The DeeDeeExperiment User’s Guide

#

The `DeeDeeExperiment` User’s Guide

# Introduction

## Why do we need a new class?

Differential expression analysis (DEA) and functional enrichment analysis (FEA) are core steps in transcriptomic workflows, helping researchers detect and interpret biological differences between conditions. However, as the number of conditions (defined by complex experimental designs) and analysis tools increases, managing the outputs of these analyses across multiple contrasts becomes increasingly overwhelming. This challenge is further amplified in single-cell RNA-seq, where pseudo-bulk analyses generate numerous results tables across cell types and contrasts, making it challenging to organize, explore, and reproduce findings, even for experienced users - we invite you to see this more in detail in the dedicated companion vignette for single cell data.

To address these issues, we introduce the DeeDeeExperiment class, an S4 object that extends the widely adopted `SingleCellExperiment` class in Bioconductor.

`DeeDeeExperiment` provides a structured and consistent framework for integrating DEA and FEA results alongside core expression data and metadata. By centralizing these results in a single container, `DeeDeeExperiment` enables users to retrieve, explore, and manage their analysis results more efficiently across multiple contrasts.

This vignette introduces the class structure, demonstrates how to create and work with `DeeDeeExperiment` objects, and walks through a typical RNA-seq workflow using the class.

## Anatomy of DeeDeeExperiment

The `DeeDeeExperiment` class inherits from the core Bioconductor `SingleCellExperiment` object. This means it inherits all the slots, methods, and compatibility with tools built for `SingleCellExperiment`, while introducing additional components to streamline downstream analysis.

Specifically, `DeeDeeExperiment` has two new slots:

- `dea` : a `SimpleList` that stores results from differential expression analysis (DEA), along with relevant metadata. This includes:

  - The used False Discovery Rate (FDR) and log2 fold change (LFC) thresholds (if specified).
  - Meta info related to both the LFC and p-value.
  - The package used to generate the DE results (e.g. DEseq2, edgeR or limma…).
  - A pointer to the original DE result object, which is kept in the `metadata` of the `DeeDeeExperiment` object.

The main columns to use in DEA in addition to the feature identifier (`log2FoldChange`, `pvalue`, and `padj`) are also stored in the `rowData` of the `DeeDeeExperiment` object.

- `fea` : a `SimpleList` that stores results from functional enrichment analysis (FEA), along with relevant metadata. This includes:

  - The name of the associated DEA contrast.
  - The FEA name.
  - A `GeneTonicList`-compatible set of `shaken_results` (ready to be used in GeneTonic).
  - The enrichment tool used to generate the results (e.g. `topGO`, `clusterProfiler...`).
  - A copy of the original enrichment result object.

The `SimpleList` slot type will bring the advantage of storing DEA/FEA related metadata (e.g., the design formula) directly into its `metadata`/`mcols`.

## Creating a `DeeDeeExperiment` object

In order to create a `DeeDeeExperiment` object, at least one of the following inputs are required:

- `sce`: A `SingleCellExperiment` object, that will be used as a scaffold to store the DE related information.
- `de_results`: A named list of DE results, in any of the formats supported by the package (currently results from `DESeq2`, `edgeR`, `limma`, or `muscat`).
- `enrich_results`: A named list of FE results, as `data.frame` generated by one of the following packages (currently `topGO`, `clusterProfiler`, `enrichR`, `gProfiler`, `fgsea`, `gsea`, `DAVID`, and output of `GeneTonic` shakers)

    # do not run
    dde <- DeeDeeExperiment(
      sce = sce, # sce is a SingleCellExperiment object
      # de_results is a named list of de results
      de_results = de_results,
      # enrich_res is a named list of enrichment results
      enrich_results = enrich_results
    )

# Getting started

To install this package, start R and enter:

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

    BiocManager::install("DeeDeeExperiment")

Once installed, the package can be loaded and attached to your current workspace as follows:

    library("DeeDeeExperiment")

# `DeeDeeExperiment` on the `macrophage` dataset

In the remainder of this vignette, we will illustrate the main features of DeeDeeExperiment on a publicly available dataset from Alasoo, et al. “Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response”, published in Nature Genetics, January 2018 (Alasoo et al. 2018) doi:10.1038/s41588-018-0046-7.

The data is made available via the macrophage Bioconductor package, which contains the files output from the Salmon quantification (version 0.12.0, with GENCODE v29 reference), as well as the values summarized at the gene level, which we will use to exemplify.

In the `macrophage` experimental setting, the samples are available from 6 different donors, in 4 different conditions (naive, treated with Interferon gamma, with SL1344, or with a combination of Interferon gamma and SL1344).

Let’s start by loading all the necessary packages:

    library("DeeDeeExperiment")

    library("macrophage")
    library("DESeq2")
    library("edgeR")
    library("DEFormats")

We will demonstrate how `DeeDeeExperiment` fits into a regular bulk RNA-seq data analysis workflow. For this we will start by processing the data and setting up the design for the Differential Expression Analysis (DEA).

    # load data
    data(gse, package = "macrophage")

If you already have your differential expression results and want to skip the DEA workflow examples, you can jump directly to the section 4 Creating DeeDeeExperiment the DeeDee way - otherwise, you can follow along in the subsequent sections where we will showcase how the typical objects from each DE framework will seamlessly fit into a `DeeDeeExperiment` object.

The remainder of this vignette assumes some knowledge of Differential Expression Analysis workflows. If you want some excellent introductions to this topic, you can refer to (Love et al. 2016) and (Law et al. 2018) - Otherwise, you can check (Ludt et al. 2022) for a comprehensive overview of these workflows, as well as the usage of tools such as `GeneTonic` or `pcaExproler` for interactive exploration.

## DEA: DESeq2 framework

Below we will demonstrate an example on how to generate DE results with `DESeq2`

    # set up design
    dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
    rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
    keep <- rowSums(counts(dds_macrophage) >= 10) >= 6

    dds_macrophage <- dds_macrophage[keep, ]

For the sake of demonstration, we will further subset the dataset for faster processing

    # set seed for reproducibility
    set.seed(42)
    # sample randomly for 1k genes to speed up the processing
    selected_genes <- sample(rownames(dds_macrophage), 1000)

    dds_macrophage <- dds_macrophage[selected_genes, ]

We then run the main `DESeq()` function and check the `resultsNames` being generated

    dds_macrophage
    #> class: DESeqDataSet
    #> dim: 1000 24
    #> metadata(7): tximetaInfo quantInfo ... txdbInfo version
    #> assays(3): counts abundance avgTxLength
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(2): gene_id SYMBOL
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    # run DESeq
    dds_macrophage <- DESeq(dds_macrophage)
    # contrasts
    resultsNames(dds_macrophage)
    #> [1] "Intercept"                      "line_eiwy_1_vs_diku_1"
    #> [3] "line_fikt_3_vs_diku_1"          "line_ieki_2_vs_diku_1"
    #> [5] "line_podx_1_vs_diku_1"          "line_qaqx_1_vs_diku_1"
    #> [7] "condition_IFNg_vs_naive"        "condition_IFNg_SL1344_vs_naive"
    #> [9] "condition_SL1344_vs_naive"

Let’s extract the DE results for each contrast. We will only use two contrasts

    FDR <- 0.05
    # IFNg_vs_naive
    IFNg_vs_naive <- results(dds_macrophage,
                             name = "condition_IFNg_vs_naive",
                             lfcThreshold = 1,
                             alpha = FDR
    )
    IFNg_vs_naive <- lfcShrink(dds_macrophage,
                               coef = "condition_IFNg_vs_naive",
                               res = IFNg_vs_naive,
                               type = "apeglm"
    )

    # Salm_vs_naive
    Salm_vs_naive <- results(dds_macrophage,
                             name = "condition_SL1344_vs_naive",
                             lfcThreshold = 1,
                             alpha = FDR
    )
    Salm_vs_naive <- lfcShrink(dds_macrophage,
                               coef = "condition_SL1344_vs_naive",
                               res = Salm_vs_naive,
                               type = "apeglm"
    )

    head(IFNg_vs_naive)
    #> log2 fold change (MAP): condition IFNg vs naive
    #> Wald test p-value: condition IFNg vs naive
    #> DataFrame with 6 rows and 5 columns
    #>                  baseMean log2FoldChange     lfcSE      pvalue        padj
    #>                 <numeric>      <numeric> <numeric>   <numeric>   <numeric>
    #> ENSG00000164741  918.0347      0.2291487 0.3006751 9.80541e-01 1.00000e+00
    #> ENSG00000078808 6684.4755     -0.0115336 0.0729923 1.00000e+00 1.00000e+00
    #> ENSG00000251034   12.1241     -0.1167013 0.4049828 9.33899e-01 1.00000e+00
    #> ENSG00000162676   10.2220      0.2691481 0.3776971 9.00817e-01 1.00000e+00
    #> ENSG00000170356   15.0772     -0.1278637 0.3522078 9.72815e-01 1.00000e+00
    #> ENSG00000204257 1922.0256      4.0550244 0.1960055 1.67298e-56 8.36489e-54
    head(Salm_vs_naive)
    #> log2 fold change (MAP): condition SL1344 vs naive
    #> Wald test p-value: condition SL1344 vs naive
    #> DataFrame with 6 rows and 5 columns
    #>                  baseMean log2FoldChange     lfcSE    pvalue      padj
    #>                 <numeric>      <numeric> <numeric> <numeric> <numeric>
    #> ENSG00000164741  918.0347      0.0114691 0.3224207  0.999632  1.000000
    #> ENSG00000078808 6684.4755      0.7893601 0.0734122  0.997559  1.000000
    #> ENSG00000251034   12.1241      1.1881497 0.4894973  0.226918  0.982042
    #> ENSG00000162676   10.2220      0.4420821 0.4228376  0.843212  1.000000
    #> ENSG00000170356   15.0772     -0.6249201 0.4252786  0.726972  1.000000
    #> ENSG00000204257 1922.0256     -0.7293810 0.2005145  0.883888  1.000000

We now have two `DESeqResults` objects: `IFNg_vs_naive` and `Salm_vs_naive`, ready to be integrated into a `DeeDeeExperiment` object.

## DEA: limma framework

Not working with `DESeq2`? No problem. `DeeDeeExperiment` is method-agnostic and easily integrates results from other frameworks like `limma`. In this section, we will walk through a typical `limma` workflow to generate DE results.

For demonstration purposes, we convert the filtered `dds_macrophage` as input for `limma`, using the `as.DGEList()` function from the `DEFormats` package.

    # create DGE list
    dge <- DEFormats::as.DGEList(dds_macrophage)

We then normalize and voom-transform the expression values, before running the `lmFit()` function:

    # normalize the counts
    dge <- calcNormFactors(dge)

    # create design for DE
    design <- model.matrix(~ line + group, data = dge$samples)
    # transform counts into logCPM
    v <- voom(dge, design)

    # fitting linear models using weighted least squares for each gene
    fit <- lmFit(v, design)

We define here as usual the contrast matrix:

    # available comparisons
    colnames(design)
    #> [1] "(Intercept)"      "lineeiwy_1"       "linefikt_3"       "lineieki_2"
    #> [5] "linepodx_1"       "lineqaqx_1"       "groupIFNg"        "groupIFNg_SL1344"
    #> [9] "groupSL1344"
    # setup comparisons
    contrast_matrix <- makeContrasts(
      IFNg_vs_Naive = groupIFNg,
      Salm_vs_Naive = groupSL1344,
      levels = design
    )

We then extract the results by applying the `contrasts.fit` function, followed by the empirical Bayes moderation as it follows:

    # apply contrast
    fit2 <- contrasts.fit(fit, contrast_matrix)

    # empirical Bayes moderation of standard errors
    fit2 <- eBayes(fit2)
    de_limma <- fit2 # MArrayLM object

We show here the top 10 DE genes, as detected in the contrast `IFNg_vs_Naive`

    # show top 10 genes, first columns
    topTable(de_limma, coef = "IFNg_vs_Naive", number = 10)[, 1:7]
    #>                 seqnames     start       end  width strand            gene_id
    #> ENSG00000204257     chr6  32948613  32969094  20482      - ENSG00000204257.14
    #> ENSG00000231389     chr6  33064569  33080775  16207      -  ENSG00000231389.7
    #> ENSG00000123992     chr2 219373527 219400022  26496      - ENSG00000123992.19
    #> ENSG00000227531     chr9 111139246 111284836 145591      -  ENSG00000227531.1
    #> ENSG00000197448     chr7 143244093 143270854  26762      + ENSG00000197448.13
    #> ENSG00000100418    chr22  41598028  41621096  23069      -  ENSG00000100418.7
    #> ENSG00000065911     chr2  74198562  74217565  19004      + ENSG00000065911.11
    #> ENSG00000233621     chr1  37454879  37474411  19533      -  ENSG00000233621.1
    #> ENSG00000143771     chr1 224356850 224379459  22610      + ENSG00000143771.11
    #> ENSG00000135124    chr12 121209857 121234106  24250      + ENSG00000135124.14
    #>                    SYMBOL
    #> ENSG00000204257   HLA-DMA
    #> ENSG00000231389  HLA-DPA1
    #> ENSG00000123992     DNPEP
    #> ENSG00000227531      <NA>
    #> ENSG00000197448     GSTK1
    #> ENSG00000100418     DESI1
    #> ENSG00000065911    MTHFD2
    #> ENSG00000233621 LINC01137
    #> ENSG00000143771     CNIH4
    #> ENSG00000135124     P2RX4

We now have a `MArrayLM` object: `de_limma`, ready to be integrated into a `DeeDeeExperiment` object.

## DEA: edgeR framework

Below we will walk you through a typical `edgeR` pipeline to produce DE results ready for integration in `DeeDeeExperiment`.

