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spatial_lda

Short Description

sm.tl.spatial_lda: This function constructs a neighborhood matrix based on user-specified categorical variables, such as cell types, and applies Latent Dirichlet Allocation (LDA) to model the latent space of cellular distributions. It returns weights that describe the spatial organization of cells, facilitating the identification of Recurrent Cellular Neighborhoods (RCNs).

The sm.tl.spatial_cluster function should be utilized to cluster these latent vectors into RCNs, offering insights into the spatial dynamics of cellular environments.

Function

spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid', z_coordinate=None, phenotype='phenotype', method='radius', radius=30, knn=10, imageid='imageid', num_motifs=10, random_state=0, subset=None, verbose=True, label='spatial_lda', **kwargs)

Parameters:

Name Type Description Default
adata AnnData

AnnData object, containing spatial gene expression data.

 required
x_coordinate (str, required)

Column name in adata denoting the x-coordinates.

 'X_centroid'
y_coordinate (str, required)

Column name in adata denoting the y-coordinates.

 'Y_centroid'
z_coordinate str

Column name in adata for z-coordinates, for 3D spatial data.

 None
phenotype (str, required)

Column name in adata indicating cell phenotype or classification.

 'phenotype'
method str

Neighborhood definition method: 'radius' for fixed distance, 'knn' for K nearest neighbors.

 'radius'
radius int

Radius defining local neighborhoods (when method='radius').

 30
knn int

Number of nearest neighbors for neighborhood definition (when method='knn').

 10
imageid str

Column name in adata specifying image identifiers, for analyses within specific images.

 'imageid'
num_motifs int

Number of latent motifs to identify.

 10
random_state int

Seed for random number generator, ensuring reproducibility.

 0
subset str

Specific image identifier for targeted analysis.

 None
verbose bool

If True, enables progress and informational messages.

 True
label str

Custom label for storing results in adata.uns.

 'spatial_lda'

Returns:

Name Type Description
adata AnnData

The input adata object, updated with spatial LDA results in adata.uns[label].
Example 
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# Analyze spatial motifs using the radius method 
adata = sm.tl.spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                    method='radius', radius=50, num_motifs=10,
                    label='lda_radius_50')

# KNN method with specific image subset
adata = sm.tl.spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                    method='knn', knn=15, num_motifs=15, subset='image_01',
                    label='lda_knn_15_image_01')

# 3D spatial data analysis using the radius method
adata = am.tl.spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid', z_coordinate='Z_centroid',
                    method='radius', radius=100, num_motifs=20, label='lda_3D_radius_100')
Source code in scimap/tools/spatial_lda.py 
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def spatial_lda (adata, 
                 x_coordinate='X_centroid',
                 y_coordinate='Y_centroid',
                 z_coordinate= None,
                 phenotype='phenotype', 
                 method='radius', 
                 radius=30, 
                 knn=10,
                 imageid='imageid',
                 num_motifs=10, 
                 random_state=0, 
                 subset=None,
                 verbose=True,
                 label='spatial_lda',**kwargs):
    """
Parameters:
        adata (anndata.AnnData):  
            AnnData object, containing spatial gene expression data.

        x_coordinate (str, required):  
            Column name in `adata` denoting the x-coordinates.

        y_coordinate (str, required):  
            Column name in `adata` denoting the y-coordinates.

        z_coordinate (str, optional):  
            Column name in `adata` for z-coordinates, for 3D spatial data.

        phenotype (str, required):  
            Column name in `adata` indicating cell phenotype or classification.

        method (str, optional):  
            Neighborhood definition method: 'radius' for fixed distance, 'knn' for K nearest neighbors.

        radius (int, optional):  
            Radius defining local neighborhoods (when method='radius').

        knn (int, optional):  
            Number of nearest neighbors for neighborhood definition (when method='knn').

        imageid (str, optional):  
            Column name in `adata` specifying image identifiers, for analyses within specific images.

        num_motifs (int, optional):  
            Number of latent motifs to identify.

        random_state (int, optional):  
            Seed for random number generator, ensuring reproducibility.

        subset (str, optional):  
            Specific image identifier for targeted analysis.

        verbose (bool, optional):  
            If True, enables progress and informational messages.

        label (str, optional):  
            Custom label for storing results in `adata.uns`.

Returns:
        adata (anndata.AnnData):  
            The input `adata` object, updated with spatial LDA results in `adata.uns[label]`.

Example:
        ```python

        # Analyze spatial motifs using the radius method 
        adata = sm.tl.spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                            method='radius', radius=50, num_motifs=10,
                            label='lda_radius_50')

        # KNN method with specific image subset
        adata = sm.tl.spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                            method='knn', knn=15, num_motifs=15, subset='image_01',
                            label='lda_knn_15_image_01')

        # 3D spatial data analysis using the radius method
        adata = am.tl.spatial_lda(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid', z_coordinate='Z_centroid',
                            method='radius', radius=100, num_motifs=20, label='lda_3D_radius_100')
        ```

    """


    # Function
    def spatial_lda_internal (adata_subset, x_coordinate,y_coordinate,z_coordinate,phenotype, 
                              method, radius, knn, imageid):

        # Print which image is being processed
        if verbose:
            print('Processing: ' + str(np.unique(adata_subset.obs[imageid])))


        # Create a dataFrame with the necessary inforamtion
        if z_coordinate is not None:
            if verbose:
                print("Including Z -axis")
            data = pd.DataFrame({'x': adata_subset.obs[x_coordinate], 'y': adata_subset.obs[y_coordinate], 'z': adata_subset.obs[z_coordinate], 'phenotype': adata_subset.obs[phenotype]})
        else:
            data = pd.DataFrame({'x': adata_subset.obs[x_coordinate], 'y': adata_subset.obs[y_coordinate], 'phenotype': adata_subset.obs[phenotype]})


