Rphenograph: RphenoGraph clustering
In iCellR: Analyzing High-Throughput Single Cell Sequencing Data

Description Usage Arguments Details Value Source References

R implementation of the PhenoGraph algorithm

1	Rphenograph(data, k = 30)

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

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.

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.