# GSVA on proteomics data

Robert Castelo1*, Axel Klenk1** and Justin Guinney2***

1Dept. of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
2Tempus Labs, Inc.

*robert.castelo@upf.edu
**axel.klenk@gmail.com
***justin.guinney@tempus.com

#### 5 May 2026

#### Abstract

Here we illustrate how to use GSVA with proteomics data.

#### Package

GSVA 2.6.1

License: Artistic-2.0

# 1 Introduction

Proteomics data, such as the one produced through the Clinical Proteomic Tumor Analysis Consortium (CPTAC), can have a massive amount of missing values, in which imputing can result in regressing to the mean, underestimating protein expression variability. In such a setting, skipping missing values from the calculations may constitute a suitable alternative to imputation. The GSVA package provides a missing data policy for the GSVA and ssGSEA methods, which we illustrate here with the proteomics data from Costa et al. (2021) and available in the GSVAdata experiment data package. We start loading the data as follows.

    library(SummarizedExperiment)
    library(GSVA)
    library(GSVAdata)

    data(geneprotExpCostaEtAl2021)
    ls()
    [1] "geneExpCostaEtAl2021" "protExpCostaEtAl2021"
    protExpCostaEtAl2021
    class: RangedSummarizedExperiment
    dim: 245 20
    metadata(1): annotation
    assays(3): logCPM log2protexp log2protexpimp
    rownames(245): 5715 9973 ... 25793 7094
    rowData names(2): Symbol UniquePeptides
    colnames(20): BS03 BS05 ... BS23 BS24
    colData names(2): FIR Sex
    assayNames(protExpCostaEtAl2021)
    [1] "logCPM"         "log2protexp"    "log2protexpimp"

The data is stored in a `SummarizedExperiment` object called `protExpCostaEtAl2021` with three assays:

- `logCPM` with the normalized log-CPM RNA-seq expression values for the 245 genes encoding the quantified proteins.
- `log2protexp` with the normalized protein units of expression, and the missing quantifications specified by `NA` values.
- `log2protexpimp` with the normalized protein units of expression, but where the missing quantifications have been imputed.

Please consult the publication in (Costa et al. 2021) for details on how these data have been produced.

# 2 Load gene sets

We will use the MSigDB C7 collection of immunologic genesets, reading them with the `readGMT()` function of the GSVA package.

    library(GSEABase)

    URL <- "https://data.broadinstitute.org/gsea-msigdb/msigdb/release/2024.1.Hs/c7.immunesigdb.v2024.1.Hs.symbols.gmt"
    c7.genesets <- readGMT(URL)

    GeneSetCollection
      names: GOLDRATH_EFF_VS_MEMORY_CD8_TCELL_DN, GOLDRATH_EFF_VS_MEMORY_CD8_TCELL_UP, ..., KAECH_NAIVE_VS_MEMORY_CD8_TCELL_UP (4872 total)
      unique identifiers: ABCA2, ABCC5, ..., LINC00841 (20457 total)
      types in collection:
        geneIdType: SymbolIdentifier (1 total)
        collectionType: NullCollection (1 total)

We filter this collection of gene sets to those formed by gene upregulated in innate leukocytes and adaptive mature lymphocytes, excluding those reported in studies on myeloid cells and the lupus autoimmune disease.

    innatepat <- c("NKCELL_VS_.+_UP", "MAST_CELL_VS_.+_UP",
                   "EOSINOPHIL_VS_.+_UP", "BASOPHIL_VS_.+_UP",
                   "MACROPHAGE_VS_.+_UP", "NEUTROPHIL_VS_.+_UP")
    innatepat <- paste(innatepat, collapse="|")
    innategsets <- names(c7.genesets)[grep(innatepat, names(c7.genesets))]
    length(innategsets)
    [1] 53

    adaptivepat <- c("CD4_TCELL_VS_.+_UP", "CD8_TCELL_VS_.+_UP", "BCELL_VS_.+_UP")
    adaptivepat <- paste(adaptivepat, collapse="|")
    adaptivegsets <- names(c7.genesets)[grep(adaptivepat, names(c7.genesets))]
    excludepat <- c("NAIVE", "LUPUS", "MYELOID")
    excludepat <- paste(excludepat, collapse="|")
    adaptivegsets <- adaptivegsets[-grep(excludepat, adaptivegsets)]
    length(adaptivegsets)
    [1] 97

    c7.genesets.filt <- c7.genesets[c(innategsets, adaptivegsets)]
    length(c7.genesets.filt)
    [1] 150

