cytof_dimReduction: Dimension reduction for high dimensional data

In JinmiaoChenLab/ClusterX: Fast clustering by automaticly search and find of density peaks

Description

Apply dimension reduction on the cytof expression data, with method pca, tsne or isomap.

Usage

cytof_dimReduction(data, method = c("tsne", "pca", "isomap"), out_dim = 2,
  isomap_distMethod = "euclidean", isomap_k = 5, isomap_ndim = NULL,
  isomapFragmentOK = TRUE, ...)

Arguments

data	Input expression data matrix.
method	Method chosed for dimensition reduction, must be one of isomap, pca or tsne.
out_dim	The dimensionality of the output.
isomap_distMethod	Method for distance calcualtion for isomap.
isomap_k	Number of shortest dissimilarities retained for a point, parameter for isomap method.
isomap_ndim	Number of axes in metric scaling, parameter for isomap method.
isomapFragmentOK	What to do if dissimilarity matrix is fragmented, parameter for isomap method.
...	Other parameters passed to Rtsne

Value

a matrix of the dimension reducted data, with colnames method_ID, and rownames same as the input data.

Examples

data(iris)
in_data <- iris[, 1:4]
out_data <- cytof_dimReduction(in_data, method = "tsne")

JinmiaoChenLab/ClusterX documentation built on May 7, 2019, 10:52 a.m.