We reuse the normalized `DGEList` and design matrix from the previous `limma` workflow (again, created via `DEFormats::as.DGEList()`):

    dge <- DEFormats::as.DGEList(dds_macrophage)

    # estimate dispersion
    dge <- estimateDisp(dge, design)
    # perform likelihood ratio test
    fit <- glmFit(dge, design)

Here as well, we define the contrasts of interest:

    # available comparisons
    colnames(design)
    #> [1] "(Intercept)"      "lineeiwy_1"       "linefikt_3"       "lineieki_2"
    #> [5] "linepodx_1"       "lineqaqx_1"       "groupIFNg"        "groupIFNg_SL1344"
    #> [9] "groupSL1344"
    # setup comparisons
    contrast_matrix <- makeContrasts(
      IFNg_vs_Naive = groupIFNg,
      Salm_vs_Naive = groupSL1344,
      levels = design
    )

… and. we create the `DGELRT` objects to store the DE results as follows:

    # DGELRT objects
    dge_lrt_IFNg_vs_naive <- glmLRT(fit,
                                    contrast = contrast_matrix[, "IFNg_vs_Naive"])
    dge_lrt_Salm_vs_naive <- glmLRT(fit,
                                    contrast = contrast_matrix[, "Salm_vs_Naive"])

The top 10 DE genes computed within the edgeR framework can be shown as in this chunk:

    # show top 10 genes, first few columns
    topTags(dge_lrt_IFNg_vs_naive, n = 10)[, 1:7]
    #> Coefficient:  1*groupIFNg
    #>                 seqnames     start       end  width strand            gene_id
    #> ENSG00000204257     chr6  32948613  32969094  20482      - ENSG00000204257.14
    #> ENSG00000231389     chr6  33064569  33080775  16207      -  ENSG00000231389.7
    #> ENSG00000123992     chr2 219373527 219400022  26496      - ENSG00000123992.19
    #> ENSG00000227531     chr9 111139246 111284836 145591      -  ENSG00000227531.1
    #> ENSG00000197448     chr7 143244093 143270854  26762      + ENSG00000197448.13
    #> ENSG00000233621     chr1  37454879  37474411  19533      -  ENSG00000233621.1
    #> ENSG00000065911     chr2  74198562  74217565  19004      + ENSG00000065911.11
    #> ENSG00000135124    chr12 121209857 121234106  24250      + ENSG00000135124.14
    #> ENSG00000163131     chr1 150730196 150765957  35762      - ENSG00000163131.10
    #> ENSG00000100418    chr22  41598028  41621096  23069      -  ENSG00000100418.7
    #>                    SYMBOL
    #> ENSG00000204257   HLA-DMA
    #> ENSG00000231389  HLA-DPA1
    #> ENSG00000123992     DNPEP
    #> ENSG00000227531      <NA>
    #> ENSG00000197448     GSTK1
    #> ENSG00000233621 LINC01137
    #> ENSG00000065911    MTHFD2
    #> ENSG00000135124     P2RX4
    #> ENSG00000163131      CTSS
    #> ENSG00000100418     DESI1

For the sake of demonstrating that `DeeDeeExperiment` can handle the different objects from the `edgeR` framework, we also compute some results with the `exactTest()` approach.

    # perform exact test
    # exact test doesn't handle multi factor models, so we have to subset

    # IFNg vs naive
    keep_samples <- dge$samples$group %in% c("naive", "IFNg")
    dge_sub <- dge[, keep_samples]
    # droplevel
    group <- droplevels(dge_sub$samples$group)
    dge_sub$samples$group <- group

    # recalculate normalization and dispersion
    dge_sub <- calcNormFactors(dge_sub)
    dge_sub <- estimateDisp(dge_sub, design = model.matrix(~group))
    # exact test
    # DGEExact object
    dge_exact_IFNg_vs_naive <- exactTest(dge_sub, pair = c("naive", "IFNg"))

    # SL1344 vs naive
    keep_samples <- dge$samples$group %in% c("naive", "SL1344")
    dge_sub <- dge[, keep_samples]
    # droplevel
    group <- droplevels(dge_sub$samples$group)
    dge_sub$samples$group <- group

    # recalculate normalization and dispersion
    dge_sub <- calcNormFactors(dge_sub)
    dge_sub <- estimateDisp(dge_sub, design = model.matrix(~group))
    # exact test
    dge_exact_Salm_vs_naive <- exactTest(dge_sub, pair = c("naive", "SL1344"))

Again, the top 10 DE genes in the `dge_exact_Salm_vs_naive` object can be shown with

    topTags(dge_exact_Salm_vs_naive, n = 10)[, 1:7]
    #> Comparison of groups:  SL1344-naive
    #>                 seqnames     start       end  width strand            gene_id
    #> ENSG00000234394     chr9  68306528  68330626  24099      +  ENSG00000234394.8
    #> ENSG00000132405     chr4   6909242   7033118 123877      + ENSG00000132405.18
    #> ENSG00000109756     chr4 159103013 159360174 257162      +  ENSG00000109756.9
    #> ENSG00000078804    chr20  34704290  34713439   9150      + ENSG00000078804.12
    #> ENSG00000105711    chr19  35030466  35040449   9984      + ENSG00000105711.11
    #> ENSG00000188211    chr11  17351726  17377341  25616      +  ENSG00000188211.8
    #> ENSG00000214113     chr6   5102593   5260939 158347      - ENSG00000214113.10
    #> ENSG00000253535     chr8  24295814  24912073 616260      -  ENSG00000253535.5
    #> ENSG00000179826    chr11  18120955  18138480  17526      +  ENSG00000179826.6
    #> ENSG00000183246    chr22  21545357  21551461   6105      -  ENSG00000183246.6
    #>                   SYMBOL
    #> ENSG00000234394     <NA>
    #> ENSG00000132405  TBC1D14
    #> ENSG00000109756  RAPGEF2
    #> ENSG00000078804 TP53INP2
    #> ENSG00000105711    SCN1B
    #> ENSG00000188211  NCR3LG1
    #> ENSG00000214113    LYRM4
    #> ENSG00000253535     <NA>
    #> ENSG00000179826  MRGPRX3
    #> ENSG00000183246  RIMBP3C

We now have two `DGELRT` & two `DGEExact` objects: `dge_lrt_IFNg_vs_naive`, `dge_lrt_Salm_vs_naive` & `dge_exact_IFNg_vs_naive`, `dge_exact_Salm_vs_naive` respectively, ready to be integrated into a `DeeDeeExperiment` object.

For demonstration purposes, we will use precomputed FEA results generated with `run_topGO()` (a practical wrapper provided by the `mosdef` package), and with the `gost()` function from `gprofiler2` package.

    # load Functional Enrichment Analysis results examples
    data("topGO_results_list", package = "DeeDeeExperiment")
    data("gost_res", package = "DeeDeeExperiment")
    data("clusterPro_res", package = "DeeDeeExperiment")

The enrichment results can be displayed in a summary way with these commands:

    # ifng vs naive
    head(topGO_results_list$ifng_vs_naive)
    #>        GO.ID
    #> 1 GO:0002822
    #> 2 GO:0002697
    #> 3 GO:0002250
    #> 4 GO:1904064
    #> 5 GO:0042098
    #> 6 GO:0070663
    #>                                                                                                                                      Term
    #> 1 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> 2                                                                                                   regulation of immune effector process
    #> 3                                                                                                                adaptive immune response
    #> 4                                                                                   positive regulation of cation transmembrane transport
    #> 5                                                                                                                    T cell proliferation
    #> 6                                                                                                   regulation of leukocyte proliferation
    #>   Annotated Significant Expected Rank in p.value_classic p.value_elim
    #> 1        11           6     0.55                       2     3.90e-06
    #> 2        13           6     0.65                       5     1.30e-05
    #> 3        17          10     0.85                       1     3.70e-05
    #> 4        10           4     0.50                      13     8.80e-04
    #> 5        10           4     0.50                      14     8.80e-04
    #> 6        11           4     0.55                      17     1.34e-03
    #>   p.value_classic
    #> 1        3.90e-06
    #> 2        1.30e-05
    #> 3        4.00e-10
    #> 4        8.80e-04
    #> 5        8.80e-04
    #> 6        1.34e-03
    #>                                                            genes
    #> 1                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> 2                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> 3 CD28,CD69,CR1L,CTSS,HLA-DMA,HLA-DPA1,IL27,LAMP3,SECTM1,TNFSF18
    #> 4                                          ANK2,CTSS,KCNE5,P2RX4
    #> 5                                     CD28,HLA-DPA1,IL27,TNFSF18
    #> 6                                     CD28,HLA-DPA1,IL27,TNFSF18

    # salmonella vs naive
    head(gost_res$result)
    #>     query significant    p_value term_size query_size intersection_size
    #> 1 query_1        TRUE 0.01425000      2551         85                30
    #> 2 query_1        TRUE 0.03856196       625         85                13
    #> 3 query_1        TRUE 0.03856196       625         85                13
    #> 4 query_1        TRUE 0.03904673      1616         85                22
    #> 5 query_1       FALSE 0.39973004       164         85                 6
    #> 6 query_1       FALSE 0.41892532       791         85                13
    #>    precision     recall    term_id source
    #> 1 0.35294118 0.01176009 GO:0042221  GO:BP
    #> 2 0.15294118 0.02080000 GO:0034097  GO:BP
    #> 3 0.15294118 0.02080000 GO:1901652  GO:BP
    #> 4 0.25882353 0.01361386 GO:0070887  GO:BP
    #> 5 0.07058824 0.03658537 GO:0071356  GO:BP
    #> 6 0.15294118 0.01643489 GO:1901701  GO:BP
    #>                                         term_name effective_domain_size
    #> 1                            response to chemical                 16175
    #> 2                            response to cytokine                 16175
    #> 3                             response to peptide                 16175
    #> 4          cellular response to chemical stimulus                 16175
    #> 5      cellular response to tumor necrosis factor                 16175
    #> 6 cellular response to oxygen-containing compound                 16175
    #>   source_order      parents
    #> 1        10049   GO:0050896
    #> 2         8408   GO:1901652
    #> 3        21619   GO:0042221
    #> 4        16358 GO:00422....
    #> 5        16608 GO:00346....
    #> 6        21654 GO:00708....
    #>                                                                                                                                                                                        evidence_codes
    #> 1 IMP,IDA,IDA IBA,IDA IMP ISS,IDA ISS IBA,IDA TAS,IMP,TAS,IDA,IBA,ISS,ISS IBA,IDA IMP IGI IBA,IDA,IMP,IDA ISS,IDA,IEP IBA,IEP,IDA IEP,IDA IEP,IMP,IBA,ISS,IDA IEP ISS IBA TAS,IDA,IMP IBA,ISS,IDA,ISS
    #> 2                                                                                                                 IMP,IDA IBA,ISS,ISS,ISS IBA,IDA IMP IGI IBA,IDA,IDA,IEP,IDA,IDA IEP ISS IBA,IDA,ISS
    #> 3                                                                                                                 IMP,IDA IBA,ISS,ISS,ISS IBA,IDA IMP IGI IBA,IDA,IDA,IEP,IDA,IDA IEP ISS IBA,IDA,ISS
    #> 4                                                                 IDA,IDA IMP ISS,IDA IBA,IDA TAS,IMP,TAS,IDA,IBA,ISS,IDA IMP IBA,IMP,ISS,IDA,IEP IBA,IDA IEP,IDA,ISS,IDA ISS IBA,IDA,IMP IBA,ISS,ISS
    #> 5                                                                                                                                                         ISS IBA,IDA IMP IGI,IDA,IEP,IEP ISS IBA,ISS
    #> 6                                                                                                                                     IDA,IDA,IDA IBA,IMP,TAS,IDA,IDA IMP IBA,IMP,IDA,ISS,IDA,IMP,ISS
    #>                                                                                                                                                                          intersection
    #> 1 LAMP3,RAPGEF2,IL12RB2,P2RX4,SHPK,CYP3A5,ADPRS,AGRN,TRERF1,SRXN1,UGCG,TNFSF18,RELA,NAIP,OSBPL7,CH25H,DNAJA1,MT1A,HAS2,PDE2A,SLC11A2,HAMP,KHK,GREM1,CCL2,LCOR,TSHR,GPR37,ADAR,OCSTAMP
    #> 2                                                                                                        LAMP3,IL12RB2,SHPK,UGCG,TNFSF18,RELA,NAIP,CH25H,HAS2,PDE2A,CCL2,ADAR,OCSTAMP
    #> 3                                                                                                        LAMP3,IL12RB2,SHPK,UGCG,TNFSF18,RELA,NAIP,CH25H,HAS2,PDE2A,CCL2,ADAR,OCSTAMP
    #> 4                                               RAPGEF2,P2RX4,SHPK,CYP3A5,ADPRS,AGRN,TRERF1,SRXN1,UGCG,RELA,OSBPL7,CH25H,DNAJA1,MT1A,PDE2A,SLC11A2,GREM1,CCL2,LCOR,TSHR,GPR37,OCSTAMP
    #> 5                                                                                                                                                 TNFSF18,RELA,NAIP,HAS2,CCL2,OCSTAMP
    #> 6                                                                                                         RAPGEF2,P2RX4,SHPK,ADPRS,AGRN,TRERF1,RELA,OSBPL7,PDE2A,CCL2,LCOR,TSHR,GPR37

