|
a |
|
b/man/proportionality.Rd |
|
|
1 |
% Generated by roxygen2: do not edit by hand |
|
|
2 |
% Please edit documentation in R/proprotionality.R |
|
|
3 |
\name{proportionality} |
|
|
4 |
\alias{proportionality} |
|
|
5 |
\title{Proportionality Distance} |
|
|
6 |
\usage{ |
|
|
7 |
proportionality(X) |
|
|
8 |
} |
|
|
9 |
\arguments{ |
|
|
10 |
\item{X}{an object of the class: \code{pca}, \code{spca}, \code{pls}, \code{spls}, \code{block.pls} or \code{block.spls}} |
|
|
11 |
} |
|
|
12 |
\value{ |
|
|
13 |
Return a list containing the following components: |
|
|
14 |
\item{propr.distance}{Square matrix with proportionality distance between pairs of features} |
|
|
15 |
\item{propr.distance.w.cluster}{distance between pairs with cluster label} |
|
|
16 |
\item{pvalue}{Wilcoxon U-test p-value comparing the medians within clusters and with the entire background set} |
|
|
17 |
} |
|
|
18 |
\description{ |
|
|
19 |
\code{proportionality} is a wrapper that compute proportionality distance for |
|
|
20 |
a clustering result (\code{pca}, \code{spca}, \code{pls}, \code{spls}, \code{block.pls}, \code{block.spls}). |
|
|
21 |
and it performs a u-test to compare the median within a cluster to the median of the entire background set. |
|
|
22 |
} |
|
|
23 |
\examples{ |
|
|
24 |
demo <- suppressWarnings(get_demo_cluster()) |
|
|
25 |
|
|
|
26 |
# pca |
|
|
27 |
X <- demo$pca |
|
|
28 |
propr.res <- proportionality(X) |
|
|
29 |
plot(propr.res) |
|
|
30 |
|
|
|
31 |
# pls |
|
|
32 |
X <- demo$spls |
|
|
33 |
propr.res <- proportionality(X) |
|
|
34 |
plot(propr.res) |
|
|
35 |
|
|
|
36 |
# block.pls |
|
|
37 |
X <- demo$block.spls |
|
|
38 |
propr.res <- proportionality(X) |
|
|
39 |
plot(propr.res) |
|
|
40 |
|
|
|
41 |
} |
|
|
42 |
\references{ |
|
|
43 |
Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S., Bähler, J. (2015). Proportionality: a valid alternative to correlation for relative data. PLoS Comput. Biol. 11, e1004075. doi: 10.1371/journal.pcbi.1004075 |
|
|
44 |
|
|
|
45 |
Quinn, T. P., Richardson, M. F., Lovell, D., Crowley, T. M. (2017). propr: an r-package for identifying proportionally abundant features using compositional data analysis. Sci. Rep. 7, 16252. doi: 10.1038/s41598-017-16520-0 |
|
|
46 |
} |