# 3 Usage and benchmark with RNA-seq data

Here we show first a quick benchmarking using the logCPM expression profiles, which are complete, by introducing `NA` values with the pattern of missing data observed in the corresponding proteins.

    logCPMsWithNAs <- assay(protExpCostaEtAl2021, "logCPM")
    logCPMsWithNAs[1:5, 1:5]
              BS03     BS05     BS06     BS07     BS08
    5715  3.826346 3.785976 3.016652 3.641209 3.547332
    9973  3.028686 2.633021 3.281683 3.382893 2.699961
    5688  5.101717 5.404292 4.314238 4.136742 4.969074
    10627 7.622114 7.779064 6.894152 7.239640 7.899282
    8672  7.077862 7.725863 6.068174 6.156980 6.970647
    logCPMsWithNAs[is.na(assay(protExpCostaEtAl2021, "log2protexp"))] <- NA
    logCPMsWithNAs[1:5, 1:5]
              BS03     BS05     BS06     BS07     BS08
    5715  3.826346 3.785976       NA 3.641209 3.547332
    9973        NA 2.633021       NA       NA       NA
    5688  5.101717       NA       NA       NA       NA
    10627       NA 7.779064       NA 7.239640       NA
    8672  7.077862 7.725863 6.068174 6.156980 6.970647

We need to add the annotation metadata to this new matrix of expression profiles with missing values.

    gsvaAnnotation(logCPMsWithNAs) <- EntrezIdentifier("org.Hs.eg.db")

When building the parameter object, GSVA will check automatically for the presence of missing values whenever the input expression data container is a non-sparse container of expression values, such as a base matrix object or a `SummarizedExperiment` object.

    gsvapar <- gsvaParam(logCPMsWithNAs, c7.genesets.filt, minSize=5)
    ! Input expression data has NA values, which will be propagated through calculations

We can force the `gsvaParam()` function to check for missing values irrespective of the input expression data container by setting the argument `checkNA="yes"`, or disable that check altogether with `checkNA="no"`. By default `checkNA="auto"`. Once missing values have been detected when we build the parameter object, the `gsva()` function (or `gsvaRanks()` and `gsvaScores()`) will apply a missing data policy specified through a parameter called `use`, which takes one of the following three possible character string values: `everything`, `all.obs` or `na.rm`. The first value (`everything`) is the default value and it propagates the missing `NA` values through the calculations.

    es_gsva_everything <- gsva(gsvapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Calculating GSVA ranks
    ℹ kcdf='auto' (default)
    ℹ GSVA dense (classical) algorithm
    ℹ Row-wise ECDF estimation with Gaussian kernels
    ℹ Calculating row ECDFs
    ℹ Calculating column ranks
    ℹ Mapping identifiers
    ℹ GSVA dense (classical) algorithm
    ℹ Calculating GSVA scores for 36 gene sets
    ✔ Calculations finished
    es_gsva_everything[1:10, 1:4]
                                                              BS03 BS05 BS06 BS07
    GSE18804_SPLEEN_MACROPHAGE_VS_BRAIN_TUMORAL_MACROPHAGE_UP   NA   NA   NA   NA
    GSE2585_THYMIC_MACROPHAGE_VS_MTEC_UP                        NA   NA   NA   NA
    GSE27786_NKCELL_VS_NEUTROPHIL_UP                            NA   NA   NA   NA
    GSE27786_NKCELL_VS_NKTCELL_UP                               NA   NA   NA   NA
    GSE27859_MACROPHAGE_VS_DC_UP                                NA   NA   NA   NA
    GSE3982_EOSINOPHIL_VS_BASOPHIL_UP                           NA   NA   NA   NA
    GSE3982_MAST_CELL_VS_BASOPHIL_UP                            NA   NA   NA   NA
    GSE3982_MAST_CELL_VS_BCELL_UP                               NA   NA   NA   NA
    GSE3982_MAST_CELL_VS_CENT_MEMORY_CD4_TCELL_UP               NA   NA   NA   NA
    GSE3982_MAST_CELL_VS_DC_UP                                  NA   NA   NA   NA
    all(is.na(es_gsva_everything))
    [1] TRUE

