Interval-Wise Testing for omics data

Author(s)	Marzia A Cremona Fabio Cumbo
Reviewers

Overview
Questions:

How to visualize high-resolution omics data in different groups of genomic regions?

How to evaluate differences in high-resolution omics data between groups of genomic regions?

How to detect locations and scales at which the significant effects unfold?

Objectives:

Pre-process high-resolution omics data

Create pointwise boxplots corresponding to different groups of genomic regions

Perform the Interval-Wise Testing between two groups of genomic regions

Visualize and inspect test results

Detect relevant locations and scales

Requirements:

Introduction to Galaxy Analyses

Time estimation: 1 hour

Supporting Materials:

Datasets

Workflows

FAQs

instances Available on these Galaxies

Known Working

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Published: Jun 14, 2018

Last modification: Nov 9, 2023

License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT

purl PURL: https://gxy.io/GTN:T00269

rating Rating: 1.5 (0 recent ratings, 2 all time)

version Revision: 17

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):

Step 1: Loading and pre-processing

Step 2: Performing Interval-Wise Testing

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
Create a new history for this tutorial

To create a new history simply click the new-history icon at the top of the history panel:
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
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).

Read more about IWTomics in the documentation (PDF).

You've Finished the Tutorial

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

Pini, A., and S. Vantini, 2017 Interval-wise testing for functional data. Journal of Nonparametric Statistics 29: 407–424. 10.1080/10485252.2017.1306627
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

Marzia A Cremona, Fabio Cumbo, Interval-Wise Testing for omics data (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/statistics/tutorials/iwtomics/tutorial.html Online; accessed TODAY
Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

@misc{statistics-iwtomics,
author = "Marzia A Cremona and Fabio Cumbo",
	title = "Interval-Wise Testing for omics data (Galaxy Training Materials)",
	year = "",
	month = "",
	day = "",
	url = "\url{https://training.galaxyproject.org/training-material/topics/statistics/tutorials/iwtomics/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol}
}

                   

Congratulations on successfully completing this tutorial!

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/statistics/tutorials/iwtomics/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: iwtomics_loadandplot
  owner: iuc
  revisions: ce633cc8f5f9
  tool_panel_section_label: Statistics
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: iwtomics_plotwithscale
  owner: iuc
  revisions: 1bc9150ed125
  tool_panel_section_label: Statistics
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: iwtomics_testandplot
  owner: iuc
  revisions: 94965e369be4
  tool_panel_section_label: Statistics
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

2 stars 1

1 stars 1

July 2021

1 stars: Disliked: Hi, unfortunately, this tutorial will only be useful to those who already know the method. As non-bioinformatician, I have no clue what this is good for. Can I do GWAS with that and how do I implement it? Such tutorials should be cross-checked by biologists, bioinformaticians can solve such problems anyway themselves.

November 2020

2 stars: Liked: Use of images and screenshots helps guide the training process, as users know what to look for. Disliked: Explanatory text needs to be substantially expanded, explaining what Interval-Wise Testing is at least in layman's terms. Additionally, explanations for data shown in images is required, else these images appear meaningless. IWT is not well-known enough to leave these things assumed.