Rphenograph: RphenoGraph clustering

Description

R implementation of the phenograph algorithm

Usage

Rphenograph(data, k = 30)

Arguments

data

Input data matrix.

Number of nearest neighbours, default is 30.

Value

print: returns the communities object itself, invisibly.
length: returns an integer scalar.
sizes: returns a numeric vector.
membership: returns a numeric vector, one number for each vertex in the graph that was the input of the community detection.
modularity: returns a numeric scalar.
algorithm: returns a character scalar.
crossing: returns a logical vector.
is_hierarchical: returns a logical scalar.
merges: returns a two-column numeric matrix.
cut_at: returns a numeric vector, the membership vector of the vertices.
as.dendrogram: returns a dendrogram object.
show_trace: returns a character vector.
code_len: returns a numeric scalar for communities found with the InfoMAP method and NULL for other methods.
plot: for communities objects returns NULL, invisibly.

Details

A simple R implementation of the phenograph [PhenoGraph](http://www.cell.com/cell/abstract/S0092-8674(15)00637-6) algorithm, which is a clustering method designed for high-dimensional single-cell data analysis. It works by creating a graph ("network") representing phenotypic similarities between cells by calclating the Jaccard coefficient between nearest-neighbor sets, and then identifying communities using the well known [Louvain method](https://sites.google.com/site/findcommunities/) in this graph.

References

Jacob H. Levine and et.al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 2015.

Examples

Run this code

iris_unique <- unique(iris) # Remove duplicates
data <- as.matrix(iris_unique[,1:4])
Rphenograph_out <- Rphenograph(data, k = 45)

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