PhenographClusterer1.0Clusters data using Phenograph clustering method.
PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") representing phenotypic similarities between cells and then identifying communities in this graph.
https://github.com/jacoblevine/PhenoGraph
Ville RantanenClusteringgitsetuptoolsphenograph
install.shData to cluster.Distance metric to define nearest neighbors.
Options include: euclidean,manhattan,correlation,cosine.Name of a column that contains a unique identifier for
each row in the input data. This column is not included in
the clustering but is copied to the output. Use an empty
string if the input does not have such a column. If this
string is empty the output will have a column called "index"
which has a running index number for each row of
input (starting from 1).Comma separated list of names of columns not to be used in
clustering. Useful if you want to ignore some attribute in
the data while clustering.Number of nearest neighbors to use in first step of graph construction. Whether to use a symmetric (default) or asymmetric ("directed") graph
The graph construction process produces a directed graph, which is symmetrized by one of two methods (see below)Whether to symmetrize by taking the average (prune=False) or product (prune=True) between the graph
and its transposeIf true, use Jaccard metric between k-neighborhoods to build graph.
If false, use a Gaussian kernel.Cells that end up in a cluster smaller than min_cluster_size are considered outliers
and are assigned to -1 in the cluster labelsNearest Neighbors and Jaccard coefficients will be computed in parallel using n_jobs. If n_jobs=-1,
the number of jobs is determined automaticallyTolerance (i.e., precision) for monitoring modularity optimizationMaximum number of seconds to run modularity optimization. If exceeded
the best result so far is returned