ClusterGVis: One-Step to Cluster and Visualize Gene Expression Data
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.
| Version: |
0.1.3 |
| Depends: |
R (≥ 2.10) |
| Imports: |
Biobase, circlize, clusterProfiler, colorRamps, ComplexHeatmap, dplyr, e1071, factoextra, ggplot2, grDevices, grid, magrittr, Matrix, methods, purrr, BiocManager, reshape2, scales, SingleCellExperiment, stats, SummarizedExperiment, TCseq, tibble |
| Suggests: |
igraph, knitr, monocle, pheatmap, rmarkdown, Seurat, WGCNA, utils |
| Published: |
2025-06-17 |
| Author: |
Jun Zhang  [aut,
cre] |
| Maintainer: |
Jun Zhang <3219030654 at stu.cpu.edu.cn> |
| BugReports: |
https://github.com/junjunlab/ClusterGVis/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/junjunlab/ClusterGVis/,
https://junjunlab.github.io/ClusterGvis-manual/ |
| NeedsCompilation: |
no |
| Citation: |
ClusterGVis citation info |
| Materials: |
README |
| CRAN checks: |
ClusterGVis results |
Documentation:
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