An introduction to Protein2Tissue

Introduction

Protein2Tissue is a Bioconductor package for tissue enrichment analysis of protein and gene lists. Given a set of proteins or genes of interest and a background set, the package identifies which tissues are significantly over-represented using statistical tests. Results can be explored through a suite of visualisation functions including heatmaps, bar charts, network plots, and GO enrichment analysis.

Gene expression reference data are derived from the Human Protein Atlas (HPA), which classifies genes into three tissue-specific expression categories:

  • Tissue enriched — expression is significantly higher in one tissue compared to all others
  • Group enriched — elevated expression in a small group of tissues
  • Tissue enhanced — expression is higher in one tissue relative to the average across all tissues
  • Not detect - no expression detected in any tissue

Installation

if (!requireNamespace("remotes", quietly = TRUE)) {
    install.packages("remotes")
}
remotes::install_github("ChrYang/Protein2Tissue")

Once accepted to Bioconductor, the package will also be installable via:

# not yet available - coming soon
BiocManager::install("Protein2Tissue")

Loading the package

library(dplyr)
library(ggplot2)
library(Protein2Tissue)

Input data

Reference database

The package ships with ee_tb, an example gene/protein tissue expression data frame derived from the Human Protein Atlas. It contains tissue-specific expression classifications for human genes. It can be inspected directly:

data("ee_tb", package = "Protein2Tissue")
head(ee_tb)
#>       Gene         Ensembl EntrezID Uniprot Is_secreted     Tissue.specificity
#> 1   TSPAN6 ENSG00000000003     7105  O43657       FALSE Low tissue specificity
#> 2     TNMD ENSG00000000005    64102  Q9H2S6       FALSE        Tissue enhanced
#> 3     DPM1 ENSG00000000419     8813  O60762       FALSE Low tissue specificity
#> 4    SCYL3 ENSG00000000457    57147  Q8IZE3       FALSE Low tissue specificity
#> 5 C1orf112 ENSG00000000460    55732  Q9NSG2       FALSE        Tissue enhanced
#> 6      FGR ENSG00000000938     2268  P09769       FALSE         Group enriched
#>    Adipose tissue          Breast Seminal vesicle Parathyroid gland
#> 1            <NA>            <NA>            <NA>              <NA>
#> 2 Tissue enhanced Tissue enhanced Tissue enhanced              <NA>
#> 3            <NA>            <NA>            <NA>              <NA>
#> 4            <NA>            <NA>            <NA>              <NA>
#> 5            <NA>            <NA>            <NA>   Tissue enhanced
#> 6            <NA>            <NA>            <NA>              <NA>
#>      Bone marrow           Lung Lymphoid tissue Liver Testis Brain Esophagus
#> 1           <NA>           <NA>            <NA>  <NA>   <NA>  <NA>      <NA>
#> 2           <NA>           <NA>            <NA>  <NA>   <NA>  <NA>      <NA>
#> 3           <NA>           <NA>            <NA>  <NA>   <NA>  <NA>      <NA>
#> 4           <NA>           <NA>            <NA>  <NA>   <NA>  <NA>      <NA>
#> 5           <NA>           <NA>            <NA>  <NA>   <NA>  <NA>      <NA>
#> 6 Group enriched Group enriched  Group enriched  <NA>   <NA>  <NA>      <NA>
#>   Gallbladder Intestine Pancreas Ovary Kidney Placenta Cervix Skin Endometrium
#> 1        <NA>      <NA>     <NA>  <NA>   <NA>     <NA>   <NA> <NA>        <NA>
#> 2        <NA>      <NA>     <NA>  <NA>   <NA>     <NA>   <NA> <NA>        <NA>
#> 3        <NA>      <NA>     <NA>  <NA>   <NA>     <NA>   <NA> <NA>        <NA>
#> 4        <NA>      <NA>     <NA>  <NA>   <NA>     <NA>   <NA> <NA>        <NA>
#> 5        <NA>      <NA>     <NA>  <NA>   <NA>     <NA>   <NA> <NA>        <NA>
#> 6        <NA>      <NA>     <NA>  <NA>   <NA>     <NA>   <NA> <NA>        <NA>
#>   Skeletal muscle Retina Tongue Heart muscle Choroid plexus Fallopian tube
#> 1            <NA>   <NA>   <NA>         <NA>           <NA>           <NA>
#> 2            <NA>   <NA>   <NA>         <NA>           <NA>           <NA>
#> 3            <NA>   <NA>   <NA>         <NA>           <NA>           <NA>
#> 4            <NA>   <NA>   <NA>         <NA>           <NA>           <NA>
#> 5            <NA>   <NA>   <NA>         <NA>           <NA>           <NA>
#> 6            <NA>   <NA>   <NA>         <NA>           <NA>           <NA>
#>   Epididymis Salivary gland Vagina Smooth muscle Adrenal gland Blood vessel
#> 1       <NA>           <NA>   <NA>          <NA>          <NA>         <NA>
#> 2       <NA>           <NA>   <NA>          <NA>          <NA>         <NA>
#> 3       <NA>           <NA>   <NA>          <NA>          <NA>         <NA>
#> 4       <NA>           <NA>   <NA>          <NA>          <NA>         <NA>
#> 5       <NA>           <NA>   <NA>          <NA>          <NA>         <NA>
#> 6       <NA>           <NA>   <NA>          <NA>          <NA>         <NA>
#>   Pituitary gland Stomach Urinary bladder Thyroid gland Prostate
#> 1            <NA>    <NA>            <NA>          <NA>     <NA>
#> 2            <NA>    <NA>            <NA>          <NA>     <NA>
#> 3            <NA>    <NA>            <NA>          <NA>     <NA>
#> 4            <NA>    <NA>            <NA>          <NA>     <NA>
#> 5            <NA>    <NA>            <NA>          <NA>     <NA>
#> 6            <NA>    <NA>            <NA>          <NA>     <NA>

