tidyomics is an open project to develop and integrate software and documentation to enable a tidy data analysis framework for omics data objects. tidyomics enables the use of familiar tidyverse verbs (select, filter, mutate, etc.) to manipulate rich data objects in the Bioconductor ecosystem. Importantly, the data objects are not modified, but tidyomics provides a tidy interface to work on the native objects, leveraging existing Bioconductor classes and algorithms.

tidyomics is a set of R packages by an international group of developers.

tidyomics allows for code such as the following:

single_cell_data |>
  filter(Phase == "G1") |>
  ggplot(aes(UMAP_1, UMAP_2, color=score)) + 
  geom_point()

(filter single cells in G1 phase and plot UMAP coordinates)

or

chip_seq_peaks |>
  filter(FDR < 0.01) |>
  join_overlap_inner(promoters) |>
  group_by(promoter_type) |>
  summarize(ave_score = mean(score))

(compute average score by the type of promoter overlap for significant peaks)

Core tidyomics packages can be installed and loaded with the tidyomics package. See the following URL for details and instructions:

https://github.com/tidyomics/tidyomics

Below find links to:

• Key Tidyomics Packages
• Comparison to base R
• Tutorials
• News
• Talks
• Tidyomics paper
• Getting Help
• Get Involved

Here we list the packages that provide a tidy data interface to manipulate native Bioconductor objects. The tidyomics project also involves other convenience packages listed below.

Consult each package homepage for a description of recent changes.

Note that many of these packages have more than one vignette, which you can find by navigating the package main page.

Convenience packages

As the tidyomics packages offer an interface to underlying R/Bioconductor function evaluations, operations carried out in tidyomics can also be performed with base R/Bioconductor. The benefit from the tidyomics approach is often in readability, interpretability, and extensability of code, gained through elimination of temporary variables, square bracket indexing ([...,...]) and control code (e.g. for, if/else, apply/sapply, etc.).

For example, a filtering and grouping operation on a SummarizedExperiment data in tidyomics would look like:

data |>
  filter(score > 0) |>
  group_by(gene_class) |>
  summarize(mean_count = mean(counts))

In comparison, we can obtain the same with base R/Bioconductor, but with more variables and some control code:

subdata <- data[rowData(data)$score > 0,]
gene_classes <- levels(rowData(subdata)$gene_class)
mean_count <- numeric(length(gene_classes))
for (i in seq_along(gene_classes)) {
  tmp_idx <- rowData(subdata)$gene_class == gene_classes[i]
  mean_count[i] <- mean(assay(subdata, "counts")[tmp_idx,])
}

This can be improved a bit if you know some more base R functions. Here is a base R alternative making use of subset and aggregate, h/t Martin Morgan:

subdata <- subset(data, score > 0)
aggregate(
  as.vector(assay(subdata)),
  list(rep(rowData(subdata)$gene_class, ncol(subdata))),
  mean
)

Even still, the tidyomics version above (filter, group_by, summarize) is likely the easiest to read and extend if the analyst wants to do additional operations, and is the easiest to directly pipe into a plot or a printed table.

For exploring this example, you can define data as follows:

set.seed(5)
data <- SummarizedExperiment(
  assay=list(counts =
    matrix(rnorm(100),10,10, 
    dimnames=list(letters[1:10],letters[1:10]))
  ), 
  rowData = DataFrame(
    score=rnorm(10), 
    gene_class=factor(rep(1:3,c(3,3,4)))
  )
)

• BioC workshop covering single cell transcriptomics and genomics: Tidy single-cell analyses
• Online book covering tidy manipulation of GRanges and more: Tidy ranges tutorial
• Quarto lecture notes introducing the concepts of tidyomics for expression and ranges: Tidy intro talk
• Short tutorial showing overlaps of GWAS SNPs with scATAC-seq peaks T1D GWAS SNPs and CD4+ peaks
• A workflow showing RNA-seq and ATAC-seq integration with plyranges: Fluent genomics workflow
• More to come...

• Tidyomics blog

• Tidy enrichment analysis with plyranges and nullranges
• Tidy analysis of genomic data

• The tidyomics ecosystem: Enhancing omic data analyses

We value community feedback and collaboration, and are happy to help you get started. Join the ongoing discussion, or you can ask specific questions about code on the support site.

• Join our Slack Channel, #tidiness_in_bioc for general discussion or pointers
• For specific coding help you can reach out on the Bioconductor support site

The tidyomics organization is open to new members and contributions; it is an effort of many developers in the Bioconductor community and beyond.

• See our tidyomics open challenges project to see what we are currently working on
• Issues tagged with good first issue are those that developers think would be good for a new developer to start working on
• Read over our Guidelines for contributing
• Read over our Code of Conduct
• As with new users, for new developers please consider joining our Slack Channel, #tidiness_in_bioc. Most of the tidyomics developers are active there and we are happy to talk through updates, PRs, or give guidance on your development of a new package in this space.