robustSingleCell (version 0.1.1)

Rphenograph: RphenoGraph clustering

Description

R implementation of the PhenoGraph algorithm

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.

Details

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.

References

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

Examples

# NOT RUN {
library(ggplot2)
iris_unique <- unique(iris) # Remove duplicates
data <- as.matrix(iris_unique[,1:4])
Rphenograph_out <- Rphenograph(data, k = 45)
igraph::modularity(Rphenograph_out[[2]])
igraph::membership(Rphenograph_out[[2]])
iris_unique$phenograph_cluster <- factor(igraph::membership(Rphenograph_out[[2]]))
ggplot(iris_unique, aes(x=Sepal.Length, y=Sepal.Width, col=Species, shape=phenograph_cluster)) +
  geom_point(size = 3)+theme_bw()

# }