Showing 4 of total 4 results (show query)
talgalili
heatmaply:Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2'
Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' to 'heatmap.2' with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'.
Maintained by Tal Galili. Last updated 9 months ago.
d3-heatmapdendextenddendrogramggplot2heatmapplotly
386 stars 14.21 score 2.0k scripts 45 dependents
yunuuuu
ggalign:A 'ggplot2' Extension for Consistent Axis Alignment
A 'ggplot2' extension offers various tools the creation of complex, multi-plot visualizations. Built on the familiar grammar of graphics, it provides intuitive tools to align and organize plots, making it ideal for complex visualizations. It excels in multi-omics research—such as genomics and microbiomes—by simplifying the visualization of intricate relationships between datasets, for example, linking genes to pathways. Whether you need to stack plots, arrange them around a central figure, or create a circular layout, 'ggalign' delivers flexibility and accuracy with minimal effort.
Maintained by Yun Peng. Last updated 13 days ago.
complex-heatmapsdendrogramdendrogram-heatmapggplotggplot-extensionggplot2heatmapheatmap-visualizationheatmapsmarginal-plotsoncoplotoncoprinttanglegramupsetupsetplot
267 stars 7.08 score 27 scripts
asa12138
pcutils:Some Useful Functions for Statistics and Visualization
Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.
Maintained by Chen Peng. Last updated 2 days ago.
22 stars 6.47 score 28 scripts 4 dependents
xiaoluo-boy
ggheatmap:Plot Heatmap
The flexibility and excellence of 'ggplot2' is unquestionable, so many drawing tools basically need 'ggplot2' as the operating object. In order to develop a heatmap drawing system based on ggplot2, we developed this tool, mainly to solve the heatmap puzzle problem and the flexible connection between the heatmap and the 'ggplot2' object. The advantages of this tool are as follows: 1. More flexible label settings; 2. Realize the linkage of heatmap and 'ggplot2' drawing system, which is helpful for operations such as puzzles; 3. Simple and easy to operate; 4. Optimization of clustering tree visualization.
Maintained by Baiwei Luo. Last updated 2 years ago.
87 stars 5.91 score 63 scripts 1 dependents