GitHub - raim/segmenTier: segmentation of clustering sequences; applicable to RNAseq time-series (original) (raw)

Similarity-Based Segmentation of Multi-Dimensional Signals

segmenTier is a dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described inMachne, Murray & Stadler (2017). 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 kmeansclustering 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)] (https://doi.org/10.1371/journal.pone.0037906), and here is used as a basis of segment similarity measures.

News

Version 0.1.2:
- more general defaults in processTimeseries(use.fft=FALSE, na2zero=FALSE) allow to set-up time-series without any transformations for clustering
- Doc and vignette have been substantially re-worked

Theory

The theory behind the package is outlined in detail inMachne, Murray & Stadler 2017 and summarized in the package vignette.

Installation

The development version can be installed from github usingdevtools:

library(devtools) install_github("raim/segmenTier", subdir = "pkg")

Usage

Quick Guide

library(segmenTier)

data(primseg436) # RNA-seq time-series data

cluster timeseries:

tset <- processTimeseries(ts=tsd, na2zero=TRUE, use.fft=TRUE, dft.range=1:7, dc.trafo="ash", use.snr=TRUE) cset <- clusterTimeseries(tset, K=12)

and segment it:

segments <- segmentClusters(seq=cset, M=100, E=2, nui=3, S="icor")

inspect results:

print(segments) plotSegmentation(tset,cset,segments)

and get segment border table for further processing

segments$segments

Demos

Usage of the package is further demonstrated in two R demos:

Demo I: Direct Interface to Algorithm

The main low level interface to the algorithm, functionsegmentClusters, is demonstrated in the filedemo/segment_test.R. It produces Supplemental Figure S1 of Machne, Murray & Stadler 2017.

To run it as a demo in R simply type:

library(segmenTier)
demo("segment_test", package = "segmenTier")

Demo II: Clustering, Batch Segmentation & Parameter Scans

A real-life data set is processed, clustered and segmented with varying parameters indemo/segment_data.R.

This demo runs quite long, since it calculates many segmentations. It provides a comprehensive overview of the effects of segmentation parameters E, M and nui, and produces (among others) Figure 3 and Supplemental Figures S4a and S4b of Machne, Murray & Stadler 2017.

demo("segment_data", package = "segmenTier")