So, in this case, there are so many missing values that the resulting enrichment scores are all `NA` values. If we try the second option `use="all.obs"` it will immediately produce an error at the parameter building step.

    gsvapar <- gsvaParam(logCPMsWithNAs, c7.genesets.filt, minSize=5,
                         use="all.obs")
    Error in `.check_for_na_values()`:
    ✖ Input expression data has NA values.

Finally, if we set `use="na.rm"`, missing `NA` values will be skipped during calculations, giving the chance to obtain non-missing enrichment scores for those gene sets with enough genes with non-missing expression values.

    gsvapar <- gsvaParam(logCPMsWithNAs, c7.genesets.filt, minSize=5,
                         use="na.rm")
    ! Input expression data has NA values, which will be discarded from calculations
    es_gsva_narm <- gsva(gsvapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Calculating GSVA ranks
    ℹ kcdf='auto' (default)
    ℹ GSVA dense (classical) algorithm
    ℹ Row-wise ECDF estimation with Gaussian kernels
    ℹ Calculating row ECDFs
    ℹ Calculating column ranks
    ℹ Mapping identifiers
    ℹ GSVA dense (classical) algorithm
    ℹ Calculating GSVA scores for 36 gene sets
    ! NA enrichment scores in gene sets with less than 5 genes after removing missing values
    ✔ Calculations finished
    round(es_gsva_narm[1:10, 1:4], digits=2)
                                                               BS03  BS05 BS06 BS07
    GSE18804_SPLEEN_MACROPHAGE_VS_BRAIN_TUMORAL_MACROPHAGE_UP    NA  0.41   NA   NA
    GSE2585_THYMIC_MACROPHAGE_VS_MTEC_UP                         NA    NA   NA   NA
    GSE27786_NKCELL_VS_NEUTROPHIL_UP                             NA -0.30   NA   NA
    GSE27786_NKCELL_VS_NKTCELL_UP                             -0.07  0.06 0.56   NA
    GSE27859_MACROPHAGE_VS_DC_UP                               0.15 -0.08   NA   NA
    GSE3982_EOSINOPHIL_VS_BASOPHIL_UP                          0.62  0.68   NA 0.29
    GSE3982_MAST_CELL_VS_BASOPHIL_UP                           0.30  0.25   NA 0.27
    GSE3982_MAST_CELL_VS_BCELL_UP                              0.12  0.20   NA   NA
    GSE3982_MAST_CELL_VS_CENT_MEMORY_CD4_TCELL_UP              0.57  0.51   NA 0.32
    GSE3982_MAST_CELL_VS_DC_UP                                   NA    NA   NA   NA

These parameters work exactly in the same way with the ssGSEA method.

    ssgseapar <- ssgseaParam(logCPMsWithNAs, c7.genesets.filt, minSize=5,
                             use="na.rm")
    ! Input expression data has NA values, which will be discarded from calculations
    es_ssgsea_narm <- gsva(ssgseapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Mapping identifiers
    ℹ Calculating ssGSEA scores for 36 gene sets
    ! NA enrichment scores in gene sets with less than 5 genes after removing missing values
    ! NA enrichment scores in gene sets with less than 5 genes after removing missing values
    ℹ Normalizing ssGSEA scores
    ✔ Calculations finished
    round(es_ssgsea_narm[1:10, 1:4], digits=2)
                                                              BS03 BS05 BS06 BS07
    GSE18804_SPLEEN_MACROPHAGE_VS_BRAIN_TUMORAL_MACROPHAGE_UP   NA 0.27   NA   NA
    GSE2585_THYMIC_MACROPHAGE_VS_MTEC_UP                        NA   NA   NA   NA
    GSE27786_NKCELL_VS_NEUTROPHIL_UP                            NA 0.13   NA   NA
    GSE27786_NKCELL_VS_NKTCELL_UP                             0.15 0.21 0.12   NA
    GSE27859_MACROPHAGE_VS_DC_UP                              0.15 0.18   NA   NA
    GSE3982_EOSINOPHIL_VS_BASOPHIL_UP                         0.52 0.60   NA 0.30
    GSE3982_MAST_CELL_VS_BASOPHIL_UP                          0.10 0.18   NA 0.18
    GSE3982_MAST_CELL_VS_BCELL_UP                             0.30 0.54   NA   NA
    GSE3982_MAST_CELL_VS_CENT_MEMORY_CD4_TCELL_UP             0.18 0.10   NA 0.09
    GSE3982_MAST_CELL_VS_DC_UP                                  NA   NA   NA   NA

