vignettes/iSEEdemo.Rmd
iSEEdemo.RmdThis package demo provides an overview of recent features added to the “iSEE universe”, the set of packages revolving around and extending the iSEE Bioconductor package.
iSEE: https://bioconductor.org/packages/iSEE
iSEEde: https://bioconductor.org/packages/iSEEde
iSEEindex: https://bioconductor.org/packages/iSEEindex
iSEEpathways: https://bioconductor.org/packages/iSEEpathways
iSEEhub: https://bioconductor.org/packages/iSEEhub
| Activity | Time |
|---|---|
| Overview of data set | 10m |
| Overview of iSEE | 10m |
| iSEEde + iSEEpathways | 10m |
| iSEEhub + iSEEindex | 15m |
iSEEde and
iSEEpathways)iSEEhub)iSEEindex)We start by loading the packages that will be used during the demo.
library(macrophage)
library(DESeq2)
library(limma)
library(edgeR)
library(tximeta)
library(org.Hs.eg.db)
library(stringr)
library(SingleCellExperiment)
library(iSEE)
library(iSEEde)
library(iSEEpathways)
library(iSEEhub)
library(iSEEindex)
library(org.Hs.eg.db)
library(fgsea)
library(GO.db)
library(ExperimentHub)
library(DuoClustering2018)
library(BiocFileCache)
library(scater)
library(yaml)macrophage data set
The data set that will be used for this demo is a bulk RNA-seq data set containing 24 samples 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. The 24 samples correspond to 6 different donors. For each of these we have four samples: one naive, one exposed to IFNgamma, one exposed to Salmonella, and one exposed to IFNgamma and Salmonella. The macrophage Bioconductor package provides the output files resulting from running Salmon on these 24 samples. Here, we load the quantifications into R using the tximeta package.
## Set the data directory
dir <- system.file("extdata", package = "macrophage")
## Read sample annotations
coldata <- read.csv(file.path(dir, "coldata.csv"))[, c(1, 2, 3, 5)]
## Create new columns indicating, respectively whether the sample was exposed
## to IFNgamma and Salmonella
coldata$IFNg <- as.character(grepl("IFNg", coldata$condition_name))
coldata$SL1344 <- as.character(grepl("SL1344", coldata$condition_name))
## Add paths to quantification files and import data
coldata$files <- file.path(dir, "quants", coldata$names, "quant.sf.gz")
se <- tximeta(coldata = coldata, type = "salmon", dropInfReps = TRUE)
#> importing quantifications
#> reading in files with read.delim (install 'readr' package for speed up)
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
#> found matching transcriptome:
#> [ GENCODE - Homo sapiens - release 29 ]
#> useHub=TRUE: checking for TxDb via 'AnnotationHub'
#> found matching TxDb via 'AnnotationHub'
#> loading from cache
#> Loading required package: GenomicFeatures
#> generating transcript ranges
## Summarize to the gene level and add additional identifiers
seg <- summarizeToGene(se)
#> loading existing TxDb created: 2023-09-12 12:23:46
#> obtaining transcript-to-gene mapping from database
#> generating gene ranges
#> gene ranges assigned by total range of isoforms, see `assignRanges`
#> summarizing abundance
#> summarizing counts
#> summarizing length
rownames(seg) <- str_replace(rownames(seg), "\\.\\d+$", "")
seg <- addIds(seg, "SYMBOL")
#> it appears the rows are gene IDs, setting 'gene' to TRUE
#> mapping to new IDs using org.Hs.eg.db
#> if all matching IDs are desired, and '1:many mappings' are reported,
#> set multiVals='list' to obtain all the matching IDs
#> 'select()' returned 1:many mapping between keys and columns
seg <- addIds(seg, "GOALL", multiVals = "list")
#> it appears the rows are gene IDs, setting 'gene' to TRUE
#> mapping to new IDs using org.Hs.eg.db
#> if all matching IDs are desired, and '1:many mappings' are reported,
#> set multiVals='list' to obtain all the matching IDs
#> 'select()' returned 1:many mapping between keys and columns
rownames(seg) <- scater::uniquifyFeatureNames(
ID = rownames(seg), names = rowData(seg)$SYMBOL
)
## Create a DESeqDataSet and filter lowly expressed genes
dds <- DESeqDataSet(seg, design = ~ IFNg * SL1344)
#> using counts and average transcript lengths from tximeta
#> Warning in DESeqDataSet(seg, design = ~IFNg * SL1344): some variables in design
#> formula are characters, converting to factors
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds
#> class: DESeqDataSet
#> dim: 29060 24
#> metadata(8): tximetaInfo quantInfo ... assignRanges version
#> assays(3): counts abundance avgTxLength
#> rownames(29060): TSPAN6 DPM1 ... ENSG00000285992 ENSG00000285994
#> rowData names(4): gene_id tx_ids SYMBOL GOALL
#> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
#> SAMEA103884949
#> colData names(6): names sample_id ... IFNg SL1344
## Apply a variance stabilizing transform followed by PCA
vst <- DESeq2::varianceStabilizingTransformation(dds, blind = TRUE)
#> using 'avgTxLength' from assays(dds), correcting for library size
pca <- DESeq2::plotPCA(vst, intgroup = "condition_name", returnData = TRUE)
## Create a SingleCellExperiment object and include the PCA representation
sce <- as(dds, "SingleCellExperiment")
assay(sce, "vst") <- assay(vst)
SingleCellExperiment::reducedDim(sce, "PCA") <- pca[, c("PC1", "PC2")]
sce
#> class: SingleCellExperiment
#> dim: 29060 24
#> metadata(8): tximetaInfo quantInfo ... assignRanges version
#> assays(4): counts abundance avgTxLength vst
#> rownames(29060): TSPAN6 DPM1 ... ENSG00000285992 ENSG00000285994
#> rowData names(4): gene_id tx_ids SYMBOL GOALL
#> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
#> SAMEA103884949
#> colData names(6): names sample_id ... IFNg SL1344
#> reducedDimNames(1): PCA
#> mainExpName: NULL
#> altExpNames(0):Below, we will illustrate how to use the iSEEde and iSEEpathways packages to visualize results from differential expression analyses. Thus, we first apply the DESeq2 and limma packages to perform differential expression analysis. We test the interaction effect between IFNgamma and Salmonella exposure, to investigate whether the effect of Salmonella exposure is different depending on whether or not the sample was also exposed to IFNgamma.