Key columns in ee_tb:

Column Description
Gene HGNC gene symbol
Ensembl Ensembl gene identifier
tissue tissue name
signal expression category
Tissue.specificity RNA specificity class
Is_secreted whether the protein is secreted

Scenario 1: Assessing Tissue-Specific Protein Expression

We are going to demonstrate how to test tissue enrichment of proteins using the example dataset within the Protein2Tissue package.

Protein2Tissue requires two inputs:

  • Input: a list of proteins or genes of interest (for example, differentially expressed proteins from a plasma proteomics experiment).
  • Background: the full set of proteins or genes having been detected in the experiment. If this is not provided, all genes or proteins in the provided database are used as the background.
# example input: gene symbols
data("plasma_data", package = "Protein2Tissue")
myList <- plasma_data %>%
  filter(Coefficient.Age > 0 & q.Age < 0.05) %>%
  pull(UniqueSymbol) %>%
  toupper()

# example background: Gene symbols of all detected proteins
myBackground <-  toupper(plasma_data$UniqueSymbol)

The identifier types used for the input and background do not have to match. Use typeKey and typeKeyBg to specify how each list should be matched to the tissue expression database.

Step 1 — Tissue enrichment analysis

tissueAnalysis() maps your input and background lists against the reference database (here we are using the shipped dataset in the package derived from the Human Protein Atlas database) and returns an Enrichment S4 object:

result <- tissueAnalysis(
    input      = myList,
    background = myBackground,
    typeKey    = "Gene",      # identifier type for input
    typeKeyBg  = "Gene"   # identifier type for background
)
#> Warning: 143 protein(s) in the background list not found in the database and
#> have been removed.
#> Warning: 38 protein(s) in the input list not found in the database and have
#> been removed.

