Project: s41467-024-46773-z | Function: seed_select (python)

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**DESCRIPTION:** Identifies dynamic feature modules through K-means clustering on average pairwise expression changes across cellular neighborhoods with permutation testing for statistical significance

**KEY TERMS:** {
  "delta_mean": "matrix Δ of average pairwise changes across neighborhoods",
  "n_clusters": "number of K-means clusters for feature modules",
  "null_iterations": "iterations for permutation testing (default 1000)",
  "dyn_feats": "dynamically expressed features passing significance test",
  "modules": "feature-cluster assignments with p-values",
  "sub_idx": "indices of subsampled representative neighborhoods"
}

**CONTEXT CODE:** import multiprocessing as mp
from functools import partial
from tqdm import tqdm
from sketchKH import *

def delve_fs(adata = None,
            k: int = 10,
            num_subsamples: int = 1000,
            n_clusters: int = 5,
            null_iterations: int = 1000,
            random_state: int = 0,
            n_random_state: int = 10,
            n_pcs = None,
            n_jobs: int = -1):
    """Performs DELVE feature selection 
        - step 1: identifies dynamic seed features to construct a between-cell affinity graph according to dynamic cell state progression
        - step 2: ranks features according to their total variation in signal along the approximate trajectory graph using the Laplacian score
    
    Parameters
    adata: anndata.AnnData
        annotated data object where adata.X is the attribute for preprocessed data (dimensions = cells x features)
    k: int (default = 10)
        number of nearest neighbors for between cell affinity kNN graph construction
    num_subsamples: int (default = 1000)
        number of neighborhoods to subsample when estimating feature dynamics  
    n_clusters: int (default = 5)
        number of feature modules
    null_iterations: int (default = 1000)
        number of iterations for gene-wise permutation testing
    random_state: int (default = 0)
        random seed parameter
    n_random_state: int (default = 10)
        number of kmeans clustering initializations
    n_pcs: int (default = None)
        number of principal components to compute pairwise Euclidean distances for between-cell affinity graph construction. If None, uses adata.X
    n_jobs = int (default = -1)
        number of tasks
    ----------
    Returns
    delta_mean: pd.DataFrame
        dataframe containing average pairwise change in expression of all features across subsampled neighborhoods (dimensions = num_subsamples x features)
    modules: pd.DataFrame
        dataframe containing feature-cluster assignments and permutation p-values (dimensions = features x 2)
    selected_features: pd.DataFrame
        dataframe containing ranked features and Laplacian scores following feature selection (dimensions = features x 1)
    ----------
    """
    # MASKED: Implements the main DELVE feature selection algorithm with two-step approach: dynamic seed identification from co-varying feature modules and Laplacian score-based feature ranking along cellular trajectory graphs implementation
    raise NotImplementedError("This function is masked for testing purposes.")

def seed_select(X = None,
                feature_names = None,
                obs_names = None,
                k: int = 10, 
                num_subsamples: int = 1000,
                n_clusters: int = 5,
                null_iterations: int = 1000,
                random_state: int = 0,
                n_random_state: int = 10,
                n_pcs = None,
                n_jobs: int = -1):
    """Identifies dynamic seed clusters
    Parameters
    X: np.ndarray (default = None)
        array containing normalized and preprocessed data (dimensions = cells x features)
    feature_names: np.ndarray (default = None)
        array containing feature names
    obs_names: np.ndarray (default = None)
        array containing cell names   
    k: int (default = 10)
        number of nearest neighbors for between cell affinity kNN graph construction
    num_subsamples: int (default = 1000)
        number of neighborhoods to subsample when estimating feature dynamics  
    n_clusters: int (default = 5)
        number of feature modules
    null_iterations: int (default = 1000)
        number of iterations for gene-wise permutation testing
    random_state: int (default = 0)
        random seed parameter
    n_random_state: int (default = 10)
        number of kmeans clustering initializations
    n_pcs: int (default = None)
        number of principal components to compute pairwise Euclidean distances for between-cell affinity graph construction. If None, uses adata.X
    n_jobs = int (default = -1)
        number of tasks
    ----------
    Returns
    sub_idx: np.andarray
        array containing indices of subsampled neighborhoods
    adata_sub: anndata.AnnData
        annotated data object containing subsampled means (dimensions = num_subsamples x features)
    delta_mean: pd.DataFrame
        dataframe containing average pairwise change in expression of all features across subsampled neighborhoods (dimensions = num_subsamples x features)
    modules: pd.DataFrame
        dataframe containing feature-cluster assignments and permutation p-values (dimensions = features x 2)
    ----------
    """
    # MASKED: Identifies dynamic feature modules through K-means clustering on average pairwise expression changes across cellular neighborhoods with permutation testing for statistical significance implementation
    raise NotImplementedError("This function is masked for testing purposes.")

def feature_select(X = None,
                    feature_names = None,
                    dyn_feats = None,
                    k: int = 10,
                    n_pcs = None, 
                    n_jobs: int = -1):
    """Ranks features along dynamic seed graph using the Laplacian score: https://papers.nips.cc/paper/2005/file/b5b03f06271f8917685d14cea7c6c50a-Paper.pdf
    Parameters
    X: np.ndarray (default = None)
        array containing normalized and preprocessed data (dimensions = cells x features)
    feature_names: np.ndarray (default = None)
        array containing feature names
    dyn_feats: np.ndarray (default = None)
        array containing features that are dynamically expressed. Can consider replacing this with a set of known regulators.
    k: int (default = 10)
        number of nearest neighbors for between cell affinity kNN graph construction
    n_pcs: int (default = None)
        number of principal components to compute pairwise Euclidean distances for between-cell affinity graph construction. If None, uses adata.X
    n_jobs = int (default = -1)
        number of tasks
    ----------
    Returns
    selected_features: pd.DataFrame
        dataframe containing ranked features and Laplacian scores for feature selection (dimensions = features x 1)
    ----------
    """
    f_idx = np.where(np.isin(feature_names, dyn_feats) == True)[0] #index of feature names to construct seed graph
    W = construct_affinity(X = X[:, f_idx], k = k, n_pcs = n_pcs, n_jobs = n_jobs) #constructs graph using dynamic seed features
    scores = laplacian_score(X = X, W = W)
    selected_features = pd.DataFrame(scores, index = feature_names, columns = ['DELVE'])
    selected_features = selected_features.sort_values(by = 'DELVE', ascending = True)

    return selected_features

def delta_exp(X = None,
            feature_names = None, 
            obs_names = None,
            k: int = 10,
            num_subsamples: int = 1000,
            random_state: int = 0,
            n_pcs = None,
            n_jobs: int = -1):
    """Estimates change in expression of features across representative cellular neighborhoods
    Parameters
    X: np.ndarray (default = None)
        array containing normalized and preprocessed data (dimensions = cells x features)
    feature_names: np.ndarray (default = None)
        array containing feature names
    obs_names: np.ndarray (default = None)
        array containing cell names   


Complete the function implementation replacing "raise NotImplementedError".