EvaluateIdent: Evaluate the cell identities using clustering

In ncchung/SeuratAddon: Unsupervised evaluation of cell identities in Seurat

Description

This function is a simple wrapper for EvaluateIdentKmeans, facilitating unsupervised evaluation of cell identities.

Usage

EvaluateIdent(object, genes.use = NULL, clustering = "kmeans",
  data.cut = NULL, do.plot = FALSE, seed = 1, ...)

Arguments

object	Seurat object
genes.use	Genes to use for clustering
clustering	Clustering method to use
data.cut	Clip all z-scores to have an absolute value below this Reduces the effect of huge outliers in the data. (default is NULL)
do.plot	Draw histograms of statistics (default is FALSE)
seed	Random seed
...	Additional parameters passed to EvaluateIdentKmeans and related functions

Details

First, run K-means clustering or mini-batch K-means clustering using ClusterCellsKmeans, which computes computationally defined cell identities, ident in the Seurat object. Second, run this function with the aforementioned Seurat object. Ensure that the same parameters (reduction.type, dims.use, k.param, ...) are used.

Value

Seurat object where the unsupervised evaluation for cell identities is stored in object@ident_prob. Furthermore, chosen probabilities (PIP, adjusted p-values, or etc) are stored in [e-mail]["ident_prob"]. See the optional argument prob.use.

See Also

EvaluateIdentKmeans

ncchung/SeuratAddon documentation built on May 3, 2019, 3:17 p.m.