The Enrichment object has two slots:

# matched input proteins with HPA annotations
getInput(result)[1:6,1:6]
#>     Gene         Ensembl EntrezID Uniprot Is_secreted     Tissue.specificity
#> 1    CGA ENSG00000135346     1081  P01215        TRUE        Tissue enriched
#> 2   SOST ENSG00000167941    50964  Q9BQB4        TRUE        Tissue enriched
#> 3 ARFIP2 ENSG00000132254    23647  P53365       FALSE Low tissue specificity
#> 4  GDF15 ENSG00000130513     9518  Q99988        TRUE        Tissue enhanced
#> 5    MLN ENSG00000096395     4295  P12872        TRUE        Tissue enriched
#> 6    PTN ENSG00000105894     5764  P21246        TRUE        Tissue enhanced

# matched background proteins with HPA annotations
getBackground(result)[1:6,1:6]
#>     Gene         Ensembl EntrezID Uniprot Is_secreted     Tissue.specificity
#> 1    CGA ENSG00000135346     1081  P01215        TRUE        Tissue enriched
#> 2   SOST ENSG00000167941    50964  Q9BQB4        TRUE        Tissue enriched
#> 3 ARFIP2 ENSG00000132254    23647  P53365       FALSE Low tissue specificity
#> 4  GDF15 ENSG00000130513     9518  Q99988        TRUE        Tissue enhanced
#> 5    MLN ENSG00000096395     4295  P12872        TRUE        Tissue enriched
#> 6 IGDCC4 ENSG00000103742    57722  Q8TDY8       FALSE        Tissue enhanced

A custom database can be substituted if needed:

result <- tissueAnalysis(
    input    = myList,
    database = myCustomDatabase
)

Enrichment results can be saved to disk as CSV or TSV files:

writeOutput(result,type = "csv","~/Enrichment_Results.csv")
#> Output written to: ~/Enrichment_Results.csv

Step 2 — RNA tissue specificity overview

Before running statistical tests, it is useful to inspect how your input genes are distributed across tissue specificity classes:

plotProteinType(result)

This bar chart shows how many of your input genes fall into each specificity class (tissue enriched, group enriched, tissue enhanced, low specificity, etc.), giving a quick overview of the tissue-specific character of your gene list.

Step 3 — Expression fingerprint heatmap

heatmapEnrich() shows the tissue expression signal for each gene in your input as a heatmap:

# all signal categories
heatmapEnrich(result)


# restrict to strongly tissue-specific signals only
heatmapEnrich(
    result,
    inclusion = c("Tissue enriched", "Tissue enhanced")
)

Rows are genes and columns are tissues. Colour indicates the signal category — orange cells indicate no tissue-specific signal for that gene-tissue combination.

Step 4 — exact test

testByTissue() performs exact test for each tissue to identify which tissues are significantly enriched in your input list relative to the background:

testResult <- testByTissue(
    result,
    inclusion     = "All",
    padj          = "BH",
    secretoryOnly = FALSE
)

head(testResult)
#>           tissue input_in input_total bg_in bg_total  input_prop     bg_prop
#> 1 Adipose tissue       21         815    50     2663 0.025766871 0.018775817
#> 2  Adrenal gland       11         815    45     2663 0.013496933 0.016898235
#> 3   Blood vessel       27         815    62     2663 0.033128834 0.023282013
#> 4    Bone marrow       37         815   169     2663 0.045398773 0.063462261
#> 5          Brain       71         815   327     2663 0.087116564 0.122793842
#> 6         Breast        5         815    25     2663 0.006134969 0.009387908
#>          OR    p_value     p_adj
#> 1 1.3820520 0.25633632 0.7839794
#> 2 0.7960185 0.63317354 0.9857820
#> 3 1.4372644 0.12812944 0.6772556
#> 4 0.7019158 0.06177884 0.4571634
#> 5 0.6817824 0.00463354 0.1714410
#> 6 0.6514436 0.51653904 0.9857820

Each row in the output corresponds to one tissue and contains:

Column Description
tissue tissue name
input_in input proteins expressed in this tissue
input_total total input proteins
bg_in background proteins expressed in this tissue
bg_total total background proteins
input_prop proportion of input proteins in tissue
bg_prop proportion of background proteins in tissue
OR odds ratio
p_value exact test p-value
p_adj adjusted p-value

To restrict the analysis to secretory proteins only:

testResultSecretory <- testByTissue(
    result,
    padj          = "BH",
    secretoryOnly = TRUE
)

Step 5 — Visualise test results

testPlot() generates a horizontal grouped bar chart comparing input and background proportions across tissues, annotated with p-values:

testPlot(testResult)

Blue bars show the proportion of input proteins expressed in each tissue; grey bars show the background proportion. P-values from exact test are annotated to the right of each tissue.

