Create UMAP of single cell features

[1]:

from livecellx.sample_data import tutorial_three_image

dic_dataset, mask_dataset = tutorial_three_image(dic_dataset_path="../datasets/test_data_STAV-A549/DIC_data", mask_dataset_path="../datasets/test_data_STAV-A549/mask_data")

3 png img file paths loaded:
3 tif img file paths loaded:

[2]:

from livecellx.core.io_sc import prep_scs_from_mask_dataset
scs = prep_scs_from_mask_dataset(mask_dataset, dic_dataset)

100%|██████████| 3/3 [00:07<00:00,  2.43s/it]

[3]:

from livecellx.trajectory.feature_extractors import compute_haralick_features, compute_skimage_regionprops
from livecellx.preprocess.utils import normalize_img_to_uint8
for sc in scs:
    features = compute_skimage_regionprops(sc, preprocess_img_func=normalize_img_to_uint8, sc_level_normalize=True)

[4]:

sc.get_feature_pd_series()

[4]:

skimage_area                 22262.000000
skimage_area_bbox            39672.000000
skimage_area_convex          23414.000000
skimage_area_filled          22262.000000
skimage_axis_major_length      263.397536
                                 ...
skimage_orientation             -1.053940
skimage_perimeter              660.825469
skimage_perimeter_crofton      629.183347
skimage_solidity                 0.950799
t                                2.000000
Length: 144, dtype: float64

[5]:

def create_sc_table(scs, normalize_features=True):
    import pandas as pd
    import numpy as np
    df = pd.DataFrame([sc.get_feature_pd_series() for sc in scs])
    if normalize_features:
        for col in df.columns:
            df[col] = (df[col] - df[col].mean())
            col_std = df[col].std()
            if col_std != 0 and not np.isnan(col_std):
                df[col] /= col_std
    # remove column t from df
    df.drop("t", axis=1, inplace=True)
    # df["sc_id"] = [sc.id for sc in scs]
    # df["t"] = [sc.timeframe for sc in scs]
    # df.set_index("sc_id", inplace=True)
    return df

sc_feature_table = create_sc_table(scs)
sc_feature_table[:2]

[5]:

	skimage_area	skimage_area_bbox	skimage_area_convex	skimage_area_filled	skimage_axis_major_length	skimage_axis_minor_length	skimage_centroid-0	skimage_centroid-1	skimage_centroid_local-0	skimage_centroid_local-1	...	skimage_moments_weighted_normalized-2-2	skimage_moments_weighted_normalized-2-3	skimage_moments_weighted_normalized-3-0	skimage_moments_weighted_normalized-3-1	skimage_moments_weighted_normalized-3-2	skimage_moments_weighted_normalized-3-3	skimage_orientation	skimage_perimeter	skimage_perimeter_crofton	skimage_solidity
0	-0.729877	-0.697465	-0.785715	-0.729877	-1.025767	-0.300223	-0.557043	-0.863007	-0.557043	-0.863007	...	-1.144537	0.705302	0.791071	0.81463	-0.703368	1.11867	-0.394298	-0.881596	-0.912129	1.276949
1	-0.729877	-0.697465	-0.785715	-0.729877	-1.025767	-0.300223	-0.557043	-0.863007	-0.557043	-0.863007	...	-1.144537	0.705302	0.791071	0.81463	-0.703368	1.11867	-0.394298	-0.881596	-0.912129	1.276949

2 rows × 143 columns

Normalize features

Perform UMAP on features

[6]:

import umap
reducer = umap.UMAP()

[7]:

# check if any columns are all NaN
sc_feature_table = sc_feature_table.dropna(axis=1, how="all")

[8]:

embedding = reducer.fit_transform(sc_feature_table)

[9]:

embedding.shape

[9]:

(42, 2)

[10]:

import matplotlib.pyplot as plt
plt.scatter(
    embedding[:, 0],
    embedding[:, 1],
    c=[sc.timeframe for sc in scs],
)

[10]:

<matplotlib.collections.PathCollection at 0x21c21c02ec0>

../../_images/livecellx_notebooks_tutorials_tutorial_umap_12_1.png

[11]:

unnormalized_img_features = create_sc_table(scs, normalize_features=False)
# drop na
unnormalized_img_features = unnormalized_img_features.dropna(axis=1, how="all")
reducer = umap.UMAP()
unnormalized_embedding = reducer.fit_transform(unnormalized_img_features)
plt.scatter(
    unnormalized_embedding[:, 0],
    unnormalized_embedding[:, 1],
    c=[sc.timeframe for sc in scs],
)

[11]:

<matplotlib.collections.PathCollection at 0x21c15a2f6a0>

../../_images/livecellx_notebooks_tutorials_tutorial_umap_13_1.png

[ ]:

list(unnormalized_img_features.keys())