Machine learning tools for automated transcriptome clustering analysis

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Documentation for package ‘omada’ version 1.11.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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