,
'y0': -1,
'x1': -1,
'y1': 1,
'line': {
'color': 'black',
'width': 2
}
}, {
'type': 'line',
'x0': -1,
'y0': -1,
'x1': 1,
'y1': -1,
'line': {
'color': 'black',
'width': 2
}
}, {
'type': 'line',
'x0': -1,
'y0': -1,
'x1': 1,
'y1': 1,
'line': {
'color': 'red',
'width': 2
}
}]
})
save.to_html(_fig=_fig,
_path=os.path.join(paths.PLOTS, save.get_module_name()),
_filename='plot_real_' + str(_real_cells) + '_static_' +
str(_static) + '_dead_dead_' + str(_dead_dead) +
'_live_dead_' + str(_live_dead) + '_dead_' + str(_dead) +
'_live_' + str(_live) + '_bead_' + str(_bead) +
'_metastasis_' + str(_metastasis) + '_bleb_' + str(_bleb) +
str(_bleb_amount_um) + '_band_' + str(_band) + '_high_time_' +
str(_high_temporal_resolution) + '_y_' + str(_offset_y))
def compute_fiber_densities(_real_cells=True, _static=False, _dead_dead=False, _live_dead=False, _dead=False,
_live=False, _bead=False, _metastasis=False, _bleb=False, _bleb_amount_um=None, _band=True,
_high_temporal_resolution=False, _pair_distance_range=None, _offset_y=0.5, _offset_z=0,
_padding_y_by=0.5, _padding_z_by=0.0, _space_y_by=0.25, _space_z_by=0.0):
if _pair_distance_range is None:
_pair_distance_range = PAIR_DISTANCE_RANGE
_experiments = all_experiments()
_experiments = filtering.by_categories(
_experiments=_experiments,
_is_single_cell=False,
_is_high_temporal_resolution=_high_temporal_resolution,
_is_bleb=_bleb,
_is_dead_dead=_dead_dead,
_is_live_dead=_live_dead,
_is_bead=_bead,
_is_metastasis=_metastasis
)
_tuples = load.experiments_groups_as_tuples(_experiments)
_tuples = filtering.by_pair_distance_range(_tuples, _pair_distance_range)
_tuples = filtering.by_real_pairs(_tuples, _real_pairs=_real_cells)
_tuples = filtering.by_fake_static_pairs(_tuples, _fake_static_pairs=_static)
if _dead_dead is not False or _live_dead is not False:
_tuples = filtering.by_dead_live(_tuples, _dead=_dead, _live=_live)
_tuples = filtering.by_band(_tuples, _band=_band)
if _bleb:
_tuples = filtering.by_bleb_amount_um(_tuples, _amount_um=_bleb_amount_um)
print('Total tuples:', len(_tuples))
_arguments = []
for _tuple in _tuples:
_experiment, _series_id, _group = _tuple
_latest_time_frame = compute.latest_time_frame_before_overlapping(_experiment, _series_id, _group, OFFSET_X)
for _cell_id in ['left_cell', 'right_cell']:
_arguments.append({
'experiment': _experiment,
'series_id': _series_id,
'group': _group,
'length_x': QUANTIFICATION_WINDOW_LENGTH_IN_CELL_DIAMETER,
'length_y': QUANTIFICATION_WINDOW_HEIGHT_IN_CELL_DIAMETER,
'length_z': QUANTIFICATION_WINDOW_WIDTH_IN_CELL_DIAMETER,
'offset_x': OFFSET_X,
'offset_y': _offset_y,
'offset_z': _offset_z,
'cell_id': _cell_id,
'direction': 'inside',
'time_points': _latest_time_frame
})
_windows_dictionary, _windows_to_compute = compute.windows(_arguments,
_keys=['experiment', 'series_id', 'group', 'cell_id'])
_fiber_densities = compute.fiber_densities(_windows_to_compute, _subtract_border=True, _padding_y_by=_padding_y_by,
_padding_z_by=_padding_z_by, _space_y_by=_space_y_by,
_space_z_by=_space_z_by)
_experiments_fiber_densities = {
_key: [_fiber_densities[_tuple] for _tuple in _windows_dictionary[_key]]
for _key in _windows_dictionary
}
_tuples_by_experiment = organize.by_experiment(_tuples)
_same_correlations_array = []
_different_correlations_array = []
_valid_tuples = []
for _experiment in _tuples_by_experiment:
print('Experiment:', _experiment)
_experiment_tuples = _tuples_by_experiment[_experiment]
for _same_index in tqdm(range(len(_experiment_tuples)), desc='Main loop'):
_same_tuple = _experiment_tuples[_same_index]
