GitHub - keyes-timothy/tidyFlowCore: tidyFlowCore: Bringing flowCore to the tidyverse (original) (raw)

tidyFlowCore

tidyFlowCore is an R package that bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore flowFrames andflowSets under this layer of abstraction.

Using this approach, tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data.

Installation instructions

Get the latest stable R release fromCRAN. Then install tidyFlowCore fromBioconductor using the following code:

if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") }

BiocManager::install("tidyFlowCore")

And the development version fromGitHub with:

BiocManager::install("keyes-timothy/tidyFlowCore")

Example

tidyFlowCore allows you to treat flowCore data structures like tidydata.frames or tibbles It does so by implementing dplyr, tidyr, and ggplot2 verbs that can be deployed directly on the flowFrame andflowSet S4 classes.

In this section, we give a brief example of how tidyFlowCore can enable a data analysis pipeline to use all the useful functions of theflowCore package and many of the functions of the dplyr, tidyr, and ggplot2 packages.

Load required packages

library(tidyFlowCore) library(flowCore)

Read data

read data from the HDCytoData package

bcr_flowset <- HDCytoData::Bodenmiller_BCR_XL_flowSet() #> see ?HDCytoData and browseVignettes('HDCytoData') for documentation #> loading from cache

Data transformation

The flowCore package natively supports multiple types of data preprocessing and transformations for cytometry data through the use of its tranform class.

For example, if we want to apply the standard arcsinh transformation often used for CyTOF data to our current dataset, we could use the following code:

asinh_transformation <- flowCore::arcsinhTransform(a = 0, b = 1/5, c = 0) transformation_list <- flowCore::transformList( colnames(bcr_flowset), asinh_transformation )

transformed_bcr_flowset <- flowCore::transform(bcr_flowset, transformation_list)

Alternatively, we can also use the tidyverse’s functional programming paradigm to perform the same transformation. For this, we use the mutate-across framework via tidyFlowCore:

transformed_bcr_flowset <- bcr_flowset |> dplyr::mutate(across(-ends_with("_id"), (.x) asinh(.x / 5)))

Cell type counting

Suppose we’re interested in counting the number of cells that belong to each cell type (encoded in the population_id column of bcr_flowset) in our dataset. Using standard flowCore functions, we could perform this calculation in a few steps:

extract all expression matrices from our flowSet

combined_matrix <- flowCore::fsApply(bcr_flowset, exprs)

take out the concatenated population_id column

combined_population_id <- combined_matrix[, 'population_id']

perform the calculation

table(combined_population_id) #> combined_population_id #> 1 2 3 4 5 6 7 8 #> 3265 6651 62890 51150 1980 18436 24518 3901

tidyFlowCore allows us to perform the same operation simply using thedplyr package’s count function:

bcr_flowset |> dplyr::count(population_id) #> # A tibble: 8 × 2 #> population_id n #> #> 1 1 3265 #> 2 2 6651 #> 3 3 62890 #> 4 4 51150 #> 5 5 1980 #> 6 6 18436 #> 7 7 24518 #> 8 8 3901

And tidyFlowCore also makes it easy to perform the counting broken down by other variables in our metadata:

bcr_flowset |>

use the .tidyFlowCore_identifier pronoun to access the name of

each experiment in the flowSet

dplyr::count(.tidyFlowCore_identifier, population_id) #> # A tibble: 128 × 3 #> .tidyFlowCore_identifier population_id n #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 31 #> 2 PBMC8_30min_patient1_BCR-XL.fcs 2 112 #> 3 PBMC8_30min_patient1_BCR-XL.fcs 3 761 #> 4 PBMC8_30min_patient1_BCR-XL.fcs 4 1307 #> 5 PBMC8_30min_patient1_BCR-XL.fcs 5 5 #> 6 PBMC8_30min_patient1_BCR-XL.fcs 6 127 #> 7 PBMC8_30min_patient1_BCR-XL.fcs 7 444 #> 8 PBMC8_30min_patient1_BCR-XL.fcs 8 51 #> 9 PBMC8_30min_patient1_Reference.fcs 1 52 #> 10 PBMC8_30min_patient1_Reference.fcs 2 132 #> # ℹ 118 more rows

Nesting and unnesting

flowFrame and flowSet data objects have a clear relationship with one another in the flowCore API - essentially nested flowFrames. In other words, flowSets are made up of multiple flowFrames!

tidyFlowCore provides a useful API for converting between flowSetand flowFrame data structures at various degrees of nesting using thegroup/nest and ungroup/unnest verbs. Note that in the dplyr and tidyr APIs, group/nest and ungroup/unnest are not synonyms (grouped data.frames are different from nested data.frames). However, because of how flowFrames and flowSets are structured,tidyFlowCore’s group/nest and ungroup/unnest functions have identical behavior, respectively.

