cell


This cell dataset contains the first three principal components of the imaging metrics for 444 cells that were engineered into four patterns. The dataset therefore should include four clusters of cell samples in theory. See the references for  more experimental and imaging analysis details of this data.


datasets

Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.

C.W. Hu

progenyClust

Finding the Optimal Cluster Number Using Progeny Clustering

cell function

A data frame with 444 observations on the following 3 variables.
  <dl>
    <dt><code>PC1</code></dt><dd>The first principal component of imaging metrics</dd> <dt><code>PC2</code></dt><dd>The second principal component of imaging metrics</dd> <dt><code>PC3</code></dt><dd>The third principal component of imaging metrics</dd> </dl> 

cell: cell imaging metrics dataset

Description

Usage

Arguments

Format

References

Examples