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Data Filtering in ResistoXplorer

In this video Achal Dhariwal talks about data Filtering in ResistoXplorer.
Hi again. Welcome to another video. In this video, we will see how we can philtre our resistome data using ResistoXplorer. Let’s quickly open ResistoXplorer through any browser of our choice. And click on the second module, which is the ARG Table module.
As we can see, this brings us to the data upload page. In the previous video, we have already see how we can upload, format, and do data sanity checking with our data. So we’re going to skip this step for this video and quickly upload the pig and broiler example data set. And click Submit to upload it directly.
Now we can see the second page, which is the data integrity check page, which will provide us the summary information of our uploaded data. We will now click on the Proceed button to go to our main page for this video, which is the data filtration page. So the main aim behind doing the data filtration of our data is to remove this low quality and uninformative features from our data and thus improving the downstream analysis, especially the statistical analysis. So this is the data philtre page in ResistoXplorer. On the top, you can see detailed text information about data filtration.
While on the bottom, you can see this blue panel, which have several option of filtering the resistome data that we can choose from. And if we look carefully on this panel, it is divided into two sections, low count philtre, and the low variance philtre. So the very first option in the low count philtre is for minimal data filtering, which, by default, will remove extremely rare features which are only present in one samples. Though, if our data is too sparse or not deeply sequenced, we can always disable this option or can change the count value. Now, the default selection for the low count philtre is based on the sample prevalence and the minimum count or abundance value.
What this basically means is the feature should be present in at least 20% of the samples with having at least two read counts. All these features which does not satisfy this criteria will be removed using this option. Additionally, we can also remove low count philtre features based on their mean and median abundance or account values. So one should select the parameters depending on how sparse or what is the statistics or the text summary of our data. In addition to this, there are also some features that do not vary or remain constant throughout the samples. Such features are highly unlikely to associate with experimental condition under investigation.
We can remove such philtre based on low variance by setting the percentage of features to removed based on their interquartile, standard deviation, or coefficient of variation. In ResistoXplorer, we can always disable any type of filtering by dragging the sliders to the left. For this demonstration purpose, I’m going to use the default parameter set for low count philtre and low variance philtre, and click Submit to perform data filtration. As you can see, on the top right, a message will appear indicating how data filtration have been performed and how many features have been removed through each data filtering option, and how many features we are left with for the next step.
In addition to this, we can also see the Proceed button in the bottom right will be enabled. By clicking on it, we can go to the next step, which is the data normalisation step, which we will go and explore in the next video. For now, you can try upload your own data or try for example data set present in ResistoXplorer and perform the data filtration step on your own. That is it for this video. Thanks for watching. Bye-bye, see you in the next video. Take care.

In this video, we will demonstrate how to remove unwanted or uninformative features (antimicrobial resistance genes) from the resistome data using ResistoXplorer.

When the video is playing, feel free to follow along on your computer!
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Exploring the Landscape of Antibiotic Resistance in Microbiomes

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