    # ifng vs naive
    head(clusterPro_res$ifng_vs_naive)
    #>                    ID
    #> GO:0002250 GO:0002250
    #> GO:0002819 GO:0002819
    #> GO:0002822 GO:0002822
    #> GO:0002460 GO:0002460
    #> GO:0002697 GO:0002697
    #> GO:0050776 GO:0050776
    #>                                                                                                                                        Description
    #> GO:0002250                                                                                                                adaptive immune response
    #> GO:0002819                                                                                                  regulation of adaptive immune response
    #> GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002460               adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002697                                                                                                   regulation of immune effector process
    #> GO:0050776                                                                                                           regulation of immune response
    #>            GeneRatio BgRatio RichFactor FoldEnrichment    zScore       pvalue
    #> GO:0002250     10/37  17/744  0.5882353      11.828299 10.325309 3.958927e-10
    #> GO:0002819      6/37  11/744  0.5454545      10.968059  7.614485 3.872646e-06
    #> GO:0002822      6/37  11/744  0.5454545      10.968059  7.614485 3.872646e-06
    #> GO:0002460      6/37  12/744  0.5000000      10.054054  7.228759 7.465887e-06
    #> GO:0002697      6/37  13/744  0.4615385       9.280665  6.885949 1.336486e-05
    #> GO:0050776      9/37  41/744  0.2195122       4.413975  5.141149 7.666847e-05
    #>                p.adjust       qvalue
    #> GO:0002250 2.561426e-07 2.233668e-07
    #> GO:0002819 8.352007e-04 7.283293e-04
    #> GO:0002822 8.352007e-04 7.283293e-04
    #> GO:0002460 1.207607e-03 1.053083e-03
    #> GO:0002697 1.729413e-03 1.508119e-03
    #> GO:0050776 8.267416e-03 7.209526e-03
    #>                                                                                                                                                                     geneID
    #> GO:0002250 ENSG00000231389/ENSG00000204257/ENSG00000078081/ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000163131/ENSG00000110848
    #> GO:0002819                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0002822                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0002460                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0002697                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0050776                 ENSG00000231389/ENSG00000204257/ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000163131/ENSG00000110848
    #>            Count
    #> GO:0002250    10
    #> GO:0002819     6
    #> GO:0002822     6
    #> GO:0002460     6
    #> GO:0002697     6
    #> GO:0050776     9

To check which formats of Functional Enrichment Analysis (FEA) results are supported natively within `DeeDeeExperiment`, simply run the following command:

    DeeDeeExperiment::supported_fea_formats()
    #>         Format         Package
    #> 1   data.frame           topGO
    #> 2 enrichResult clusterProfiler
    #> 3   gseaResult clusterProfiler
    #> 4  fgseaResult           fgsea
    #> 5   data.frame      gprofiler2
    #> 6   data.frame         enrichR
    #> 7   data.frame           DAVID
    #> 8   data.frame       GeneTonic

By now, we collected DEA results from three different frameworks and generated multiple FEA results. Each result has its own structure, columns, metadata, …

Managing and exploring this growing stack of outputs can quickly become overwhelming. So let’s now see how `DeeDeeExperiment` brings all these pieces together.

# Creating `DeeDeeExperiment` the DeeDee way

In the following example we construct the dde object using a `dds` object and named list of DE results

    # initialize DeeDeeExperiment with dds object and DESeq results (as a named list)
    dde <- DeeDeeExperiment(
      sce = dds_macrophage,
      de_results = list(
        IFNg_vs_naive = IFNg_vs_naive,
        Salm_vs_naive = Salm_vs_naive
      )
    )

    dde
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(8): tximetaInfo quantInfo ... version singlecontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(58): gene_id SYMBOL ... Salm_vs_naive_pvalue
    #>   Salm_vs_naive_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(2): IFNg_vs_naive, Salm_vs_naive
    #> fea(0):

You can see from the summary overview that the information on the two `dea` has been added to the original object.

## Adding results to a `dde` object

### Adding DEA results

Additional DE results can be added using the `addDEA()` method. These results are stored in both the `dea` slot and in the `rowData`.

We can also add results as individual element of class `DESeqResults`, `MArrayLM`, `DGEExact` or `DGELRT`.

    # add a named list of results from edgeR
    dde <- addDEA(dde, dea = list(
      dge_exact_IFNg_vs_naive = dge_exact_IFNg_vs_naive,
      dge_lrt_IFNg_vs_naive = dge_lrt_IFNg_vs_naive
    ))

    # add results from limma as a MArrayLM object
    dde <- addDEA(dde, dea = de_limma)

    dde
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(8): tximetaInfo quantInfo ... version singlecontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(67): gene_id SYMBOL ... de_limma_pvalue de_limma_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(5): IFNg_vs_naive, Salm_vs_naive, dge_exact_IFNg_vs_naive, dge_lrt_IFNg_vs_naive, de_limma
    #> fea(0):
    # inspect the columns of the rowData
    names(rowData(dde))
    #>  [1] "gene_id"
    #>  [2] "SYMBOL"
    #>  [3] "baseMean"
    #>  [4] "baseVar"
    #>  [5] "allZero"
    #>  [6] "dispGeneEst"
    #>  [7] "dispGeneIter"
    #>  [8] "dispFit"
    #>  [9] "dispersion"
    #> [10] "dispIter"
    #> [11] "dispOutlier"
    #> [12] "dispMAP"
    #> [13] "Intercept"
    #> [14] "line_eiwy_1_vs_diku_1"
    #> [15] "line_fikt_3_vs_diku_1"
    #> [16] "line_ieki_2_vs_diku_1"
    #> [17] "line_podx_1_vs_diku_1"
    #> [18] "line_qaqx_1_vs_diku_1"
    #> [19] "condition_IFNg_vs_naive"
    #> [20] "condition_IFNg_SL1344_vs_naive"
    #> [21] "condition_SL1344_vs_naive"
    #> [22] "SE_Intercept"
    #> [23] "SE_line_eiwy_1_vs_diku_1"
    #> [24] "SE_line_fikt_3_vs_diku_1"
    #> [25] "SE_line_ieki_2_vs_diku_1"
    #> [26] "SE_line_podx_1_vs_diku_1"
    #> [27] "SE_line_qaqx_1_vs_diku_1"
    #> [28] "SE_condition_IFNg_vs_naive"
    #> [29] "SE_condition_IFNg_SL1344_vs_naive"
    #> [30] "SE_condition_SL1344_vs_naive"
    #> [31] "WaldStatistic_Intercept"
    #> [32] "WaldStatistic_line_eiwy_1_vs_diku_1"
    #> [33] "WaldStatistic_line_fikt_3_vs_diku_1"
    #> [34] "WaldStatistic_line_ieki_2_vs_diku_1"
    #> [35] "WaldStatistic_line_podx_1_vs_diku_1"
    #> [36] "WaldStatistic_line_qaqx_1_vs_diku_1"
    #> [37] "WaldStatistic_condition_IFNg_vs_naive"
    #> [38] "WaldStatistic_condition_IFNg_SL1344_vs_naive"
    #> [39] "WaldStatistic_condition_SL1344_vs_naive"
    #> [40] "WaldPvalue_Intercept"
    #> [41] "WaldPvalue_line_eiwy_1_vs_diku_1"
    #> [42] "WaldPvalue_line_fikt_3_vs_diku_1"
    #> [43] "WaldPvalue_line_ieki_2_vs_diku_1"
    #> [44] "WaldPvalue_line_podx_1_vs_diku_1"
    #> [45] "WaldPvalue_line_qaqx_1_vs_diku_1"
    #> [46] "WaldPvalue_condition_IFNg_vs_naive"
    #> [47] "WaldPvalue_condition_IFNg_SL1344_vs_naive"
    #> [48] "WaldPvalue_condition_SL1344_vs_naive"
    #> [49] "betaConv"
    #> [50] "betaIter"
    #> [51] "deviance"
    #> [52] "maxCooks"
    #> [53] "IFNg_vs_naive_log2FoldChange"
    #> [54] "IFNg_vs_naive_pvalue"
    #> [55] "IFNg_vs_naive_padj"
    #> [56] "Salm_vs_naive_log2FoldChange"
    #> [57] "Salm_vs_naive_pvalue"
    #> [58] "Salm_vs_naive_padj"
    #> [59] "dge_exact_IFNg_vs_naive_log2FoldChange"
    #> [60] "dge_exact_IFNg_vs_naive_pvalue"
    #> [61] "dge_exact_IFNg_vs_naive_padj"
    #> [62] "dge_lrt_IFNg_vs_naive_log2FoldChange"
    #> [63] "dge_lrt_IFNg_vs_naive_pvalue"
    #> [64] "dge_lrt_IFNg_vs_naive_padj"
    #> [65] "de_limma_log2FoldChange"
    #> [66] "de_limma_pvalue"
    #> [67] "de_limma_padj"

The `dde` object now contains 5 DEAs in its `dea` slot. The `rowData` also includes a record of the `log2FoldChange`, `pvalue`, and `padj` values for each contrast.

It is possible to overwrite the results, when the argument `force` is set to `TRUE`

    dde <- addDEA(dde, dea = list(same_contrast = dge_lrt_Salm_vs_naive))

    # overwrite results with the same name
    # e.g. if the content of the same object has changed
    dde <- addDEA(dde,
                   dea = list(same_contrast = dge_exact_Salm_vs_naive),
                   force = TRUE)

    dde
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(8): tximetaInfo quantInfo ... version singlecontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(70): gene_id SYMBOL ... same_contrast_pvalue
    #>   same_contrast_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(6): IFNg_vs_naive, Salm_vs_naive, dge_exact_IFNg_vs_naive, dge_lrt_IFNg_vs_naive, de_limma, same_contrast
    #> fea(0):

### Adding multi-contrast DEA results

In `limma`, an `MArrayLM` object may contain multiple contrasts, each represented as a column in the `coefficients` matrix. When such an object is supplied to `DeeDeeExperiment`, only one contrast (by default, coefficient 2) can be extracted at a time. The constructor therefore issues a warning when it detects more than one coefficient:

    dde_limma <- DeeDeeExperiment(sce = dds_macrophage,
                                  de_results = de_limma)

    dde_limma
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(8): tximetaInfo quantInfo ... version singlecontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(55): gene_id SYMBOL ... de_limma_pvalue de_limma_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(1): de_limma
    #> fea(0):

To work with all contrasts contained in a multi-contrast `MArrayLM` object, the results must first be expanded into a list of per-contrast tables. This is achieved using the `limma_list_for_dde()` helper function:

    dde_limma_list <- limma_list_for_dde(fit = de_limma,
                                         number = nrow(de_limma),
                                         sort.by = "none")

    names(dde_limma_list)
    #> [1] "IFNg_vs_Naive" "Salm_vs_Naive"

The output is a named list, where each element corresponds to one contrast in the original `MArrayLM` object. Now we can insert all contrast directly into a `DeeDeeExperiment` object at once:

    dde_limma <- addDEA(dde_limma,
                        dea = dde_limma_list)

    dde_limma
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(9): tximetaInfo quantInfo ... singlecontrast multicontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(61): gene_id SYMBOL ... Salm_vs_Naive_pvalue
    #>   Salm_vs_Naive_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(3): de_limma, IFNg_vs_Naive, Salm_vs_Naive
    #> fea(0):

### Adding FEA results

FEA results can be added to a `dde` object using the `addFEA()` method. These results are stored in the `fea` slot.

If the `fea_tool` argument was not specified, the internal function used to generate the standardized format of FEA result is automatically detected - this should work in most cases, but it can be beneficial to explicitly specify the `fea_tool` used, also for simple bookkeeping reasons.

    # add FEA results as a named list
    dde <- addFEA(dde,
                   fea = list(IFNg_vs_naive = topGO_results_list$ifng_vs_naive))

    # add FEA results as a single object
    dde <- addFEA(dde, fea = gost_res$result)

    # add FEA results and specify the FEA tool
    dde <- addFEA(dde, fea = clusterPro_res, fea_tool = "clusterProfiler")

    dde
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(8): tximetaInfo quantInfo ... version singlecontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(70): gene_id SYMBOL ... same_contrast_pvalue
    #>   same_contrast_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(6): IFNg_vs_naive, Salm_vs_naive, dge_exact_IFNg_vs_naive, dge_lrt_IFNg_vs_naive, de_limma, same_contrast
    #> fea(4): IFNg_vs_naive, gost_res$result, ifng_vs_naive, salmonella_vs_naive

## Linking DE and FE Analysis in a `dde` object

Once DE and FE results have been added, FEAs can be linked to specific DE contrasts using the `linkDEAandFEA()` method. This maintains a clear association between the analyses and helps document it within the `dde` object.

    dde <- linkDEAandFEA(dde,
                            dea_name = "Salm_vs_naive",
                            fea_name = c("salmonella_vs_naive", "gost_res$result")
    )

This is then directly visible in the object summary, provided with the dedicated `summary()` method:

    summary(dde)
    #> DE Results Summary:
    #>                 DEA_name  Up Down  FDR
    #>            IFNg_vs_naive  36   17 0.05
    #>            Salm_vs_naive  90   34 0.05
    #>  dge_exact_IFNg_vs_naive  91   67 0.05
    #>    dge_lrt_IFNg_vs_naive 156  145 0.05
    #>                 de_limma 503   98 0.05
    #>            same_contrast 166  184 0.05
    #>
    #> FE Results Summary:
    #>             FEA_Name     Linked_DE         FE_Type Term_Number
    #>        IFNg_vs_naive IFNg_vs_naive           topGO         955
    #>      gost_res$result Salm_vs_naive       gProfiler        2095
    #>        ifng_vs_naive             . clusterProfiler          20
    #>  salmonella_vs_naive Salm_vs_naive clusterProfiler          11

## Adding contextual information

It is possible to add context to specific DEA results that are stored in a `dde` object using the `addScenarioInfo()` method. This information can help document the experimental setup, clarify comparisons, or maybe provided as additional element to a large language model that can benefit of the extra bits to assist in the interpretation.

    dde <- addScenarioInfo(dde,
                             dea_name = "IFNg_vs_naive",
                             info = "This results contains the output of a Differential Expression Analysis performed on data from the `macrophage` package, more precisely contrasting the counts from naive macrophage to those associated with IFNg."
    )

## Summary of a `dde` object

All results stored in a `dde` object can be easily inspected with the `summary()` method

    # minimal summary
    summary(dde)
    #> DE Results Summary:
    #>                 DEA_name  Up Down  FDR
    #>            IFNg_vs_naive  36   17 0.05
    #>            Salm_vs_naive  90   34 0.05
    #>  dge_exact_IFNg_vs_naive  91   67 0.05
    #>    dge_lrt_IFNg_vs_naive 156  145 0.05
    #>                 de_limma 503   98 0.05
    #>            same_contrast 166  184 0.05
    #>
    #> FE Results Summary:
    #>             FEA_Name     Linked_DE         FE_Type Term_Number
    #>        IFNg_vs_naive IFNg_vs_naive           topGO         955
    #>      gost_res$result Salm_vs_naive       gProfiler        2095
    #>        ifng_vs_naive             . clusterProfiler          20
    #>  salmonella_vs_naive Salm_vs_naive clusterProfiler          11

It is possible to customize the output of `summary()` using optional arguments. For example, the `FDR` threshold can be adjusted to display the number of up- and downregulated genes for each comparison.

