plot_single_gene: Plot a single gene expression histogram with best fitted...

In EMMIXgene: A Mixture Model-Based Approach to the Clustering of Microarray Expression Data

Description

Plot a single gene expression histogram with best fitted mixture of t-distributions according to the EMMIX-gene algorithm.

Usage

plot_single_gene(dat, gene_id, g = NULL, random_starts = 8,
  max_it = 100, ll_thresh = 8, min_clust_size = 8, tol = 1e-04,
  start_method = "both", three = TRUE, min = -4, max = 2)

Arguments

dat	matrix of gene expression data.
gene_id	row number of gene to be plotted.
g	force number of components, default = NULL
random_starts	The number of random initializations used per gene when fitting mixtures of t-distributions. Initialization uses k-means by default.
max_it	The maximum number of iterations per mixture fit. Default value is 100.
ll_thresh	The difference in -2 log lambda used as a threshold for selecting between g=1 and g=2 for each gene. Default value is 8, which was chosen arbitrarily in the original paper.
min_clust_size	The minimum number of observations per cluster used when fitting mixtures of t-distributions for each gene. Default value is 8.
tol	Tolerance value used for detecting convergence of EMMIX fits.
start_method	Default value is "both". Can also choose "random" for purely random starts.
three	Also test g=2 vs g=3 where appropriate. Defaults to TRUE.
min, max	Minimum and maximum x-axis values for the plot window.

Value

A ggplot2 histogram with fitted t-distributions overlayed.

Examples

example <- plot_single_gene(alon_data,1) 
#plot(example)

EMMIXgene documentation built on March 26, 2020, 7:12 p.m.