# Finding Marker Genes with DeconvoBuddies

# Introduction

## What are Marker Genes?

Cell type marker genes have cell type specific expression, that is high expression in the target cell type, and low expression in all other cell types. Sub-setting the genes considered in a cell type deconvolution analysis helps reduce noise and can improve the accuracy of a deconvolution method.

### How can we select marker genes?

There are several approaches to select marker genes.

One popular method is “1 vs. All” differential expression (Lun et al., n.d.), where genes are tested for differential expression between the target cell type, and a combined group of all “other” cell types. Statistically significant differentially expressed genes (DEGs) can be selected as a set of marker genes, DEGs can be ranked by high log fold change.

However in some cases 1vAll can select genes with high expression in non-target cell types, especially in cell types related to the target cell types (such as Neuron sub-types), or when there is a smaller number of cells in the cell type and the signal is disguised within the other group.

For example, in our snRNA-seq dataset from Human DLPFC (Huuki-Myers et al. 2024) selecting marker gene for the cell type Oligodendrocyte (Oligo), MBP has a high log fold change when testing by 1vALL (see illustration below). But, when the expression of MBP is observed by individual cell types there is also expression in the related cell types Microglia (Micro) and Oligodendrocyte precursor cells (OPC).

Figure 1. 1vALL Marker Gene differential expression vs. MeanRatio method for marker gene selection.

## The Mean Ratio Method

To capture genes with more cell type specific expression and less noise, we developed the Mean Ratio method. The Mean Ratio method works by selecting genes with large differences between gene expression in the target cell type and the closest non-target cell type, by evaluating genes by their `MeanRatio` metric.

We calculate the `MeanRatio` for a target cell type for each gene by dividing the mean expression of the target cell by the mean expression of the next highest non-target cell type. Genes with the highest `MeanRatio` values are selected as marker genes.

In the above example, Oligo is the target cell type. Micro has the highest mean expression out of the other non-target (not Oligo) cell types. The `MeanRatio = (mean expression Oligo) / (mean expression Micro)`, for MBP `MeanRatio` = 2.68 for gene FOLH1 `MeanRatio` is much higher 21.6 showing FOLH1 is the better marker gene (in contrast to ranking by 1vALL log FC). In the violin plots you can see that expression of FOLH1 is much more specific to Oligo than MBP, supporting the ranking by `MeanRatio`.

We have implemented the Mean Ratio method in this R package with the function `get_mean_ratio()`. This vignette will cover our process for marker gene selection.

## Goals of this Vignette

We will be demonstrating how to use `DeconvoBuddies` tools when finding cell type marker genes in single cell RNA-seq data via the MeanRatio method.

1. Install and load required packages
2. Download DLPFC snRNA-seq data
3. Find MeanRatio marker genes with `DeconvoBuddies::get_mean_ratio()`
4. Find 1vALL marker genes with `DeconvoBuddies::findMarkers_1vALL()`
5. Compare marker gene selection
6. Visualize marker genes expression with `DeconcoBuddies::plot_gene_express()` and related functions

# 1. Install and load required packages

`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:

## Install `DeconvoBuddies`

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

    BiocManager::install("DeconvoBuddies")

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

## Load Other Packages

Let’s load the packages will use in this vignette.

    ## Packages for different types of RNA-seq data structures in R
    library("SingleCellExperiment")

    ## For downloading data
    library("spatialLIBD")

    ## Other helper packages for this vignette
    library("dplyr")
    library("ggplot2")

    ## Our main package
    library("DeconvoBuddies")

# 2. Download DLPFC snRNA-seq data.

Here we will download single nucleus RNA-seq data from the Human DLPFC with 77k nuclei x 36k genes (Huuki-Myers et al. 2024). This data is stored in a `SingleCellExperiment` object. The nuclei in this dataset are labeled by cell types at a few resolutions, we will focus on the “broad” resolution that contains seven cell types.

