tidyomics (original) (raw)

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tidyomics is an open project to develop and integrate software and documentation to enable atidy dataanalysis framework for omics data objects. tidyomics enables the use of familiartidyverse verbs (select, filter, mutate, etc.) to manipulate rich data objects in theBioconductor 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)

Installer

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

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.

Package Intro GitHub Description
tidySummarizedExperiment Vignette GitHub Tidy manipulation of SummarizedExperiment objects
tidySingleCellExperiment Vignette GitHub Tidy manipulation of SingleCellExperiment objects
tidySeurat Vignette GitHub Tidy manipulation of Seurat objects
tidySpatialExperiment Vignette GitHub Tidy manipulation of SpatialExperiment objects
tidytof Vignette GitHub Tidy manipulation of high-dimensional cytometry data
plyranges Vignette GitHub Tidy manipulation of genomics ranges
plyinteractions Vignette GitHub Tidy manipulation of genomic interactions
plyxp Vignette GitHub Data-masking-based interface to experiment data

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

Package Intro GitHub Description
tidybulk Vignette GitHub Tidy bulk RNA-seq data analysis
nullranges Vignette GitHub Generation of null genomic range sets
easylift Vignette GitHub Perform genomic liftover

Comparison to base R

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 andaggregate, 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)))
  )
)

Comparison to Bioconductor

A key innovation in Bioconductor is the use of object-oriented programming and specific data structures. As described inGentleman et al 2004,

An exprSet is a data structure that binds together array-based expression measurements with covariate and administrative data for a collection of [experiments]... [its] design facilitates a three-tier architecture for providing analysis tools for new microarray platforms: low-level data are bridged to high-level analysis manipulations via the exprSet structure.

In Bioconductor, rich, structured data about experiments is maintained throughout analyses by passing data objects from one method to another. E.g. estimateDispersions adds dispersion information to therowData slot of a DESeqDataSet which is a sub-class of aSummarizedExperiment therefore inheriting the structure and methods of that class. The structure of the data is preserved after running the function (like many Biodonctor methods, it is an _endomorphic_function).

The goal of tidyomics is to preserve the object-oriented programming style and stucture of Bioconductor data objects, while allowing users to manipulate these data objects with expressive commands, familiar to tidyverse users.

Tidyomics aims to allow users to flexibly explore and plot biological datasets, by combining simple functions with human-readable names in a modular fashion to perform complex operations, including grouping and summarization tasks. Operations should still be performed with comparable efficiency to the underlying base R/Bioconductor code.

Tutorials

News

Talks

Tidyomics paper

Getting Help

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.

Get Involved

diagram of tidyomics community

The tidyomics organization is open to new members and contributions; it is an effort ofmany developersin the Bioconductor community and beyond.