Step 6 — Network visualisation

networkPlot() creates a bipartite network connecting genes to their associated tissues, coloured by signal category:

networkPlot(result)

In the network:

  • Large circles are tissue hubs, labelled with the tissue name
  • Small points are genes
  • Edge colour encodes the signal category (tissue enriched, group enriched, or tissue enhanced)

This is particularly useful for identifying groups of genes that share tissue associations.

Step 7 — GO enrichment analysis

goEnriched() performs Gene Ontology enrichment analysis on genes showing tissue-specific expression in a tissue of interest:

# biological process enrichment for liver-associated genes
goResult <- goEnriched(
    result,
    type       = "BP",
    tissueTest = "Liver",
    inclusion  = "All"
)

# view top enriched terms
head(as.data.frame(methods::slot(goResult, "result")))
#>                    ID                                    Description GeneRatio
#> GO:0010876 GO:0010876                             lipid localization      8/51
#> GO:0006869 GO:0006869                                lipid transport      7/51
#> GO:0006091 GO:0006091 generation of precursor metabolites and energy      6/51
#> GO:0005975 GO:0005975                 carbohydrate metabolic process      7/51
#> GO:0019221 GO:0019221            cytokine-mediated signaling pathway      5/51
#> GO:0070371 GO:0070371                          ERK1 and ERK2 cascade      5/51
#>            BgRatio RichFactor FoldEnrichment oddsRatio   zScore     pvalue
#> GO:0010876  13/161  0.6153846       1.942685  3.906977 2.406335 0.02066865
#> GO:0006869  12/161  0.5833333       1.841503  3.340909 2.056847 0.04466949
#> GO:0006091  11/161  0.5454545       1.721925  2.800000 1.683804 0.09102668
#> GO:0005975  15/161  0.4666667       1.473203  2.028409 1.306367 0.15401954
#> GO:0019221  10/161  0.5000000       1.578431  2.282609 1.282071 0.17348017
#> GO:0070371  10/161  0.5000000       1.578431  2.282609 1.282071 0.17348017
#>            p.adjust qvalue                                           geneID
#> GO:0010876        1      1 PLA2G12B/APOF/CRP/PLA2G2A/APOB/GLTPD2/FABP1/SYT7
#> GO:0006869        1      1     PLA2G12B/APOF/PLA2G2A/APOB/GLTPD2/FABP1/SYT7
#> GO:0006091        1      1                LEPR/FBP1/UGP2/PPP1R3B/PDIA5/PYGL
#> GO:0005975        1      1           GSTO1/CHI3L1/FBP1/UGP2/PPP1R3B/PC/PYGL
#> GO:0019221        1      1                        EPO/GREM2/LEPR/THPO/CCL16
#> GO:0070371        1      1                      EPO/FGA/PLA2G2A/THPO/CHI3L1
#>            Count
#> GO:0010876     8
#> GO:0006869     7
#> GO:0006091     6
#> GO:0005975     7
#> GO:0019221     5
#> GO:0070371     5

The type argument accepts:

  • "BP" — Biological Process (default)
  • "MF" — Molecular Function
  • "CC" — Cellular Component

Complete example workflow

library(Protein2Tissue)

# --- inputs ---
data("plasma_data", package = "Protein2Tissue")

myList <- plasma_data %>%
  filter(Coefficient.Age > 0 & q.Age < 0.05) %>%
  pull(UniqueSymbol) %>%
  toupper()

# example background: Ensembl IDs representing the detectable proteome
myBackground <-  toupper(plasma_data$UniqueSymbol)

# --- step 1: tissue enrichment ---
result <- tissueAnalysis(
    input      = myList,
    background = myBackground,
    typeKey    = "Gene",      # identifier type for input
    typeKeyBg  = "Gene",   # identifier type for background
)
#> Warning: 143 protein(s) in the background list not found in the database and
#> have been removed.
#> Warning: 38 protein(s) in the input list not found in the database and have
#> been removed.

