clusterNames

aggregatedData

The number of top features in descending order of rank of importance.

Num_TopFeatures


Get the top features that differentiate the clusters based on the maximum cluster signal levels across all samples. The maximum signal levels of all features of each cluster are found, and then the maximum signal levels are normalized across clusters. Next, within each cluster, the ranks of the features are obtained based on the normalized signal levels. Top ranked features are used to annotate each cluster


Implements Partition-Assisted Clustering, which utilizes a collection of partition based nonparametric density estimation techniques to improve the robustness and accuracy of downstream clustering.
The package also provides functions for effectively visualizing the clustering results.
It is particularly useful for finding and visualizing subpopulations in single-cell data analysis.

Dangna Li

Partition-Assisted Clustering

clusterNames function

Get the top features that differentiate the clusters based on the maximum cluster signal levels across all samples. The maximum signal levels of all features of each cluster are found, and then the maximum signal levels are normalized across clusters. Next, within each cluster, the ranks of the features are obtained based on the normalized signal levels. Top ranked features are used to annotate each cluster

Description

Usage

Arguments

Value