Since we are doing calculations on the RNA-seq data, for which we have the complete logCPM values, we can compare how close are the enrichment scores obtained by skipping `NA` values from the ones obtained with the complete data, for both GSVA and ssGSEA. First, we calculate the enrichment scores with the complete data for each of the two methods.

    gsvaAnnotation(protExpCostaEtAl2021) <- EntrezIdentifier("org.Hs.eg.db")
    gsvapar <- gsvaParam(protExpCostaEtAl2021, c7.genesets.filt,
                         assay="logCPM", minSize=5)
    es_gsva <- gsva(gsvapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Calculating GSVA ranks
    ℹ kcdf='auto' (default)
    ℹ GSVA dense (classical) algorithm
    ℹ Row-wise ECDF estimation with Gaussian kernels
    ℹ Calculating row ECDFs
    ℹ Calculating column ranks
    ℹ Mapping identifiers
    ℹ GSVA dense (classical) algorithm
    ℹ Calculating GSVA scores for 36 gene sets
    ✔ Calculations finished
    ssgseapar <- ssgseaParam(protExpCostaEtAl2021, c7.genesets.filt,
                             assay="logCPM", minSize=5)
    es_ssgsea <- gsva(ssgseapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Mapping identifiers
    ℹ Calculating ssGSEA scores for 36 gene sets
    ℹ Normalizing ssGSEA scores
    ✔ Calculations finished

Second, we calculate and plot the correlations between the enrichment scores obtained from the data with missing values with the ones obtained from the complete data.

    r_es_gsva <- r_es_ssgsea <- numeric(nrow(es_gsva))
    for (i in 1:nrow(es_gsva)) {
      r_es_gsva[i] <- cor(assay(es_gsva)[i, ], es_gsva_narm[i, ],
                  use="complete.obs")
      r_es_ssgsea[i] <- cor(assay(es_ssgsea)[i, ], es_ssgsea_narm[i, ],
                use="complete.obs")
    }
    boxplot(list(GSVA=r_es_gsva, ssGSEA=r_es_ssgsea),
        ylab="Correlation with complete data", las=1)

Figure 1: Correlation between enrichment scores calculated from RNA-seq expression profiles with missing values, and those calculated from the complete version of the same data

As we can see in Figure 1 GSVA scores calculated by skipping missing values correlate better with those calculated from the complete data, than ssGSEA scores between incomplete and complete data.

# 4 Usage and benchmark with proteomics data

Here we show the usage of GSVA with the actual proteomics data from Costa et al. (2021), comparing the results obtained by skipping missing values with the results calculated from the imputed data. The normalized proteomics expression values before imputation are stored in the `log2protexp` assay, and after imputation in the `log2protexpimp` assay.

    assays(protExpCostaEtAl2021)$log2protexp[1:5, 1:5]
              BS03     BS05     BS06     BS07     BS08
    5715  20.22729 20.97647       NA 20.42439 19.83030
    9973        NA 18.31250       NA       NA       NA
    5688  19.90413       NA       NA       NA       NA
    10627       NA 21.41412       NA 20.38215       NA
    8672  21.78837 21.60210 22.53073 22.16052 20.33004
    assays(protExpCostaEtAl2021)$log2protexpimp[1:5, 1:5]
              BS03     BS05     BS06     BS07     BS08
    5715  20.22729 20.97647 20.07473 20.42439 19.83030
    9973  19.52460 18.31250 19.67240 18.96282 19.57746
    5688  19.90413 20.61876 20.59096 21.51491 19.91715
    10627 20.13780 21.41412 20.53676 20.38215 19.84890
    8672  21.78837 21.60210 22.53073 22.16052 20.33004

