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Refactor and format code
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@Soorya19Pradeep
Soorya19Pradeep committed Sep 23, 2024
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Showing 1 changed file with 169 additions and 129 deletions.
298 changes: 169 additions & 129 deletions applications/contrastive_phenotyping/evaluation/PC_vs_CF.py
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import matplotlib.pyplot as plt
import seaborn as sns

from scipy.stats import spearmanr
import pandas as pd
import plotly.express as px

# %%
features_path = Path(
"/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/time_sampling_strategies/time_interval/predict/feb_test_time_interval_1_epoch_178.zarr"
Expand Down Expand Up @@ -62,13 +66,15 @@

# %% PCA analysis of the features

pca = PCA(n_components=3)
pca = PCA(n_components=5)
pca_features = pca.fit_transform(features.values)
features = (
features.assign_coords(PCA1=("sample", pca_features[:, 0]))
.assign_coords(PCA2=("sample", pca_features[:, 1]))
.assign_coords(PCA3=("sample", pca_features[:, 2]))
.set_index(sample=["PCA1", "PCA2", "PCA3"], append=True)
.assign_coords(PCA4=("sample", pca_features[:, 3]))
.assign_coords(PCA5=("sample", pca_features[:, 4]))
.set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"], append=True)
)

# %% convert the xarray to dataframe structure and add columns for computed features
Expand Down Expand Up @@ -100,59 +106,16 @@

# %% compute the computed features and add them to the dataset

# fov_names_list = [
# name for name in embedding_dataset["fov_name"].values if name.startswith("/B/4")
# ]
fov_names_list = features["fov_name"].unique()
unique_fov_names = sorted(list(set(fov_names_list)))

# features = pd.DataFrame()

for fov_name in unique_fov_names:

# all_tracks_FOV = embedding_dataset.sel(fov_name=fov_name)

# unique_track_ids = list(all_tracks_FOV["track_id"].values)
unique_track_ids = features[features["fov_name"] == fov_name]["track_id"].unique()
unique_track_ids = list(set(unique_track_ids))

for track_id in unique_track_ids:
# a_track_in_FOV = all_tracks_FOV.sel(track_id=track_id)
# indices = np.arange(a_track_in_FOV.sizes["sample"])
# features_track = a_track_in_FOV["features"]
# time_stamp = features_track["t"][indices].astype(str)

# feature_track_values = features_track.values

# perform PCA analysis of features

# pca = PCA(n_components=4)
# if feature_track_values.shape[0] > 5:
# pca_features = pca.fit_transform(feature_track_values)
# else:
# continue

# features_track = (
# features_track.assign_coords(PCA1=("sample", pca_features[:, 0]))
# .assign_coords(PCA2=("sample", pca_features[:, 1]))
# .assign_coords(PCA3=("sample", pca_features[:, 2]))
# .assign_coords(PCA4=("sample", pca_features[:, 3]))
# .set_index(sample=["PCA1", "PCA2", "PCA3", "PCA4"], append=True)
# )

# umap = UMAP()
# if feature_track_values.shape[0] > 5:
# umap_features = umap.fit_transform(features_track.values)
# else:
# continue

# features_track = (
# features_track.assign_coords(UMAP1=("sample", umap_features[:, 0]))
# .assign_coords(UMAP2=("sample", umap_features[:, 1]))
# .set_index(sample=["UMAP1", "UMAP2"], append=True)
# )

# load the image patches

prediction_dataset = dataset_of_tracks(
data_path,
Expand All @@ -165,31 +128,6 @@
whole = np.stack([p["anchor"] for p in prediction_dataset])
phase = whole[:, 0, 3]
fluor = np.max(whole[:, 1], axis=1)
# phase = np.stack([p["anchor"][0, 3].numpy() for p in predict_dataset])
# fluor = np.stack([np.max(p["anchor"][1].numpy(), axis=0) for p in predict_dataset])

