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The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.
Building a complete picture of cell state requires measuring different properties of the cells, such as their gene expression, morphology, etc., and understanding 1) how these properties relate to each other, 2) how they change over time, 3) how they are affected by different perturbations. It is often difficult to collect this information through experimentation alone. High-throughput single-cell assays such as single-cell RNA-sequencing are destructive to cells, making it difficult to make other observations of the same cells at other time points or using different measurement tools. In this thesis, I develop new machine learning methodology to integrate and translate between single-cell data. In the first half, I develop methods based on generative modeling, representation learning and optimal transport to learn mappings between cells collected at different time points. In the second half, I develop methods based on generative modeling and representation learning to map between different data modalities, including both observational measurements and interventions. Overall, this body of work progresses towards the larger goal of complete cell models that predict cell state under different measurements, time points, and perturbations.
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
This thesis discusses novel statistical methods for analyzing high-dimensional multimodal data. Part one discusses three methods for multimodal data learning. First, we propose a model-based probabilistic approach for correlation and canonical correlation estimation for two sparse count datasets motivated by integer-valued data from next-generation sequencing platforms. Second, in many scientific problems such as video surveillance, modern genomics, and finance, data are often collected from diverse domains across time that exhibits time-dependent heterogeneous properties. We propose a generative model based on variational autoencoder and recurrent neural network to infer the latent dynamics for multi-view longitudinal data. This method allows us to identify the disentangled latent embeddings across multiple modalities while accounting for the time factor to achieve interpretable results. Third, we propose a deep interpretable variational canonical correlation analysis model for multi-view learning. This model is designed to disentangle both the shared and view-specific variations for multi-view data and achieve model interpretability. For all the methods, simulated and real experiments show our algorithms' advantages across domains. Part two discusses two methods for multiple hypothesis testing. First, we propose incorporating the feature hierarchy in a probabilistic black-box model to control FDR for two-group multiple hypothesis testing problems. The deep learning architecture enables efficient optimization and gracefully handles high-dimensional hypothesis features. The extensive simulation studies on synthetic and real datasets demonstrate that our algorithm yields more discoveries while controlling the FDR than state-of-the-art methods. Further, we propose a Bayesian differential analysis framework for multiple group problems. The simulation studies demonstrate that this model can recover the truth from learning the data very well. We conclude the thesis with discussions and future works.