mds_plot: MDS Plot

In dunbarlabNIH/barcodetrackR: Functions for Analyzing Cellular Barcoding Data

Description

Calculates a simmilarity/dissimlarity index or metrix for each sample-sample pair and reduces the resulting dist matrix into two dimensions

Usage

mds_plot(
  your_SE,
  group_by = "SAMPLENAME",
  method_dist = "bray",
  assay = "proportions",
  your_title = NULL,
  point_size = 3,
  text_size = 12,
  return_table = FALSE,
  kmeans_cluster = FALSE,
  k.param = 3,
  draw_ellipses = FALSE
)

Arguments

your_SE	Summarized Experiment object containing clonal tracking data as created by the barcodetrackR 'create_SE' function.
group_by	Column of metadata to color samples by. Can also specify "kmeans_cluster" if kmeans_cluster argument is set to TRUE, and then the grouping variables will be the clusterinng result.
method_dist	Dissimilarity index from vegan. One of "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", or "cao".
assay	The assay to calculate the index on
your_title	Character. The title for the plot.
point_size	Numeric. The size of the points.
text_size	Numeric. Size of text in plot.
return_table	Logical. If set to true, the function will return a dataframe containing each samples reduced measure of dissimilarity coordinates.
kmeans_cluster	Logical. If set to true, each sample will be assigned a cluster computed by kmeans on the chosen assay.
k.param	Numeric. If kmeans_cluster is TRUE, provide the number of kmeans clusters to identify.
draw_ellipses	Logical. If kmeans_cluster is TRUE, draw ellipses around the different kmeans clusters.

Value

Plots dissimilarity indices between samples in your_SE. Or if return table is set to TRUE, returns a dataframe of each sample's reduced measures of dissimilarity coordinates.

Examples

data(wu_subset)
mds_plot(your_SE = wu_subset, method_dist = "bray", group_by = "celltype")
# "

dunbarlabNIH/barcodetrackR documentation built on April 26, 2021, 6:20 p.m.