scHicCluster (version 4.1)

Matrix to compute on:

Cluster method::

Apply dimension reduction::

Number of clusters:

How many clusters should be computed by kmeans or spectral clustering

Number of nearest neighbors:

How many nearest neighbors should be computed for the k-nn graph?

List of chromosomes to consider:

Please separate the chromosomes by space

Clustering on raw data

scHicCluster uses kmeans or spectral clustering to associate each cell to a cluster and therefore to its cell cycle. The clustering can be run on the raw data, on a kNN computed via the exact euclidean distance or via PCA. Please consider also the other clustering and dimension reduction approaches of the scHicExplorer suite such as scHicCluster, scHicClusterMinHash and scHicClusterSVL. They can give you better results, can be faster or less memory demanding.

For more information about scHiCExplorer please consider our documentation on readthedocs.io