    # specify FDR threshold for subsetting DE genes based on adjusted p-values
    summary(dde, FDR = 0.01)
    #> DE Results Summary:
    #>                 DEA_name  Up Down  FDR
    #>            IFNg_vs_naive  30   15 0.01
    #>            Salm_vs_naive  80   29 0.01
    #>  dge_exact_IFNg_vs_naive  68   42 0.01
    #>    dge_lrt_IFNg_vs_naive 123  107 0.01
    #>                 de_limma 415   69 0.01
    #>            same_contrast 128  126 0.01
    #>
    #> FE Results Summary:
    #>             FEA_Name     Linked_DE         FE_Type Term_Number
    #>        IFNg_vs_naive IFNg_vs_naive           topGO         955
    #>      gost_res$result Salm_vs_naive       gProfiler        2095
    #>        ifng_vs_naive             . clusterProfiler          20
    #>  salmonella_vs_naive Salm_vs_naive clusterProfiler          11

It is also possible to include any scenario information associated with each DEA by setting `show_scenario_info = TRUE`

    # show contextual information, if available
    summary(dde, show_scenario_info = TRUE)
    #> DE Results Summary:
    #>                 DEA_name  Up Down  FDR
    #>            IFNg_vs_naive  36   17 0.05
    #>            Salm_vs_naive  90   34 0.05
    #>  dge_exact_IFNg_vs_naive  91   67 0.05
    #>    dge_lrt_IFNg_vs_naive 156  145 0.05
    #>                 de_limma 503   98 0.05
    #>            same_contrast 166  184 0.05
    #>
    #> FE Results Summary:
    #>             FEA_Name     Linked_DE         FE_Type Term_Number
    #>        IFNg_vs_naive IFNg_vs_naive           topGO         955
    #>      gost_res$result Salm_vs_naive       gProfiler        2095
    #>        ifng_vs_naive             . clusterProfiler          20
    #>  salmonella_vs_naive Salm_vs_naive clusterProfiler          11
    #>
    #> Scenario Info:
    #>  - IFNg_vs_naive :
    #>  This results contains the output of a Differential Expression Analysis
    #>   performed on data from the `macrophage` package, more precisely contrasting
    #>   the counts from naive macrophage to those associated with IFNg.
    #>
    #>
    #> No scenario info for: Salm_vs_naive, dge_exact_IFNg_vs_naive, dge_lrt_IFNg_vs_naive, de_limma, same_contrast

## Renaming results in a `dde` object

Specific results can be renamed after being added using the `renameDEA()` and `renameFEA()` methods. The corresponding column names in the `rowData` will be updated accordingly.

    # rename dea, one element
    dde <- renameDEA(dde,
                      old_name = "de_limma",
                      new_name = "ifng_vs_naive_&_salm_vs_naive"
    )

    # multiple entries at once
    dde <- renameDEA(dde,
                      old_name = c("dge_exact_IFNg_vs_naive", "dge_lrt_IFNg_vs_naive"),
                      new_name = c("exact_IFNg_vs_naive", "lrt_IFNg_vs_naive")
    )

    # rename fea
    dde <- renameFEA(dde,
                      old_name = "gost_res$result",
                      new_name = "salm_vs_naive"
    )

## Removing results in a `dde` object

The method `removeDEA()` and `removeFEA()` can be used to delete results from a `dde` object.

    # removing dea
    dde <- removeDEA(dde, c(
      "ifng_vs_naive_&_salm_vs_naive",
      "exact_IFNg_vs_naive",
      "dge_Salm_vs_naive"
    ))

    # removing fea
    dde <- removeFEA(dde, c("salmonella_vs_naive"))

    dde
    #> class: DeeDeeExperiment
    #> dim: 1000 24
    #> metadata(8): tximetaInfo quantInfo ... version singlecontrast
    #> assays(7): counts abundance ... H cooks
    #> rownames(1000): ENSG00000164741 ENSG00000078808 ... ENSG00000273680
    #>   ENSG00000073536
    #> rowData names(64): gene_id SYMBOL ... same_contrast_pvalue
    #>   same_contrast_padj
    #> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
    #>   SAMEA103884949
    #> colData names(15): names sample_id ... condition line
    #> reducedDimNames(0):
    #> mainExpName: NULL
    #> altExpNames(0):
    #> dea(4): IFNg_vs_naive, Salm_vs_naive, lrt_IFNg_vs_naive, same_contrast
    #> fea(3): IFNg_vs_naive, salm_vs_naive, ifng_vs_naive

## Accessing results stored in a `dde` object

Several accessor methods are available to retrieve DEA and FEA results stored within a `dde` object.

`getDEANames()` and `getFEANames()` are the methods to quickly retrieve the names of available DEAs and FEAs

    # get DEA names
    getDEANames(dde)
    #> [1] "IFNg_vs_naive"     "Salm_vs_naive"     "lrt_IFNg_vs_naive"
    #> [4] "same_contrast"

    # get FEA names
    getFEANames(dde)
    #> [1] "IFNg_vs_naive" "salm_vs_naive" "ifng_vs_naive"

It is possible to directly access specific DEA results either in their original format or in minimal format. Use `format = "original"` to retrieve results in the same class as originally provided (e.g., `DESeqResults`, `MArrayLM`…).

    # access the 1st DEA if dea_name is not specified (default: minimal format)
    getDEA(dde) |> head()
    #> DataFrame with 6 rows and 3 columns
    #>                 IFNg_vs_naive_log2FoldChange IFNg_vs_naive_pvalue
    #>                                    <numeric>            <numeric>
    #> ENSG00000164741                    0.2291487          9.80541e-01
    #> ENSG00000078808                   -0.0115336          1.00000e+00
    #> ENSG00000251034                   -0.1167013          9.33899e-01
    #> ENSG00000162676                    0.2691481          9.00817e-01
    #> ENSG00000170356                   -0.1278637          9.72815e-01
    #> ENSG00000204257                    4.0550244          1.67298e-56
    #>                 IFNg_vs_naive_padj
    #>                          <numeric>
    #> ENSG00000164741        1.00000e+00
    #> ENSG00000078808        1.00000e+00
    #> ENSG00000251034        1.00000e+00
    #> ENSG00000162676        1.00000e+00
    #> ENSG00000170356        1.00000e+00
    #> ENSG00000204257        8.36489e-54

    # access the 1st DEA, in original format
    getDEA(dde, format = "original") |> head()
    #> log2 fold change (MAP): condition IFNg vs naive
    #> Wald test p-value: condition IFNg vs naive
    #> DataFrame with 6 rows and 5 columns
    #>                  baseMean log2FoldChange     lfcSE      pvalue        padj
    #>                 <numeric>      <numeric> <numeric>   <numeric>   <numeric>
    #> ENSG00000164741  918.0347      0.2291487 0.3006751 9.80541e-01 1.00000e+00
    #> ENSG00000078808 6684.4755     -0.0115336 0.0729923 1.00000e+00 1.00000e+00
    #> ENSG00000251034   12.1241     -0.1167013 0.4049828 9.33899e-01 1.00000e+00
    #> ENSG00000162676   10.2220      0.2691481 0.3776971 9.00817e-01 1.00000e+00
    #> ENSG00000170356   15.0772     -0.1278637 0.3522078 9.72815e-01 1.00000e+00
    #> ENSG00000204257 1922.0256      4.0550244 0.1960055 1.67298e-56 8.36489e-54

