Fig. 1

The SC3s framework for single cell consensus clustering. SC3s takes as input the gene-by-cell expression matrix, after preprocessing and dimensionality reduction via PCA using Scanpy commands. To achieve consensus clustering, SC3s attempts to combine the results of multiple clustering runs, where the number of principal components is changed (d range). All this information is then encoded into a binary matrix, which can be efficiently used to produce the final k cell clusters. The key difference from the original SC3 is that for each d, the cells are first grouped into microclusters which can be reused for multiple values of k, saving time in computation