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Sm.tl.spatial lda

Short Description

sm.tl.spatial_lda: The function allows users to compute a neighbourhood matrix using any categorical variable (e.g. cell-types) as input and then perform Latent Dirichlet Allocation (LDA) modelling. The latent space weights are then then returned which can be clustered to identify Reccurent Cellular Neighbourhoods (RCNs).

Use the [spatial_cluster] function to further group the neighbourhoods into Reccurent Cellular Neighbourhoods (RCNs)

Function

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

Parameters:

Name Type Description Default
adata

AnnData object

required
x_coordinate

float, required
Column name containing the x-coordinates values.

'X_centroid'
y_coordinate

float, required
Column name containing the y-coordinates values.

'Y_centroid'
phenotype

string, required
Column name of the column containing the phenotype information. It could also be any categorical assignment given to single cells.

'phenotype'
method

string, optional
Two options are available: a) 'radius', b) 'knn'.
a) radius - Identifies the neighbours within a given radius for every cell.
b) knn - Identifies the K nearest neigbours for every cell.

'radius'
radius

int, optional
The radius used to define a local neighbhourhood.

30
knn

int, optional
Number of cells considered for defining the local neighbhourhood.

10
imageid

string, optional
Column name of the column containing the image id.

'imageid'
subset

string, optional
imageid of a single image to be subsetted for analyis.

None
num_motifs

int, optional
The number of requested latent motifs to be extracted from the training corpus.

10
random_state

int, optional
Either a randomState object or a seed to generate one. Useful for reproducibility.

0
label

string, optional
Key for the returned data, stored in adata.uns.

'spatial_lda'

Returns:

Name Type Description
adata

AnnData object
Updated AnnData object with the results stored in adata.uns ['spatial_lda'].

1
2
    # Running the radius method
    adata = sm.tl.spatial_lda (adata, num_motifs=10, 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',
                 phenotype='phenotype', method='radius', radius=30, knn=10,
                 imageid='imageid',num_motifs=10, random_state=0, subset=None,
                 label='spatial_lda',**kwargs):
    """
Parameters:
    adata : AnnData object

    x_coordinate : float, required  
        Column name containing the x-coordinates values.

    y_coordinate : float, required  
        Column name containing the y-coordinates values.

    phenotype : string, required  
        Column name of the column containing the phenotype information. 
        It could also be any categorical assignment given to single cells.

    method : string, optional  
        Two options are available: a) 'radius', b) 'knn'.  
        a) radius - Identifies the neighbours within a given radius for every cell.  
        b) knn - Identifies the K nearest neigbours for every cell.  

    radius : int, optional  
        The radius used to define a local neighbhourhood.

    knn : int, optional  
        Number of cells considered for defining the local neighbhourhood.

    imageid : string, optional  
        Column name of the column containing the image id.

    subset : string, optional  
        imageid of a single image to be subsetted for analyis.

    num_motifs : int, optional  
        The number of requested latent motifs to be extracted from the training corpus.

    random_state : int, optional  
        Either a randomState object or a seed to generate one. Useful for reproducibility.

    label : string, optional  
        Key for the returned data, stored in `adata.uns`.

Returns:
    adata : AnnData object  
        Updated AnnData object with the results stored in `adata.uns ['spatial_lda']`.

Example:
```python
    # Running the radius method
    adata = sm.tl.spatial_lda (adata, num_motifs=10, radius=100)
```
    """

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

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

        # 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':
            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':
            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,
                                                            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
    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
    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
    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()
    print('\nCoherence Score: ', coherence_lda)

    # isolate the latent features
    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