Introduction | Installation | Example | Data Input | Divergence Matrix Calculation | More on the density method: | More on the divergence method | Visualization | Testing and Confidence intervals | Parallelization and Random Seeds | Random seed | References | SessionInfo

Introduction | The RSEC clustering workflow | Quickstart | The Data | Filtering and normalization | Clustering with RSEC | The output | Visualizing the output | Visualizing many clusterings | Barplots & contingency tables | Co-Clustering | Plot of Hierarchy of Clusters | 2D plot of clusters | Rerunning RSEC with different parameters | Finding Features related to the clusters | Heatmaps of features | Overview of the clustering workflow | Step 1: Clustering with clusterMany | Step 2: Find a consensus with makeConsensus | Step 3: Merge clusters together with makeDendrogram and mergeClusters | RSEC | ClusterExperiment Objects | Subsetting ClusterExperiment objects | Samples not assigned to a cluster (Negative Valued Cluster Assignments) | Dimensionality reduction and SingleCellExperiment Class | Visualizing the data | Cluster Alignment plot with plotClusters | Manipulations of colors | Saving Assignment of colors from plotClusters | Manual Assignment of colors | Using only the assigned colors | Choosing a different set of colors | Variant version: plotClustersWorkflow | Heatmap including the clusters with plotHeatmap | Displaying clustering or sample information | Additional options for clustering/ordering samples | Using separate input data for clustering and for visualization | Setting the breaks | Dendrogram of clusters with plotDendrogram | Changing the leaf and plot type | Changing the information plotted with dendrogram | Additional clusters | Merge Information | Sample data | Node information | The clustering workflow | clusterMany | Base clustering algorithms and the ClusterFunction class | inputType | algorithmType | requiredArgs | Internal clustering procedures | Main Clustering (Step 2): mainClustering | Subsampling (step 1) subsampleClustering | Sequential Detection of Clusters (Step 3): seqCluster | Arguments of clusterMany | Example changing the distance function | Example using a user-defined clustering algorithm | Dealing with large numbers of clusterings | Create a unified cluster from many clusters with makeConsensus | Consensus from subsets of clusterings | Creating a Hierarchy of Clusters with makeDendrogram | Making a past run the current one. | More about how the dendrogram is saved | Merging clusters with mergeClusters | Requiring a certain log-fold change | Keeping track of and rerunning elements of the workflow | Designate a Final Clustering | RSEC | Finding Features related to a Clustering | Types of Significance Tests (Contrasts) | All Pairwise | One Against All | Dendrogram | DE Analysis for count and other RNASeq data | Piping into other DE routines | Multiple Testing adjustments | Working with other assays() | Session Information | References

Avoid NxN matrices | Choice of Clustering Routine | Subsampling and Consensus clustering | Technical details | Data in Memory versus HDF5