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DendroX: multi-level multi-cluster selection in dendrograms.

Feiling Feng1, Qiaonan Duan2, Xiaoqing Jiang1

  • 1Department of Biliary Tract Surgery I, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.

BMC Genomics
|February 2, 2024
PubMed
Summary

DendroX is a web app for interactive dendrogram visualization, simplifying cluster identification in heatmaps. It aids in discovering novel compound clusters with shared bioactivities, like anticancer and anti-inflammatory effects.

Keywords:
Cluster analysisDendrogramLINCS L1000Natural medicine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • Cluster heatmaps are crucial for pattern discovery in biological data.
  • Identifying appropriate dendrogram cuts for cluster analysis is challenging.
  • Existing tools often require multiple cuts for clusters at different levels.

Purpose of the Study:

  • To develop an interactive web application, DendroX, for improved dendrogram visualization and cluster analysis.
  • To facilitate the identification and functional analysis of clusters from heatmaps.
  • To address the difficulty of matching visual and computational cluster determinations.

Main Methods:

  • Developed DendroX, a web app with interactive dendrogram visualization.
  • Implemented functions to extract linkage matrices from R/Python objects.
  • Created a GUI for input file preparation from text files.
  • Tested scalability on dendrograms with tens of thousands of nodes.

Main Results:

  • DendroX enables users to divide dendrograms at any level into multiple clusters.
  • A case study on 297 chemical compounds identified 17 biologically meaningful clusters.
  • Discovered a novel cluster of natural compounds with anticancer, anti-inflammatory, and antioxidant activities.

Conclusions:

  • DendroX effectively bridges the gap between visual and computational cluster identification in heatmaps.
  • The tool aids users in navigating complex dendrogram structures.
  • Identified a natural compound cluster with significant shared bioactivities, suggesting convergent biological effects.