    ## Use spatialLIBD to fetch the snRNA-seq dataset
    sce_path_zip <- fetch_deconvo_data("sce")
    #> 2026-04-29 21:02:47.245673 loading file /github/home/.cache/R/BiocFileCache/27685a8425c5_sce_DLPFC_annotated.zip%3Fdl%3D1

    ## unzip and load the data
    sce_path <- unzip(sce_path_zip, exdir = tempdir())
    sce <- HDF5Array::loadHDF5SummarizedExperiment(
        file.path(tempdir(), "sce_DLPFC_annotated")
    )

    # lobstr::obj_size(sce)
    # 172.28 MB

    ## exclude Ambiguous cell type
    sce <- sce[, sce$cellType_broad_hc != "Ambiguous"]
    sce$cellType_broad_hc <- droplevels(sce$cellType_broad_hc)

    ## Check the broad cell type distribution
    table(sce$cellType_broad_hc)
    #>
    #>     Astro EndoMural     Micro     Oligo       OPC     Excit     Inhib
    #>      3979      2157      1601     10894      1940     24809     11067

    ## We're going to subset to the first 5k genes to save memory
    ## In a real application you'll want to use the full dataset
    sce <- sce[seq_len(5000), ]

    ## check the final dimensions of the dataset
    dim(sce)
    #> [1]  5000 56447

# 3. Find MeanRatio marker genes

To find Mean Ratio marker genes for the data in `sce` we’ll use the function `DeconvoBuddies::get_mean_ratio()`, this function takes a `SingleCellExperiment` object `sce` the name of the column in the `colData(sce)` that contains the cell type annotations of interest (here we’ll use `cellType_broad_hc`), and optionally you can also supply additional column names from the `rowData(sce)` to add the `gene_name` and/or `gene_ensembl` information to the table output of `get_mean_ratio`. While the speedup is notably worse than linear with the number of cores provided, computation can nevertheless be parallelized with a `BPPARAM` argument from `BiocParallel` to save some time.

    # calculate the Mean Ratio of genes for each cell type in sce
    marker_stats_MeanRatio <- get_mean_ratio(
        sce = sce, # sce is the SingleCellExperiment with our data
        assay_name = "logcounts", ## assay to use, we recommend logcounts [default]
        cellType_col = "cellType_broad_hc", # column in colData with cell type info
        gene_ensembl = "gene_id", # column in rowData with ensembl gene ids
        gene_name = "gene_name" # column in rowData with gene names/symbols
    )
    #> Warning in get_mean_ratio(sce = sce, assay_name = "logcounts", cellType_col = "cellType_broad_hc", : Assay is a
    #> DelayedMatrix. For considerably faster marker finding, consider bringing in memory as a dgCMatrix.

The function `get_mean_ratio()` returns a `tibble` with the following columns:

- `gene` is the name of the gene (from rownames(`sce`)).
- `cellType.target` is the cell type we’re finding marker genes for.
- `mean.target` is the mean expression of `gene` for `cellType.target`.
- `cellType.2nd` is the second highest non-target cell type.
- `mean.2nd` is the mean expression of `gene` for `cellType.2nd`.
- `MeanRatio` is the ratio of `mean.target/mean.2nd`.
- `MeanRatio.rank` is the rank of `MeanRatio` for the cell type.
- `MeanRatio.anno` is an annotation of the `MeanRatio` calculation helpful for plotting.
- `gene_ensembl` & `gene_name` optional cols from `rowData(sce)` specified by the user to add gene information

