Cameron Martino

Dec 18, 2024 | genomebiology.biomedcentral.com | Pixu Shi |Cameron Martino |Rungang Han |Stefan Janssen |Gregory Buck |Myrna G. Serrano | +4 more

In this section, we provide details of our methods, including data preprocessing, the TEMPTED algorithm, tensor reconstruction, and how the dimension reduction learned from training data can be transferred to new testing data. Additionally, we present details of case studies (Pax5 Mice Leukemia data, shotgun metagenomic vaginal data, 16S Vaginal data), and data-driven simulation studies.

Dec 2, 2024 | biorxiv.org | Pixu Shi |Cameron Martino |Rungang Han |Stefan Janssen

AbstractLongitudinal studies are crucial for understanding complex microbiome dynamics and their link to health. We introduce TEMPoral TEnsor Decomposition (TEMPTED), a time-informed dimensionality reduction method for high-dimensional longitudinal data that treats time as a continuous variable, effectively characterizing temporal information and handling varying temporal sampling.

Feb 6, 2024 | biorxiv.org | Pixu Shi |Cameron Martino |Rungang Han |Stefan Janssen

AbstractComplex dynamics of microbial communities underlie their essential roles in health and disease, but our understanding of these dynamics remains incomplete. To bridge this gap, longitudinal microbiome data are being rapidly generated, yet their power is limited by technical challenges in design and analysis, such as missing temporal samples, complex correlation structure, and high dimensionality.

Jul 29, 2023 | biorxiv.org | Cameron Martino |Stefan Janssen |Pixu Shi |Rungang Han

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