We first create the parameter objects for the proteomics data before imputation.

    gsvapar <- gsvaParam(protExpCostaEtAl2021, c7.genesets.filt,
                 assay="log2protexp", minSize=5, use="na.rm")
    ! Input expression data has NA values, which will be discarded from calculations
    ssgseapar <- ssgseaParam(protExpCostaEtAl2021, c7.genesets.filt,
                     assay="log2protexp", minSize=5, use="na.rm")
    ! Input expression data has NA values, which will be discarded from calculations

We can use the method `anyNA()` on the parameter object to programmatically confirm anytime the presence of missing values.

    anyNA(gsvapar)
    [1] TRUE
    anyNA(ssgseapar)
    [1] TRUE

Next, we calculate enrichment scores skipping missing values.

    es_gsva <- gsva(gsvapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Calculating GSVA ranks
    ℹ kcdf='auto' (default)
    ℹ GSVA dense (classical) algorithm
    ℹ Row-wise ECDF estimation with Gaussian kernels
    ℹ Calculating row ECDFs
    ℹ Calculating column ranks
    ℹ Mapping identifiers
    ℹ GSVA dense (classical) algorithm
    ℹ Calculating GSVA scores for 36 gene sets
    ! NA enrichment scores in gene sets with less than 5 genes after removing missing values
    ✔ Calculations finished
    es_ssgsea <- gsva(ssgseapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Mapping identifiers
    ℹ Calculating ssGSEA scores for 36 gene sets
    ! NA enrichment scores in gene sets with less than 5 genes after removing missing values
    ! NA enrichment scores in gene sets with less than 5 genes after removing missing values
    ℹ Normalizing ssGSEA scores
    ✔ Calculations finished

Now we do calculations again on the imputed proteomics data stored in the `log2protexpimp` assay.

    gsvapar <- gsvaParam(protExpCostaEtAl2021, c7.genesets.filt,
                 assay="log2protexpimp", minSize=5)
    anyNA(gsvapar)
    [1] FALSE
    es_gsva_imp <- gsva(gsvapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Calculating GSVA ranks
    ℹ kcdf='auto' (default)
    ℹ GSVA dense (classical) algorithm
    ℹ Row-wise ECDF estimation with Gaussian kernels
    ℹ Calculating row ECDFs
    ℹ Calculating column ranks
    ℹ Mapping identifiers
    ℹ GSVA dense (classical) algorithm
    ℹ Calculating GSVA scores for 36 gene sets
    ✔ Calculations finished
    ssgseapar <- ssgseaParam(protExpCostaEtAl2021, c7.genesets.filt,
                     assay="log2protexpimp", minSize=5)
    anyNA(ssgseapar)
    [1] FALSE
    es_ssgsea_imp <- gsva(ssgseapar)
    ℹ GSVA version 2.6.1
    ℹ Searching for rows with constant values
    ℹ Mapping identifiers
    ℹ Calculating ssGSEA scores for 36 gene sets
    ℹ Normalizing ssGSEA scores
    ✔ Calculations finished

Finally, in a similar way we did in the previous section, we compare the enrichment scores calculated before and after imputation. As shown in Figure 2 below, GSVA scores calculated by skipping missing values correlated better with those calculated from the imputed data, than ssGSEA scores calculated by skipping missing values before imputation and on the imputed data.

    r_es_gsva <- r_es_ssgsea <- numeric(nrow(es_gsva))
    for (i in 1:nrow(es_gsva)) {
      r_es_gsva[i] <- cor(assay(es_gsva)[i, ], assay(es_gsva_imp)[i, ],
                  use="complete.obs")
      r_es_ssgsea[i] <- cor(assay(es_ssgsea)[i, ], assay(es_ssgsea_imp)[i, ],
                use="complete.obs")
    }
    boxplot(list(GSVA=r_es_gsva, ssGSEA=r_es_ssgsea),
        ylab="Correlation before/after imputation", las=1)

Figure 2: Correlation between enrichment scores calculated from normalized MS proteomics data before and after imputing missing values

