Function reference • cola

All functions
`ATC()`	Ability to correlate to other rows
`ATC_approx()`	Ability to correlate to other rows - an approximated method
`ConsensusPartition` `ConsensusPartition-class`	The ConsensusPartition class
`ConsensusPartitionList` `ConsensusPartitionList-class`	The ConsensusPartitionList class
`DownSamplingConsensusPartition` `DownSamplingConsensusPartition-class`	The DownSamplingConsensusPartition class
`[`(<ConsensusPartitionList>)	Subset a ConsensusPartitionList object
`[`(<HierarchicalPartition>)	Subset the HierarchicalPartition object
`[[`(<ConsensusPartitionList>)	Subset a ConsensusPartitionList object
`[[`(<HierarchicalPartition>)	Subset the HierarchicalPartition object
`FCC()`	Flatness of the CDF curve
`HierarchicalPartition` `HierarchicalPartition-class`	The HierarchicalPartition class
`PAC()`	The proportion of ambiguous clustering (PAC score)
`aPAC()`	Adapted PAC scores
`adjust_matrix()`	Remove rows with low variance and impute missing values
`adjust_outlier()`	Adjust outliers
`all_leaves(<HierarchicalPartition>)`	All leaves in the hierarchy
`all_nodes(<HierarchicalPartition>)`	All nodes in the hierarchy
`all_partition_methods()`	All supported partitioning methods
`all_top_value_methods()`	All supported top-value methods
`cola()`	A bottle of cola
`cola_opt()`	Global parameters
`cola_report(<ConsensusPartition>)`	Make HTML report from the ConsensusPartition object
`cola_report(<ConsensusPartitionList>)`	Make HTML report from the ConsensusPartitionList object
`cola_report(<HierarchicalPartition>)`	Make HTML report from the HierarchicalPartition object
`cola_report`	Method dispatch page for cola_report
`cola_rl`	Example ConsensusPartitionList object
`collect_classes(<ConsensusPartition>)`	Collect subgroups from ConsensusPartition object
`collect_classes(<ConsensusPartitionList>)`	Collect classes from ConsensusPartitionList object
`collect_classes(<HierarchicalPartition>)`	Collect classes from HierarchicalPartition object
`collect_classes`	Method dispatch page for collect_classes
`collect_plots(<ConsensusPartition>)`	Collect plots from ConsensusPartition object
`collect_plots(<ConsensusPartitionList>)`	Collect plots from ConsensusPartitionList object
`collect_plots`	Method dispatch page for collect_plots
`collect_stats(<ConsensusPartition>)`	Draw and compare statistics for a single method
`collect_stats(<ConsensusPartitionList>)`	Draw and compare statistics for multiple methods
`collect_stats`	Method dispatch page for collect_stats
`colnames(<ConsensusPartition>)`	Column names of the matrix
`colnames(<ConsensusPartitionList>)`	Column names of the matrix
`colnames(<DownSamplingConsensusPartition>)`	Column names of the matrix
`colnames(<HierarchicalPartition>)`	Column names of the matrix
`colnames`	Method dispatch page for colnames
`compare_partitions(<ConsensusPartition>)`	Compare two partitionings
`compare_signatures(<ConsensusPartition>)`	Compare Signatures from Different k
`compare_signatures(<HierarchicalPartition>)`	Compare Signatures from Different Nodes
`compare_signatures`	Method dispatch page for compare_signatures
`concordance()`	Concordance to the consensus partition
`config_ATC()`	Adjust parameters for default ATC method
`consensus_heatmap(<ConsensusPartition>)`	Heatmap of the consensus matrix
`consensus_partition()`	Consensus partition
`consensus_partition_by_down_sampling()`	Consensus partitioning only with a subset of columns
`correspond_between_rankings()`	Correspond between a list of rankings
`correspond_between_two_rankings()`	Correspond two rankings
`david_enrichment()`	Perform DAVID enrichment analysis
`dim(<ConsensusPartition>)`	Dimension of the matrix
`dim(<ConsensusPartitionList>)`	Dimension of the matrix
`dim(<DownSamplingConsensusPartition>)`	Dimension of the matrix
`dim(<HierarchicalPartition>)`	Dimension of the matrix
`dimension_reduction(<ConsensusPartition>)`	Visualize column after dimension reduction
`dimension_reduction(<DownSamplingConsensusPartition>)`	Visualize column after dimension reduction
