Rphenograph | R Documentation |
R implementation of the PhenoGraph algorithm
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.
Author: Chen Hao, 2015 Copied from https://github.com/JinmiaoChenLab/Rphenograph/blob/master/R/phenograph.R for use in briDiscovr on 200611 under the Artistic-2.0 license
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. |
Jacob H. Levine and et.al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 2015.
## Not run:
iris_unique <- unique(iris) # Remove duplicates
data <- as.matrix(iris_unique[,1:4])
Rphenograph_out <- Rphenograph(data, k = 45)
modularity(Rphenograph_out[[2]])
membership(Rphenograph_out[[2]])
iris_unique$phenograph_cluster <- factor(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()
## End(Not run)
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