    ## Explore the tibble output
    marker_stats_MeanRatio
    #> # A tibble: 1,721 × 10
    #>    gene     cellType.target mean.target cellType.2nd mean.2nd MeanRatio MeanRatio.rank MeanRatio.anno      gene_ensembl
    #>    <chr>    <fct>                 <dbl> <fct>           <dbl>     <dbl>          <int> <chr>               <chr>
    #>  1 LYPD6B   Inhib                 1.20  Excit          0.0967     12.4               1 Inhib/Excit: 12.385 ENSG00000150…
    #>  2 GREM2    Inhib                 1.20  Excit          0.271       4.44              2 Inhib/Excit: 4.442  ENSG00000180…
    #>  3 IGSF3    Inhib                 0.899 Excit          0.241       3.72              3 Inhib/Excit: 3.724  ENSG00000143…
    #>  4 GALNT14  Inhib                 1.86  Excit          0.503       3.69              4 Inhib/Excit: 3.69   ENSG00000158…
    #>  5 LYPD6    Inhib                 1.09  Astro          0.435       2.51              5 Inhib/Astro: 2.506  ENSG00000187…
    #>  6 SLC35D1  Inhib                 0.781 OPC            0.375       2.08              6 Inhib/OPC: 2.081    ENSG00000116…
    #>  7 FLVCR1   Inhib                 0.576 Excit          0.336       1.72              7 Inhib/Excit: 1.715  ENSG00000162…
    #>  8 RAVER2   Inhib                 1.09  OPC            0.651       1.68              8 Inhib/OPC: 1.676    ENSG00000162…
    #>  9 ARHGEF11 Inhib                 1.81  Excit          1.10        1.64              9 Inhib/Excit: 1.641  ENSG00000132…
    #> 10 VAV3     Inhib                 1.68  Astro          1.03        1.63             10 Inhib/Astro: 1.631  ENSG00000134…
    #> # ℹ 1,711 more rows
    #> # ℹ 1 more variable: gene_name <chr>

    ## genes with the highest MeanRatio are the best marker genes for each cell type
    marker_stats_MeanRatio |>
        filter(MeanRatio.rank == 1)
    #> # A tibble: 7 × 10
    #>   gene  cellType.target mean.target cellType.2nd mean.2nd MeanRatio MeanRatio.rank MeanRatio.anno gene_ensembl gene_name
    #>   <chr> <fct>                 <dbl> <fct>           <dbl>     <dbl>          <int> <chr>          <chr>        <chr>
    #> 1 LYPD… Inhib                  1.20 Excit          0.0967     12.4               1 Inhib/Excit: … ENSG0000015… LYPD6B
    #> 2 AC01… Oligo                  2.37 OPC            0.147      16.1               1 Oligo/OPC: 16… ENSG0000022… AC012494…
    #> 3 MIR3… OPC                    1.58 Excit          0.205       7.69              1 OPC/Excit: 7.… ENSG0000022… MIR3681HG
    #> 4 AC01… Excit                  1.01 Inhib          0.135       7.51              1 Excit/Inhib: … ENSG0000023… AC011995…
    #> 5 PRDM… Astro                  1.97 EndoMural      0.142      13.9               1 Astro/EndoMur… ENSG0000014… PRDM16
    #> 6 SLC2… EndoMural              1.49 Astro          0.146      10.2               1 EndoMural/Ast… ENSG0000011… SLC2A1
    #> 7 LINC… Micro                  1.57 Excit          0.0640     24.5               1 Micro/Excit: … ENSG0000023… LINC01141

# 4. Find 1vALL marker genes

To further explore cell type marker genes it can be helpful to also calculate the 1vALL stats for the dataset. To help with this we have included the function `DeconvoBuddies::findMarkers_1vALL()`, which is a wrapper for `scran::findMarkers()` that iterates through cell types and creates an table output in a compatible with the output `DeconvoBuddies::get_mean_ratio()`.

Similarity to `get_mean_ratio` this function requires the `sce` object, `cellType_col` to define cell types, and `assay_name`. But `findMarkers_1vALL()` also takes a model (`mod`) to use as design in `scran::findMarkers()`, in this example we will control for donor which is stored as `BrNum`.

Note this function can by default take a bit of time to run. However, the computation can be parallelized by cell type with a nearly linear speedup with the number of provided cores, by providing a `BPPARAM` argument as supported through `BiocParallel`. We have saved the output as `data("marker_stats_1vAll")` to speed-up the runtime of this vignette.

