About HuBMAP HIVE Tools Component at Carnegie Mellon University

Project Mission: To develop, extend & collect comprehensive, flexible, and fair computational tools  for the analysis and integration of data across the Tissue Mapping Components (TMCs) and HIVE (HuBMAP Integration, Visualization & Engagement). View our project page here.

About the HuBMAP Consortium: Based upon new imaging and biomolecular sequencing technologies, multiple national and international efforts are underway to more comprehensively understand human cells. The Human BioMolecular Atlas Program (HuBMAP) is a consortium composed of diverse research teams funded by the National Institutes of Health (https://commonfund.nih.gov/HuBMAP). The overall goals of the NIH Common Fund HuBMAP are to Accelerate development of the next generation of tools and techniques for constructing high-resolution spatial tissue maps that quantify multiple types of biomolecule

1. Accelerate development of the next generation of tools and techniques for constructing high-resolution spatial tissue maps that quantify multiple types of biomolecules

2. Generate foundational 3D tissue maps using validated high-content, high-throughput imaging and omics assays

3. Establish an open data platform that will develop novel approaches to integrating, visualizing, and modeling imaging and omics data to build multidimensional maps, and making data rapidly findable, accessible, interoperable, and reusable by the global research community

4. Coordinate and collaborate with other funding agencies, programs, and the biomedical research community to build the architecture and tools for mapping the human body with cellular resolution; and

5. Support projects that demonstrate the value of the resources developed by the program to study individual variation and tissue changes across the lifespan and the health-disease continuum.


Private Investigators

Matthew Ruffalo, PI & Systems Scientist, Carnegie Mellon University


Ziv Bar-Joseph, PI, Carnegie Mellon University,


Sarah Teichmann, PI, Wellcome Sanger UK


Benedict Paten, PI, University of California Santa Cruz


Robert Murphy, CO-PI, Carnegie Mellon University


Carl Kingsford, Co-PI, Carnegie Mellon University


Jian Ma, Co-PI, Carnegie Mellon University


Research Staff

Sean Donahue, Research Programmer, Carnegie Mellon University


Ted Zhang, Research Programmer, Carnegie Mellon University


Allyson Ricarte, Project Manager, HIVE Tools Component


Cecilia Cisar, Research Assistant


Nick Keener, Research Assistant, University of Santa Cruz


Walt Shands, Software Engineer, jshands@ucsc.edu

Past Collaborators

Sushma Akoju, Software Developer, Carnegie Mellon University

Haoran Chen, Research Assistant, Carnegie Mellon University

Vladimir Kiselev, Informatics Team Leader, Wellcome Sanger UK

Maria Keays, Software Engineer, Wellcome Sanger UK

Daniele Muraro, Senior Bioinformatician, Wellcome Sanger UK

Vasyl Vaskivsky, Bioimage Analyst

Data Portal

HIVE TC-CMU Publications

Euxhen Hasanaj, Jingtao Wang, Arjun Sarathi, Ziv Bar-Joseph. et al. Interactive single-cell data analysis using Cellar. Nat Commun 13, 1998 (2022).

Euxhen Hasanaj, Jingtao Wang, Arjun Sarathi, Jun Ding, Ziv Bar-Joseph. Cellar: Interactive single cell data annotation tool. Nature Communications. , in press, 2022

M.T. Dayao, M. Brusko, C. Wasserfall, and Z. Bar-Joseph. Membrane marker selection for segmenting single cell spatial proteomics data. Nature Communications. , in press, 2022

Jouni Sirén, Jean Monlong, Xian Chang, Adam M. Novak, Jordan M. Eizenga, Charles Markello, Jonas A. Sibbesen, Glenn Hickey, Pi-Chuan Chang, Andrew Carroll, Namrata Gupta, Stacey Gabriel, Thomas W. Blackwell, Aakrosh Ratan, Kent D. Taylor, Stephen S. Rich, Jerome I. Rotter, David Haussler, Erik Garrison, Benedict Paten, Pangenomics enables genotyping of known structural variants in 5202 diverse genomes, Science. In press, 2021.

Shafin K, Pesout T, Chang PC, Nattestad M, Kolesnikov A, Goel S, Baid G, Kolmogorov M, Eizenga JM, Miga KH, Carnevali P, Jain M, Carroll A, Paten B. Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nat Methods. 2021 Nov;18(11):1322-1332. https://doi.org/10.1038/s41592-021-01299-w. Epub 2021 Nov 1. PMID: 34725481; PMCID: PMC8571015.