    # access specific DEA by name, in original format
    getDEA(dde,
        dea_name = "lrt_IFNg_vs_naive",
        format = "original"
    ) |> head()
    #> An object of class "DGELRT"
    #> $coefficients
    #>                 (Intercept) lineeiwy_1  linefikt_3  lineieki_2 linepodx_1
    #> ENSG00000164741    5.175854 0.67213278 -0.05259369 -0.01339329 1.29681950
    #> ENSG00000078808    8.278650 0.00402456 -0.03709687  0.21932115 0.11747637
    #> ENSG00000251034    1.014486 0.15070576  0.74924951  0.25503447 0.07053757
    #> ENSG00000162676    1.289952 0.33790749  0.40315514 -0.31901449 0.45655192
    #> ENSG00000170356    2.726918 0.72918602 -3.77848148 -1.12811382 0.22642076
    #> ENSG00000204257    5.014865 0.06189526  0.55228532  0.40284292 0.02648805
    #>                 lineqaqx_1    groupIFNg groupIFNg_SL1344 groupSL1344
    #> ENSG00000164741  2.6465010  0.209413707        0.2092308  0.00836375
    #> ENSG00000078808 -0.1422926 -0.008349654        0.4510972  0.54987336
    #> ENSG00000251034 -0.9178745 -0.152022802        2.0525732  0.93647602
    #> ENSG00000162676 -0.1853235  0.281742319        1.3644068  0.36717291
    #> ENSG00000170356 -1.2678329 -0.134690209        0.3165192 -0.48906881
    #> ENSG00000204257 -0.2266840  2.824388471        2.8415187 -0.52552374
    #>
    #> $fitted.values
    #>                 SAMEA103885102 SAMEA103885347 SAMEA103885043 SAMEA103885392
    #> ENSG00000164741     331.655702     462.039895     263.438037      254.91171
    #> ENSG00000078808    6870.927681    6678.848818    8959.632232     6786.72381
    #> ENSG00000251034       4.905110       4.102499       9.200383       23.86174
    #> ENSG00000162676       6.482184       8.483674       6.859531       15.69818
    #> ENSG00000170356      31.739065      28.581517      17.925568       24.66855
    #> ENSG00000204257     257.657335    4470.300829     116.231942     2839.34248
    #>                 SAMEA103885182 SAMEA103885136 SAMEA103885413 SAMEA103884967
    #> ENSG00000164741     541.659383     683.537995      512.97562      304.28235
    #> ENSG00000078808    6344.399964    5486.055195     8924.93577     5199.85040
    #> ENSG00000251034       5.158169       3.845204       10.59328       20.80845
    #> ENSG00000162676       8.344330       9.757418        9.62274       16.69992
    #> ENSG00000170356      59.952194      46.966007       29.04831       28.29474
    #> ENSG00000204257     267.293328    3939.478849      119.97375     2260.78958
    #>                 SAMEA103885368 SAMEA103885218 SAMEA103885319 SAMEA103885004
    #> ENSG00000164741     359.172061    296.1174498    185.9324214    135.7370135
    #> ENSG00000078808    6581.055519   4732.6198238   6470.3613511   4342.1462486
    #> ENSG00000251034      10.679763      6.5705973     15.3048388     33.3440929
    #> ENSG00000162676       9.770194      9.3988092      8.1610872     15.4164277
    #> ENSG00000170356       0.448430      0.2804508      0.1503089      0.1402329
    #> ENSG00000204257     458.910965   5724.3165612    152.9668030   3171.8565864
    #>                 SAMEA103885284 SAMEA103885059 SAMEA103884898 SAMEA103885157
    #> ENSG00000164741     198.852309     262.446302     157.716956     121.188309
    #> ENSG00000078808    7091.291206    5612.865507    7289.094965    4792.281251
    #> ENSG00000251034       4.658328       3.164519       7.229445      15.865144
    #> ENSG00000162676       3.751586       3.996576       3.242031       6.341893
    #> ENSG00000170356       3.740692       5.307931       3.447916       3.879532
    #> ENSG00000204257     324.869282    4257.347028     110.551863    2388.281074
    #>                 SAMEA103885111 SAMEA103884919 SAMEA103885276 SAMEA103885021
    #> ENSG00000164741    1116.173877     804.006709     654.324347      584.55272
    #> ENSG00000078808    7292.391215    5268.451176    9534.169029     5255.72416
    #> ENSG00000251034       4.560251       2.919708       8.786939       15.94287
    #> ENSG00000162676       4.360673       9.585626      10.152537       17.05258
    #> ENSG00000170356      35.381627      24.447762      18.115629       18.49445
    #> ENSG00000204257     251.760556    3038.849970     109.736481     1860.38020
    #>                 SAMEA103885262 SAMEA103885228 SAMEA103885308 SAMEA103884949
    #> ENSG00000164741    4676.003495    5469.556117    3263.934946    3152.021611
    #> ENSG00000078808    7507.930361    6225.812870    8557.053834    5724.071935
    #> ENSG00000251034       2.285887       1.627765       3.870953       9.082268
    #> ENSG00000162676       6.721858       7.482278       6.309935      13.572609
    #> ENSG00000170356       4.303653       7.585661       2.123053       7.346422
    #> ENSG00000204257     276.832068    3840.528622     105.090593    2239.078240
    #>
    #> $deviance
    #> [1] 17.781168  5.254107 16.617720 12.086169 19.370343 14.245392
    #>
    #> $iter
    #> [1] 8 4 8 7 8 9
    #>
    #> $failed
    #> [1] FALSE FALSE FALSE FALSE FALSE FALSE
    #>
    #> $method
    #> [1] "levenberg"
    #>
    #> $unshrunk.coefficients
    #>                 (Intercept)  lineeiwy_1  linefikt_3  lineieki_2 linepodx_1
    #> ENSG00000164741    5.386057 0.672384945 -0.05264553 -0.01339574 1.29719196
    #> ENSG00000078808    8.492342 0.004024739 -0.03709713  0.21932492 0.11747852
    #> ENSG00000251034    1.194149 0.150285771  0.75707908  0.25656718 0.06892552
    #> ENSG00000162676    1.477140 0.341417291  0.40780179 -0.32581702 0.46190147
    #> ENSG00000170356    2.928045 0.732167607 -4.12610418 -1.14204104 0.22791311
    #> ENSG00000204257    5.226801 0.061960308  0.55257129  0.40298303 0.02649441
    #>                 lineqaqx_1    groupIFNg groupIFNg_SL1344  groupSL1344
    #> ENSG00000164741  2.6469822  0.209484397        0.2094209  0.008415412
    #> ENSG00000078808 -0.1422951 -0.008349908        0.4511057  0.549883290
    #> ENSG00000251034 -0.9476348 -0.157304723        2.0840705  0.956531485
    #> ENSG00000162676 -0.1890086  0.288167022        1.3819958  0.374787459
    #> ENSG00000170356 -1.2851178 -0.136782408        0.3184693 -0.495824479
    #> ENSG00000204257 -0.2268297  2.824948958        2.8420928 -0.525920738
    #>
    #> $df.residual
    #> [1] 15 15 15 15 15 15
    #>
    #> $design
    #>                (Intercept) lineeiwy_1 linefikt_3 lineieki_2 linepodx_1
    #> SAMEA103885102           1          0          0          0          0
    #> SAMEA103885347           1          0          0          0          0
    #> SAMEA103885043           1          0          0          0          0
    #> SAMEA103885392           1          0          0          0          0
    #> SAMEA103885182           1          1          0          0          0
    #>                lineqaqx_1 groupIFNg groupIFNg_SL1344 groupSL1344
    #> SAMEA103885102          0         0                0           0
    #> SAMEA103885347          0         1                0           0
    #> SAMEA103885043          0         0                0           1
    #> SAMEA103885392          0         0                1           0
    #> SAMEA103885182          0         0                0           0
    #> 19 more rows ...
    #>
    #> $offset
    #>           [,1]      [,2]       [,3]        [,4]      [,5]      [,6]       [,7]
    #> [1,] 0.4180404 0.5401098 0.17934576 -0.05456072 0.2361954 0.2593559 0.17337094
    #> [2,] 0.3427124 0.3227088 0.05825912 -0.12072419 0.2589610 0.1219479 0.05035433
    #> [3,] 0.3961281 0.3747517 0.06856426 -0.10594365 0.2961466 0.1596962 0.05925339
    #> [4,] 0.3919171 0.3728362 0.07371127 -0.10559112 0.3030246 0.1713031 0.07078391
    #> [5,] 0.5295032 0.5614976 0.45400752 -0.04098516 0.4333348 0.3259938 0.20457207
    #> [6,] 0.3248296 0.3534611 0.05470751 -0.11756594 0.2995854 0.1650935 0.02443249
    #>            [,8]      [,9]      [,10]      [,11]      [,12]       [,13]
    #> [1,] -0.5499069 0.5503901 0.14786028 -0.1164436 -0.6321131 -0.08009890
    #> [2,] -0.3910874 0.3367055 0.01533919 -0.2301410 -0.5302266  0.15495575
    #> [3,] -0.3931466 0.4171223 0.08868107 -0.1795908 -0.5284183  0.08794008
    #> [4,] -0.3851496 0.3943942 0.06747383 -0.1603522 -0.5315043  0.17085523
    #> [5,] -0.6360060 0.3960564 0.06348445 -0.2011793 -1.0848610 -0.46673327
    #> [6,] -0.4073846 0.3494840 0.04815727 -0.2232306 -0.5593926  0.15363894
    #>            [,14]       [,15]      [,16]      [,17]       [,18]        [,19]
    #> [1,] -0.01209918 -0.32027468 -0.7847366  0.3344130 -0.20312576 -0.208061219
    #> [2,] -0.07050040 -0.36741559 -0.6880109  0.2847662 -0.03197896  0.002933508
    #> [3,] -0.14141082 -0.42908574 -0.7706625  0.2543029 -0.03428650 -0.046339940
    #> [4,] -0.05405233 -0.34991078 -0.6861418 -0.4664154  0.03305583  0.003894374
    #> [5,]  0.01998052 -0.05240958 -0.7487589  0.4102345  0.17736293  0.236641356
    #> [6,] -0.09833143 -0.39837846 -0.6935476  0.2751831 -0.05900987 -0.029292735
    #>           [,20]      [,21]     [,22]       [,23]       [,24]
    #> [1,] -0.5218229  0.4171598 0.3644291  0.04923413 -0.18666088
    #> [2,] -0.4938532  0.5736682 0.3947623  0.15458104 -0.14871694
    #> [3,] -0.5781334  0.5802395 0.3979984  0.15045482 -0.12426103
    #> [4,] -0.4847359  0.6172329 0.4362385  0.17920613 -0.06207378
    #> [5,] -0.5569570 -0.1834631 0.5201149 -0.39424768  0.03281681
    #> [6,] -0.5668520  0.6234398 0.4284451  0.18077229 -0.12824446
    #> attr(,"class")
    #> [1] "CompressedMatrix"
    #> attr(,"Dims")
    #> [1]  6 24
    #> attr(,"repeat.row")
    #> [1] FALSE
    #> attr(,"repeat.col")
    #> [1] FALSE
    #>
    #> $dispersion
    #> [1] 0.15542388 0.01552404 0.14311039 0.11310960 0.11347812 0.05257298
    #>
    #> $prior.count
    #> [1] 0.125
    #>
    #> $samples
    #>                      group lib.size norm.factors          names sample_id
    #> SAMEA103885102       naive  2451140            1 SAMEA103885102    diku_A
    #> SAMEA103885347        IFNg  2319742            1 SAMEA103885347    diku_B
    #> SAMEA103885043      SL1344  2275254            1 SAMEA103885043    diku_C
    #> SAMEA103885392 IFNg_SL1344  2041202            1 SAMEA103885392    diku_D
    #> SAMEA103885182       naive  2415265            1 SAMEA103885182    eiwy_A
    #>                line_id replicate condition_name macrophage_harvest
    #> SAMEA103885102  diku_1         1          naive          11/6/2015
    #> SAMEA103885347  diku_1         1           IFNg          11/6/2015
    #> SAMEA103885043  diku_1         1         SL1344          11/6/2015
    #> SAMEA103885392  diku_1         1    IFNg_SL1344          11/6/2015
    #> SAMEA103885182  eiwy_1         1          naive         11/25/2015
    #>                salmonella_date ng_ul_mean rna_extraction rna_submit
    #> SAMEA103885102      11/13/2015   293.9625     11/30/2015  12/9/2015
    #> SAMEA103885347      11/13/2015   293.9625     11/30/2015  12/9/2015
    #> SAMEA103885043      11/13/2015   293.9625     11/30/2015  12/9/2015
    #> SAMEA103885392      11/13/2015   293.9625     11/30/2015  12/9/2015
    #> SAMEA103885182       12/2/2015   193.5450      12/3/2015  12/9/2015
    #>                       library_pool chemistry rna_auto   line
    #> SAMEA103885102 3226_RNAauto-091215   V4_auto        1 diku_1
    #> SAMEA103885347 3226_RNAauto-091215   V4_auto        1 diku_1
    #> SAMEA103885043 3226_RNAauto-091215   V4_auto        1 diku_1
    #> SAMEA103885392 3226_RNAauto-091215   V4_auto        1 diku_1
    #> SAMEA103885182 3226_RNAauto-091215   V4_auto        1 eiwy_1
    #> 19 more rows ...
    #>
    #> $genes
    #>                 seqnames     start       end  width strand            gene_id
    #> ENSG00000164741     chr8  13083361  13604610 521250      - ENSG00000164741.14
    #> ENSG00000078808     chr1   1216908   1232031  15124      - ENSG00000078808.16
    #> ENSG00000251034     chr8  22540845  22545405   4561      -  ENSG00000251034.1
    #> ENSG00000162676     chr1  92474762  92486876  12115      - ENSG00000162676.11
    #> ENSG00000170356     chr7 144250045 144256244   6200      -  ENSG00000170356.9
    #> ENSG00000204257     chr6  32948613  32969094  20482      - ENSG00000204257.14
    #>                  SYMBOL   baseMean      baseVar allZero dispGeneEst
    #> ENSG00000164741    DLC1  918.03469 1.919478e+06   FALSE 0.176900497
    #> ENSG00000078808    SDF4 6684.47548 4.121476e+06   FALSE 0.005336141
    #> ENSG00000251034    <NA>   12.12408 2.083055e+02   FALSE 0.139939060
    #> ENSG00000162676    GFI1   10.22204 8.849548e+01   FALSE 0.088009082
    #> ENSG00000170356 OR2A20P   15.07716 2.865438e+02   FALSE 0.086413772
    #> ENSG00000204257 HLA-DMA 1922.02562 3.556107e+06   FALSE 0.050271572
    #>                 dispGeneIter    dispFit dispersion dispIter dispOutlier
    #> ENSG00000164741            8 0.07822483 0.16262236       10       FALSE
    #> ENSG00000078808            8 0.07420142 0.00763372        9       FALSE
    #> ENSG00000251034           13 0.42671547 0.19197295        8       FALSE
    #> ENSG00000162676           30 0.49242762 0.14920745        6       FALSE
    #> ENSG00000170356            8 0.35754498 0.15108319        8       FALSE
    #> ENSG00000204257            8 0.07578857 0.05282007        7       FALSE
    #>                    dispMAP Intercept line_eiwy_1_vs_diku_1
    #> ENSG00000164741 0.16262236  7.770353           0.970022375
    #> ENSG00000078808 0.00763372 12.251844           0.005781728
    #> ENSG00000251034 0.19197295  1.755162           0.175340690
    #> ENSG00000162676 0.14920745  2.136581           0.480446534
    #> ENSG00000170356 0.15108319  4.233279           1.056913201
    #> ENSG00000204257 0.05282007  7.540662           0.089423375
    #>                 line_fikt_3_vs_diku_1 line_ieki_2_vs_diku_1
    #> ENSG00000164741           -0.07577429           -0.01929912
    #> ENSG00000078808           -0.05342186            0.31633010
    #> ENSG00000251034            1.05452572            0.33475857
    #> ENSG00000162676            0.58406093           -0.47081704
    #> ENSG00000170356           -5.53704332           -1.66265291
    #> ENSG00000204257            0.79727383            0.58138974
    #>                 line_podx_1_vs_diku_1 line_qaqx_1_vs_diku_1
    #> ENSG00000164741            1.87146580             3.8188294
    #> ENSG00000078808            0.16953519            -0.2053341
    #> ENSG00000251034            0.05036760            -1.4165928
    #> ENSG00000162676            0.66518266            -0.2720575
    #> ENSG00000170356            0.32751605            -1.8548902
    #> ENSG00000204257            0.03820932            -0.3272907
    #>                 condition_IFNg_vs_naive condition_IFNg_SL1344_vs_naive
    #> ENSG00000164741              0.30218964                      0.3025714
    #> ENSG00000078808             -0.01205631                      0.6508812
    #> ENSG00000251034             -0.22261594                      3.0195724
    #> ENSG00000162676              0.42160612                      1.9873533
    #> ENSG00000170356             -0.19625391                      0.4465885
    #> ENSG00000204257              4.07555133                      4.1002828
    #>                 condition_SL1344_vs_naive SE_Intercept SE_line_eiwy_1_vs_diku_1
    #> ENSG00000164741                 0.0121682   0.35946727               0.41475882
    #> ENSG00000078808                 0.7933721   0.07791249               0.09000068
    #> ENSG00000251034                 1.3720821   0.52662721               0.57749189
    #> ENSG00000162676                 0.5395388   0.47091496               0.51421255
    #> ENSG00000170356                -0.7301748   0.40909698               0.43816712
    #> ENSG00000204257                -0.7587539   0.20930500               0.24109955
    #>                 SE_line_fikt_3_vs_diku_1 SE_line_ieki_2_vs_diku_1
    #> ENSG00000164741               0.41637071               0.41707883
    #> ENSG00000078808               0.09007559               0.09001577
    #> ENSG00000251034               0.55423062               0.59092538
    #> ENSG00000162676               0.51540968               0.58148644
    #> ENSG00000170356               1.08119676               0.55495255
    #> ENSG00000204257               0.24021347               0.24106904
    #>                 SE_line_podx_1_vs_diku_1 SE_line_qaqx_1_vs_diku_1
    #> ENSG00000164741               0.41426348               0.41357906
    #> ENSG00000078808               0.08998452               0.08995828
    #> ENSG00000251034               0.58993435               0.64576993
    #> ENSG00000162676               0.52198431               0.52861599
    #> ENSG00000170356               0.44840747               0.53506242
    #> ENSG00000204257               0.24138447               0.24133054
    #>                 SE_condition_IFNg_vs_naive SE_condition_IFNg_SL1344_vs_naive
    #> ENSG00000164741                 0.33811564                        0.33917339
    #> ENSG00000078808                 0.07354079                        0.07356888
    #> ENSG00000251034                 0.55185235                        0.47411418
    #> ENSG00000162676                 0.45132259                        0.43087159
    #> ENSG00000170356                 0.42631745                        0.43364895
    #> ENSG00000204257                 0.19471168                        0.19486534
    #>                 SE_condition_SL1344_vs_naive WaldStatistic_Intercept
    #> ENSG00000164741                   0.33870933               21.616302
    #> ENSG00000078808                   0.07340909              157.251345
    #> ENSG00000251034                   0.49674549                3.332836
    #> ENSG00000162676                   0.45764794                4.537084
    #> ENSG00000170356                   0.44708739               10.347861
    #> ENSG00000204257                   0.20193856               36.027147
    #>                 WaldStatistic_line_eiwy_1_vs_diku_1
    #> ENSG00000164741                          2.33876251
    #> ENSG00000078808                          0.06424094
    #> ENSG00000251034                          0.30362451
    #> ENSG00000162676                          0.93433451
    #> ENSG00000170356                          2.41212351
    #> ENSG00000204257                          0.37089814
    #>                 WaldStatistic_line_fikt_3_vs_diku_1
    #> ENSG00000164741                          -0.1819876
    #> ENSG00000078808                          -0.5930781
    #> ENSG00000251034                           1.9026840
    #> ENSG00000162676                           1.1331974
    #> ENSG00000170356                          -5.1212171
    #> ENSG00000204257                           3.3190221
    #>                 WaldStatistic_line_ieki_2_vs_diku_1
    #> ENSG00000164741                         -0.04627211
    #> ENSG00000078808                          3.51416314
    #> ENSG00000251034                          0.56649888
    #> ENSG00000162676                         -0.80967846
    #> ENSG00000170356                         -2.99602717
    #> ENSG00000204257                          2.41171465
    #>                 WaldStatistic_line_podx_1_vs_diku_1
    #> ENSG00000164741                          4.51757370
    #> ENSG00000078808                          1.88404850
    #> ENSG00000251034                          0.08537831
    #> ENSG00000162676                          1.27433458
    #> ENSG00000170356                          0.73039829
    #> ENSG00000204257                          0.15829236
    #>                 WaldStatistic_line_qaqx_1_vs_diku_1
    #> ENSG00000164741                            9.233614
    #> ENSG00000078808                           -2.282548
    #> ENSG00000251034                           -2.193649
    #> ENSG00000162676                           -0.514660
    #> ENSG00000170356                           -3.466680
    #> ENSG00000204257                           -1.356193
    #>                 WaldStatistic_condition_IFNg_vs_naive
    #> ENSG00000164741                             0.8937464
    #> ENSG00000078808                            -0.1639404
    #> ENSG00000251034                            -0.4033977
    #> ENSG00000162676                             0.9341569
    #> ENSG00000170356                            -0.4603469
    #> ENSG00000204257                            20.9312117
    #>                 WaldStatistic_condition_IFNg_SL1344_vs_naive
    #> ENSG00000164741                                    0.8920846
    #> ENSG00000078808                                    8.8472347
    #> ENSG00000251034                                    6.3688717
    #> ENSG00000162676                                    4.6124028
    #> ENSG00000170356                                    1.0298387
    #> ENSG00000204257                                   21.0416218
    #>                 WaldStatistic_condition_SL1344_vs_naive WaldPvalue_Intercept
    #> ENSG00000164741                              0.03592521        1.261922e-103
    #> ENSG00000078808                             10.80754512         0.000000e+00
    #> ENSG00000251034                              2.76214305         8.596573e-04
    #> ENSG00000162676                              1.17893863         5.703737e-06
    #> ENSG00000170356                             -1.63318139         4.279378e-25
    #> ENSG00000204257                             -3.75735038        3.144547e-284
    #>                 WaldPvalue_line_eiwy_1_vs_diku_1
    #> ENSG00000164741                       0.01934773
    #> ENSG00000078808                       0.94877838
    #> ENSG00000251034                       0.76141398
    #> ENSG00000162676                       0.35013137
    #> ENSG00000170356                       0.01585991
    #> ENSG00000204257                       0.71071340
    #>                 WaldPvalue_line_fikt_3_vs_diku_1
    #> ENSG00000164741                     8.555925e-01
    #> ENSG00000078808                     5.531289e-01
    #> ENSG00000251034                     5.708179e-02
    #> ENSG00000162676                     2.571313e-01
    #> ENSG00000170356                     3.035699e-07
    #> ENSG00000204257                     9.033327e-04
    #>                 WaldPvalue_line_ieki_2_vs_diku_1
    #> ENSG00000164741                     0.9630933700
    #> ENSG00000078808                     0.0004411418
    #> ENSG00000251034                     0.5710546971
    #> ENSG00000162676                     0.4181250005
    #> ENSG00000170356                     0.0027352206
    #> ENSG00000204257                     0.0158777027
    #>                 WaldPvalue_line_podx_1_vs_diku_1
    #> ENSG00000164741                     6.255226e-06
    #> ENSG00000078808                     5.955842e-02
    #> ENSG00000251034                     9.319606e-01
    #> ENSG00000162676                     2.025449e-01
    #> ENSG00000170356                     4.651468e-01
    #> ENSG00000204257                     8.742264e-01
    #>                 WaldPvalue_line_qaqx_1_vs_diku_1
    #> ENSG00000164741                     2.616515e-20
    #> ENSG00000078808                     2.245698e-02
    #> ENSG00000251034                     2.826063e-02
    #> ENSG00000162676                     6.067907e-01
    #> ENSG00000170356                     5.269289e-04
    #> ENSG00000204257                     1.750379e-01
    #>                 WaldPvalue_condition_IFNg_vs_naive
    #> ENSG00000164741                       3.714576e-01
    #> ENSG00000078808                       8.697780e-01
    #> ENSG00000251034                       6.866557e-01
    #> ENSG00000162676                       3.502229e-01
    #> ENSG00000170356                       6.452673e-01
    #> ENSG00000204257                       2.783309e-97
    #>                 WaldPvalue_condition_IFNg_SL1344_vs_naive
    #> ENSG00000164741                              3.723476e-01
    #> ENSG00000078808                              8.971461e-19
    #> ENSG00000251034                              1.904239e-10
    #> ENSG00000162676                              3.980406e-06
    #> ENSG00000170356                              3.030857e-01
    #> ENSG00000204257                              2.728839e-98
    #>                 WaldPvalue_condition_SL1344_vs_naive betaConv betaIter deviance
    #> ENSG00000164741                         9.713420e-01     TRUE        5 315.9203
    #> ENSG00000078808                         3.170408e-27     TRUE        3 358.9627
    #> ENSG00000251034                         5.742331e-03     TRUE        5 124.7960
    #> ENSG00000162676                         2.384226e-01     TRUE        5 124.1491
    #> ENSG00000170356                         1.024309e-01     TRUE        9 129.6075
    #> ENSG00000204257                         1.717220e-04     TRUE        4 307.6203
    #>                 maxCooks
    #> ENSG00000164741       NA
    #> ENSG00000078808       NA
    #> ENSG00000251034       NA
    #> ENSG00000162676       NA
    #> ENSG00000170356       NA
    #> ENSG00000204257       NA
    #>
    #> $prior.df
    #> [1] 3.945018
    #>
    #> $AveLogCPM
    #> [1]  9.002025 11.648884  2.516565  2.454987  3.149487  9.783568
    #>
    #> $table
    #>                      logFC    logCPM           LR       PValue
    #> ENSG00000164741  0.3021201  9.002025   0.82710072 3.631122e-01
    #> ENSG00000078808 -0.0120460 11.648884   0.01332429 9.081036e-01
    #> ENSG00000251034 -0.2193225  2.516565   0.19045944 6.625345e-01
    #> ENSG00000162676  0.4064682  2.454987   0.95391159 3.287256e-01
    #> ENSG00000170356 -0.1943169  3.149487   0.25037488 6.168112e-01
    #> ENSG00000204257  4.0747312  9.783568 347.09044460 1.822821e-77
    #>
    #> $comparison
    #> [1] "1*groupIFNg"
    #>
    #> $df.test
    #> [1] 1 1 1 1 1 1

