diagnostic.cor.eigs: Determine the dimension of low dimensional embedding

In ProFAST: Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

Determine the dimension of low dimensional embedding

Description

This function estimate the dimension of low dimensional embedding for a given cell by gene expression matrix. For more details, see Franklin et al. (1995) and Crawford et al. (2010).

Usage

diagnostic.cor.eigs(object, ...)

## Default S3 method:
diagnostic.cor.eigs(
  object,
  q_max = 50,
  plot = TRUE,
  n.sims = 10,
  parallel = TRUE,
  ncores = 10,
  seed = 1,
  ...
)

## S3 method for class 'Seurat'
diagnostic.cor.eigs(
  object,
  assay = NULL,
  slot = "data",
  nfeatures = 2000,
  q_max = 50,
  seed = 1,
  ...
)

Arguments

object	A Seurat or matrix object
...	Other arguments passed to diagnostic.cor.eigs.default.
q_max	the upper bound of low dimensional embedding. Default is 50.
plot	a indicator of whether plot eigen values.
n.sims	number of simulaton times. Default is 10.
parallel	a indicator of whether use parallel analysis.
ncores	the number of cores used in parallel analysis. Default is 10.
seed	a postive integer, specify the random seed for reproducibility
assay	an optional string, specify the name of assay in the Seurat object to be used.
slot	an optional string, specify the name of slot.
nfeatures	an optional integer, specify the number of features to select as top variable features. Default is 2000.

Value

A data.frame with attribute 'q_est' and 'plot', which is the estimated dimension of low dimensional embedding. In addition, this data.frame containing the following components:

• q - The index of eigen values.

• eig_value - The eigen values on observed data.

• eig_sim - The mean value of eigen values of n.sims simulated data.

• q_est - The selected dimension in attr(obj, 'q_est').

• plot - The plot saved in attr(obj, 'plot').

References

1. Franklin, S. B., Gibson, D. J., Robertson, P. A., Pohlmann, J. T., & Fralish, J. S. (1995). Parallel analysis: a method for determining significant principal components. Journal of Vegetation Science, 6(1), 99-106.

2. Crawford, A. V., Green, S. B., Levy, R., Lo, W. J., Scott, L., Svetina, D., & Thompson, M. S. (2010). Evaluation of parallel analysis methods for determining the number of factors.Educational and Psychological Measurement, 70(6), 885-901.

Examples

n <- 100
p <- 50
d <- 15
object <- matrix(rnorm(n*d), n, d) %*% matrix(rnorm(d*p), d, p)
diagnostic.cor.eigs(object, n.sims=2)

ProFAST documentation built on May 29, 2024, 7:15 a.m.