Rphenograph function

RphenoGraph clustering

R implementation of the PhenoGraph algorithm

Source

https://github.com/JinmiaoChenLab/Rphenograph

Rphenograph(data, k = 30)

Arguments

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

Returns

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.

Details

A simple R implementation of the PhenoGraph 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 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.