R: Machine learning tools for automated transcriptome clustering analysis

# Machine learning tools for automated transcriptome clustering analysis

## Documentation for package ‘omada’ version 1.15.0

- 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