segmenTier
segmentation of clustering sequences; applicable to RNAseq time-series
Science Score: 33.0%
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Repository
segmentation of clustering sequences; applicable to RNAseq time-series
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- Stars: 3
- Watchers: 4
- Forks: 1
- Open Issues: 0
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Metadata Files
README.md
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 in
Machne, 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 kmeans
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), 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.
- more general defaults in
- Version 0.1.3:
- faster implementation of SNR calculation processTimeseries.
Theory
The theory behind the package is outlined in detail in Machne, Murray & Stadler 2017 and summarized in the package vignette.
Installation
The development version can be installed from github using
devtools:
R
library(devtools)
install_github("raim/segmenTier", subdir = "pkg")
Usage
Quick Guide
```R 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, function
segmentClusters, is demonstrated in the file
demo/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 in demo/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")
Karl, the segmenTier
Owner
- Name: Rainer Machne
- Login: raim
- Kind: user
- Website: www.tbi.univie.ac.at/~raim
- Repositories: 3
- Profile: https://github.com/raim
theoretical biologist and experimental bioinformatician
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Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Rainer Machne | r****m@t****t | 642 |
| Popa, Ovidiu (ovpop100) | o****0@h****e | 3 |
Committer Domains (Top 20 + Academic)
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Last synced: 11 months ago
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Packages
- Total packages: 1
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Total downloads:
- cran 209 last-month
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- Total versions: 1
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cran.r-project.org: segmenTier
Similarity-Based Segmentation of Multidimensional Signals
- Homepage: https://github.com/raim/segmenTier
- Documentation: http://cran.r-project.org/web/packages/segmenTier/segmenTier.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
-
Latest release: 0.1.2
published over 7 years ago
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Dependencies
- Rcpp >= 0.12.7 imports
- flowClust * suggests
- flowCore * suggests
- flowMerge * suggests
- knitr * suggests
- rmarkdown * suggests