# Compute Fourier descriptors for phase image
# data = {
# "Phase Symmetry Score": [],
# "Fluor Symmetry Score": [],
# "Sensor Area": [],
# "Masked Sensor Intensity": [],
# "Entropy Phase": [],
# "Entropy Fluor": [],
# "Contrast Phase": [],
# "Dissimilarity Phase": [],
# "Homogeneity Phase": [],
# "Contrast Fluor": [],
# "Dissimilarity Fluor": [],
# "Homogeneity Fluor": [],
# "Phase IQR": [],
# "Fluor Mean Intensity": [],
# "Phase Standard Deviation": [],
# "Fluor Standard Deviation": [],
# "Phase radial profile": [],
# "Fluor radial profile": [],
# "Time": [],
# }

for t in range(phase.shape[0]):
# Compute Fourier descriptors for phase image
Expand Down Expand Up @@ -217,16 +155,6 @@
FE.compute_glcm_features(fluor[t])
)

# # Compute edge detection using Canny
# edges_phase = FE.detect_edges(phase[t])
# edges_fluor = FE.detect_edges(fluor[t])

# Quantify the amount of edge feature in the phase image
# edge_density_phase = np.sum(edges_phase) / (edges_phase.shape[0] * edges_phase.shape[1])

# Quantify the amount of edge feature in the fluor image
# edge_density_fluor = np.sum(edges_fluor) / (edges_fluor.shape[0] * edges_fluor.shape[1])

# Compute interqualtile range of pixel intensities
iqr = FE.compute_iqr(phase[t])

Expand All @@ -241,31 +169,7 @@
phase_radial_profile = FE.compute_radial_intensity_gradient(phase[t])
fluor_radial_profile = FE.compute_radial_intensity_gradient(fluor[t])

# Append the computed features to the data dictionary
# data["Phase Symmetry Score"].append(phase_symmetry_score)
# data["Fluor Symmetry Score"].append(fluor_symmetry_score)
# data["Sensor Area"].append(area)
# data["Masked Sensor Intensity"].append(masked_intensity)
# data["Entropy Phase"].append(entropy_phase)
# data["Entropy Fluor"].append(entropy_fluor)
# data["Contrast Phase"].append(contrast_phase)
# data["Dissimilarity Phase"].append(dissimilarity_phase)
# data["Homogeneity Phase"].append(homogeneity_phase)
# data["Contrast Fluor"].append(contrast_fluor)
# data["Dissimilarity Fluor"].append(dissimilarity_fluor)
# data["Homogeneity Fluor"].append(homogeneity_fluor)
# # data["Edge Density Phase"].append(edge_density_phase)
# # data["Edge Density Fluor"].append(edge_density_fluor)
# data["Phase IQR"].append(iqr)
# data["Fluor Mean Intensity"].append(fluor_mean_intensity)
# data["Phase Standard Deviation"].append(phase_std_dev)
# data["Fluor Standard Deviation"].append(fluor_std_dev)
# data["Phase radial profile"].append(phase_radial_profile)
# data["Fluor radial profile"].append(fluor_radial_profile)
# data["Time"].append(t)

# update the features dataframe with the computed features

features.loc[
(features["fov_name"] == fov_name)
& (features["track_id"] == track_id)
Expand Down Expand Up @@ -375,22 +279,6 @@
"Fluor radial profile",
] = fluor_radial_profile

# Create a dataframe to store the computed features
# data_df = pd.DataFrame(data)

# compute correlation between PCA features and computed features

# Create a dataframe with PCA results
# pca_results = pd.DataFrame(
# pca_features, columns=["PCA1", "PCA2", "PCA3", "PCA4"]
# )
# umap_results = pd.DataFrame(umap_features, columns=["UMAP1", "UMAP2"])
# combined_df = pd.concat([data_df, pca_results, umap_results], axis=1)
# combined_df["fov_name"] = fov_name
# combined_df["track_id"] = track_id

