clusterCor_c

data

clusters

<p>Calculates Pearson's product-moment correlation coefficients
between rows in <code>data</code>  and <code>cluster</code>, and is used to calculate
the position-cluster similarity matrix for the scoring function "icor".
This is implemented in Rcpp for calculation speed, using 
<code><a rd-options="" href="/link/myPearson?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::myPearson">myPearson</a></code> to calculate correlations.</p>

A dynamic programming solution to segmentation based on
maximization of arbitrary similarity measures within segments.
The general idea, theory and this implementation are described in
Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.
In addition to the core algorithm, the package provides time-series
processing and clustering functions as described in the publication.
These are generally applicable where a `k-means` clustering yields
meaningful results, and have been specifically developed for
clustering of the Discrete Fourier Transform of periodic gene
expression data (`circadian' or `yeast metabolic oscillations').
This clustering approach is outlined in the supplemental material of
Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here
is used as a basis of segment similarity measures. Notably, the
time-series processing and clustering functions can also be used as
stand-alone tools, independent of segmentation, e.g., for
transcriptome data already mapped to genes.

Rainer Machne

segmenTier

Similarity-Based Segmentation of Multidimensional Signals

clusterCor_c function

<p>Calculates Pearson's product-moment correlation coefficients
between rows in <code>data</code>  and <code>cluster</code>, and is used to calculate
the position-cluster similarity matrix for the scoring function "icor".
This is implemented in Rcpp for calculation speed, using 
<code><a rd-options='' href='myPearson'>myPearson</a></code> to calculate correlations.</p>

clusterCor_c: Calculates position-cluster correlations for scoring function "icor".

Description

Usage

Arguments

Value