        # Create a DataFrame with the necessary inforamtion
        #data = pd.DataFrame({'x': adata_subset.obs[x_coordinate], 'y': adata_subset.obs[y_coordinate], 'phenotype': adata_subset.obs[phenotype]})

        # Identify neighbourhoods based on the method used
        # a) KNN method

        if method == 'knn':
            if verbose:
                print("Identifying the " + str(knn) + " nearest neighbours for every cell")
            if z_coordinate is not None:
                tree = BallTree(data[['x','y','z']], leaf_size= 2)
                ind = tree.query(data[['x','y','z']], k=knn, return_distance= False)
            else:
                tree = BallTree(data[['x','y']], leaf_size= 2)
                ind = tree.query(data[['x','y']], k=knn, return_distance= False)
            ind = list(np.array(item) for item in ind)


        # b) Local radius method
        if method == 'radius':
            if verbose:
                print("Identifying neighbours within " + str(radius) + " pixels of every cell")
            if z_coordinate is not None:
                kdt = BallTree(data[['x','y','z']], metric='euclidean') 
                ind = kdt.query_radius(data[['x','y','z']], r=radius, return_distance=False)
            else:
                kdt = BallTree(data[['x','y']], metric='euclidean') 
                ind = kdt.query_radius(data[['x','y']], r=radius, return_distance=False)


# =============================================================================
#         if method == 'knn':
#             if verbose:
#                 print("Identifying the " + str(knn) + " nearest neighbours for every cell")
#             tree = BallTree(data[['x','y']], leaf_size= 2)
#             ind = tree.query(data[['x','y']], k=knn, return_distance= False)
#             #ind = [np.array(x) for x in ind]
#             ind = list(np.array(item) for item in ind)
#             
#         # b) Local radius method
#         if method == 'radius':
#             if verbose:
#                 print("Identifying neighbours within " + str(radius) + " pixels of every cell")
#             kdt = BallTree(data[['x','y']], leaf_size= 2) 
#             ind = kdt.query_radius(data[['x','y']], r=radius, return_distance=False)
#             
# =============================================================================

        # Map phenotype
        phenomap = dict(zip(list(range(len(ind))), data['phenotype'])) # Used for mapping
        for i in range(len(ind)):
            ind[i] = [phenomap[letter] for letter in ind[i]]

        # return
        return ind

    # Subset a particular image if needed
    if subset is not None:
        adata_list = [adata[adata.obs[imageid] == subset]]
    else:
        adata_list = [adata[adata.obs[imageid] == i] for i in adata.obs[imageid].unique()]

    # Apply function to all images
    # Create lamda function 
    r_spatial_lda_internal = lambda x: spatial_lda_internal(adata_subset=x,
                                                            x_coordinate=x_coordinate,
                                                            y_coordinate=y_coordinate,
                                                            z_coordinate=z_coordinate,
                                                            phenotype=phenotype, 
                                                            method=method, 
                                                            radius=radius, 
                                                            knn=knn, 
                                                            imageid=imageid) 
    all_data = list(map(r_spatial_lda_internal, adata_list)) # Apply function 

    # combine all the data into one
    texts = np.concatenate( all_data, axis=0 ).tolist()

    # LDA pre-processing
    if verbose:
        print ('Pre-Processing Spatial LDA')
    # Create Dictionary
    id2word = corpora.Dictionary(texts)

    # Term Document Frequency
    corpus = [id2word.doc2bow(text) for text in texts]

    # Build LDA model
    if verbose:
        print ('Training Spatial LDA')
    try:
        lda_model = gensim.models.ldamulticore.LdaMulticore(corpus=corpus,
                                                   id2word=id2word,
                                                   num_topics=num_motifs, 
                                                   random_state=random_state,**kwargs)
    except:
        lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                                   id2word=id2word,
                                                   num_topics=num_motifs, 
                                                   random_state=random_state,**kwargs)

    # Compute Coherence Score
    if verbose:
        print ('Calculating the Coherence Score')
    coherence_model_lda = CoherenceModel(model=lda_model, texts=texts, dictionary=id2word, coherence='c_v')
    coherence_lda = coherence_model_lda.get_coherence()
    if verbose:
        print('\nCoherence Score: ', coherence_lda)

    # isolate the latent features
    if verbose:
        print ('Gathering the latent weights')
    topic_weights = []
    for row_list in lda_model[corpus]:
        tmp = np.zeros(num_motifs)
        for i, w in row_list:
            tmp[i] = w
        topic_weights.append(tmp)
    # conver to dataframe
    arr = pd.DataFrame(topic_weights, index=adata.obs.index).fillna(0)
    arr = arr.add_prefix('Motif_')

    # isolate the weights of phenotypes
    pattern = "(\d\.\d+).\"(.*?)\""
    cell_weight = pd.DataFrame(index=np.unique(adata.obs[phenotype]))
    for i in range(0, len(lda_model.print_topics())):
        level1 = lda_model.print_topics()[i][1]
        tmp = pd.DataFrame(re.findall(pattern, level1))
        tmp.index = tmp[1]
        tmp = tmp.drop(columns=1)
        tmp.columns = ['Motif_'+ str(i)]
        cell_weight = cell_weight.merge(tmp, how='outer', left_index=True, right_index=True)
    # fill zeros
    cell_weight = cell_weight.fillna(0).astype(float)

    # save the results in anndata object
    adata.uns[label] = arr # save the weight for each cell
    adata.uns[str(label)+'_probability'] = cell_weight # weights of each cell type
    #adata.uns[str(label)+'_model'] = lda_model

    # return
    return adata