`dimension_reduction(<HierarchicalPartition>)`	Visualize columns after dimension reduction
`dimension_reduction`	Method dispatch page for dimension_reduction
`dimension_reduction(<matrix>)`	Visualize columns after dimension reduction
`find_best_km()`	Find a best k for the k-means clustering
`functional_enrichment(<ANY>)`	Perform functional enrichment on signature genes
`functional_enrichment(<ConsensusPartition>)`	Perform functional enrichment on signature genes
`functional_enrichment(<ConsensusPartitionList>)`	Perform functional enrichment on signature genes
`functional_enrichment(<HierarchicalPartition>)`	Perform functional enrichment on signature genes
`functional_enrichment`	Method dispatch page for functional_enrichment
`get_anno(<ConsensusPartition>)`	Get annotations
`get_anno(<ConsensusPartitionList>)`	Get annotations
`get_anno(<DownSamplingConsensusPartition>)`	Get annotations
`get_anno(<HierarchicalPartition>)`	Get annotations
`get_anno`	Method dispatch page for get_anno
`get_anno_col(<ConsensusPartition>)`	Get annotation colors
`get_anno_col(<ConsensusPartitionList>)`	Get annotation colors
`get_anno_col(<HierarchicalPartition>)`	Get annotation colors
`get_anno_col`	Method dispatch page for get_anno_col
`get_children_nodes(<HierarchicalPartition>)`	Test whether a node is a leaf node
`get_classes(<ConsensusPartition>)`	Get subgroup labels
`get_classes(<ConsensusPartitionList>)`	Get subgroup labels
`get_classes(<DownSamplingConsensusPartition>)`	Get subgroup labels
`get_classes(<HierarchicalPartition>)`	Get class IDs from the HierarchicalPartition object
`get_classes`	Method dispatch page for get_classes
`get_consensus(<ConsensusPartition>)`	Get consensus matrix
`get_matrix(<ConsensusPartition>)`	Get the original matrix
`get_matrix(<ConsensusPartitionList>)`	Get the original matrix
`get_matrix(<DownSamplingConsensusPartition>)`	Get the original matrix
`get_matrix(<HierarchicalPartition>)`	Get the original matrix
`get_matrix`	Method dispatch page for get_matrix
`get_membership(<ConsensusPartition>)`	Get membership matrix
`get_membership(<ConsensusPartitionList>)`	Get membership matrix
`get_membership`	Method dispatch page for get_membership
`get_param(<ConsensusPartition>)`	Get parameters
`get_signatures(<ConsensusPartition>)`	Get signature rows
`get_signatures(<DownSamplingConsensusPartition>)`	Get signature rows
`get_signatures(<HierarchicalPartition>)`	Get signatures rows
`get_signatures`	Method dispatch page for get_signatures
`get_stats(<ConsensusPartition>)`	Get statistics
`get_stats(<ConsensusPartitionList>)`	Get statistics
`get_stats`	Method dispatch page for get_stats
`golub_cola`	Example ConsensusPartitionList object from Golub dataset
`golub_cola_ds`	Example DownSamplingConsensusPartition object from Golub dataset
`golub_cola_rh`	Example HierarchicalPartition object from Golub dataset
`hierarchical_partition()`	Hierarchical partition
`is_best_k(<ConsensusPartition>)`	Test whether the current k is the best/optional k
`is_best_k(<ConsensusPartitionList>)`	Test whether the current k is the best/optional k
`is_best_k`	Method dispatch page for is_best_k
`is_leaf_node(<HierarchicalPartition>)`	Test whether a node is a leaf node
`is_stable_k(<ConsensusPartition>)`	Test whether the current k corresponds to a stable partition
`is_stable_k(<ConsensusPartitionList>)`	Test whether the current k corresponds to a stable partition
`is_stable_k`	Method dispatch page for is_stable_k
`knee_finder2()`	Find the knee/elbow of a list of sorted points
`knitr_add_tab_item()`	Add JavaScript tab in the report
`knitr_insert_tabs()`	Generate the HTML fragment for the JavaScript tabs
`map_to_entrez_id()`	Map to Entrez IDs
`max_depth(<HierarchicalPartition>)`	Max depth of the hierarchy
`membership_heatmap(<ConsensusPartition>)`	Heatmap of membership in each partition
`merge_node(<HierarchicalPartition>)`	Merge node
`merge_node_param()`	Parameters to merge branches in subgroup dendrogram.
