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Related Experiment Video

Updated: Aug 9, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

ClonoCluster: A method for using clonal origin to inform transcriptome clustering.

Lee P Richman^1,2, Yogesh Goyal^3,4,5, Connie L Jiang⁶

¹Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.

|February 23, 2023

Summary

This summary is machine-generated.

ClonoCluster integrates cellular barcoding and transcriptome data to form hybrid cell clusters, revealing distinct cell states and markers. This computational method offers a novel approach to cell type identification and analysis.

Keywords:

Bioinformatics clonality clustering lineage tracing single cell

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iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

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Last Updated: Aug 9, 2025

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Published on: April 30, 2011

Area of Science:

Computational Biology
Genomics
Cell Biology

Background:

Cellular state inference relies on high-dimensional profiling and clustering.
Cellular barcoding enables grouping cells by clonal origin, offering an alternative to transcriptome-based clustering.

Purpose of the Study:

To develop a computational method, ClonoCluster, that integrates both clonal origin and transcriptome data for hybrid cell clustering.
To introduce Warp Factor, a tool for incorporating clonal information into 2D visualization techniques like UMAP.

Main Methods:

Developed ClonoCluster, a method combining clone and transcriptome information with a tunable parameter for hybrid clustering.
Applied ClonoCluster across six independent datasets.
Developed Warp Factor to integrate clone information into UMAP visualizations.

Main Results:

ClonoCluster generated qualitatively different hybrid clusters across all tested datasets.
Hybrid clusters exhibited distinct markers with equivalent fidelity to transcriptome-only clusters.
Ribosomal function and extracellular matrix genes were strongly associated with rearrangements in hybrid clusters.

Conclusions:

Integrating clonal and transcriptome data provides a novel approach to cell clustering and identification.
ClonoCluster and Warp Factor reveal biologically relevant markers of cell identity.
Hybrid clustering offers a complementary perspective to traditional transcriptome-only methods.