Rphenograph: RphenoGraph clustering

In JhuangLab/CytoTree: A Toolkit for Flow And Mass Cytometry Data

Description

R implementation of the PhenoGraph algorithm

A simple R implementation of the [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.

This function is developed by Hao Chen and updated by Yuting Dai.

Usage

Rphenograph(data, k = 30)

Arguments

data	matrix; input data matrix
k	integer; number of nearest neighbours (default:30)

Value

a list contains an igraph graph object for graph_from_data_frame and 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.

cluster information

Author(s)

Hao Chen <[e-mail]>

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[, seq_len(4)])
Rphenograph_out <- Rphenograph(data, k = 45)
modularity(Rphenograph_out[[2]])
membership(Rphenograph_out[[2]])
iris_unique$phenograph_cluster <- factor(membership(Rphenograph_out[[2]]))

JhuangLab/CytoTree documentation built on Nov. 16, 2020, 7:23 a.m.