Visualization of descriptive multiblock analysis. (31st July 2018)

• Record Type: Journal Article
• Authors: Skotare, Tomas; Sjögren, Rickard; Surowiec, Izabella; Nilsson, David; Trygg, Johan
• Is Part Of: Journal of chemometrics. Volume 34:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-31
• Subjects: data fusion -- descriptive analytics -- multiblock analysis -- OnPLS -- visualization; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3071
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system. Abstract : In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We demonstrate our visualizations on an industrial case study in vibration spectroscopy as well as a multiomics integration study. We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.