plot_ica | R Documentation |
This function plots a low-dimensional projection of an omic data matrix using independent component analysis.
plot_ica( dat, group = NULL, covar = NULL, top = NULL, dims = c(1L, 2L), label = FALSE, pal_group = "npg", pal_covar = "Blues", size = NULL, alpha = NULL, title = "ICA", legend = "right", hover = FALSE, D3 = FALSE )
dat |
Omic data matrix or matrix-like object with rows corresponding to
probes and columns to samples. It is strongly recommended that data be
filtered and normalized prior to plotting. Raw counts stored in |
group |
Optional character or factor vector of length equal to sample size, or up to two such vectors organized into a list or data frame. Supply legend title(s) by passing a named list or data frame. |
covar |
Optional continuous covariate. If non- |
top |
Optional number (if > 1) or proportion (if < 1) of most variable probes to be used for ICA. |
dims |
Vector specifying which independent components to plot. Must be
of length two unless |
label |
Label data points by sample name? Defaults to |
pal_group |
String specifying the color palette to use if |
pal_covar |
String specifying the color palette to use if |
size |
Point size. |
alpha |
Point transparency. |
title |
Optional plot title. |
legend |
Legend position. Must be one of |
hover |
Show sample name by hovering mouse over data point? If |
D3 |
Render plot in three dimensions? |
This function plots the samples of an omic data matrix in a two- or three-dimensional independent component subspace. ICA is an easy and popular projection method that can aid in identifying clusters, detecting outliers, and visualizing the latent structure of a dataset.
ICA is a general method for separating a multivariate signal into additive
subcomponents. ICA algorithms differ in their objective functions and
optimization methods. plot_ica
relies on Cardoso's JADE ICA algorithm,
as implemented in the JADE
package.
By default, plot_ica
decomposes the complete dat
matrix.
Limit the ICA to only the most variable probes by using the top
argument.
Miettinen, J., Nordhausen, K. & Taskinen, S. (2017). Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp. Journal of Statistical Software, 76: 1–31.
JADE
, plot_pca
, plot_tsne
n <- 10L p <- 1000L x <- matrix(rnorm(n * p), ncol = n) plot_ica(x)
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