ClonoCluster: A method for using clonal origin to inform transcriptome clustering
Summary: Clustering cells based on their high-dimensional profiles is an important data reduction process by which researchers infer distinct cellular states. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoC...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
|
| Series: | Cell Genomics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666979X22002105 |
| _version_ | 1811167219950813184 |
|---|---|
| author | Lee P. Richman Yogesh Goyal Connie L. Jiang Arjun Raj |
| author_facet | Lee P. Richman Yogesh Goyal Connie L. Jiang Arjun Raj |
| author_sort | Lee P. Richman |
| collection | DOAJ |
| description | Summary: Clustering cells based on their high-dimensional profiles is an important data reduction process by which researchers infer distinct cellular states. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoCluster, a computational method that combines both clone and transcriptome information to create hybrid clusters that weight both kinds of data with a tunable parameter. We generated hybrid clusters across six independent datasets and found that ClonoCluster generated qualitatively different clusters in all cases. The markers of these hybrid clusters were different but had equivalent fidelity to transcriptome-only clusters. The genes most strongly associated with the rearrangements in hybrid clusters were ribosomal function and extracellular matrix genes. We also developed the complementary tool Warp Factor that incorporates clone information in popular 2D visualization techniques like UMAP. Integrating ClonoCluster and Warp Factor revealed biologically relevant markers of cell identity. |
| first_indexed | 2024-04-10T16:05:35Z |
| format | Article |
| id | doaj.art-1c329a8139cd4528af193d452f7b6331 |
| institution | Directory Open Access Journal |
| issn | 2666-979X |
| language | English |
| last_indexed | 2024-04-10T16:05:35Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Genomics |
| spelling | doaj.art-1c329a8139cd4528af193d452f7b63312023-02-10T04:23:35ZengElsevierCell Genomics2666-979X2023-02-0132100247ClonoCluster: A method for using clonal origin to inform transcriptome clusteringLee P. Richman0Yogesh Goyal1Connie L. Jiang2Arjun Raj3Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USADepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USAGenetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USADepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Corresponding authorSummary: Clustering cells based on their high-dimensional profiles is an important data reduction process by which researchers infer distinct cellular states. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoCluster, a computational method that combines both clone and transcriptome information to create hybrid clusters that weight both kinds of data with a tunable parameter. We generated hybrid clusters across six independent datasets and found that ClonoCluster generated qualitatively different clusters in all cases. The markers of these hybrid clusters were different but had equivalent fidelity to transcriptome-only clusters. The genes most strongly associated with the rearrangements in hybrid clusters were ribosomal function and extracellular matrix genes. We also developed the complementary tool Warp Factor that incorporates clone information in popular 2D visualization techniques like UMAP. Integrating ClonoCluster and Warp Factor revealed biologically relevant markers of cell identity.http://www.sciencedirect.com/science/article/pii/S2666979X22002105single cellBioinformaticslineage tracingclonalityclustering |
| spellingShingle | Lee P. Richman Yogesh Goyal Connie L. Jiang Arjun Raj ClonoCluster: A method for using clonal origin to inform transcriptome clustering Cell Genomics single cell Bioinformatics lineage tracing clonality clustering |
| title | ClonoCluster: A method for using clonal origin to inform transcriptome clustering |
| title_full | ClonoCluster: A method for using clonal origin to inform transcriptome clustering |
| title_fullStr | ClonoCluster: A method for using clonal origin to inform transcriptome clustering |
| title_full_unstemmed | ClonoCluster: A method for using clonal origin to inform transcriptome clustering |
| title_short | ClonoCluster: A method for using clonal origin to inform transcriptome clustering |
| title_sort | clonocluster a method for using clonal origin to inform transcriptome clustering |
| topic | single cell Bioinformatics lineage tracing clonality clustering |
| url | http://www.sciencedirect.com/science/article/pii/S2666979X22002105 |
| work_keys_str_mv | AT leeprichman clonoclusteramethodforusingclonalorigintoinformtranscriptomeclustering AT yogeshgoyal clonoclusteramethodforusingclonalorigintoinformtranscriptomeclustering AT connieljiang clonoclusteramethodforusingclonalorigintoinformtranscriptomeclustering AT arjunraj clonoclusteramethodforusingclonalorigintoinformtranscriptomeclustering |