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Printable Handouts
Navigable Slide Index
- Introduction
- Talk outline
- Data integration
- Different data sets measured on same samples
- Goals of data fusion
- Methods of data fusion
- Chemical and biological correlations
- Observed biological variability and correlations
- Interpretation of biological correlations
- STOCSY
- Statistical heterospectroscopy (SHY)
- Novel regulation of potato pigmentation
- Gene–metabolite correlation network
- Simultaneous component analysis methods
- Arabidopsis thaliana and phytophtora infestans
- Metabolic and transcriptomic correlations
- Separate PCA analyses
- Simultaneous component analysis
- Scores and loadings
- Methods for common and distinct variation
- Common, distinctive and residual variation
- Common vs. distinct variation
- Joint and individual variation explained (JIVE)
- JIVE: common results
- JIVE: distinct results
- DISCO-SCA
- Optimal orthogonal rotation of loadings
- DISCO-SCA: results
- OnPLS / O2PLS
- OnPLS common results
- OnPLS distinctive results
- Differences in explained variation
- Differences between JIVE, DISCO and OnPLS
- Biological interpretations
- Complex Issues in data fusion
- Sample alignment
- Block scaling
- Model complexity
- Multi-block data fusion: Listeria Monocytogenes
- Block scores
- SCA model complexity selection
- Data fusion summary
- Three methods for variation analysis
- Acknowledgements
Topics Covered
- Data integration/fusion in metabolomics
- Correlation based data fusion methods
- Methods based on simultaneous component analysis
- Methods that can distinguish common and distinctive variation
- Application of methods to metabolic and transcriptional response of Arabidopsis thaliana mutants to Phytophtora infestans
- Sample alignment
- Block scaling
- Model complexity
Links
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Talk Citation
Westerhuis, J.A. (2017, November 30). Data fusion: examples in fusing metabolomics and transcriptomics data [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved October 12, 2024, from https://doi.org/10.69645/GXWV4185.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Johan A. Westerhuis has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Other Talks in the Series: Bioinformatics for Metabolomics
Transcript
Please wait while the transcript is being prepared...
0:00
Hello, my name is Johan A. Westerhuis and I'm from the University of Amsterdam.
I'm going to give you an overview of Data Fusion Methods,
and I will show some examples of fusing Metabolomics and Transcriptomics Data.
0:13
On this next slide,
you can see the content of my talk.
I will first introduce the difference between data integration and data fusion.
and we'll discuss some of the goals of data fusion,
and after that I will show some of the methods
used and some complex issues in data fusion.
0:31
In this next slide,
you can see an example of data integration by multiple sources of omics data,
and also ontologies and databases that are combined.
On this map, you can see an overview of the glycolysis of some data
and where we have measured metabolomics data, RNA-seq,
miRNA, proteomics and even more sources of data.
Now, because this is the glycolysis,
we will have to know information about each gene and
where it's active in this map.
And therefore, we need the ontologies,
and we might even use some of the databases
for example, consisting of mRNA and miRNA associations.
So, together the whole overview of all of these different data sources,
is what I call data integration.
1:14
On this next slide,
I'm going into the data fusion methods.
Data fusion methods are sort of statistical methods which can combine different datasets,
measured on the same samples or on the same individuals.
And the most important sort of issue, is that measurements on
the same samples should appear in all of the datasets simultaneously.
Here, you see an example
of fusing Metabolomics data measured on GCMS, LCMS and NMR.
And here you see, indicated by this line,
that samples on the same row in each of these sets,
that they belong together;
so, to the same individual or to the same sample.
And there are other examples of fusing metabolomics with gene expression and proteomics,
or even fusing different compartments.
And here we see an example of kidney, plasma,
and liver tissues all measured from the same individual.
And then, sort of the same individual,
has the same level of information that we want to combine.
So, what are the goals of data fusion?
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