# Session information

Here is the output of `sessionInfo()` on the system on which this document was compiled running pandoc 3.9.0.2:

    sessionInfo()
    R version 4.6.0 Patched (2026-04-24 r89963)
    Platform: aarch64-apple-darwin23
    Running under: macOS Tahoe 26.3.1

    Matrix products: default
    BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

    locale:
    [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

    time zone: America/New_York
    tzcode source: internal

    attached base packages:
    [1] stats4    stats     graphics  grDevices utils     datasets  methods
    [8] base

    other attached packages:
     [1] sva_3.60.0                  BiocParallel_1.46.0
     [3] genefilter_1.94.0           mgcv_1.9-4
     [5] nlme_3.1-169                RColorBrewer_1.1-3
     [7] edgeR_4.10.0                limma_3.68.2
     [9] GSVAdata_1.47.3             SummarizedExperiment_1.42.0
    [11] GenomicRanges_1.64.0        Seqinfo_1.2.0
    [13] MatrixGenerics_1.24.0       matrixStats_1.5.0
    [15] GSEABase_1.74.0             graph_1.90.0
    [17] annotate_1.90.0             XML_3.99-0.23
    [19] org.Hs.eg.db_3.23.1         AnnotationDbi_1.74.0
    [21] IRanges_2.46.0              S4Vectors_0.50.0
    [23] Biobase_2.72.0              BiocGenerics_0.58.0
    [25] generics_0.1.4              GSVA_2.6.1
    [27] BiocStyle_2.40.0

    loaded via a namespace (and not attached):
     [1] blob_1.3.0                  Biostrings_2.80.0
     [3] fastmap_1.2.0               SingleCellExperiment_1.34.0
     [5] digest_0.6.39               rsvd_1.0.5
     [7] lifecycle_1.0.5             survival_3.8-6
     [9] statmod_1.5.1               KEGGREST_1.52.0
    [11] RSQLite_2.4.6               magrittr_2.0.5
    [13] compiler_4.6.0              rlang_1.2.0
    [15] sass_0.4.10                 tools_4.6.0
    [17] yaml_2.3.12                 knitr_1.51
    [19] S4Arrays_1.12.0             bit_4.6.0
    [21] DelayedArray_0.38.1         abind_1.4-8
    [23] HDF5Array_1.40.0            memuse_4.2-3
    [25] grid_4.6.0                  beachmat_2.28.0
    [27] xtable_1.8-8                Rhdf5lib_2.0.0
    [29] tinytex_0.59                cli_3.6.6
    [31] rmarkdown_2.31              crayon_1.5.3
    [33] otel_0.2.0                  httr_1.4.8
    [35] rjson_0.2.23                DelayedMatrixStats_1.34.0
    [37] DBI_1.3.0                   cachem_1.1.0
    [39] rhdf5_2.56.0                splines_4.6.0
    [41] parallel_4.6.0              BiocManager_1.30.27
    [43] XVector_0.52.0              vctrs_0.7.3
    [45] Matrix_1.7-5                jsonlite_2.0.0
    [47] bookdown_0.46               BiocSingular_1.28.0
    [49] bit64_4.8.0                 irlba_2.3.7
    [51] magick_2.9.1                locfit_1.5-9.12
    [53] h5mread_1.4.0               jquerylib_0.1.4
    [55] glue_1.8.1                  codetools_0.2-20
    [57] ScaledMatrix_1.20.0         pillar_1.11.1
    [59] htmltools_0.5.9             rhdf5filters_1.24.0
    [61] R6_2.6.1                    sparseMatrixStats_1.24.0
    [63] evaluate_1.0.5              lattice_0.22-9
    [65] png_0.1-9                   SpatialExperiment_1.22.0
    [67] memoise_2.0.1               bslib_0.10.0
    [69] Rcpp_1.1.1-1.1              SparseArray_1.12.2
    [71] xfun_0.57                   pkgconfig_2.0.3

# References

Costa, Daniel, Núria Bonet, Amanda Solé, et al. 2021. “Genome-Wide Postnatal Changes in Immunity Following Fetal Inflammatory Response.” The FEBS Journal 288 (7): 2311–31. https://doi.org/10.1111/febs.15578.