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.


Data Portal

HIVE TC-CMU Publications

Y. Yuan and Z. Bar-Joseph. Deep learning of gene relationships from single cell time-course expression data. Briefings in Bioinformatics. In press, 2021

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

C. Lin, J. Ding, Z. Bar-Joseph.
Inferring TF activation order in time series scRNA-Seq studies
PLoS Comput Biol. , 16(2):e1007644, 2020

H. Zafar, C. Lin, Z. Bar-Joseph.
Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data
Nature Communications , 11:3055, 2020

J Ding, Z Bar-Joseph.
Analysis of time series regulatory networks .
Current Opinion in Systems Biology. , 21, Pages 16-24, 2020

A. Alavi, Z. Bar-Joseph.
Iterative point set registration for aligning scRNA-seq data.
PLoS Comput Biol. , 16(10):e1007939, 2020

D. Li, J. Ding, Z. Bar-Joseph
Identifying signaling genes in spatial single cell expression data.
Bioinformatics , in press, 2020

Y. Yuan, Z. Bar-Joseph
GCNG: Graph convolutional networks for inferring cell-cell interactions
Genome Biology , 21(1):300, 2020

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, Hamim, Lin, C., & Bar-Joseph, Z. (2019). Single-Cell Lineage Tracing by Integrating CRISPR-Cas9 Mutations with Transcriptomic Data. BioRxiv, 7 May 2019, www.biorxiv.org/content/10.1101/630814v1.full.pdf.