writeOutput(result,type = "csv","~/Enrichment_Results.csv")
#> Output written to: ~/Enrichment_Results.csv

# --- step 2: overview ---
plotProteinType(result)


# --- step 3: heatmap ---
heatmapEnrich(result)


# --- step 4: exact test ---
testResult <- testByTissue(result, padj = "BH")

# --- step 5: bar chart ---
testPlot(testResult)


# --- step 6: network ---
networkPlot(result)


# --- step 7: GO enrichment ---
goResult <- goEnriched(result, type = "BP", tissueTest = "Liver")

#Scenario 2: Tissue-Specific Expression Analysis of Differentially Expressed #Genes This package can also identify genes or proteins enriched in specified tissues from lists of differentially expressed genes (DEGs) derived from RNA‑seq data or differentially expressed proteins (DEPs) obtained from tissue‑specific proteomics experiments.


result2 <- tissueAnalysis2(
  input         = c("BRCA1", "EGFR", "ETDB", 
                    "CCDC7", "TSPY9", "CCDC148"), #List of DEG/DEPs
  typeKey       = "Gene",
  database      = NULL,      # NULL uses bundled HPA database
  inclusion     = "All",
  secretoryOnly = FALSE,
  tissueToTest  = "Testis"   # tissue name must match the database
)
#> All input proteins found in the database.

The results can be written to disk by the write.csv() function.

write.csv(result2,"~/result2.csv",row.names=F)

Scenario 3: Customising Your Own Tissue Expression Database

In addition to using the bundled Human Protein Atlas–derived tissue expression database, this package allows you to construct a custom tissue expression database from a gene‑by‑tissue average expression matrix. This is useful when working with user‑generated datasets, alternative expression atlases, or organism‑specific expression resources. Help we are using the gene-by-tissue average transcripts per million (TPM) matrix from GTEx bundled to this package as an example.

Classifying tissue specificity from an expression matrix

The geneClassification() function classifies genes or proteins based on their tissue‑specific expression patterns. The input expression matrix should be formatted with genes (or proteins) as rows and tissues as columns.

# Example expression matrix (genes x tissues)
#Download pre-processing average tissue expression levels among tissues 
url <- "https://raw.githubusercontent.com/ChrYang/DataSet/main/tissue_tpm_gtex.csv"
tissue_tpm_gtex <- read.csv(url, row.names=1)

# Classify tissue specificity using gene symbols
res <- geneClassification(
  expression          = tissue_tpm_gtex,
  typeKey             = "Ensembl",
  min_expression      = 1,
  secretory_analysis  = FALSE
)

head(res)

Using predefined tissue groups

Related tissues can be grouped prior to classification, allowing tissue specificity to be assessed at the designated group rather than the automatically ranked group by expression levels.

data("tissue_map")

# Classify genes using grouped tissues
res_grouped <- geneClassification(
  expression          = tissue_tpm_gtex,
  tissue_groups       = tissue_map,
  typeKey             = "Ensembl",
  min_expression      = 1,
  secretory_analysis  = FALSE
)

The resulting classification output can be used directly as a custom tissue expression database in downstream analyses, including tissue enrichment testing with tissueAnalysis().

Data source

The bundled tissue expression database uses tissue-specific gene expression data from the Human Protein Atlas. If you use Protein2Tissue in your work, please also cite the HPA:

Uhlen M et al. (2015). Tissue-based map of the human proteome. Science, 347(6220). https://doi.org/10.1126/science.1260419

Uhlen M et al. (2017). A pathology atlas of the human cancer transcriptome. Science, 357(6352). https://doi.org/10.1126/science.aan2507

GTEx Consortium (2013). The Genotype-Tissue Expression (GTEx) project. Nature Genetics, 45(6). https://doi.org/10.1038/ng.2653

