Dimension reduction techniques for the integrative analysis of multi-omics data

By C. Meng, O. Zeleznik, G. Thallinger, B. Küster, A. Gholami, A. Culhane. Briefings Bioinform., 2016

355 citations recorded by the source corpus.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3f3b339a7b57cf17129924a8e301fa3fe932bf86

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Citations in the corpus

Integrative single-cell analysis (2019, background): “Statistical approaches for multimodal single-cell integration are likely to be inspired by bulk approaches (reviewed elsewhere 76 ) that are used to perform joint dimensionality reduction on multiple omics data sets to identify conserved or divergent patterns and can be readily extended to…”
The Application of Deep Learning in Cancer Prognosis Prediction (2020, background): “Dimension reduction assists the integrative analysis of multi-omics data [64].”
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2015, methodology): “Algorithms such as the principal component analysis or clustering methods are widely used in bioinformatics [39].”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “Unlike PCA in which decomposition matrices have both positive and negative values, the resulting matrices from NMF only have positive values; thus, original data is represented only by additive combinations of latent variables. t-distributed stochastic neighbor embedding (t-SNE) [37] is an FE algori”
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets (2018, methodology): “Although factor models that aim to address this have previously been proposed (e.g. Meng et al , 2014, 2016; Tenenhaus et al , 2014; preprint: Singh et al , 2018), these methods either lack sparsity, which can reduce interpretability, or require a substantial number of parameters to be determined…”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, neutral): “(13) reviews only dimension reduction multi-omics methods.”
Ten quick tips for effective dimensionality reduction (2019, positive): “To assist practitioners, we also include in Table 2 a list of stable implementations of methods discussed in this article.”
Precision Oncology: The Road Ahead (2017, methodology): “Other methods formulate the problem as a “multi-view” matrix factorization and dimension reduction, or as a probabilistic model (reviewed in [82]).”