_same_experiment, _same_series, _same_group = _same_tuple
_same_left_cell_fiber_densities = \
_experiments_fiber_densities[
(_same_experiment, _same_series, _same_group, 'left_cell')
]
_same_right_cell_fiber_densities = \
_experiments_fiber_densities[
(_same_experiment, _same_series, _same_group, 'right_cell')
]
_same_properties = \
load.group_properties(_same_experiment, _same_series, _same_group)
_same_left_cell_fiber_densities = compute.remove_blacklist(
_same_experiment,
_same_series,
_same_properties['cells_ids']['left_cell'],
_same_left_cell_fiber_densities
)
_same_right_cell_fiber_densities = compute.remove_blacklist(
_same_experiment,
_same_series,
_same_properties['cells_ids']['right_cell'],
_same_right_cell_fiber_densities
)
_same_left_cell_fiber_densities_filtered, _same_right_cell_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_same_left_cell_fiber_densities, _same_right_cell_fiber_densities
)
# ignore small arrays
if len(_same_left_cell_fiber_densities_filtered) < compute.minimum_time_frames_for_correlation(_same_experiment):
continue
_same_correlation = compute_lib.correlation(
compute_lib.derivative(_same_left_cell_fiber_densities_filtered, _n=DERIVATIVE),
compute_lib.derivative(_same_right_cell_fiber_densities_filtered, _n=DERIVATIVE)
)
for _different_index in range(len(_experiment_tuples)):
if _same_index != _different_index:
_different_tuple = _experiment_tuples[_different_index]
_different_experiment, _different_series, _different_group = \
_different_tuple
for _same_cell_id, _different_cell_id in product(['left_cell', 'right_cell'],
['left_cell', 'right_cell']):
_same_fiber_densities = _experiments_fiber_densities[(
_same_experiment,
_same_series,
_same_group,
_same_cell_id
)]
_different_fiber_densities = _experiments_fiber_densities[(
_different_experiment,
_different_series,
_different_group,
_different_cell_id
)]
_different_properties = load.group_properties(
_different_experiment, _different_series, _different_group
)
_same_fiber_densities = compute.remove_blacklist(
_same_experiment,
_same_series,
_same_properties['cells_ids'][_same_cell_id],
_same_fiber_densities
)
_different_fiber_densities = compute.remove_blacklist(
_different_experiment,
_different_series,
_different_properties['cells_ids'][_different_cell_id],
_different_fiber_densities
)
_same_fiber_densities_filtered, _different_fiber_densities_filtered = \
compute.longest_same_indices_shared_in_borders_sub_array(
_same_fiber_densities, _different_fiber_densities
)
# ignore small arrays
if len(_same_fiber_densities_filtered) < compute.minimum_time_frames_for_correlation(_different_experiment):
continue
_different_correlation = compute_lib.correlation(
compute_lib.derivative(_same_fiber_densities_filtered, _n=DERIVATIVE),
compute_lib.derivative(_different_fiber_densities_filtered, _n=DERIVATIVE)
)
_same_correlations_array.append(_same_correlation)
_different_correlations_array.append(_different_correlation)
if _same_tuple not in _valid_tuples:
_valid_tuples.append(_same_tuple)
print('Offset Y:', _offset_y, 'Offset Z:', _offset_z)
print('Total tuples:', len(_valid_tuples))
print('Total points:', len(_same_correlations_array))
_same_minus_different = \
np.array(_same_correlations_array) - np.array(_different_correlations_array)
print('Wilcoxon of same minus different around the zero:')
print(wilcoxon(_same_minus_different))
print('Higher same amount:', (_same_minus_different > 0).sum() /
len(_same_minus_different))
return _same_correlations_array, _different_correlations_array