# features = pd.concat([features, combined_df], ignore_index=True)

# %%

# Save the features dataframe to a CSV file
Expand All @@ -399,6 +287,14 @@
index=False,
)

# # read the features dataframe from the CSV file
# features = pd.read_csv(
# "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/features_twoChan.csv"
# )

# remove the rows with missing values
features = features.dropna()

# sub_features = features[features["Time"] == 20]
feature_df_removed = features.drop(
columns=["fov_name", "track_id", "t", "id", "parent_track_id", "parent_id"]
Expand All @@ -407,25 +303,51 @@
# Compute correlation between PCA features and computed features
correlation = feature_df_removed.corr(method="spearman")

# %% find the best correlated computed features with PCA features

# Find the best correlated computed features with PCA features
# best_correlated_features = correlation.loc["PCA1":"PCA4", :].idxmax()
# best_correlated_features

# %% display PCA correlation as a heatmap

plt.figure(figsize=(20, 5))
sns.heatmap(
correlation.drop(columns=["PCA1", "PCA2", "PCA3", "PCA4"]).loc["PCA1":"PCA4", :],
correlation.drop(columns=["PCA1", "PCA2", "PCA3", "PCA4", "PCA5"]).loc[
"PCA1":"PCA5", :
],
annot=True,
cmap="coolwarm",
fmt=".2f",
)
plt.title("Correlation between PCA features and computed features")
plt.xlabel("Computed Features")
plt.ylabel("PCA Features")
plt.show()
plt.savefig(
"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/PC_vs_CF_2chan_pca.svg"
)


# %% plot PCA vs set of computed features

set_features = [
"Sensor Area",
"Fluor radial profile",
"Contrast Fluor",
"Phase radial profile",
"Phase Standard Deviation",
"Phase IQR",
"Homogeneity Phase",
"Homogeneity Fluor",
]

plt.figure(figsize=(8, 10))
sns.heatmap(
correlation.loc[set_features, "PCA1":"PCA5"],
annot=True,
cmap="coolwarm",
fmt=".2f",
vmin=-1,
vmax=1,
)

plt.savefig(
"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/PC_vs_CF_2chan_pca_setfeatures.svg"
)

# %% display UMAP correlation as a heatmap

Expand All @@ -443,4 +365,122 @@
"/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/cell_division/PC_vs_CF_2chan.svg"
)

# %%
# %% find the cell patches with the highest and lowest value in each feature

import os


def save_patches(fov_name, track_id):
data_path = Path(
"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/registered_test.zarr"
)
tracks_path = Path(
"/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/8-train-test-split/track_test.zarr"
)
source_channel = ["Phase3D", "RFP"]
prediction_dataset = dataset_of_tracks(
data_path,
tracks_path,
[fov_name],
[track_id],
source_channel=source_channel,
)
whole = np.stack([p["anchor"] for p in prediction_dataset])
phase = whole[:, 0]
fluor = whole[:, 1]
out_dir = "/hpc/projects/comp.micro/infected_cell_imaging/Single_cell_phenotyping/ContrastiveLearning/Figure_panels/data/computed_features/"
fov_name_out = fov_name.replace("/", "_")
np.save(
(os.path.join(out_dir, "phase" + fov_name_out + "_" + str(track_id) + ".npy")),
phase,
)
np.save(
(os.path.join(out_dir, "fluor" + fov_name_out + "_" + str(track_id) + ".npy")),
fluor,
)


