In LieberInstitute/DeconvoBuddies: Helper Functions for LIBD Deconvolution

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "man/figures/README-",
    out.width = "100%"
)

DeconvoBuddies

The goal of DeconvoBuddies is to provide helper functions for the deconvolution process, as well as a paired dataset designed to test the performance of deconvolution arguments. The dataset is from Human DLPFC, and contains bulk RNA-seq, single nucleus RNA-seq, and estimated cell type proportions from RNAScope/IF.

Installation instructions

Get the latest stable R release from CRAN. Then install DeconvoBuddies using from Bioconductor the following code:

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

BiocManager::install("DeconvoBuddies")

And the development version from GitHub with:

BiocManager::install("LieberInstitute/DeconvoBuddies")

Application of DeconvoBuddies

suppressMessages({
    library("DeconvoBuddies")
})

Access Datasets

DeconvoBuddies contains paired snRNA-seq, bulk RNA-seq, and cell type proportion data from the human DLPFC from this study.

## Access data with fetch_deconvo_data
sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")

## explore the single cell experiment object
sce_DLPFC_example

Find Marker Genes and Visualize Expression

DeconvoBuddies has tools for finding marker genes ideal for deconvolution, and plotting functions to quickly visualize the expression of selected genes in a snRNA-seq data.

plot_gene_express(
    sce = sce_DLPFC_example,
    category = "cellType_broad_hc",
    genes = c("GAD2", "CD22")
)

Plot Deconvoltion Cell Type Proportions

Create composition bar plots of predicted cell type proportions.

set.seed(123)
## load example data
data("est_prop")

## pivot data to long format and join with test estimated proportion data
est_prop_long <- est_prop |>
    tibble::rownames_to_column("RNum") |>
    tidyr::pivot_longer(!RNum, names_to = "cell_type", values_to = "prop")

## composition bar plot
plot_composition_bar(
    est_prop_long |>
        dplyr::filter(RNum %in% sample(rownames(est_prop), 10)),
    x_col = "RNum"
)

Citation

Below is the citation output from using citation('DeconvoBuddies') in R. Please run this yourself to check for any updates on how to cite DeconvoBuddies.

print(citation("DeconvoBuddies"), bibtex = TRUE)

Please note that the DeconvoBuddies 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 DeconvoBuddies project is released with a Contributor 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 actions through r BiocStyle::CRANpkg('usethis'), r BiocStyle::CRANpkg('remotes'), and r BiocStyle::CRANpkg('rcmdcheck') customized to use Bioconductor's docker containers and r BiocStyle::Biocpkg('BiocCheck').
Code coverage assessment is possible thanks to codecov and r BiocStyle::CRANpkg('covr').
The documentation website is automatically updated thanks to r BiocStyle::CRANpkg('pkgdown').
The code is styled automatically thanks to r BiocStyle::CRANpkg('styler').
The documentation is formatted thanks to r BiocStyle::CRANpkg('devtools') and r BiocStyle::CRANpkg('roxygen2').