`ncol(<ConsensusPartition>)`	Number of columns in the matrix
`ncol(<ConsensusPartitionList>)`	Number of columns in the matrix
`ncol(<DownSamplingConsensusPartition>)`	Number of columns in the matrix
`ncol(<HierarchicalPartition>)`	Number of columns in the matrix
`ncol`	Method dispatch page for ncol
`node_info(<HierarchicalPartition>)`	Information on the nodes
`node_level(<HierarchicalPartition>)`	Information on the nodes
`nrow(<ConsensusPartition>)`	Number of rows in the matrix
`nrow(<ConsensusPartitionList>)`	Number of rows in the matrix
`nrow(<HierarchicalPartition>)`	Number of rows in the matrix
`nrow`	Method dispatch page for nrow
`plot_ecdf(<ConsensusPartition>)`	Plot the empirical cumulative distribution (eCDF) curve of the consensus matrix
`predict_classes(<ConsensusPartition>)`	Predict classes for new samples based on cola classification
`predict_classes`	Method dispatch page for predict_classes
`predict_classes(<matrix>)`	Predict classes for new samples based on signature centroid matrix
`print(<hc_table_suggest_best_k>)`	Print the hc_table_suggest_best_k object
`recalc_stats()`	Recalculate statistics in the ConsensusPartitionList object
`register_NMF()`	Register NMF partitioning method
`register_SOM()`	Register SOM partitioning method
`register_partition_methods()`	Register user-defined partitioning methods
`register_top_value_methods()`	Register user-defined top-value methods
`relabel_class()`	Relabel class labels according to the reference labels
`remove_partition_methods()`	Remove partitioning methods
`remove_top_value_methods()`	Remove top-value methods
`rownames(<ConsensusPartition>)`	Row names of the matrix
`rownames(<ConsensusPartitionList>)`	Row names of the matrix
`rownames(<HierarchicalPartition>)`	Row names of the matrix
`rownames`	Method dispatch page for rownames
`run_all_consensus_partition_methods()`	Consensus partitioning for all combinations of methods
`select_partition_number(<ConsensusPartition>)`	Several plots for determining the optimized number of subgroups
`show(<ConsensusPartition>)`	Print the ConsensusPartition object
`show(<ConsensusPartitionList>)`	Print the ConsensusPartitionList object
`show(<DownSamplingConsensusPartition>)`	Print the DownSamplingConsensusPartition object
`show(<HierarchicalPartition>)`	Print the HierarchicalPartition object
`show`	Method dispatch page for show
`split_node(<HierarchicalPartition>)`	Split node
`suggest_best_k(<ConsensusPartition>)`	Suggest the best number of subgroups
`suggest_best_k(<ConsensusPartitionList>)`	Suggest the best number of subgroups
`suggest_best_k(<HierarchicalPartition>)`	Guess the best number of partitions
`suggest_best_k`	Method dispatch page for suggest_best_k
`test_between_factors()`	Test whether a list of factors are correlated
`test_to_known_factors(<ConsensusPartition>)`	Test correspondance between predicted subgroups and known factors
`test_to_known_factors(<ConsensusPartitionList>)`	Test correspondance between predicted classes and known factors
`test_to_known_factors(<DownSamplingConsensusPartition>)`	Test correspondance between predicted subgroups and known factors
`test_to_known_factors(<HierarchicalPartition>)`	Test correspondance between predicted classes and known factors
`test_to_known_factors`	Method dispatch page for test_to_known_factors
`top_elements_overlap()`	Overlap of top elements from different metrics
`top_rows_heatmap(<ConsensusPartition>)`	Heatmap of top rows
`top_rows_heatmap(<ConsensusPartitionList>)`	Heatmap of top rows from different top-value methods
`top_rows_heatmap(<HierarchicalPartition>)`	Heatmap of top rows from different top-value methods
`top_rows_heatmap`	Method dispatch page for top_rows_heatmap
`top_rows_heatmap(<matrix>)`	Heatmap of top rows from different top-value methods
`top_rows_overlap(<ConsensusPartitionList>)`	Overlap of top rows from different top-value methods
`top_rows_overlap(<HierarchicalPartition>)`	Overlap of top rows on different nodes
`top_rows_overlap`	Method dispatch page for top_rows_overlap
`top_rows_overlap(<matrix>)`	Overlap of top rows from different top-value methods

Reference

All functions