# Get Started with DeconvoBuddies

# Introduction

`DeconvoBuddies` is an R package developed to assist in running bulk RNA-seq deconvolution. This package provides functions to access a data set designed to evaluate deconvolution method performance, find marker genes, and create plots useful in deconvolution.

This package is associated with “Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex” from Huuki-Myers et al. (10.1101/2024.02.09.579665v2)[https://www.biorxiv.org/content/10.1101/2024.02.09.579665v2].

# Basics

## Install `DeconvoBuddies`

`R` is an open-source statistical environment which can be easily modified to enhance its functionality via packages. DeconvoBuddies is a `R` package available via the Bioconductor repository for packages. `R` can be installed on any operating system from CRAN after which you can install DeconvoBuddies by using the following commands in your `R` session:

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

    BiocManager::install("DeconvoBuddies")

    ## Check that you have a valid Bioconductor installation
    BiocManager::valid()

## Required knowledge

DeconvoBuddies is based on many other packages and in particular in those that have implemented the infrastructure needed for dealing with snRNA-seq data. That is, packages like SingleCellExperiment.

If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.

## Asking for help

As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But `R` and `Bioconductor` have a steep learning curve so it is critical to learn where to ask for help. The blog post quoted above mentions some but we would like to highlight the Bioconductor support site as the main resource for getting help: remember to use the `DeconvoBuddies` tag and check the older posts. Other alternatives are available such as creating GitHub issues and tweeting. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.

## Citing `DeconvoBuddies`

We hope that DeconvoBuddies will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

    ## Citation info
    citation("DeconvoBuddies")
    #> To cite package 'DeconvoBuddies' in publications use:
    #>
    #>   Huuki-Myers LA, Maynard KR, Hicks SC, Zandi P, Kleinman JE, Hyde TM,
    #>   Goes FS, Collado-Torres L (2026). _DeconvoBuddies: a R/Bioconductor
    #>   package with deconvolution helper functions_.
    #>   doi:10.18129/B9.bioc.DeconvoBuddies
    #>   <https://doi.org/10.18129/B9.bioc.DeconvoBuddies>.
    #>   https://github.com/LieberInstitute/DeconvoBuddies/DeconvoBuddies - R
    #>   package version 1.5.0,
    #>   <http://www.bioconductor.org/packages/DeconvoBuddies>.
    #>
    #>   Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ,
    #>   Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard
    #>   KR, Collado-Torres L (2025). "Benchmark of cellular deconvolution
    #>   methods using a multi-assay dataset from postmortem human prefrontal
    #>   cortex." _Genome Biol_. doi:10.1186/s13059-025-03552-3
    #>   <https://doi.org/10.1186/s13059-025-03552-3>.
    #>   <https://doi.org/10.1186/s13059-025-03552-3>.
    #>
    #> To see these entries in BibTeX format, use 'print(<citation>,
    #> bibtex=TRUE)', 'toBibtex(.)', or set
    #> 'options(citation.bibtex.max=999)'.

# Quick start to using `DeconvoBuddies`

Let’s load some packages we’ll use in this vignette.

    suppressMessages({
        library("DeconvoBuddies")
        library("SummarizedExperiment")
        library("dplyr")
        library("tidyr")
        library("tibble")
    })

## Access Data

Use `fetch_deconvo_data` to download RNA sequencing data from the Human DLPFC (Huuki-Myers, Montgomery, Kwon, Cinquemani, Eagles, Gonzalez-Padilla, Maden, Kleinman, Hyde, Hicks, Maynard, and Collado-Torres, 2025).

- `rse_gene`: 110 samples of bulk RNA-seq. [110 bulk RNA-seq samples x 21k genes] (41 MB).

- `sce` : snRNA-seq data from the Human DLPFC. [77k nuclei x 36k genes] (172 MB)

- `sce_DLPFC_example`: Sub-set of `sce` useful for testing. [10k nuclei x 557 genes] (49 MB)

    ## Access and snRNA-seq example data
    if (!exists("sce_DLPFC_example")) sce_DLPFC_example <- fetch_deconvo_data("sce_DLPFC_example")
    #> 2026-05-28 06:25:46.722881 Access ExperimentHub EH9626
    #> see ?DeconvoBuddies and browseVignettes('DeconvoBuddies') for documentation
    #> downloading 1 resources
    #> retrieving 1 resource
    #> loading from cache
    #> require("SingleCellExperiment")

