Tidyomics - a tool for multiple omics analysis?

Min Hyung Ryu

October 18, 2023

CDNM Multiple Omics Meeting

Tidyverse and tidyomics

https://github.com/tidyomics

https://r4ds.hadley.nz/

Slides adapted from below:

Credit: Mike Love, Maria Doyle, Stefano Mangiola

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Resources:

https://tidyomics.github.io/tidyomicsWorkshopBioc2023/articles/tidyGenomicsTranscriptomics.html

https://stemangiola.github.io/tidySingleCellExperiment/

https://github.com/tidyomics/tidy-genomics-talk/tree/main

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Consider dataset below:

There are three variables:

• Person
• Treatment
• Test result

An Example: tidy version

Tibble data structure

Advantages of Tidyverse

1. Consistency:

• promotes a consistent and coherent approach to data analysis.
• follows a unified grammar and design philosophy.

2. Readability:

• code is more readable and expressive.
• follows conventions resembling natural language.

3. Piping:

• uses the `%>%` or ‘|>’ (pipe) operator for chaining data manipulation operations.
• enhances code clarity and flow.

4. Data Manipulation:

• packages like dplyr and tidyr simplify data wrangling.
• includes functions for filtering, grouping, summarizing, and reshaping data.

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Verb-based operations

Summarize after grouping

Summarized output

Advantages of Tidyverse

5. Visualization:

• ggplot2 creates customizable, publication-quality visualizations
• adheres to a "grammar of graphics" for complex plots

6. Data Import:

• readr and readxl simplify importing data from various formats.
• Supports CSV, Excel, and more.

7. Data Exploration:

• dplyr and tidyr facilitate data exploration and pattern identification.

8. Package Ecosystem:

• integrates well with other R packages and libraries.
• extends the power and capabilities of R.

Tibble data structure

Example: genomic and transcriptomic data integration

g <- ensembldb::genes(edb)

Only include genes that were found in the scRNAseq data

Call in all the gene_names in your scRNA-seq data

Clean the data from ChIP-seq

Chromatin accessibility

Let’s see if genes near peaks of active chromatin marks (H3K4me3 measured with ChIP-seq)

Derive the distance from gene (scRNAseq) to H3K4me3 peaks (ChiP-seq)

Make the gene-to-peak distance into a categorical variable

Summarize nested data for each cell type

Application:

You can nest for cell-type, disease, donor, etc.

Visualize the results

Other applications?

Summary

• RNA-seq and methylation integration
• CITE-seq (RNA-seq and surface protein markers)
• Single-cell multiple omics integration
• Metabolomics and proteomics

• Tidy data paradigm allows clean and scalable data analysis
• Powerful visualization for exploration and publications
• Integration of genomics and transcriptomics
• Tidyomics is an expanding tool designed to apply “tidy” to omics research