Overview | Package workflows | Featured methods | Fast correlation analysis | Network reconstruction | From a data frame | From an adjacency matrix | From an incidence matrix | From a SIF file | Calculation of centrality measures | Network vertices | Degree centrality | Betweenness centrality | Neighborhood connectivity | H-index | Local H-index | Collective Influence | ClusterRank | Assessment of the association of centrality measures | Conditional probability of deviation from means | Nature of association (considering dependent and independent) | Nature of association (without considering dependence direction) | Identification of the most influential network nodes | Integrated Value of Influence (IVI) from centrality measures | Integrated Value of Influence (IVI) from a graph | Network visualization | IVI shiny app | Identification of the most important network spreaders | Identification of the most important network hubs | Ranking the influence of nodes on the topology of a network based on the SIRIR model | Experimental data-based classification and ranking of top candidate features | Preparing the differential/regression data | Assembling the Diff_data | Preparing the experimental data (Exptl_data) | Optional pseudo-sampling for large datasets | Conservative feature filtering | Running the ExIR model | Running exir on single-cell RNA-seq data | ExIR visualization | ExIR shiny app | Computational manipulation of cells | References

1 Overview | 1.1 What you will learn | 2 Core Workflows | 2.1 Joint clustering and marker discovery | 2.2 Cluster and cell subset marker identification, assessment and visualization | 2.3 Cell type annotation | 2.4 CelliVerse Agent | 2.5 Helper and other visualization functions | 3 CelliVerse Agent | 3.1 Preparing the Agent | 3.2 Launching the Agent | 3.3 A typical Agent workflow | 4 Example Workflow | 4.1 Running ClustoCell | 4.2 Adding cluster labels to the dataset | 4.3 Dataset feature selection | 4.4 Cluster marker evaluation | 4.5 Cell type annotation | 4.6 Running ClustoCell on large datasets