    ## Run 1vALL DE to find markers for each cell type - takes 5min+

    # marker_stats_1vAll <- findMarkers_1vAll(
    #     sce = sce, # sce is the SingleCellExperiment with our data
    #     assay_name = "counts",
    #     cellType_col = "cellType_broad_hc", # column in colData with cell type info
    #     mod = "~BrNum" # Control for donor stored in "BrNum" with mod
    #     raw_logFC = TRUE # also retain raw logFC in addition to standardized
    # )

    ## load 1vAll data to save time, data is equivalent to the above code
    data("marker_stats_1vAll")

The function `findMarkers_1vALL()` returns a `tibble` with the following columns:

- `gene` is the name of the gene (from rownames(`sce`)).
- `std.logFC` the standard logFC
- `log.p.value` the log of the p-value of the DE test
- `log.FDR` the log of the False Discovery Rate adjusted p.value
- `logFC` the log fold change from the DE test, added when `raw_logFC=TRUE`
- `cellType.target` the cell type we’re finding marker genes for
- `std.logFC.rank` the rank of `std.logFC` for each cell type
- `std.logFC.anno` is an annotation of the `std.logFC` value helpful for plotting.

    ## Explore the tibble output
    marker_stats_1vAll
    #> # A tibble: 35,000 × 8
    #> # Groups:   cellType.target [7]
    #>    gene    logFC log.p.value log.FDR std.logFC cellType.target std.logFC.rank std.logFC.anno
    #>    <chr>   <dbl>       <dbl>   <dbl>     <dbl> <fct>                    <int> <chr>
    #>  1 SILC1   2.77       -6747.  -6739.      1.42 Inhib                        1 " std logFC = 1.423"
    #>  2 LYPD6B  2.48       -6338.  -6330.      1.37 Inhib                        2 " std logFC = 1.374"
    #>  3 LYPD6   2.18       -6147.  -6140.      1.35 Inhib                        3 " std logFC = 1.351"
    #>  4 ALK     4.56       -5707.  -5700.      1.30 Inhib                        4 " std logFC = 1.296"
    #>  5 WLS     3.26       -5111.  -5104.      1.22 Inhib                        5 " std logFC = 1.22"
    #>  6 PLD5    5.85       -4711.  -4704.      1.17 Inhib                        6 " std logFC = 1.167"
    #>  7 KIRREL1 1.04       -4539.  -4532.      1.14 Inhib                        7 " std logFC = 1.143"
    #>  8 TRIM67  0.622      -4326.  -4319.      1.11 Inhib                        8 " std logFC = 1.114"
    #>  9 GREM2   2.11       -4048.  -4042.      1.07 Inhib                        9 " std logFC = 1.075"
    #> 10 IGSF3   1.27       -3783.  -3777.      1.04 Inhib                       10 " std logFC = 1.037"
    #> # ℹ 34,990 more rows

    ## genes with the highest MeanRatio are the best marker genes for each cell type
    marker_stats_1vAll |>
        filter(std.logFC.rank == 1)
    #> # A tibble: 7 × 8
    #> # Groups:   cellType.target [7]
    #>   gene       logFC log.p.value log.FDR std.logFC cellType.target std.logFC.rank std.logFC.anno
    #>   <chr>      <dbl>       <dbl>   <dbl>     <dbl> <fct>                    <int> <chr>
    #> 1 SILC1      2.77       -6747.  -6739.      1.42 Inhib                        1 " std logFC = 1.423"
    #> 2 RNF220    10.7        -8373.  -8365.      1.52 Oligo                        1 " std logFC = 1.524"
    #> 3 BX284613   7.42      -12763. -12754.      4.20 OPC                          1 " std logFC = 4.204"
    #> 4 KIAA1211L  7.89       -8371.  -8362.      1.23 Excit                        1 " std logFC = 1.23"
    #> 5 PRDM16     1.90       -8853.  -8844.      2.38 Astro                        1 " std logFC = 2.383"
    #> 6 EPAS1      6.50       -9480.  -9471.      3.31 EndoMural                    1 " std logFC = 3.308"
    #> 7 CSF3R      0.808      -9670.  -9661.      3.86 Micro                        1 " std logFC = 3.859"

As this is a differential expression test, you can create volcano plots to explore the outputs. 🌋

Note that with the default option “up” for direction only up-regulated genes are considered marker candidates, so all genes with logFC<1 will have a p.value=0. As a results these plots will only have the right half of the volcano shape. If you’d like all p-values set `findMarkers_1vALL(direction="any")`.

    # Create volcano plots of DE stats from 1vALL
    marker_stats_1vAll |>
        ggplot(aes(logFC, -log.p.value)) +
        geom_point() +
        facet_wrap(~cellType.target) +
        geom_vline(xintercept = c(1, -1), linetype = "dashed", color = "red")

# 5. Compare Marker Gene Selection

Let’s join the two `marker_stats` tables together to compare the findings of the two methods.

Note as we are using a subset of data for this example, for some genes there is not enough data to test and 1vALL will have some missing results.

    ## join the two marker_stats tables
    marker_stats <- marker_stats_MeanRatio |>
        left_join(marker_stats_1vAll, by = join_by(gene, cellType.target))

    ## Check stats for our top genes
    marker_stats |>
        filter(MeanRatio.rank == 1) |>
        select(gene, cellType.target, MeanRatio, MeanRatio.rank, std.logFC, std.logFC.rank)
    #> # A tibble: 7 × 6
    #>   gene       cellType.target MeanRatio MeanRatio.rank std.logFC std.logFC.rank
    #>   <chr>      <fct>               <dbl>          <int>     <dbl>          <int>
    #> 1 LYPD6B     Inhib               12.4               1      1.37              2
    #> 2 AC012494.1 Oligo               16.1               1     NA                NA
    #> 3 MIR3681HG  OPC                  7.69              1      1.42             11
    #> 4 AC011995.2 Excit                7.51              1     NA                NA
    #> 5 PRDM16     Astro               13.9               1      2.38              1
    #> 6 SLC2A1     EndoMural           10.2               1      2.30              4
    #> 7 LINC01141  Micro               24.5               1      2.07              8

## Hockey Stick Plots

Plotting the values of Mean Ratio vs. standard log fold change (from 1vAll) we create what we call “hockey stick plots” 🏒. These plots help visualize the distribution of `MeanRatio` and standard `logFC` values.

Figure 2. Hockey Stick plots compare values of 1vALL logFC vs. MeanRatio and help select marker genes.

Typically for a cell type see most genes have low Mean Ratio and low fold change, these genes are not marker genes (red box in illustration above on the bottom left side).

Genes with higher fold change from 1vALL are better marker gene candidates, but most have low MeanRatio values indicating that one or more non-target cell types have high expression for that gene, causing noise (orange box on the top left side).

Genes with high MeanRatio typically also have high 1vALL standard fold changes, these are cell type specific marker genes we are selecting for (green box on the top right side).

    # create hockey stick plots to compare MeanRatio and standard logFC values.
    marker_stats |>
        ggplot(aes(MeanRatio, std.logFC)) +
        geom_point() +
        facet_wrap(~cellType.target)
    #> Warning: Removed 29 rows containing missing values or values outside the scale range (`geom_point()`).

We can see a “hockey stick” shape in most of the cell types, with a few marker genes with high values for both `logFC` and `MeanRatio`.

# 6. Visualize Marker Genes Expression

An important step for ensuring you have selected high quality marker genes is to visualize their expression over the cell types in the dataset. `DeconvoBuddies` has several functions to help quickly plot gene expression at a few levels:

`plot_gene_express()` plots the expression of one or more genes as a violin plot.

    ## plot expression of two genes from a list
    plot_gene_express(
        sce = sce,
        category = "cellType_broad_hc",
        genes = c("SLC2A1", "CREG2")
    )

`plot_marker_express()` plots the top n marker genes for a specified cell type based on the values from `marker_stats`. Annotations for the details of the MeanRatio value + calculation are added to each panel.