    # access specific DEA by name (default: minimal format)
    getDEA(dde, dea_name = "lrt_IFNg_vs_naive") |> head()
    #> DataFrame with 6 rows and 3 columns
    #>                 lrt_IFNg_vs_naive_log2FoldChange lrt_IFNg_vs_naive_pvalue
    #>                                        <numeric>                <numeric>
    #> ENSG00000164741                         0.302120              3.63112e-01
    #> ENSG00000078808                        -0.012046              9.08104e-01
    #> ENSG00000251034                        -0.219323              6.62535e-01
    #> ENSG00000162676                         0.406468              3.28726e-01
    #> ENSG00000170356                        -0.194317              6.16811e-01
    #> ENSG00000204257                         4.074731              1.82282e-77
    #>                 lrt_IFNg_vs_naive_padj
    #>                              <numeric>
    #> ENSG00000164741            5.52682e-01
    #> ENSG00000078808            9.54582e-01
    #> ENSG00000251034            7.99366e-01
    #> ENSG00000162676            5.18373e-01
    #> ENSG00000170356            7.76822e-01
    #> ENSG00000204257            1.82282e-74

`getDEAList()` retrieves all available DEAs stored in a dde object as a list.

    # get dea results as a list, (default: minimal format)
    lapply(getDEAList(dde), head)
    #> $IFNg_vs_naive
    #>                 log2FoldChange       pvalue         padj
    #> ENSG00000164741     0.22914866 9.805414e-01 1.000000e+00
    #> ENSG00000078808    -0.01153363 1.000000e+00 1.000000e+00
    #> ENSG00000251034    -0.11670132 9.338990e-01 1.000000e+00
    #> ENSG00000162676     0.26914813 9.008170e-01 1.000000e+00
    #> ENSG00000170356    -0.12786371 9.728148e-01 1.000000e+00
    #> ENSG00000204257     4.05502442 1.672977e-56 8.364885e-54
    #>
    #> $Salm_vs_naive
    #>                 log2FoldChange    pvalue      padj
    #> ENSG00000164741     0.01146911 0.9996325 1.0000000
    #> ENSG00000078808     0.78936008 0.9975592 1.0000000
    #> ENSG00000251034     1.18814969 0.2269175 0.9820422
    #> ENSG00000162676     0.44208214 0.8432117 1.0000000
    #> ENSG00000170356    -0.62492010 0.7269723 1.0000000
    #> ENSG00000204257    -0.72938097 0.8838883 1.0000000
    #>
    #> $lrt_IFNg_vs_naive
    #>                 log2FoldChange       pvalue         padj
    #> ENSG00000164741      0.3021201 3.631122e-01 5.526822e-01
    #> ENSG00000078808     -0.0120460 9.081036e-01 9.545819e-01
    #> ENSG00000251034     -0.2193225 6.625345e-01 7.993656e-01
    #> ENSG00000162676      0.4064682 3.287256e-01 5.183734e-01
    #> ENSG00000170356     -0.1943169 6.168112e-01 7.768221e-01
    #> ENSG00000204257      4.0747312 1.822821e-77 1.822821e-74
    #>
    #> $same_contrast
    #>                 log2FoldChange       pvalue         padj
    #> ENSG00000164741     -0.7170767 4.147125e-01 5.751906e-01
    #> ENSG00000078808      0.8006679 7.888783e-06 6.466216e-05
    #> ENSG00000251034      1.3949657 2.997726e-02 7.746062e-02
    #> ENSG00000162676      0.7154035 1.330873e-01 2.524108e-01
    #> ENSG00000170356     -0.3280016 7.520222e-01 8.663851e-01
    #> ENSG00000204257     -0.7804270 4.436564e-02 1.066482e-01

The next command can be quite verbose in its output, but it gives full access to the original objects provided when creating and updating the `dde` container.

    # get dea results as a list, in original format
    lapply(getDEAList(dde, format = "original"), head)

`getDEAInfo()` returns the whole content of the `dea` slot, including the DEA metadata - the following chunk is left unevaluated for clarity as its output can be very verbose.

    # extra info
    getDEAInfo(dde)

It is easy, still, to retrieve some specific set of information, such as the `package` used for a specific `dea_name`:

    # retrieve specific information like the package name used for specific dea
    dea_name <- "Salm_vs_naive"
    getDEAInfo(dde)[[dea_name]][["package"]]
    #> [1] "DESeq2"

In this case, both contrasts point to the same stored object (`key`), but with different `coef` values. This avoids duplicating the original `MArrayLM` fit while still allowing each contrast to be resolved back to its corresponding results.