J. Ding, A. Alavi, M.R. Ebrahimkhani, Z. Bar-Joseph. Computational tools for analyzing single-cell data in pluripotent cell differentiation studies. Cell Reports Methods, Volume 1, Issue 6, 25 October 2021. https://doi.org/10.1016/j.crmeth.2021.100087.

H. Chen and R. F. Murphy, Evaluation of cell segmentation methods without reference segmentations. bioRxiv 2021.09.17.460800. https://doi.org/10.1101/2021.09.17.460800.

H. Teng, Y. Yuan, Z. Bar-Joseph. Clustering Spatial Transcriptomics Data. Bioinformatics. In press, 2021. https://doi.org/10.1093/bioinformatics/btab704

J. Ding, N. Sharon and Z. Bar-Joseph. Temporal modeling using single cell transcriptomic. Nature Reviews Genetics. In press, 2021.

Y. Yuan and Z. Bar-Joseph. Deep learning of gene relationships from single cell time-course expression data. Briefings in Bioinformatics, Volume 22, Issue 5, September 2021. https://doi.org/10.1093/bib/bbab142.

Börner, K., Teichmann, S.A., Quardokus, E.M. et al. Anatomical structures, cell types and biomarkers of the Human Reference Atlas. Nat Cell Biol 23, 1117–1128 (2021). https://doi.org/10.1038/s41556-021-00788-6

Sungnak, W., Huang, N., Bécavin, C. et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat Med 26, 681–687 (2020). https://doi.org/10.1038/s41591-020-0868-6

G. Songwei, H. Wang, A. Alavi, E. Xing and Z. Bar-Joseph. Supervised Adversarial Alignment of Single-Cell RNA-seq Data. Journal of Computational Biology, Online ahead of print, 2021. Original version appeared in Proceedings of the 24th Annual International Conference on Research in Computational Molecular Biology (RECOMB), pp 72-87, 2020. https://doi.org/10.1089/cmb.2020.0439.

C. Lin, J. Ding, Z. Bar-Joseph. Inferring TF activation order in time series scRNA-Seq studies. PLoS Comput Biol. , 16(2):e1007644, 2020. https://doi.org/10.1371/journal.pcbi.1007644.

H. Zafar, C. Lin, Z. Bar-Joseph. Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nature Communications, 11:3055, 2020. https://doi.org/10.1038/s41467-020-16821-5.

J Ding, Z Bar-Joseph. Analysis of time series regulatory networks. Current Opinion in Systems Biology, 21, Pages 16-24, 2020. https://doi.org/10.1016/j.coisb.2020.07.005.

A. Alavi, Z. Bar-Joseph. Iterative point set registration for aligning scRNA-seq data.
PLoS Comput Biol. , 16(10):e1007939, 2020. https://doi.org/10.1371/journal.pcbi.1007939.

D. Li, J. Ding, Z. Bar-Joseph, Identifying signaling genes in spatial single-cell expression data, Bioinformatics, Volume 37, Issue 7, 1 April 2021, Pages 968–975. https://doi.org/10.1093/bioinformatics/btaa769.

Y. Yuan, Z. Bar-Joseph GCNG: Graph convolutional networks for inferring cell-cell interactions. Genome Biology, 21(1):300, 2020. https://doi.org/10.1186/s13059-020-02214-w.

Snyder, M.P., Lin, S., Posgai, A. et al. The human body at cellular resolution: the NIH Human Biomolecular Atlas ProgramNature 574, 187–192 (2019). https://doi.org/10.1038/s41586-019-1629-x.

Lin, C., & Bar-Joseph, Z. (2019). Continuous-state HMMs for modeling time-series single-cell RNA-Seq dataBioinformatics 35 (22), 15 November 2019, 4707 – 4715. https://doi.org/10.1093/bioinformatics/btz296.

Ding, J., Lin, C., & Bar-Joseph, Z. (2019). Cell lineage inference from SNP and scRNA-Seq dataNucleic acids research47(10), e56. https://doi.org/10.1093/nar/gkz146.

Zafar, H., Lin, C. & Bar-Joseph, Z. Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nat Commun ,113055 (2020). https://doi.org/10.1038/s41467-020-16821-5.