cytofkit (version 1.4.4)

Rphenograph: RphenoGraph clustering

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.

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.

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

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