omada

DOI: 10.18129/B9.bioc.omada    

Machine learning tools for automated transcriptome clustering analysis

Bioconductor version: Release (3.16)

Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found.

Author: Sokratis Kariotis [aut, cre]

Maintainer: Sokratis Kariotis <sokratiskariotis at gmail.com>

Citation (from within R, enter citation("omada")):

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("omada")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("omada")

 

HTML R Script my-vignette
PDF   Reference Manual
Text   NEWS
Text   LICENSE

Details

biocViews Clustering, GeneExpression, RNASeq, Software
Version 1.0.0
In Bioconductor since BioC 3.16 (R-4.2) (< 6 months)
License GPL-3
Depends pdfCluster (>= 1.0-3), kernlab (>= 0.9-29), R (>= 4.2), fpc (>= 2.2-9), Rcpp (>= 1.0.7), diceR (>= 0.6.0), ggplot2 (>= 3.3.5), reshape (>= 0.8.8), clusterCrit (>= 1.2.8), clValid (>= 0.7), glmnet (>= 4.1.3), dplyr (>= 1.0.7), stats (>= 4.1.2)
Imports
LinkingTo
Suggests rmarkdown, knitr, testthat
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package omada_1.0.0.tar.gz
Windows Binary omada_1.0.0.zip
macOS Binary (x86_64) omada_1.0.0.tgz
macOS Binary (arm64) omada_1.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/omada
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/omada
Package Short Url https://bioconductor.org/packages/omada/
Package Downloads Report Download Stats

Documentation »

Bioconductor

R / CRAN packages and documentation

Support »

Please read the posting guide. Post questions about Bioconductor to one of the following locations: