Rphenograph: RphenoGraph clustering

In raman91/cytofkit2: cytofkit: an integrated mass cytometry data analysis pipeline

Description

R implementation of the phenograph algorithm

Usage

Rphenograph(data, k = 30)

Arguments

data	Input data matrix.
k	Number of nearest neighbours, default is 30.

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 calculating 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.

Value

a communities object, the operations of this class contains:

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.

Author(s)

Chen Hao

References

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

Examples

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

raman91/cytofkit2 documentation built on Dec. 22, 2021, 12:54 p.m.