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Performing ordination analysis of resistome data using ResistoXplorer

Performing ordination analysis of resistome data using ResistoXplorer
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Hi again. Welcome back to another video. In this video, we will learn how we can perform beta diversity or ordination analysis in order to find out the differences between the samples based on their [INAUDIBLE] composition using ResistoXplorer. So let’s quickly open ResistoXplorer. As you can see, I am already on Analysis Panel page. We have already learned how to process format and upload the data in ResistoXplorer. So we have skipped those steps for this video. But just to let you know, here I’m using the pig and [INAUDIBLE] example data set already present in the ResistoXplorer. And I have selected default parameter in order to reach to this Analysis Panel page.
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So now let’s quickly click on the Ordination Analysis module in order to reach the Beta Diversity Analysis main page, which is present under the Composition Profiling section.
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And here it is. As you can see, on the top of the page, we have several options or drop down menus which enable us to explore different distance methods in order to calculate the dissimilarities or distance between samples. Additionally, we can also choose at which functional level do we want beta diversity analysis to be performed. Apart from this, we can also select different ordination method, and we can assess the statistical significance of these beta diversity differences using various multivariate analysis approaches. By default in ResistoXplorer, the beta diversity analysis or ordination analysis is performed using [INAUDIBLE] distance and PCoA ordination. And the significant difference of statistical testing between the [INAUDIBLE] based on the selected experimental factor is done using PERMANOVA.
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Alternatively, we can also perform [INAUDIBLE] recommended beta diversity analysis. In order to do that, we have to go to the Normalisation page. Select the Central Log Ratio Transformation and then click Submit to re-normalise the data. And then jump back to the Ordination Analysis page again and select the PCA as a ordination method. And after that, we have to click Submit in order to execute it. So as you can see here, this is the results of PCoA. While in the bottom half of the page, we have the results for beta diversity or ordination analysis which are represented as 2D and 3D sampled scatter plots. And on the top of the 2D plots, we have also the statistics for significance testing.
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Here in this plot, each dot represent the sample and the distance between those two sample describes the dissimilarity in their composition. From these results, we can easily see that the pig samples are distance from the [INAUDIBLE] or poultry samples, indicating there’s significant differences in their [INAUDIBLE] composition irrespective of the country. Additionally, we can also choose and go to the 3D plots. And we can hover on any point or any sample to know its name, the ordination score, and the percentage variance explained. Lastly, we can always go to the Download Session page to download all the results of beta diversity analysis.
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Additionally, we can also identify some underlying pattern in the data sets by colouring samples based on experimental factor or alpha diversity measures. So as of now, you can try exploring beta diversity analysis with your own data set or try the publicly available example data set in ResistoXplorer and try to answer some of the basic questions present in the exercise. That’s it for this video. Hope you enjoyed it. We’ll see you very soon. Bye bye, and take care.

In this video, you will learn how to perform and infer results from beta diversity analysis in ResistoXplorer. Based on it, you need to complete the exercise present below.

Exercise:

Go to the ResistoXplorer and upload your own data or use an example dataset, such as the ‘Pig and Broiler’, in ARG table module and complete the data filtration and normalisation step. Once you reached the Analysis Panel page, you can see the “Ordination analysis” module under Composition profiling section. Click on it and visualise the differences in resistome composition between the samples or groups using different distance measures, functional levels and ordination methods. Based on that, try to answer some questions:

  • Do you observe any grouping pattern in your data? If yes, then which group are more distinct from each other?
  • What kind of hypothesis can you test on your data with beta diversity or ordination analysis?
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