Recent publications (2016 - present)


  • K. W. Govek*, E. C. Troisi*, S. Woodhouse, and P. G. Cámara. Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping. (2019). [*authors contributed equally]. BiorXiv. [GitHub] [Web].

  • K. W. Govek, V. S. Yamajala, and P. G. Cámara. Spectral Simplicial Theory for Feature Selection and Applications to Genomics. (2018). arXiv:1811.03377. [GitHub].

  • Y. Ho, P. Hu, M. T. Peel, P. G. Cámara, H. Wu, and S. A. Liebhaber. Single cell transcriptomic analysis of the adult mouse pituitary reveals a novel multi-hormone cell cluster and physiologic demand-induced lineage plasticity. (2018). DOI: 10.1101/475558 (bioRxiv).

Research articles

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  • A.H. Rizvi*, P. G. Cámara*, E. K. Kandror, T. J. Roberts, I. Scheiren, T. Maniatis, and R. Rabadán. Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nature Biotechnology 35 (2017) 551-560. DOI: 10.1038/nbt.3854. [*authors contributed equally]. [GitHub].

    Abstract: Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity in cellular identity over time and identified four transient states (pluripotent, precursor, progenitor, and fully differentiated cells) based on changes in stage-dependent combinations of transcription factors, RNA-binding proteins, and long noncoding RNAs (lncRNAs). scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.

  • J.-K. Lee, J. Wang, J. K. Sa, E. Ladewig, H.-O. Lee, I.-H. Lee, H.-J. Kang, D. S. Rosenbloom, P. G. Cámara, Z. Liu, P. van Nieuwenhuizen, S. W. Jung, S. W. Choi, J. Kim, A. Chen, K.-T. Kim, S. Shin, Y. J. Seo, J. M. Oh, Y. J. Shin, D.-S. Kong, H. J. Seol, A. Blumberg, J.-I. Lee, A. Iavarone, W.-Y. Park, R. Rabadán, and D.-H. Nam. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature Genetics 49 (2017) 594 - 599. DOI: 10.1038/ng.3806.

    Abstract: Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. However, this proposition is complicated by spatial and temporal heterogeneity. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.

  • P. G. Cámara, D. I. S. Rosenbloom, K. J. Emmett, A. J. Levine, and R. Rabadán. Topological Data Analysis Generates High-Resolution, Genome-wide Maps of Human Recombination. Cell Systems 3 (2016) 83-94. DOI: 10.1016/j.cels.2016.05.008.

    Abstract: Meiotic recombination is a fundamental evolutionary process driving diversity in eukaryotes. In mammals, recombination is known to occur preferentially at specific genomic regions. Using topological data analysis (TDA), a branch of applied topology that extracts global features from large data sets, we developed an efficient method for mapping recombination at fine scales. When compared to standard linkage-based methods, TDA can deal with a larger number of SNPs and genomes without incurring prohibitive computational costs. We applied TDA to 1,000 Genomes Project data and constructed high-resolution whole-genome recombination maps of seven human populations. Our analysis shows that recombination is generally under-represented within transcription start sites. However, the binding sites of specific transcription factors are enriched for sites of recombination. These include transcription factors that regulate the expression of meiosis- and gametogenesis-specific genes, cell cycle progression, and differentiation blockage. Additionally, our analysis identifies an enrichment for sites of recombination at repeat-derived loci matched by piwi-interacting RNAs.

  • P. G. Cámara, A. J. Levine, and R. Rabadán. Inference of ancestral recombination graphs through topological data analysis. PLOS Computational Biology 12 (2016) 8. DOI: 10.1371/journal.pcbi.1005071. [GitHub].

    Abstract: The recent explosion of genomic data has underscored the need for interpretable and comprehensive analyses that can capture complex phylogenetic relationships within and across species. Recombination, reassortment and horizontal gene transfer constitute examples of pervasive biological phenomena that cannot be captured by tree-like representations. Starting from hundreds of genomes, we are interested in the reconstruction of potential evolutionary histories leading to the observed data. Ancestral recombination graphs represent potential histories that explicitly accommodate recombination and mutation events across orthologous genomes. However, they are computationally costly to reconstruct, usually being infeasible for more than few tens of genomes. Recently, Topological Data Analysis (TDA) methods have been proposed as robust and scalable methods that can capture the genetic scale and frequency of recombination. We build upon previous TDA developments for detecting and quantifying recombination, and present a novel framework that can be applied to hundreds of genomes and can be interpreted in terms of minimal histories of mutation and recombination events, quantifying the scales and identifying the genomic locations of recombinations. We implement this framework in a software package, called TARGet, and apply it to several examples, including small migration between different populations, human recombination, and horizontal evolution in finches inhabiting the Galápagos Islands.

Review articles

  • J. H. Moore, M. R. Boland, P. G. Cámara, H. Chervitz, G. Gonzalez, B. E. Himes, D. Kim, D. L. Mowery, M. D. Ritchie, L. Shen, R. J. Urbanowicz, and J. H. Holmes. Preparing next-generation scientists for biomedical big data: artificial intelligence approaches. Personalized Medicine 16 (2019) 247-257. DOI:10.2217/pme-2018-0145.

  • P. G. Cámara. Methods and challenges in the analysis of single-cell RNA-sequencing data. Current Opinion in Systems Biology 7 (2018) 47 - 53. DOI: 10.1016/j.coisb.2017.12.007.

  • P. G. Cámara. Topological methods for genomics: present and future directions. Current Opinion in Systems Biology 1 (2017) 95 - 101. DOI: 10.1016/j.coisb.2016.12.007.

  • D. S. Rosenbloom, P. G. Cámara, T. Chu, and R. Rabadán. Evolutionary scalpels for dissecting tumor ecosystems. Biochim. Biophys. Acta Rev. Cancer. 1867 (2017) 69 - 83. DOI: 10.1016/j.bbcan.2016.11.005.

For publications prior to 2016, please visit my Google Scholar profile. A word cloud of my publications in physics can be found here.