myPearson

<p>Incremental calculation of the Pearson correlation coefficient between
two vectors for calculation within Rcpp functions
<code><a rd-options="" href="/link/clusterCor_c?package=segmenTier&version=0.1.2" data-mini-rdoc="segmenTier::clusterCor_c">clusterCor_c</a></code>.</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

myPearson function

<p>Incremental calculation of the Pearson correlation coefficient between
two vectors for calculation within Rcpp functions
<code><a rd-options='' href='clusterCor_c'>clusterCor_c</a></code>.</p>

myPearson: Pearson product-moment correlation coefficient

Description

Usage

Arguments

Details