| plot_sammon | R Documentation |
This function plots a low-dimensional projection of an omic data matrix using Sammon mapping.
plot_sammon( dat, group = NULL, covar = NULL, dist = "euclidean", p = 2L, top = 500L, filter_method = "pairwise", dims = c(1L, 2L), label = FALSE, pal_group = "npg", pal_covar = "Blues", size = NULL, alpha = NULL, title = "Sammon Map", 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- |
dist |
Distance measure to be used. Supports all methods available in
|
p |
Power of the Minkowski distance. |
top |
Optional number (if > 1) or proportion (if < 1) of top probes to be used for mapping. |
filter_method |
String specifying whether to apply a |
dims |
Vector specifying which dimensions 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? |
Sammon mapping is a variant of nonmetric MDS with a cost function designed to better preserve local structure.
The projection is calculated using the MASS::sammon
function, which takes a distance matrix as input. Available distance
measures include: "euclidean", "maximum", "manhattan",
"canberra", "minkowski", "cosine", "pearson",
"kendall", "spearman", "bray", "kulczynski",
"jaccard", "gower", "altGower", "morisita",
"horn", "mountford", "raup", "binomial",
"chao", "cao", "mahalanobis", "MI", or "KLD".
Some distance measures are unsuitable for certain types of data. See
dist_mat for more details on these methods and links to documentation
on each.
The top argument optionally filters data using either probewise
variance (if filter_method = "common") or the leading fold change
method of Smyth et al. (if filter_method = "pairwise"). See
plot_mds for more details.
Sammon, J.W. (1969). A non-linear mapping for data structure analysis. IEE Trans. Comput., C-18: 401-409.
plot_mds, plot_tsne
mat <- matrix(rnorm(1000 * 5), nrow = 1000, ncol = 5) plot_sammon(mat) library(DESeq2) dds <- makeExampleDESeqDataSet() dds <- rlog(dds) plot_sammon(dds, group = colData(dds)$condition)
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