STCC

consensus clustering enhances spatial domain detection for spatial transcriptomics data.

1. Installation

pip install STCC==1.0.2

2. Data preparation

The input of STCC is a matrix composed of label vectors of different clustering results, where the rows represent spots and the columns represent the indices of different clustering results.

df = pd.read_csv('STCC/test data.csv',index_col=0)

3. Running STCC

n_clusters = 15   # cluster numbers
method = 'wNMF-based' #  'Average-based','Onehot-based','wNMF-based'
seed = 2024 # random seed
if method == 'wNMF-based':
    labels_consensus, contributions = consensus_STCC(df,n_clusters,methods=method,seed=seed)
    print(f'Consensus labels are: {labels_consensus}')
    print(f'weight of {method} is:{contributions}')
else:
    labels_consensus = consensus_STCC(df,n_clusters,methods=method,seed=seed)
    print(f'Consensus labels are: {labels_consensus}')