    ## Explore snRNA-seq data in sce_DLPFC_example
    sce_DLPFC_example
    #> class: SingleCellExperiment
    #> dim: 557 10000
    #> metadata(3): Samples cell_type_colors cell_type_colors_broad
    #> assays(1): logcounts
    #> rownames(557): GABRD PRDM16 ... AFF2 MAMLD1
    #> rowData names(7): source type ... gene_type binomial_deviance
    #> colnames(10000): 8_AGTGACTGTAGTTACC-1 17_GCAGCCAGTGAGTCAG-1 ...
    #>   12_GGACGTCTCTGACAGT-1 1_GGTTAACTCTCTCTAA-1
    #> colData names(32): Sample Barcode ... cellType_layer layer_annotation
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):

    ## Access Bulk RNA-seq data
    if (!exists("rse_gene")) rse_gene <- fetch_deconvo_data("rse_gene")
    #> 2026-05-28 06:25:51.478061 Access ExperimentHub EH9625
    #> see ?DeconvoBuddies and browseVignettes('DeconvoBuddies') for documentation
    #> downloading 1 resources
    #> retrieving 1 resource
    #> loading from cache

    ## Explore bulk data in rse_gene
    rse_gene
    #> class: RangedSummarizedExperiment
    #> dim: 21745 110
    #> metadata(1): SPEAQeasy_settings
    #> assays(2): counts logcounts
    #> rownames(21745): ENSG00000227232.5 ENSG00000278267.1 ...
    #>   ENSG00000210195.2 ENSG00000210196.2
    #> rowData names(11): Length gencodeID ... gencodeTx passExprsCut
    #> colnames(110): 2107UNHS-0291_Br2720_Mid_Bulk
    #>   2107UNHS-0291_Br2720_Mid_Cyto ... AN00000906_Br8667_Mid_Cyto
    #>   AN00000906_Br8667_Mid_Nuc
    #> colData names(80): SAMPLE_ID Sample ... diagnosis qc_class

For more details on this dataset, and an example deconvolution run check out the Vignette: Deconvolution Benchmark in Human DLPFC.

## Marker Finding

### Using MeanRatio to Find Cell Type Markers

Accurate deconvolution requires highly specific marker genes for each cell type to be defined. To select genes specific for each cell type, you can evaluate the MeanRatio for each gene x each cell type, where `MeanRatio = mean(Expression of target cell type) / mean(Expression of highest non-target cell type)`.

These values can be calculated for a single cell RNA-seq dataset using `get_mean_ratio()`. This can also work for spatially-resolved transcriptomics datasets. That is, `get_mean_ratio()` can also work with `SpatialExperiment::SpatialExperiment()` objects.

    ## find marker genes with get_mean_ratio
    marker_stats <- get_mean_ratio(
        sce_DLPFC_example,
        cellType_col = "cellType_broad_hc",
        gene_name = "gene_name",
        gene_ensembl = "gene_id"
    )

    ## explore tibble output, gene with high MeanRatio values are good marker genes
    marker_stats
    #> # A tibble: 762 × 10
    #>    gene       cellType.target mean.target cellType.2nd mean.2nd MeanRatio
    #>    <chr>      <fct>                 <dbl> <fct>           <dbl>     <dbl>
    #>  1 CD22       Oligo                  1.36 OPC            0.0730      18.6
    #>  2 LINC01608  Oligo                  2.39 Micro          0.142       16.8
    #>  3 FOLH1      Oligo                  1.59 OPC            0.101       15.7
    #>  4 SLC5A11    Oligo                  2.14 Micro          0.145       14.7
    #>  5 AC012494.1 Oligo                  2.42 OPC            0.169       14.3
    #>  6 ST18       Oligo                  4.65 OPC            0.329       14.1
    #>  7 MAG        Oligo                  1.44 Astro          0.103       14.0
    #>  8 ANLN       Oligo                  1.60 Micro          0.115       13.9
    #>  9 CLDN11     Oligo                  1.82 EndoMural      0.146       12.5
    #> 10 MOG        Oligo                  2.06 OPC            0.185       11.1
    #> # ℹ 752 more rows
    #> # ℹ 4 more variables: MeanRatio.rank <int>, MeanRatio.anno <chr>,
    #> #   gene_ensembl <chr>, gene_name <chr>

For more discussion of finding marker genes with `DeconvoBuddies` check out the Vignette: Finding Marker Genes with DeconvoBuddies.

## Plotting Tools

### Creating A Cell Type Color palette

As you work with single-cell data and deconvolution outputs, it is very useful to establish a consistent color palette to use across different plots. The function `create_cell_colors()` returns a named vector of hex values, corresponding to the names of cell types. This list is compatible with functions like `ggplot2::scale_color_manual()`.

