Pablo G Cámara, PhD

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Assistant Professor of Genetics
Department: Genetics

Contact information
A302 Richards Building
3700 Hamilton Walk
Philadelphia, PA 19104-6145
Office: 215-746-3545
BS (Theoretical Physics)
Universidad Autonoma de Madrid, Spain, 2002.
PhD (Theoretical Physics)
Universidad Autonoma de Madrid, Spain, 2006.
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> Perelman School of Medicine   > Faculty   > Details

Description of Research Expertise

Research in the Camara lab is focused on the development and application of innovative computational approaches to the study of cellular heterogeneity and its role in disease. To this end, we draw ideas from topology, geometry, statistics, physics, and computer science. Combining these approaches with the use of high-throughput single-cell technologies and large-scale population studies, we aim to achieve a better understanding of the cellular composition and cell-to-cell signaling networks of tumors in relation to their genomes. We are particularly interested in deciphering the cellular ecosystem and heterotypic signaling networks of tumors of the central nervous system, both in adults and children; however, we expect our methods to be of utility to a broader community and collaborate with groups working in related areas of research.

Selected Publications

K. W. Govek, E. C. Troisi, Z. Miao, R. G. Aubin, S. Woodhouse, and P. G. Cámara: Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping. Science Advances 7(10): eabc5464, 2021.

Rabadan R., Mohamedi Y., Rubin U., Chu T., Alghalith A. N., Elliott O., Arnes L., Cal S., Obaya A. J., Levine A. J., Camara P. G.: Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis. Nature Communications 11(3808), 2020.

Govek K. W., Yamajala V. S., Camara P. G.: Clustering-Independent Analysis of Genomic Data using Spectral Simplicial Theory. PLOS Computational Biology 15(11): e1007509, 2019.

Rizvi, A. H.*, Camara, P. G.*, Kandror, E. K., Roberts, T. J., Schieren, I., Maniatis, T., Rabadan, R.: Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nature Biotechnology 35(6): 551-560, 2017 Notes: *equal contributing authors.

Lee, J.-K., Wang, J., Sa, J. K., Ladewig, E., Lee, H.-O., Lee, I.-H., Kang, H. J., Rosenbloom, D. S., Camara, P. G., Liu, Z., van Nieuwenhuizen, P., Jung, S. W., Choi, S. W., Kim, J., Chen, A., Kim, K. T., Shin, S., Seo, Y. J., Oh, J. M., Shin, Y. J., Park, C. K., Kong, D. S., Seol, H. J., Blumberg, A., Lee, J. I., Iavarone, A., Park, W. Y., Rabadan, R., Nam, D. H.: Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature Genetics 49(4): 594-599, 2017.

Camara, P. G.: Topological methods for genomics: present and future directions. Current Opinion in Systems Biology 1: 95-101, 2017.

Camara, P. G., Levine, A. J., Rabadan, R.: Inference of ancestral recombination graphs through topological data analysis. PLOS Computational Biology 12(8): e1005071, 2016.

Camara, P. G., Rosenbloom, D. S., Emmett, K. J., Levine, A. J., Rabadan, R.: Topological data analysis generates high-resolution, genome-wide maps of human recombination. Cell Systems 3(1): 83-94, 2016.

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Last updated: 08/09/2022
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