Browsing by Author "Li, Zuqi"

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    Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data
    (Oxford University Press, 2025) Li, Zuqi; Windels, Sam F. L.; Malod-Dognin, Noël; Weinberg, Seth M.; Marazita, Mary L.; Walsh, Susan; Shriver, Mark D.; Fardo, David W.; Claes, Peter; Pržulj, Nataša; Van Steen, Kristel; Biology, School of Science
    Motivation: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). Results: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. Availability and implementation: INMTD is freely available at https://github.com/ZuqiLi/INMTD.
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    netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity
    (bioRxiv, 2023-05-05) Li, Zuqi; Melograna, Federico; Hoskens, Hanne; Duroux, Diane; Marazita, Mary L.; Walsh, Susan; Weinberg, Seth M.; Shriver, Mark D.; Müller-Myhsok, Bertram; Claes, Peter; Van Steen, Kristel; Biology, School of Science
    Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.