`getFEA()` directly accesses specific FEA results either in their original format or in minimal format

    # access the 1st FEA, (default: minimal format)
    getFEA(dde) |> head()
    #>                 gs_id
    #> GO:0002822 GO:0002822
    #> GO:0002697 GO:0002697
    #> GO:0002250 GO:0002250
    #> GO:1904064 GO:1904064
    #> GO:0042098 GO:0042098
    #> GO:0070663 GO:0070663
    #>                                                                                                                                     gs_description
    #> GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002697                                                                                                   regulation of immune effector process
    #> GO:0002250                                                                                                                adaptive immune response
    #> GO:1904064                                                                                   positive regulation of cation transmembrane transport
    #> GO:0042098                                                                                                                    T cell proliferation
    #> GO:0070663                                                                                                   regulation of leukocyte proliferation
    #>            gs_pvalue
    #> GO:0002822  3.90e-06
    #> GO:0002697  1.30e-05
    #> GO:0002250  3.70e-05
    #> GO:1904064  8.80e-04
    #> GO:0042098  8.80e-04
    #> GO:0070663  1.34e-03
    #>                                                                  gs_genes
    #> GO:0002822                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> GO:0002697                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> GO:0002250 CD28,CD69,CR1L,CTSS,HLA-DMA,HLA-DPA1,IL27,LAMP3,SECTM1,TNFSF18
    #> GO:1904064                                          ANK2,CTSS,KCNE5,P2RX4
    #> GO:0042098                                     CD28,HLA-DPA1,IL27,TNFSF18
    #> GO:0070663                                     CD28,HLA-DPA1,IL27,TNFSF18
    #>            gs_de_count gs_bg_count gs_Expected
    #> GO:0002822           6          11        0.55
    #> GO:0002697           6          13        0.65
    #> GO:0002250          10          17        0.85
    #> GO:1904064           4          10        0.50
    #> GO:0042098           4          10        0.50
    #> GO:0070663           4          11        0.55

    # access the 1st FEA, in original format
    getFEA(dde, format = "original") |> head()
    #>        GO.ID
    #> 1 GO:0002822
    #> 2 GO:0002697
    #> 3 GO:0002250
    #> 4 GO:1904064
    #> 5 GO:0042098
    #> 6 GO:0070663
    #>                                                                                                                                      Term
    #> 1 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> 2                                                                                                   regulation of immune effector process
    #> 3                                                                                                                adaptive immune response
    #> 4                                                                                   positive regulation of cation transmembrane transport
    #> 5                                                                                                                    T cell proliferation
    #> 6                                                                                                   regulation of leukocyte proliferation
    #>   Annotated Significant Expected Rank in p.value_classic p.value_elim
    #> 1        11           6     0.55                       2     3.90e-06
    #> 2        13           6     0.65                       5     1.30e-05
    #> 3        17          10     0.85                       1     3.70e-05
    #> 4        10           4     0.50                      13     8.80e-04
    #> 5        10           4     0.50                      14     8.80e-04
    #> 6        11           4     0.55                      17     1.34e-03
    #>   p.value_classic
    #> 1        3.90e-06
    #> 2        1.30e-05
    #> 3        4.00e-10
    #> 4        8.80e-04
    #> 5        8.80e-04
    #> 6        1.34e-03
    #>                                                            genes
    #> 1                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> 2                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> 3 CD28,CD69,CR1L,CTSS,HLA-DMA,HLA-DPA1,IL27,LAMP3,SECTM1,TNFSF18
    #> 4                                          ANK2,CTSS,KCNE5,P2RX4
    #> 5                                     CD28,HLA-DPA1,IL27,TNFSF18
    #> 6                                     CD28,HLA-DPA1,IL27,TNFSF18

    # access specific FEA by name
    getFEA(dde, fea_name = "ifng_vs_naive") |> head()
    #>                 gs_id
    #> GO:0002250 GO:0002250
    #> GO:0002819 GO:0002819
    #> GO:0002822 GO:0002822
    #> GO:0002460 GO:0002460
    #> GO:0002697 GO:0002697
    #> GO:0050776 GO:0050776
    #>                                                                                                                                     gs_description
    #> GO:0002250                                                                                                                adaptive immune response
    #> GO:0002819                                                                                                  regulation of adaptive immune response
    #> GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002460               adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002697                                                                                                   regulation of immune effector process
    #> GO:0050776                                                                                                           regulation of immune response
    #>               gs_pvalue
    #> GO:0002250 3.958927e-10
    #> GO:0002819 3.872646e-06
    #> GO:0002822 3.872646e-06
    #> GO:0002460 7.465887e-06
    #> GO:0002697 1.336486e-05
    #> GO:0050776 7.666847e-05
    #>                                                                                                                                                                   gs_genes
    #> GO:0002250 ENSG00000231389,ENSG00000204257,ENSG00000078081,ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000163131,ENSG00000110848
    #> GO:0002819                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0002822                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0002460                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0002697                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0050776                 ENSG00000231389,ENSG00000204257,ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000163131,ENSG00000110848
    #>            gs_de_count gs_bg_count gs_ontology gs_GeneRatio gs_BgRatio
    #> GO:0002250          10          17          BP        10/37     17/744
    #> GO:0002819           6          11          BP         6/37     11/744
    #> GO:0002822           6          11          BP         6/37     11/744
    #> GO:0002460           6          12          BP         6/37     12/744
    #> GO:0002697           6          13          BP         6/37     13/744
    #> GO:0050776           9          41          BP         9/37     41/744
    #>             gs_p.adjust    gs_qvalue
    #> GO:0002250 2.561426e-07 2.233668e-07
    #> GO:0002819 8.352007e-04 7.283293e-04
    #> GO:0002822 8.352007e-04 7.283293e-04
    #> GO:0002460 1.207607e-03 1.053083e-03
    #> GO:0002697 1.729413e-03 1.508119e-03
    #> GO:0050776 8.267416e-03 7.209526e-03

    # access specific FEA by name, in original format
    getFEA(dde, fea_name = "ifng_vs_naive", format = "original") |> head()
    #>                    ID
    #> GO:0002250 GO:0002250
    #> GO:0002819 GO:0002819
    #> GO:0002822 GO:0002822
    #> GO:0002460 GO:0002460
    #> GO:0002697 GO:0002697
    #> GO:0050776 GO:0050776
    #>                                                                                                                                        Description
    #> GO:0002250                                                                                                                adaptive immune response
    #> GO:0002819                                                                                                  regulation of adaptive immune response
    #> GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002460               adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002697                                                                                                   regulation of immune effector process
    #> GO:0050776                                                                                                           regulation of immune response
    #>            GeneRatio BgRatio RichFactor FoldEnrichment    zScore       pvalue
    #> GO:0002250     10/37  17/744  0.5882353      11.828299 10.325309 3.958927e-10
    #> GO:0002819      6/37  11/744  0.5454545      10.968059  7.614485 3.872646e-06
    #> GO:0002822      6/37  11/744  0.5454545      10.968059  7.614485 3.872646e-06
    #> GO:0002460      6/37  12/744  0.5000000      10.054054  7.228759 7.465887e-06
    #> GO:0002697      6/37  13/744  0.4615385       9.280665  6.885949 1.336486e-05
    #> GO:0050776      9/37  41/744  0.2195122       4.413975  5.141149 7.666847e-05
    #>                p.adjust       qvalue
    #> GO:0002250 2.561426e-07 2.233668e-07
    #> GO:0002819 8.352007e-04 7.283293e-04
    #> GO:0002822 8.352007e-04 7.283293e-04
    #> GO:0002460 1.207607e-03 1.053083e-03
    #> GO:0002697 1.729413e-03 1.508119e-03
    #> GO:0050776 8.267416e-03 7.209526e-03
    #>                                                                                                                                                                     geneID
    #> GO:0002250 ENSG00000231389/ENSG00000204257/ENSG00000078081/ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000163131/ENSG00000110848
    #> GO:0002819                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0002822                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0002460                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0002697                                                                 ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000110848
    #> GO:0050776                 ENSG00000231389/ENSG00000204257/ENSG00000141574/ENSG00000197721/ENSG00000178562/ENSG00000197272/ENSG00000120337/ENSG00000163131/ENSG00000110848
    #>            Count
    #> GO:0002250    10
    #> GO:0002819     6
    #> GO:0002822     6
    #> GO:0002460     6
    #> GO:0002697     6
    #> GO:0050776     9

Similarly to `getDEAList()`, `getFEAList()` returns all FEAs stored in a `dde` object. Optionally, if `dea_name` is set, it returns all FEAs linked to a specific DEA. This is illustrated in the following chunk

    # get fea results as a list (default: minimal format)
    lapply(getFEAList(dde), head)
    #> $IFNg_vs_naive
    #>                 gs_id
    #> GO:0002822 GO:0002822
    #> GO:0002697 GO:0002697
    #> GO:0002250 GO:0002250
    #> GO:1904064 GO:1904064
    #> GO:0042098 GO:0042098
    #> GO:0070663 GO:0070663
    #>                                                                                                                                     gs_description
    #> GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002697                                                                                                   regulation of immune effector process
    #> GO:0002250                                                                                                                adaptive immune response
    #> GO:1904064                                                                                   positive regulation of cation transmembrane transport
    #> GO:0042098                                                                                                                    T cell proliferation
    #> GO:0070663                                                                                                   regulation of leukocyte proliferation
    #>            gs_pvalue
    #> GO:0002822  3.90e-06
    #> GO:0002697  1.30e-05
    #> GO:0002250  3.70e-05
    #> GO:1904064  8.80e-04
    #> GO:0042098  8.80e-04
    #> GO:0070663  1.34e-03
    #>                                                                  gs_genes
    #> GO:0002822                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> GO:0002697                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> GO:0002250 CD28,CD69,CR1L,CTSS,HLA-DMA,HLA-DPA1,IL27,LAMP3,SECTM1,TNFSF18
    #> GO:1904064                                          ANK2,CTSS,KCNE5,P2RX4
    #> GO:0042098                                     CD28,HLA-DPA1,IL27,TNFSF18
    #> GO:0070663                                     CD28,HLA-DPA1,IL27,TNFSF18
    #>            gs_de_count gs_bg_count gs_Expected
    #> GO:0002822           6          11        0.55
    #> GO:0002697           6          13        0.65
    #> GO:0002250          10          17        0.85
    #> GO:1904064           4          10        0.50
    #> GO:0042098           4          10        0.50
    #> GO:0070663           4          11        0.55
    #>
    #> $salm_vs_naive
    #>                 gs_id                                  gs_description
    #> GO:0042221 GO:0042221                            response to chemical
    #> GO:0034097 GO:0034097                            response to cytokine
    #> GO:1901652 GO:1901652                             response to peptide
    #> GO:0070887 GO:0070887          cellular response to chemical stimulus
    #> GO:0071356 GO:0071356      cellular response to tumor necrosis factor
    #> GO:1901701 GO:1901701 cellular response to oxygen-containing compound
    #>             gs_pvalue
    #> GO:0042221 0.01425000
    #> GO:0034097 0.03856196
    #> GO:1901652 0.03856196
    #> GO:0070887 0.03904673
    #> GO:0071356 0.39973004
    #> GO:1901701 0.41892532
    #>                                                                                                                                                                                       gs_genes
    #> GO:0042221 LAMP3,RAPGEF2,IL12RB2,P2RX4,SHPK,CYP3A5,ADPRS,AGRN,TRERF1,SRXN1,UGCG,TNFSF18,RELA,NAIP,OSBPL7,CH25H,DNAJA1,MT1A,HAS2,PDE2A,SLC11A2,HAMP,KHK,GREM1,CCL2,LCOR,TSHR,GPR37,ADAR,OCSTAMP
    #> GO:0034097                                                                                                        LAMP3,IL12RB2,SHPK,UGCG,TNFSF18,RELA,NAIP,CH25H,HAS2,PDE2A,CCL2,ADAR,OCSTAMP
    #> GO:1901652                                                                                                        LAMP3,IL12RB2,SHPK,UGCG,TNFSF18,RELA,NAIP,CH25H,HAS2,PDE2A,CCL2,ADAR,OCSTAMP
    #> GO:0070887                                               RAPGEF2,P2RX4,SHPK,CYP3A5,ADPRS,AGRN,TRERF1,SRXN1,UGCG,RELA,OSBPL7,CH25H,DNAJA1,MT1A,PDE2A,SLC11A2,GREM1,CCL2,LCOR,TSHR,GPR37,OCSTAMP
    #> GO:0071356                                                                                                                                                 TNFSF18,RELA,NAIP,HAS2,CCL2,OCSTAMP
    #> GO:1901701                                                                                                         RAPGEF2,P2RX4,SHPK,ADPRS,AGRN,TRERF1,RELA,OSBPL7,PDE2A,CCL2,LCOR,TSHR,GPR37
    #>            gs_de_count gs_bg_count gs_adj_pvalue gs_ontology
    #> GO:0042221          30        2551    0.01425000       GO:BP
    #> GO:0034097          13         625    0.03856196       GO:BP
    #> GO:1901652          13         625    0.03856196       GO:BP
    #> GO:0070887          22        1616    0.03904673       GO:BP
    #> GO:0071356           6         164    0.39973004       GO:BP
    #> GO:1901701          13         791    0.41892532       GO:BP
    #>
    #> $ifng_vs_naive
    #>                 gs_id
    #> GO:0002250 GO:0002250
    #> GO:0002819 GO:0002819
    #> GO:0002822 GO:0002822
    #> GO:0002460 GO:0002460
    #> GO:0002697 GO:0002697
    #> GO:0050776 GO:0050776
    #>                                                                                                                                     gs_description
    #> GO:0002250                                                                                                                adaptive immune response
    #> GO:0002819                                                                                                  regulation of adaptive immune response
    #> GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002460               adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> GO:0002697                                                                                                   regulation of immune effector process
    #> GO:0050776                                                                                                           regulation of immune response
    #>               gs_pvalue
    #> GO:0002250 3.958927e-10
    #> GO:0002819 3.872646e-06
    #> GO:0002822 3.872646e-06
    #> GO:0002460 7.465887e-06
    #> GO:0002697 1.336486e-05
    #> GO:0050776 7.666847e-05
    #>                                                                                                                                                                   gs_genes
    #> GO:0002250 ENSG00000231389,ENSG00000204257,ENSG00000078081,ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000163131,ENSG00000110848
    #> GO:0002819                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0002822                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0002460                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0002697                                                                 ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000110848
    #> GO:0050776                 ENSG00000231389,ENSG00000204257,ENSG00000141574,ENSG00000197721,ENSG00000178562,ENSG00000197272,ENSG00000120337,ENSG00000163131,ENSG00000110848
    #>            gs_de_count gs_bg_count gs_ontology gs_GeneRatio gs_BgRatio
    #> GO:0002250          10          17          BP        10/37     17/744
    #> GO:0002819           6          11          BP         6/37     11/744
    #> GO:0002822           6          11          BP         6/37     11/744
    #> GO:0002460           6          12          BP         6/37     12/744
    #> GO:0002697           6          13          BP         6/37     13/744
    #> GO:0050776           9          41          BP         9/37     41/744
    #>             gs_p.adjust    gs_qvalue
    #> GO:0002250 2.561426e-07 2.233668e-07
    #> GO:0002819 8.352007e-04 7.283293e-04
    #> GO:0002822 8.352007e-04 7.283293e-04
    #> GO:0002460 1.207607e-03 1.053083e-03
    #> GO:0002697 1.729413e-03 1.508119e-03
    #> GO:0050776 8.267416e-03 7.209526e-03