    # plot the top 10 MeanRatio genes for Excit
    plot_marker_express(
        sce = sce,
        stats = marker_stats,
        cell_type = "Excit",
        n_genes = 10,
        cellType_col = "cellType_broad_hc"
    )

In these violin plots we can see these genes have high expression in the target cell type Excit , and mostly low expression nuclei in the other cell types, sometimes even no expression.

This function defaults to selecting genes by the MeanRatio stats, but can also be used to plot the 1vAll genes.

    # plot the top 10 1vAll genes for Excit
    plot_marker_express(
        sce = sce,
        stats = marker_stats,
        cell_type = "Excit",
        n_genes = 10,
        rank_col = "std.logFC.rank", ## use logFC cols from 1vALL
        anno_col = "std.logFC.anno",
        cellType_col = "cellType_broad_hc"
    )

We can see in the top 1vALL genes there is some expression of these genes in Inhib nuclei in addition to the target cell type Excit.

`plot_marker_express_ALL()` plots the top marker genes for all cell types. This is a quick and easy way to look at the the top markers in your dataset, which is an important step and can help identify genes with multimodal distributions that may confound the MeanRatio method.

    # plot the top 10 1vAll genes for all cell types
    print(plot_marker_express_ALL(
        sce = sce,
        stats = marker_stats,
        n_genes = 10,
        cellType_col = "cellType_broad_hc"
    ))

The violin plots can also be directly printed to a PDF file using the built in argument `plot_marker_express_ALL(pdf = "my_marker_genes.pdf")` for portability and easy sharing.

# Summary

In this vignette we covered the importance of finding marker genes, and introduced our method for finding cell type specific genes MeanRatio. We covered how to find and compare MeanRatio marker genes with `get_mean_ratio()`, and 1vALL marker genes with `findMarkers_1vALL()`. And finally, how to visualize the expression of these marker genes with `plot_marker_express()` and related functions.

Next Steps

For an example of how to use these marker genes in a deconvolution workflow, check out Vignette: Deconvolution Benchmark in Human DLPFC.

We hope this vignette and DeconvoBuddies helps you with your research goals! Thanks for reading 😁

# Reproducibility

The DeconvoBuddies package (Huuki-Myers, Maynard, Hicks et al., 2026) was made possible thanks to:

- R (R Core Team, 2026)
- BiocStyle (Oleś, 2026)
- knitr (Xie, 2025)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux et al., 2026)
- sessioninfo (Wickham, Chang, Flight et al., 2025)
- testthat (Wickham, 2011)

This package was developed using biocthis.

`R` session information.

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    #>  version  R version 4.6.0 (2026-04-24)
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    #> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

# Bibliography

This vignette was generated using BiocStyle (Oleś, 2026) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux et al., 2026) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.31. 2026. URL: https://github.com/rstudio/rmarkdown.

[2] L. A. Huuki-Myers, K. R. Maynard, S. C. Hicks, et al. DeconvoBuddies: a R/Bioconductor package with deconvolution helper functions. https://github.com/LieberInstitute/DeconvoBuddies/DeconvoBuddies - R package version 1.4.0. 2026. DOI: 10.18129/B9.bioc.DeconvoBuddies. URL: http://www.bioconductor.org/packages/DeconvoBuddies.

[3] M. W. McLean. “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software (2017). DOI: 10.21105/joss.00338.

[4] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.40.0. 2026. URL: https://github.com/Bioconductor/BiocStyle.

[5] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (ROR: <https://ror.org/05qewa988>;). Vienna, Austria, 2026. DOI: 10.32614/R.manuals. URL: https://www.R-project.org/.

[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/articles/RJ-2011-002/.

[7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.3. 2025. URL: https://github.com/r-lib/sessioninfo#readme.

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

Huuki-Myers, Louise A., Abby Spangler, Nicholas J. Eagles, et al. 2024. “A Data-Driven Single-Cell and Spatial Transcriptomic Map of the Human Prefrontal Cortex.” Science 384 (6698): eadh1938. https://doi.org/10.1126/science.adh1938.
Lun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. n.d. A Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-Seq Data with Bioconductor. https://doi.org/10.12688/f1000research.9501.2.