There are three palettes to choose from to generate colors or users can provide their own color palette:

- “classic” (default): classic set of 8 cell type colors from LIBD, checked for visability and color blind accessibility.

- “gg”: Equi-distant hues, same process for selecting colors as `ggplot` - no maximum number

- “tableau”: tableau20 color set - max 20 colors

    test_cell_types <- c("cell_A", "cell_B", "cell_C", "cell_D", "cell_E")

    ## Preview "classic" colors
    test_cell_colors_classic <- create_cell_colors(
        cell_types = test_cell_types,
        palette_name = "classic",
        preview = TRUE
    )
    #> Creating classic palette for 5 broad cell types

    ## Preview "gg" colors
    test_cell_colors_gg <- create_cell_colors(
        cell_types = test_cell_types,
        palette_name = "gg",
        preview = TRUE
    )
    #> Creating gg palette for 5 broad cell types

    ## Preview "tableau" colors
    test_cell_colors_tableau <- create_cell_colors(
        cell_types = test_cell_types,
        palette_name = "tableau",
        preview = TRUE
    )
    #> Creating tableau palette for 5 broad cell types

    ## Check the color hex codes for "tableau"
    test_cell_colors_tableau
    #>    cell_A    cell_B    cell_C    cell_D    cell_E
    #> "#1F77B4" "#AEC7E8" "#FF7F0E" "#FFBB78" "#2CA02C"

    ## Provide a palette from RColorBrewer
    test_cell_colors_brew <- create_cell_colors(
        cell_types = test_cell_types,
        palette = RColorBrewer::brewer.pal(n = length(test_cell_types), name = "Dark2"),
        preview = TRUE
    )
    #> Creating custom palette for 5 broad cell types

If there are sub-cell types with consistent delimiters, the `split` argument creates a scale of related colors. This helps expand on the maximum number of colors and makes your palette flexible when considering different ‘resolutions’ of cell types. This works by ignoring any prefixes after the `split` character. In this example below, `Excit_01` and `Excit_02` will just be considered as `Excit` since `split = "_"`.

    my_cell_types <- levels(sce_DLPFC_example$cellType_hc)
    ## Ignore any suffix after the "_" character by using the "split" argument
    my_cell_colors <- create_cell_colors(
        cell_types = my_cell_types,
        palette_name = "classic",
        preview = TRUE,
        split = "_"
    )
    #> Creating classic palette for 8 broad cell types
    #> Creating fine cell type gradients for 4 cell types

### Plot Expression of Top Markers

The function `plot_marker_express()` helps quickly visualize expression of top marker genes, by ordering and annotating violin plots of expression over cell type. Here we’ll plot the expression of the top 6 marker genes for Astrocytes.

    # plot expression of the top 6 Astro marker genes
    plot_marker_express(
        sce = sce_DLPFC_example,
        stats = marker_stats,
        cell_type = "Astro",
        n_genes = 6,
        cellType_col = "cellType_broad_hc",
        color_pal = my_cell_colors
    )
    #> Warning: No shared levels found between `names(values)` of the manual scale and the
    #> data's fill values.
    #> No shared levels found between `names(values)` of the manual scale and the
    #> data's fill values.

The violin plots of gene expression confirm the cell type specificity of these marker genes, most of the nuclei with high expression of these six genes are astrocytes (Astro).

### Plot Composition Bar Plot

The output of deconvolution are cell type estimates that sum to 1. A good visulization for these predictions is a stacked bar plot. The function `plot_composition_bar()` creates a stacked bar plot showing the cell type proportion for each sample, or the average proportion for a group of samples. In this example data, the `RNum` is a sample (donor) identifier and `Dx` is a group variable for the diagnosis status of the donors.

    # load example data
    data("rse_bulk_test")
    data("est_prop_test")

    # access the colData of a test rse dataset
    pd <- colData(rse_bulk_test) |>
        as.data.frame()

    ## pivot data to long format and join with test estimated proportion data
    est_prop_long <- est_prop_test |>
        rownames_to_column("RNum") |>
        pivot_longer(!RNum, names_to = "cell_type", values_to = "prop") |>
        left_join(pd)
    #> Joining with `by = join_by(RNum)`

