cytof_dimReduction: Dimension reduction for high dimension data

In raman91/cytofkit2: 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, markers = NULL, method = c("umap", "tsne",
  "pca", "isomap", "diffusionmap", "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 , 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 reduced data, with colnames method_ID, and rownames same as the input data.

Note

Currently, diffusionmap will not work with R 3.4.0, due to an issue with the latest CRAN release of its dependency igraph If this is the case, consider manually updating igraph using; install.packages("https://github.com/igraph/rigraph/releases/download/v1.1.0/igraph_1.1.0.zip", repos=NULL, method="libcurl")

Examples

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

raman91/cytofkit2 documentation built on Dec. 22, 2021, 12:54 p.m.