cytof_dimReduction: Dimension reduction for high dimensional data

In haoeric/cytofkit_devel: cytofkit: an integrated mass cytometry data analysis pipeline

Description

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

Usage

cytof_dimReduction(data, method = c("tsne", "pca", "isomap", "diffusionmap",
  "NULL"), distMethod = "euclidean", out_dim = 2, tsneSeed = 42,
  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 , diffusionmap or tsne.
distMethod	Method for distance calcualtion, default is "euclidean", other choices like "manhattan", "cosine", "rankcor"....
out_dim	The dimensionality of the output.
tsneSeed	Set a seed if you want reproducible t-SNE results.
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 the method, check Rtsne, DiffusionMap, isomap.

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")

haoeric/cytofkit_devel documentation built on May 17, 2019, 2:29 p.m.