cytof_dimReduction: Dimension reduction for high dimension data

View source: R/cytof_dimensionReduction.R

cytof_dimReductionR Documentation

Dimension reduction for high dimension data

Description

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

Usage

cytof_dimReduction(
  data,
  markers = NULL,
  method = c("umap", "tsne", "pca", "isomap", "NULL"),
  distMethod = "euclidean",
  out_dim = 2,
  umap_neighbor = 30,
  umap_min_dist = 0.3,
  tsneSeed = 42,
  isomap_k = 5,
  isomap_ndim = NULL,
  isomapFragmentOK = TRUE,
  ...
)

Arguments

data

Input expression data matrix.

markers

Selected markers for dimension reduction, either marker names/descriptions or marker IDs.

method

Method chosen for dimensition reduction, must be one of isomap, pca , or tsne.

distMethod

Method for distance calcualtion, default is "euclidean", other choices like "manhattan", "cosine", "rankcor"....

out_dim

The dimensionality of the output.

umap_neighbor

This parameter controls how UMAP balances local versus global structure in the data.

umap_min_dist

Controls how tightly UMAP is allowed to pack points together.

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, umap, isomap.

Value

A matrix of the dimension reduced data, with colnames method_ID, and rownames same as the input data.

Examples

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

JinmiaoChenLab/cytofkit2 documentation built on May 12, 2022, 8:09 a.m.