One-Step to Cluster and Visualize Gene Expression Data
Cell Data Set Class
Check and Install Required Packages
Cluster Data Based on Different Methods
Perform GO/KEGG Enrichment Analysis for Multiple Clusters
Method to access cds count matrix
Generic to access cds count matrix
using filter.std to filter low expression genes
Determine Optimal Clusters for Gene Expression or Pseudotime Data
Return a size-factor normalized and (optionally) log-transformed expre...
Pipe operator
Create a heatmap to demonstrate the bifurcation of gene expression alo...
Create a heatmap to demonstrate the bifurcation of gene expression alo...
Plots a pseudotime-ordered, row-centered heatmap which is slightly mod...
Calculate and return a smoothed pseudotime matrix for the given gene l...
Prepare scRNA Data for clusterGvis Analysis
Method to extract pseudotime from CDS object
Generic to extract pseudotime from CDS object
Get the size factors from a cds object.
traverseTree function
using visCluster to visualize cluster results from clusterData and enr...
Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as 'Seurat', 'Monocle', and 'WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) <doi:10.6026/97320630002005> for more details.
Useful links