    # get all FEAs for this de_name, in original format
    lapply(getFEAList(dde, dea_name = "IFNg_vs_naive", format = "original"), head)
    #> $IFNg_vs_naive
    #>        GO.ID
    #> 1 GO:0002822
    #> 2 GO:0002697
    #> 3 GO:0002250
    #> 4 GO:1904064
    #> 5 GO:0042098
    #> 6 GO:0070663
    #>                                                                                                                                      Term
    #> 1 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
    #> 2                                                                                                   regulation of immune effector process
    #> 3                                                                                                                adaptive immune response
    #> 4                                                                                   positive regulation of cation transmembrane transport
    #> 5                                                                                                                    T cell proliferation
    #> 6                                                                                                   regulation of leukocyte proliferation
    #>   Annotated Significant Expected Rank in p.value_classic p.value_elim
    #> 1        11           6     0.55                       2     3.90e-06
    #> 2        13           6     0.65                       5     1.30e-05
    #> 3        17          10     0.85                       1     3.70e-05
    #> 4        10           4     0.50                      13     8.80e-04
    #> 5        10           4     0.50                      14     8.80e-04
    #> 6        11           4     0.55                      17     1.34e-03
    #>   p.value_classic
    #> 1        3.90e-06
    #> 2        1.30e-05
    #> 3        4.00e-10
    #> 4        8.80e-04
    #> 5        8.80e-04
    #> 6        1.34e-03
    #>                                                            genes
    #> 1                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> 2                             CD28,CD69,CR1L,IL27,SECTM1,TNFSF18
    #> 3 CD28,CD69,CR1L,CTSS,HLA-DMA,HLA-DPA1,IL27,LAMP3,SECTM1,TNFSF18
    #> 4                                          ANK2,CTSS,KCNE5,P2RX4
    #> 5                                     CD28,HLA-DPA1,IL27,TNFSF18
    #> 6                                     CD28,HLA-DPA1,IL27,TNFSF18

`getFEAInfo()` returns the whole content of the `fea` slot, including the FEA tables and their associated metadata. Again, the following chunk is left unevaluated for clarity as its output can be very verbose:

    # extra info
    getFEAInfo(dde)

Yet, specific set of information, such as the `fe_tool` used for a specific `fea_name` (here, “ifng_vs_naive”) can be easily retrieved with:

    # the tool used to perform the FEA
    getFEAInfo(dde)[["ifng_vs_naive"]][["fe_tool"]]
    #> [1] "clusterProfiler"

It is possible to export DEA and FEA results, as well as the available assay that are stored in a `DeeDeeExperiment` object using `export_result_for_dde`

    # export only DEAs
    out_dir <- "path/to/results_folder"

    export_result_for_dde(dde,
                          res_type = "dea",
                          res_format = "minimal",
                          output_dir = out_dir,
                          file_name = "macrophage_dataset")

    # export both DEAs and FEAs
    export_result_for_dde(dde,
                          res_type = c("dea","fea"),
                          res_format = "minimal",
                          output_dir = out_dir,
                          file_name = "macrophage_dataset")

    # export DEAs, FEAs, and assays
    export_result_for_dde(dde,
                          res_type = "all",
                          res_format = "minimal",
                          output_dir = out_dir,
                          file_name = "macrophage_dataset",
                          force = TRUE)

# Downstream operations with `DeeDeeExperiment` objects

Since `DeeDeeExperiment` inherits from the `SingleCellExperiment` class, it can be seamlessly used with other Bioconductor tools such as `iSEE` or `GeneTonic`, or many more.

With the `dde` object now containing all key elements of your analysis, from the counts to enrichment results, metadata, and scenario details, you can save it and plug it into these tools for interactive exploration and visualization.

    saveRDS(dde, "dde_macrophage.RDS")

To explore the `dde` object within `iSEE`, simply run these lines of code:

    library("iSEE")
    iSEE::iSEE(dde)

Similarly, to see more on `dde` within `GeneTonic`, you can run the following chunk:

    library("GeneTonic")
    my_dde <- dde

    res_de <- getDEA(my_dde, dea_name = "IFNg_vs_naive", format = "original")
    res_enrich <- getFEA(my_dde, fea_name = "ifng_vs_naive")
    annotation_object <- mosdef::get_annotation_orgdb(dds_macrophage,
                                                      "org.Hs.eg.db",
                                                      "ENSEMBL")

    GeneTonic::GeneTonic(
      dds = dds_macrophage,
      res_de = res_de,
      res_enrich = res_enrich,
      annotation_obj = annotation_object,
      project_id = "GeneTonic_with_DeeDee"
    )

Of course, these packages need to be installed as usual on the same machine via `BiocManager::install()`.

# FAQs

Do I need to process/filter my DE results before adding them to a `dde` object?

→ `DeeDeeExperiment` is just a container, it does not alter your results. Any filtering or DEG selection should be done beforehand, giving you full control over how your analysis is conducted.

What happens if I forget to name my DE or FE result when adding it?

→ If no name is provided, `DeeDeeExperiment` will attempt to assign a default one, which is the variable name in the current environment. However, it’s strongly recommended to name each result clearly.

I am using a different enrichment analysis tool, and this is currently not supported by DeeDeeExperiment, what can I do?

→ The quick-and-easy option would be to rename the output of the tabular format from this tool into the format expected by `DeeDeeExperiment` (which is the same adopted by `GeneTonic`). If you think a conversion function would be useful to yourself and many other users, feel free to submit a pull request within the `DeeDeeExperiment` Github repository (https://github.com/imbeimainz/DeeDeeExperiment/pulls)!

# Session info

    sessionInfo()
    #> R version 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 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-r0.3.26.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] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] DEFormats_1.40.0            edgeR_4.10.0
    #>  [3] DESeq2_1.52.0               macrophage_1.26.0
    #>  [5] limma_3.68.0                muscat_1.26.0
    #>  [7] scater_1.40.0               ggplot2_4.0.3
    #>  [9] scuttle_1.22.0              ExperimentHub_3.2.0
    #> [11] AnnotationHub_4.2.0         BiocFileCache_3.2.0
    #> [13] dbplyr_2.5.2                DeeDeeExperiment_1.2.0
    #> [15] SingleCellExperiment_1.34.0 SummarizedExperiment_1.42.0
    #> [17] Biobase_2.72.0              GenomicRanges_1.64.0
    #> [19] Seqinfo_1.2.0               IRanges_2.46.0
    #> [21] S4Vectors_0.50.0            BiocGenerics_0.58.0
    #> [23] generics_0.1.4              MatrixGenerics_1.24.0
    #> [25] matrixStats_1.5.0           BiocStyle_2.40.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RcppAnnoy_0.0.23         splines_4.6.0            bitops_1.0-9
    #>   [4] filelock_1.0.3           tibble_3.3.1             enrichit_0.1.4
    #>   [7] lifecycle_1.0.5          httr2_1.2.2              Rdpack_2.6.6
    #>  [10] doParallel_1.0.17        lattice_0.22-9           MASS_7.3-65
    #>  [13] backports_1.5.1          magrittr_2.0.5           sass_0.4.10
    #>  [16] rmarkdown_2.31           jquerylib_0.1.4          yaml_2.3.12
    #>  [19] otel_0.2.0               DBI_1.3.0                buildtools_1.0.0
    #>  [22] minqa_1.2.8              RColorBrewer_1.1-3       abind_1.4-8
    #>  [25] EnvStats_3.1.0           glmmTMB_1.1.14           purrr_1.2.2
    #>  [28] yulab.utils_0.2.4        rappdirs_0.3.4           sandwich_3.1-1
    #>  [31] circlize_0.4.18          ggrepel_0.9.8            pbkrtest_0.5.5
    #>  [34] irlba_2.3.7              maketools_1.3.2          RSpectra_0.16-2
    #>  [37] codetools_0.2-20         DelayedArray_0.38.0      DOSE_4.6.0
    #>  [40] tidyselect_1.2.1         shape_1.4.6.1            farver_2.1.2
    #>  [43] lme4_2.0-1               ScaledMatrix_1.20.0      viridis_0.6.5
    #>  [46] jsonlite_2.0.0           GetoptLong_1.1.1         BiocNeighbors_2.6.0
    #>  [49] iterators_1.0.14         bbmle_1.0.25.1           foreach_1.5.2
    #>  [52] tools_4.6.0              progress_1.2.3           Rcpp_1.1.1-1.1
    #>  [55] blme_1.0-7               glue_1.8.1               gridExtra_2.3
    #>  [58] SparseArray_1.12.0       xfun_0.57                mgcv_1.9-4
    #>  [61] dplyr_1.2.1              withr_3.0.2              numDeriv_2016.8-1.1
    #>  [64] BiocManager_1.30.27      fastmap_1.2.0            boot_1.3-32
    #>  [67] caTools_1.18.3           digest_0.6.39            rsvd_1.0.5
    #>  [70] R6_2.6.1                 colorspace_2.1-2         GO.db_3.22.0
    #>  [73] gtools_3.9.5             RSQLite_2.4.6            RhpcBLASctl_0.23-42
    #>  [76] tidyr_1.3.2              variancePartition_1.42.0 data.table_1.18.2.1
    #>  [79] corpcor_1.6.10           prettyunits_1.2.0        httr_1.4.8
    #>  [82] S4Arrays_1.12.0          uwot_0.2.4               pkgconfig_2.0.3
    #>  [85] gtable_0.3.6             blob_1.3.0               ComplexHeatmap_2.28.0
    #>  [88] S7_0.2.2                 XVector_0.52.0           sys_3.4.3
    #>  [91] remaCor_0.0.20           htmltools_0.5.9          TMB_1.9.21
    #>  [94] clue_0.3-68              scales_1.4.0             png_0.1-9
    #>  [97] fANCOVA_0.6-1            reformulas_0.4.4         knitr_1.51
    #> [100] reshape2_1.4.5           rjson_0.2.23             coda_0.19-4.1
    #> [103] checkmate_2.3.4          nlme_3.1-169             curl_7.1.0
    #> [106] nloptr_2.2.1             bdsmatrix_1.3-7          cachem_1.1.0
    #> [109] zoo_1.8-15               GlobalOptions_0.1.4      stringr_1.6.0
    #> [112] BiocVersion_3.23.1       KernSmooth_2.23-26       parallel_4.6.0
    #> [115] vipor_0.4.7              AnnotationDbi_1.74.0     apeglm_1.34.0
    #> [118] pillar_1.11.1            grid_4.6.0               vctrs_0.7.3
    #> [121] gplots_3.3.0             BiocSingular_1.28.0      beachmat_2.28.0
    #> [124] cluster_2.1.8.2          beeswarm_0.4.0           evaluate_1.0.5
    #> [127] mvtnorm_1.3-7            cli_3.6.6                locfit_1.5-9.12
    #> [130] compiler_4.6.0           rlang_1.2.0              crayon_1.5.3
    #> [133] emdbook_1.3.14           fs_2.1.0                 plyr_1.8.9
    #> [136] ggbeeswarm_0.7.3         stringi_1.8.7            writexl_1.5.4
    #> [139] viridisLite_0.4.3        BiocParallel_1.46.0      lmerTest_3.2-1
    #> [142] Biostrings_2.80.0        aod_1.3.3                GOSemSim_2.38.0
    #> [145] Matrix_1.7-5             hms_1.1.4                bit64_4.8.0
    #> [148] KEGGREST_1.52.0          statmod_1.5.1            rbibutils_2.4.1
    #> [151] broom_1.0.12             memoise_2.0.1            bslib_0.10.0
    #> [154] bit_4.6.0

# References

Alasoo, Kaur, Julia Rodrigues, Subhankar Mukhopadhyay, et al. 2018. “Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response.” Nature Genetics 50 (3): 424–31. https://doi.org/10.1038/s41588-018-0046-7.
Law, Charity W., Monther Alhamdoosh, Shian Su, et al. 2018. “RNA-Seq Analysis Is Easy as 1-2-3 with Limma, Glimma and edgeR.” F1000Research 5 (December): 1408. https://doi.org/10.12688/f1000research.9005.3.
Love, Michael I., Simon Anders, Vladislav Kim, and Wolfgang Huber. 2016. “RNA-Seq Workflow: Gene-Level Exploratory Analysis and Differential Expression.” F1000Research 4 (November): 1070. https://doi.org/10.12688/f1000research.7035.2.
Ludt, Annekathrin, Arsenij Ustjanzew, Harald Binder, Konstantin Strauch, and Federico Marini. 2022. “Interactive and Reproducible Workflows for Exploring and Modeling RNA‐seq Data with pcaExplorer, Ideal, and GeneTonic.” Current Protocols 2 (4). https://doi.org/10.1002/cpz1.411.