Session information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] Protein2Tissue_0.99.6 ggplot2_4.0.3         dplyr_1.2.1          
#> [4] BiocStyle_2.41.0     
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3      sys_3.4.3               jsonlite_2.0.0         
#>   [4] tidydr_0.0.7            magrittr_2.0.5          ggtangle_0.1.4         
#>   [7] farver_2.1.2            rmarkdown_2.32          fs_2.1.0               
#>  [10] vctrs_0.7.3             memoise_2.0.1           ggtree_4.3.0           
#>  [13] rstatix_1.1.0           htmltools_0.5.9         BiocBaseUtils_1.15.1   
#>  [16] broom_1.0.13            Formula_1.2-6           gridGraphics_0.5-1     
#>  [19] sass_0.4.10             bslib_0.12.0            htmlwidgets_1.6.4      
#>  [22] plyr_1.8.9              httr2_1.3.0             rootSolve_1.8.2.4      
#>  [25] cachem_1.1.0            buildtools_1.0.0        igraph_2.3.4           
#>  [28] lifecycle_1.0.5         pkgconfig_2.0.3         Matrix_1.7-6           
#>  [31] R6_2.6.1                fastmap_1.2.0           gson_0.2.2             
#>  [34] digest_0.6.39           Exact_3.3               aplot_0.3.2            
#>  [37] enrichplot_1.99.8       ggnewscale_0.5.2        patchwork_1.3.2        
#>  [40] AnnotationDbi_1.75.2    S4Vectors_0.51.10       aisdk_1.4.12           
#>  [43] ps_1.9.3                RSQLite_3.53.3          org.Hs.eg.db_3.23.1    
#>  [46] ggpubr_1.0.0            labeling_0.4.3          abind_1.4-8            
#>  [49] httr_1.4.9              polyclip_1.10-7         compiler_4.6.1         
#>  [52] bit64_4.8.6             fontquiver_0.2.1        withr_3.0.3            
#>  [55] backports_1.5.1         S7_0.2.2                carData_3.0-6          
#>  [58] viridis_0.6.5           DBI_1.3.0               ggforce_0.5.0          
#>  [61] ggsignif_0.6.4          MASS_7.3-66             rappdirs_0.3.4         
#>  [64] tools_4.6.1             otel_0.2.0              ape_5.8-1              
#>  [67] scatterpie_0.2.6        glue_1.8.1              callr_3.8.0            
#>  [70] nlme_3.1-171            GOSemSim_2.39.4         grid_4.6.1             
#>  [73] cluster_2.1.8.3         reshape2_1.4.5          generics_0.1.4         
#>  [76] gtable_0.3.6            tidyr_1.3.2             car_3.1-5              
#>  [79] tidygraph_1.3.1         XVector_0.53.0          BiocGenerics_0.59.12   
#>  [82] ggrepel_0.9.8           pillar_1.11.1           stringr_1.6.0          
#>  [85] yulab.utils_0.2.5       splines_4.6.1           tweenr_2.0.3           
#>  [88] treeio_1.37.1           lattice_0.23-1          bit_4.6.0              
#>  [91] tidyselect_1.2.1        fontLiberation_0.1.0    GO.db_3.23.1           
#>  [94] maketools_1.3.2         Biostrings_2.81.9       knitr_1.52             
#>  [97] fontBitstreamVera_0.1.1 gridExtra_2.3.1         IRanges_2.47.5         
#> [100] Seqinfo_1.3.2           stats4_4.6.1            xfun_0.62              
#> [103] graphlayouts_1.2.5      Biobase_2.73.2          stringi_1.8.9          
#> [106] lazyeval_0.2.3          ggfun_0.2.2             yaml_2.3.12            
#> [109] evaluate_1.0.5          ggraph_2.2.2            gdtools_0.5.2          
#> [112] tibble_3.3.1            qvalue_2.45.0           BiocManager_1.30.27    
#> [115] ggplotify_0.1.4         cli_3.6.6               systemfonts_1.3.2      
#> [118] processx_3.9.0          jquerylib_0.1.4         Rcpp_1.1.2             
#> [121] png_0.1-9               parallel_4.6.1          blob_1.3.0             
#> [124] clusterProfiler_4.21.4  DOSE_4.7.3              viridisLite_0.4.3      
#> [127] tidytree_0.4.8          ggiraph_0.9.6           enrichit_0.2.6         
#> [130] scales_1.4.0            purrr_1.2.2             crayon_1.5.3           
#> [133] rlang_1.3.0             KEGGREST_1.53.6