unnesting a flowSet results in a flowFrame with an additional column,

'tidyFlowCore_name` that identifies cells based on which experiment in the

original flowSet they come from

bcr_flowset |> dplyr::ungroup() #> flowFrame object 'file8c8539ae19b6' #> with 172791 cells and 40 observables: #> name desc range minRange maxRange #> $P1 Time Time 2399633 0.0000 2399632 #> $P2 Cell_length Cell_length 69 0.0000 68 #> $P3 CD3(110:114)Dd CD3(110:114)Dd 9383 -61.6796 9382 #> $P4 CD45(In115)Dd CD45(In115)Dd 5035 0.0000 5034 #> $P5 BC1(La139)Dd BC1(La139)Dd 14306 -100.8797 14305 #> ... ... ... ... ... ... #> $P36 group_id group_id 3 0 2 #> $P37 patient_id patient_id 9 0 8 #> $P38 sample_id sample_id 17 0 16 #> $P39 population_id population_id 9 0 8 #> $P40 .tidyFlowCore_name .tidyFlowCore_name 17 0 16 #> 297 keywords are stored in the 'description' slot

flowSets can be unnested and renested for various analyses

bcr_flowset |> dplyr::ungroup() |>

group_by cell type

dplyr::group_by(population_id) |>

calculate the mean HLA-DR expression of each cell population

dplyr::summarize(mean_expression = mean(HLA-DR(Yb174)Dd)) |> dplyr::select(population_id, mean_expression) #> # A tibble: 8 × 2 #> population_id mean_expression #> #> 1 3 3.67 #> 2 7 3.33 #> 3 4 4.33 #> 4 2 87.1 #> 5 6 88.2 #> 6 8 3.12 #> 7 1 51.4 #> 8 5 18.0

Plotting

tidyFlowCore also provides a direct interface between ggplot2 andflowFrame or flowSet data objects. For example…

cell population names, from the HDCytoData documentation

population_names <- c( "B-cells IgM-", "B-cells IgM+", "CD4 T-cells", "CD8 T-cells", "DC", "monocytes",
"NK cells",
"surface-" )

calculate mean CD20 expression across all cells

mean_cd20_expression <- bcr_flowset |> dplyr::ungroup() |> dplyr::summarize(mean_expression = mean(asinh(CD20(Sm147)Dd / 5))) |> dplyr::pull(mean_expression)

calculate mean CD4 expression across all cells

mean_cd4_expression <- bcr_flowset |> dplyr::ungroup() |> dplyr::summarize(mean_expression = mean(asinh(CD4(Nd145)Dd / 5))) |> dplyr::pull(mean_expression)

bcr_flowset |>

preprocess all columns that represent protein measurements

dplyr::mutate(dplyr::across(-ends_with("_id"), (.x) asinh(.x / 5))) |>

plot a CD4 vs. CD45 scatterplot

ggplot2::ggplot(ggplot2::aes(x = CD20(Sm147)Dd, y = CD4(Nd145)Dd)) +

add some reference lines

ggplot2::geom_hline( yintercept = mean_cd4_expression, color = "red", linetype = "dashed" ) + ggplot2::geom_vline( xintercept = mean_cd20_expression, color = "red", linetype = "dashed" ) + ggplot2::geom_point(size = 0.1, alpha = 0.1) +

facet by cell population

ggplot2::facet_wrap( facets = ggplot2::vars(population_id), labeller = ggplot2::as_labeller( (population_id) population_names[as.numeric(population_id)] ) ) +

axis labels

ggplot2::labs( x = "CD20 expression (arcsinh)", y = "CD4 expression (arcsinh)" )

Using some standard functions from the ggplot2 library, we can create a scatterplot of CD4 vs. CD20 expression in the different cell populations included in the bcr_flowset flowSet. We can see, unsurprisingly, that both B-cell populations are highest for CD20 expression, whereas CD4+ T-helper cells are highest for CD4 expression.

Citation

Below is the citation output from running citation('tidyFlowCore') in R. Please run this yourself to check for any updates on how to citetidyFlowCore.

print(citation('tidyFlowCore'), bibtex = TRUE) #> To cite package 'tidyFlowCore' in publications use: #> #> Keyes TJ (2024). tidyFlowCore: Bringing flowCore to the tidyverse. #> doi:10.18129/B9.bioc.tidyFlowCore #> https://doi.org/10.18129/B9.bioc.tidyFlowCore, #> https://github.com/keyes-timothy/tidyflowCore/tidyFlowCore - R #> package version 0.99.1, #> http://www.bioconductor.org/packages/tidyFlowCore. #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {tidyFlowCore: Bringing flowCore to the tidyverse}, #> author = {Timothy J Keyes}, #> year = {2024}, #> url = {http://www.bioconductor.org/packages/tidyFlowCore}, #> note = {https://github.com/keyes-timothy/tidyflowCore/tidyFlowCore - R package version 0.99.1}, #> doi = {10.18129/B9.bioc.tidyFlowCore}, #> }

Please note that the tidyFlowCore was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

Code of Conduct

Please note that the tidyFlowCore project is released with aContributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Development tools

• Continuous code testing is possible thanks to GitHub actionsthrough usethis,remotes, and_rcmdcheck_ customized to use Bioconductor’s docker containers and_BiocCheck_.
• Code coverage assessment is possible thanks tocodecov and_covr_.
• The documentation website is automatically updated thanks to_pkgdown_.
• The code is styled automatically thanks to_styler_.
• The documentation is formatted thanks to_devtools_ and_roxygen2_.

For more details, check the dev directory.

This package was developed using_biocthis_.