Introduction

IWTomics (Cremona et al. 2018) implements the Interval-Wise Testing (IWT; Pini and Vantini 2017) for omics data. This inferential procedure tests for differences in “Omics” data between two groups of genomic regions, and does not require fixing location and scale at the outset.

In this tutorial we use real data about endogenous retroviruses (ERVs) in mouse. In particular, we compare recombination hotspots in the flanking regions of fixed ETn versus control regions. The dataset contains two region datasets “ETn fixed”, “Control” and one feature “Recombination hotspots content”. The region dataset “ETn fixed” contains 1296 genomic regions of 64 kb surrounding fixed ETns elements (32-kb flanking sequences upstream and 32-kb flanking sequences downstream of each element). The region dataset “Control” contains 1142 regions of 64 kb without elements, used as control in the test. The regions are aligned around their center (i.e. around the ETn integration sites).

Recombination hotspots measurements are associated to each “ETn fixed” and “Control” region. In particular, this feature is measured in 1-kb windows, so that each region is associated to a recombination hotspots curve made of 64 values. The measurement used is the feature content, i.e. the fraction of the 1-kb window that is covered by recombination hotspots

The data we use in this tutorial is available at Zenodo.

Agenda

IWTomics is composed of three steps (corresponding to three tools):

1. Introduction
2. Step 1: Loading and pre-processing
3. Step 2: Performing Interval-Wise Testing
4. Step 3: Selecting test scale

Step 1: Loading and pre-processing

The first tool (IWTomics Load Smooth and Plot) imports a collection of genomic region datasets, and associates to each region multiple genomic feature measurements. It allows to align the regions in multiple ways (center, left, right or scale alignment), to smooth the feature curves (possibly filling gaps in the measurements) and to create a graphical representation of the feature measurements in each region datasets (aligned curves or pointwise quantile curves).

Hands-on: Get the data

1. Create a new history for this tutorial

   Tip: Creating a new history

   Click the new-history icon at the top of the history panel.

   If the new-history is missing:

   1. Click on the galaxy-gear icon (History options) on the top of the history panel
   2. Select the option Create New from the menu

2. Import datasets:
   • Fixed ETn (ETn_fixed.bed) regions
   • Control (Control.bed) regions
   • Recombination Hotspot (Recombination_hotspots.txt) content

   https://zenodo.org/record/5589610/files/ETn_fixed.bed
   https://zenodo.org/record/5589610/files/Control.bed
   https://zenodo.org/record/5589610/files/Recombination_hotspots.txt
   

3. Import header files for regions and features:
   • regions_header.tabular
   • features_header.tabular

   https://zenodo.org/record/5589610/files/regions_header.tabular
   https://zenodo.org/record/5589610/files/features_header.tabular
   

   Please note that the file names in the first column of the header files must exactly match the dataset names in the Galaxy history.

Hands-on: Pre-process data and create pointwise boxplot

Load, Smooth and Plot tool: Run Load, Smooth and Plot with:

• Select the two region datasets ETn_fixed.bed and Control.bed.
• Select the region header file regions_header.tabular.
• Select the feature file Recombination_hotspot.txt.
• Select the feature header file features_header.tabular.
• Start positions are 0-based in this example; all region alignments are equivalent in this case.
• You can choose to smooth the feature curves.
• Select the graphical representation type Pointwise quantile curves (boxplot).

Step 2: Performing Interval-Wise Testing

The second tool (IWTomics Test and Plot) statistically evaluates differences in genomic features between groups of regions along the genome. In particular, it implements the Interval-Wise Testing for omics data, an extended version of the Interval-Wise Testing for functional data presented in Pini and Vantini (2017).

It allows to perform multiple two sample permutation tests between pairs of region datasets, on several features. It returns the adjusted p-value curves for every test and all possible scales. Moreover, it creates a graphical representation of the Interval-Wise Testing results and a summary plot (optional) with p-values at the maximum scale. The tool IWTomics Plot with Threshold on Test Scale permits to select the scale to be used in the plots.

Hands-on: Test for difference between ETn and Control regions

Test and Plot tool: Run Test and Plot with:

• Select the IWTomicsData Object, Region Dataset IDs, and Feature IDs created during Step 1.
• Select pairs of genomic region datasets to be compared using two-sample Interval-Wise Testing. In this example we consider one two-sample test contrasting ETn_fixed (select it as Region 1) to Control (select it as Region 2).
• Select feature measurements to be tested. In this example, select Recomb_hot feature.
• You can choose the test statistic, the number of permutations, and the significance level of the test (alpha).
• Select the graphical representation type Pointwise quantile curves (boxplot).

Step 3: Selecting test scale

The third tool (IWTomics Plot with Threshold on Test Scale) allows to select the scale for the Interval-Wise Testing results. In particular it returns the p-value curves for the different tests performed at the selected scale, and it creates a graphical representation of the Interval-Wise Testing results and a summary plot (optional) at the selected scale.

Hands-on: Change scale for test results

Plot with Threshold on Test Scale tool: Run Plot with Threshold on Test Scale with:

• Select the IWTomicsData Object with Test Results, Test IDs, and Feature IDs created during Step 2.
• Select the scale to be used in Interval-Wise Testing results and plot. Select the test identifier ETn_fixed vs Control, the feature identifier Recomb_hot, and the scale 8 (8-kb scale).
• Select the graphical representation type Pointwise quantile curves (boxplot).

Details: Additional resources

Read more about IWTomics here.

Key points

• High-resolution omics data can be represented as curves along the genome

• IWTomics permits to visualize these data

• The Interval-Wise Testing detects statistically significant differences between pairs of genomic regions

• The Interval-Wise Testing corrects p-values taking into consideration the ordered nature of measurements along the genome

• Locations and scales do not need to be specified at the outset, indeed IWTomics permits to detect them

Frequently Asked Questions

Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Statistics and machine learning topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help Forum

References

1. Pini, A., and S. Vantini, 2017 Interval-wise testing for functional data. Journal of Nonparametric Statistics 29: 407–424. 10.1080/10485252.2017.1306627
2. Cremona, M. A., A. Pini, F. Cumbo, K. D. Makova, F. Chiaromonte et al., 2018 IWTomics: testing high-resolution sequence-based ‘Omics’ data at multiple locations and scales (I. Birol, Ed.). Bioinformatics 34: 2289–2291. 10.1093/bioinformatics/bty090

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Citing this Tutorial

1. Marzia A Cremona, Fabio Cumbo, 2022 Interval-Wise Testing for omics data (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/statistics/tutorials/iwtomics/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{statistics-iwtomics,
author = "Marzia A Cremona and Fabio Cumbo",
title = "Interval-Wise Testing for omics data (Galaxy Training Materials)",
year = "2022",
month = "10",
day = "18"
url = "\url{https://training.galaxyproject.org/training-material/topics/statistics/tutorials/iwtomics/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
    doi = {10.1016/j.cels.2018.05.012},
    url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}
}
                   

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