Kim Anh-le Cao - Multivariate models for dimension reduction and biomarker selection in omics data
of 40/40
Intro PCA: the workhorse Variable selection for single ... and multiple ’omics Conclu Multivariate models for dimension reduction and biomarker selection in omics data Kim-Anh Lê Cao The University of Queensland Diamantina Institute Brisbane, Australia Kim-Anh Lê Cao 2014 Winter School in Mathematical & Computational Biology
Kim Anh-le Cao - Multivariate models for dimension reduction and biomarker selection in omics data
Recent advances in high throughput ’omics’ technologies enable quantitative measurements of expression or abundance of biological molecules of a whole biological system. The transcriptome, proteome and metabolome are dynamic entities, with the presence, abundance and function of each transcript, protein and metabolite being critically dependent on its temporal and spatial location. Whilst single omics analyses are commonly performed to detect between‐groups difference from either static or dynamic experiments, the integration or combination of multi‐layer information is required to fully unravel the complexities of a biological system. Data integration relies on the currently accepted biological assumption that each functional level is related to each other. Therefore, considering all the biological entities (transcripts, proteins, metabolites) as part of a whole biological system is crucial to unravel the complexity of living organisms. With many contributors and collaborators, we have further developed several multivariate approaches to project high dimensional data into a smaller space and select relevant biological features, while capturing the largest sources of variation in the data. These approaches are based on variants of Partial Least Squares regression and Canonical Correlation Analysis and enable the integration of several types of omics data. In this presentation, I will illustrate how various techniques enable exploration, biomarker selection and visualisation for different types of analytical frameworks. First presented at the 2014 Winter School in Mathematical and Computational Biology http://bioinformatics.org.au/ws14/program/
Text of Kim Anh-le Cao - Multivariate models for dimension reduction and biomarker selection in omics data
Intro PCA: the workhorse Variable selection for single ... and
multiple omics Conclu Multivariate models for dimension reduction
and biomarker selection in omics data Kim-Anh L Cao The University
of Queensland Diamantina Institute Brisbane, Australia Kim-Anh L
Cao 2014 Winter School in Mathematical & Computational
Biology
Intro PCA: the workhorse Variable selection for single ... and
multiple omics Conclu Challenges Challenges Close interaction
between statisticians, bioinformaticians and molecular biologists
Understand the biological problem Try answer complex biological
questions Irrelevant (noisy) variables n Large Seminar Room,
Institute for Molecular Bioscience, Queensland Biosciences
Precinct, The University of Queensland, St Lucia Campus * You are
invited to a short course in biostatistics Four presentations over
four months will cover 1. Summarizing and presenting data 2.
Hypothesis testing and statistical inference 3. Comparing two
groups 4. Comparing more than two groups IMB Career Advantage 2014
Free registration: www.di.uq.edu.au/statistics [email protected]
http://www.math.univ-toulouse.fr/biostat/mixOmics Kim-Anh L Cao
2014 Winter School in Mathematical & Computational Biology
![BIOMARKER SIGNATURE IDENTIFICATION IN “OMICS” DATA … · E-mail address: vlagani@ics.forth.gr (Vincenzo Lagani) 1 . supported by large scale evaluations [4,5] that have experimentally](https://img.pdfslide.net/doc/110x75/5e7dd8d441d5485e6b5983d1/biomarker-signature-identification-in-aoeomicsa-data-e-mail-address-vlaganiicsforthgr.jpg?t=1669733289)