    ## explore est_prop_long
    est_prop_long
    #> # A tibble: 500 × 7
    #>    RNum  cell_type    prop BrNum Sex   Dx        Age
    #>    <chr> <chr>       <dbl> <chr> <chr> <chr>   <dbl>
    #>  1 R913  cell_A    0.431   Br001 F     Case     71.9
    #>  2 R913  cell_B    0.242   Br001 F     Case     71.9
    #>  3 R913  cell_C    0.110   Br001 F     Case     71.9
    #>  4 R913  cell_D    0.174   Br001 F     Case     71.9
    #>  5 R913  cell_E    0.0426  Br001 F     Case     71.9
    #>  6 R602  cell_A    0.286   Br002 F     Control  73.1
    #>  7 R602  cell_B    0.392   Br002 F     Control  73.1
    #>  8 R602  cell_C    0.207   Br002 F     Control  73.1
    #>  9 R602  cell_D    0.00186 Br002 F     Control  73.1
    #> 10 R602  cell_E    0.113   Br002 F     Control  73.1
    #> # ℹ 490 more rows

    ## the composition bar plot shows cell type composition for Sample
    plot_composition_bar(est_prop_long,
        x_col = "RNum",
        add_text = FALSE
    ) +
        ggplot2::scale_fill_manual(values = test_cell_colors_classic)

    ## the composition bar plot shows the average cell type composition for each Dx
    plot_composition_bar(est_prop_long, x_col = "Dx") +
        ggplot2::scale_fill_manual(values = test_cell_colors_classic)

We can see that the mean proportions of cell types A through E are very similar across the `Dx` groups (`Case` and `Control`). In this case, this is expected given that we are using simulated data. Although if you look across each donor with `RNum` we can see more variability across the simulated data.

Since you are now familiar with the basic overview of `DeconvoBuddies`, you are now ready to dive deeper into:

- the selection of marker genes,
- followed by the application of `DeconvoBuddies` to the Human Brain (DLPFC) Deconvolution dataset.

# Reproducibility

The DeconvoBuddies package (Huuki-Myers, Maynard, Hicks, Zandi, Kleinman, Hyde, Goes, and Collado-Torres, 2026) was made possible thanks to:

- R (R Core Team, 2026)
- AnnotationHub (Morgan and Shepherd, 2026)
- BiocFileCache (Shepherd and Morgan, 2026)
- DelayedMatrixStats (Hickey, 2026)
- dplyr (Wickham, François, Henry, Müller, and Vaughan, 2026)
- ExperimentHub (Morgan and Shepherd, 2026)
- ggplot2 (Wickham, 2016)
- graphics (R Core Team, 2026)
- grDevices (R Core Team, 2026)
- MatrixGenerics (Ahlmann-Eltze, Hickey, and Pagès, 2026)
- methods (R Core Team, 2026)
- purrr (Wickham and Henry, 2026)
- rafalib (Irizarry and Love, 2025)
- reshape2 (Wickham, 2007)
- S4Vectors (Pagès, Lawrence, and Aboyoun, 2026)
- scran (Lun, McCarthy, and Marioni, 2016)
- SingleCellExperiment (Amezquita, Lun, Becht, Carey, Carpp, Geistlinger, Marini, Rue-Albrecht, Risso, Soneson, Waldron, Pages, Smith, Huber, Morgan, Gottardo, and Hicks, 2020)
- spatialLIBD (Pardo, Spangler, Weber, Hicks, Jaffe, Martinowich, Maynard, and Collado-Torres, 2022)
- stats (R Core Team, 2026)
- stringr (Wickham, 2025)
- SummarizedExperiment (Morgan, Obenchain, Hester, and Pagès, 2026)
- tibble (Müller and Wickham, 2026)
- utils (R Core Team, 2026)

This vignette was generated using BiocStyle (Oleś, 2026) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2026) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

This package was developed using biocthis.

`R` session information.

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[11] M. Morgan and L. Shepherd. AnnotationHub: Client to access AnnotationHub resources. R package version 4.3.0. 2026.

[12] M. Morgan and L. Shepherd. ExperimentHub: Client to access ExperimentHub resources. R package version 3.3.0. 2026. URL: https://github.com/Bioconductor/ExperimentHub.

[13] K. Müller and H. Wickham. tibble: Simple Data Frames. R package version 3.3.1. 2026. URL: https://tibble.tidyverse.org/.

[14] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.41.0. 2026. URL: https://github.com/Bioconductor/BiocStyle.

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[22] H. Wickham, R. François, L. Henry, et al. dplyr: A Grammar of Data Manipulation. R package version 1.2.1. 2026. URL: https://dplyr.tidyverse.org.

[23] H. Wickham and L. Henry. purrr: Functional Programming Tools. R package version 1.2.2. 2026. URL: https://purrr.tidyverse.org/.

[24] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.51. 2025. URL: https://yihui.org/knitr/.