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Bioconductor - omada

omada

This is the released version of omada; for the devel version, see omada.

Machine learning tools for automated transcriptome clustering analysis

Bioconductor version: Release (3.21)

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 toolkitâs 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] ORCID: 0000-0001-9993-6017

Maintainer: Sokratis Kariotis <sokratiskariotis at gmail.com>

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

To install this package, start R (version "4.5") 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")

Details biocViews Clustering, GeneExpression, RNASeq, Software Version 1.10.0 In Bioconductor since BioC 3.16 (R-4.2) (2.5 years) 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), genieclust (>= 1.1.3), clValid (>= 0.7), glmnet (>= 4.1.3), dplyr (>= 1.0.7), stats (>= 4.1.2), clValid (>= 0.7) Imports System Requirements URL See More Package Archives

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

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