# PCA1: Fluor radial profile
highest_fluor_radial_profile = features.loc[features["Fluor radial profile"].idxmax()]
print("Row with highest 'Fluor radial profile':")
# print(highest_fluor_radial_profile)
print(
f"fov_name: {highest_fluor_radial_profile['fov_name']}, time: {highest_fluor_radial_profile['t']}"
)
save_patches(
highest_fluor_radial_profile["fov_name"], highest_fluor_radial_profile["track_id"]
)

lowest_fluor_radial_profile = features.loc[features["Fluor radial profile"].idxmin()]
print("Row with lowest 'Fluor radial profile':")
# print(lowest_fluor_radial_profile)
print(
f"fov_name: {lowest_fluor_radial_profile['fov_name']}, time: {lowest_fluor_radial_profile['t']}"
)
save_patches(
lowest_fluor_radial_profile["fov_name"], lowest_fluor_radial_profile["track_id"]
)

# PCA2: Entropy phase
highest_entropy_phase = features.loc[features["Entropy Phase"].idxmax()]
print("Row with highest 'Entropy Phase':")
# print(highest_entropy_phase)
print(
f"fov_name: {highest_entropy_phase['fov_name']}, time: {highest_entropy_phase['t']}"
)
save_patches(highest_entropy_phase["fov_name"], highest_entropy_phase["track_id"])

lowest_entropy_phase = features.loc[features["Entropy Phase"].idxmin()]
print("Row with lowest 'Entropy Phase':")
# print(lowest_entropy_phase)
print(
f"fov_name: {lowest_entropy_phase['fov_name']}, time: {lowest_entropy_phase['t']}"
)
save_patches(lowest_entropy_phase["fov_name"], lowest_entropy_phase["track_id"])

# PCA3: Phase IQR
highest_phase_iqr = features.loc[features["Phase IQR"].idxmax()]
print("Row with highest 'Phase IQR':")
# print(highest_phase_iqr)
print(f"fov_name: {highest_phase_iqr['fov_name']}, time: {highest_phase_iqr['t']}")
save_patches(highest_phase_iqr["fov_name"], highest_phase_iqr["track_id"])

tenth_lowest_phase_iqr = features.nsmallest(10, "Phase IQR").iloc[9]
print("Row with tenth lowest 'Phase IQR':")
# print(tenth_lowest_phase_iqr)
print(
f"fov_name: {tenth_lowest_phase_iqr['fov_name']}, time: {tenth_lowest_phase_iqr['t']}"
)
save_patches(tenth_lowest_phase_iqr["fov_name"], tenth_lowest_phase_iqr["track_id"])

# PCA4: Phase Standard Deviation
highest_phase_std_dev = features.loc[features["Phase Standard Deviation"].idxmax()]
print("Row with highest 'Phase Standard Deviation':")
# print(highest_phase_std_dev)
print(
f"fov_name: {highest_phase_std_dev['fov_name']}, time: {highest_phase_std_dev['t']}"
)
save_patches(highest_phase_std_dev["fov_name"], highest_phase_std_dev["track_id"])

lowest_phase_std_dev = features.loc[features["Phase Standard Deviation"].idxmin()]
print("Row with lowest 'Phase Standard Deviation':")
# print(lowest_phase_std_dev)
print(
f"fov_name: {lowest_phase_std_dev['fov_name']}, time: {lowest_phase_std_dev['t']}"
)
save_patches(lowest_phase_std_dev["fov_name"], lowest_phase_std_dev["track_id"])

# PCA5: Sensor area
highest_sensor_area = features.loc[features["Sensor Area"].idxmax()]
print("Row with highest 'Sensor Area':")
# print(highest_sensor_area)
print(f"fov_name: {highest_sensor_area['fov_name']}, time: {highest_sensor_area['t']}")
save_patches(highest_sensor_area["fov_name"], highest_sensor_area["track_id"])

tenth_lowest_sensor_area = features.nsmallest(10, "Sensor Area").iloc[9]
print("Row with tenth lowest 'Sensor Area':")
# print(tenth_lowest_sensor_area)
print(
f"fov_name: {tenth_lowest_sensor_area['fov_name']}, time: {tenth_lowest_sensor_area['t']}"
)
save_patches(tenth_lowest_sensor_area["fov_name"], tenth_lowest_sensor_area["track_id"])

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