Package: omada 1.7.0

Sokratis Kariotis

omada: Machine learning tools for automated transcriptome clustering analysis

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.

Authors:Sokratis Kariotis [aut, cre]

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NEWS

# Install 'omada' in R:

install.packages('omada', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

toy_gene_memberships - Cluster memberships for toy gene data for package examples
toy_genes - Toy gene data for package examples

On BioConductor:omada-1.7.0(bioc 3.20)omada-1.6.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

37 exports 0.91 score 142 dependencies

Last updated 2 months agofrom:3d3c329917

Exports:clusteringMethodSelection clusterVoting feasibilityAnalysis feasibilityAnalysisDataBased featureSelection get_agreement_scores get_average_feature_k_stabilities get_average_stabilities_per_k get_average_stability get_cluster_memberships_k get_cluster_voting_k_votes get_cluster_voting_memberships get_cluster_voting_metric_votes get_cluster_voting_scores get_feature_selection_optimal_features get_feature_selection_optimal_number_of_features get_feature_selection_scores get_generated_dataset get_internal_metric_scores get_max_stability get_metric_votes_k get_optimal_features get_optimal_memberships get_optimal_number_of_features get_optimal_parameter_used get_optimal_stability_score get_partition_agreement_scores get_sample_memberships get_vote_frequencies_k omada optimalClustering partitionAgreement plot_average_stabilities plot_cluster_voting plot_feature_selection plot_partition_agreement plot_vote_frequencies

Dependencies:abind ade4 assertthat base64enc Biobase BiocGenerics BiocManager bit bit64 bslib cachem class classInt cli clipr clue cluster clusterSim clv clValid codetools colorspace combinat commonmark cpp11 crayon DEoptimR diceR digest diptest doParallel dplyr e1071 fansi farver fastmap flexmix fontawesome forcats foreach fpc fs generics genieclust geometry ggplot2 glmnet glue gridBase gtable gtools hardhat haven highr hms htmltools httpuv infotheo isoband iterators jquerylib jsonlite kernlab KernSmooth klaR labeling labelled later lattice lifecycle linprog lpSolve magic magrittr MASS Matrix mclust memoise mgcv mime miniUI modeltools munsell nlme NMF nnet pdfCluster pillar pixmap pkgconfig plyr prabclus prettyunits progress promises proxy purrr questionr R.cache R.methodsS3 R.oo R.utils R2HTML R6 RankAggreg rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppProgress readr registry reshape reshape2 rlang rngtools robustbase rprojroot rstudioapi sass scales shape shiny sourcetools sp stringi stringr styler survival tibble tidyr tidyselect tzdb utf8 vctrs viridisLite vroom withr xfun xtable yardstick

Omada, An unsupervised machine learning toolkit for automated sample clustering of gene expression profiles

Sokratis Kariotis

Rendered fromomada-vignette.Rmdusingknitr::rmarkdownon Jun 24 2024.

Last update: 2024-04-10
Started: 2022-05-23

Help page	Topics
Method Selection through intra-method Consensus Partition Consistency	clusteringMethodSelection
Estimating number of clusters through internal exhaustive ensemble majority voting	clusterVoting
Simulating dataset and calculate stabilities over different number of clusters	feasibilityAnalysis
Simulating dataset based on existing dataset's dimensions, mean and standard deviation	feasibilityAnalysisDataBased
Predictor variable subsampling sets and bootstrapping stability set selection	featureSelection
Get a dataframe of partition agreement scores for a set of random parameters clustering runs across different methods	get_agreement_scores
Get a dataframe of average bootstrap stabilities	get_average_feature_k_stabilities
Get average stabilities for all numbers of clusters(k)	get_average_stabilities_per_k
Get the average stability(over all k)	get_average_stability
Get cluster memberships for every k	get_cluster_memberships_k
Get k vote frequencies	get_cluster_voting_k_votes
Get cluster memberships for every k	get_cluster_voting_memberships
Get a dataframe with the k votes for every index	get_cluster_voting_metric_votes
Get a matrix with metric scores for every k and internal index	get_cluster_voting_scores
Get the optimal features	get_feature_selection_optimal_features
Get the optimal number of features	get_feature_selection_optimal_number_of_features
Get a dataframe of average bootstrap stabilities	get_feature_selection_scores
Get the simulated dataset	get_generated_dataset
Get a matrix with metric scores for every k and internal index	get_internal_metric_scores
Get the maximum stability	get_max_stability
Get a dataframe with the k votes for every index	get_metric_votes_k
Get the optimal features	get_optimal_features
Get a dataframe with the memberships of the samples found in the input data	get_optimal_memberships
Get the optimal number of features	get_optimal_number_of_features
Get the optimal parameter used	get_optimal_parameter_used
Get the optimal stability score	get_optimal_stability_score
Get a dataframe of partition agreement scores for a set of random parameters clustering runs across different methods	get_partition_agreement_scores
Get a dataframe with the memberships of the samples found in the input data	get_sample_memberships
Get k vote frequencies	get_vote_frequencies_k
A wrapper function that utilizes all tools to produce the optimal sample memberships	omada
Clustering with the optimal parameters estimated by these tools	optimalClustering
Partition Agreement calculation between two clustering runs	partitionAgreement
Plot the average bootstrap stabilities	plot_average_stabilities
Plot k vote frequencies	plot_cluster_voting
Plot the average bootstrap stabilities	plot_feature_selection
Plot of partition agreement scores	plot_partition_agreement
Plot k vote frequencies	plot_vote_frequencies
Cluster memberships for toy gene data for package examples	toy_gene_memberships
Toy gene data for package examples	toy_genes

Package: omada 1.7.0

omada: Machine learning tools for automated transcriptome clustering analysis

Omada, An unsupervised machine learning toolkit for automated sample clustering of gene expression profiles

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