Usage Example

This document explains how to use three functions to calculate kernel density estimation (KDE), Moran's I statistic, Correlation, Wasserstein distance.

Function Imports

First, we need to import the necessary libraries and functions.

import numpy as np
import pandas as pd
from scipy.spatial import KDTree
from scipy.stats import wasserstein_distance
from sklearn.neighbors import KernelDensity
from scipy.sparse import csr_matrix
from tqdm import tqdm

# Import custom functions
from Spatialspots import calculate_kde, calculate_morans_i, calculate_wasserstein_distance

# Generate sample data
data = {
    "dim_1": np.random.rand(100),
    "dim_2": np.random.rand(100),
    "gene": np.random.choice(["geneA", "geneB", "geneC"], 100)
}
df = pd.DataFrame(data)
genes = ["geneA", "geneB", "geneC"]

# Calculate Kernel Density Estimation (KDE)
xy, densities = calculate_kde(df, genes, bandwidth=2, grid_size=10)

# Calculate Moran's I Statistic
morans_i_values = []
for density in densities:
    morans_i = calculate_morans_i(xy, density, threshold_distance=50)
    morans_i_values.append(morans_i)

# Calculate Wasserstein Distance
w_dis = calculate_wasserstein_distance(densities, xy, genes)

# Calculate Correlation
correlation = calculate_correlation(densities, genes=genes)

Moran's I

Calculate Moran's I for gene or cell distribution.

Wasserstein metric

Calculate the distribution distance between spots using the Wasserstein metric.

Correlation

Calculate the Correlation between spots