## Run DESeq2
dds <- DESeq2::DESeq(dds)
#> estimating size factors
#> using 'avgTxLength' from assays(dds), correcting for library size
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
DESeq2::resultsNames(dds)
#> [1] "Intercept" "IFNg_TRUE_vs_FALSE" "SL1344_TRUE_vs_FALSE"
#> [4] "IFNgTRUE.SL1344TRUE"
res <- DESeq2::results(dds, name = "IFNgTRUE.SL1344TRUE",
lfcThreshold = 0)
## Embed the contrast results obtained from DESeq2 into the SCE
stopifnot(rownames(res) == rownames(sce))
sce <- embedContrastResults(res,
sce,
name = "IFNgTRUE.SL1344TRUE.DESeq2")
sce
#> class: SingleCellExperiment
#> dim: 29060 24
#> metadata(8): tximetaInfo quantInfo ... assignRanges version
#> assays(4): counts abundance avgTxLength vst
#> rownames(29060): TSPAN6 DPM1 ... ENSG00000285992 ENSG00000285994
#> rowData names(5): gene_id tx_ids SYMBOL GOALL iSEEde
#> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
#> SAMEA103884949
#> colData names(6): names sample_id ... IFNg SL1344
#> reducedDimNames(1): PCA
#> mainExpName: NULL
#> altExpNames(0):
## Run limma-trend
dge <- tximeta::makeDGEList(seg)
dge <- dge[rownames(dds), ]
logCPM <- edgeR::cpm(dge, log = TRUE, prior.count = 3)
design <- model.matrix(~ IFNg * SL1344, data = dge$samples)
fit <- limma::lmFit(logCPM, design = design)
fit <- eBayes(fit, trend = TRUE)
tt <- topTable(fit, coef = ncol(design), number = Inf, sort.by = "none")
## Embed the results obtained from limma into the SCE
stopifnot(rownames(tt) == rownames(sce))
sce <- embedContrastResults(tt,
sce,
name = "IFNgTRUE.SL1344TRUE.limma",
class = "limma")
sce
#> class: SingleCellExperiment
#> dim: 29060 24
#> metadata(8): tximetaInfo quantInfo ... assignRanges version
#> assays(4): counts abundance avgTxLength vst
#> rownames(29060): TSPAN6 DPM1 ... ENSG00000285992 ENSG00000285994
#> rowData names(5): gene_id tx_ids SYMBOL GOALL iSEEde
#> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
#> SAMEA103884949
#> colData names(6): names sample_id ... IFNg SL1344
#> reducedDimNames(1): PCA
#> mainExpName: NULL
#> altExpNames(0):
## Where's the information stored?
rowData(sce)$iSEEde
#> DataFrame with 29060 rows and 2 columns
#> IFNgTRUE.SL1344TRUE.DESeq2 IFNgTRUE.SL1344TRUE.limma
#> <iSEEDESeq2Results> <iSEELimmaResults>
#> 1 <iSEEDESeq2Results> <iSEELimmaResults>
#> 2 <iSEEDESeq2Results> <iSEELimmaResults>
#> 3 <iSEEDESeq2Results> <iSEELimmaResults>
#> 4 <iSEEDESeq2Results> <iSEELimmaResults>
#> 5 <iSEEDESeq2Results> <iSEELimmaResults>
#> ... ... ...
#> 29056 <iSEEDESeq2Results> <iSEELimmaResults>
#> 29057 <iSEEDESeq2Results> <iSEELimmaResults>
#> 29058 <iSEEDESeq2Results> <iSEELimmaResults>
#> 29059 <iSEEDESeq2Results> <iSEELimmaResults>
#> 29060 <iSEEDESeq2Results> <iSEELimmaResults>We also run fgsea to
perform a gene set enrichment analysis of GO terms, based on the
DESeq2 results obtained above.
## Extract GO term composition
pathways <- select(org.Hs.eg.db, keys(org.Hs.eg.db, "SYMBOL"), c("GOALL"),
keytype = "SYMBOL")
#> 'select()' returned 1:many mapping between keys and columns
pathways <- subset(pathways, ONTOLOGYALL == "BP")
pathways <- unique(pathways[, c("SYMBOL", "GOALL")])
pathways <- split(pathways$SYMBOL, pathways$GOALL)
len_pathways <- lengths(pathways)
pathways <- pathways[len_pathways > 15 & len_pathways < 200]
length(pathways)
#> [1] 4454
## Get test statistics and apply fgsea
feature_stats <- res$stat
names(feature_stats) <- rownames(res)
set.seed(42)
fgseaRes <- fgsea(pathways = pathways,
stats = feature_stats,
minSize = 15,
maxSize = 200)
fgseaRes <- fgseaRes[order(pval), ]
head(fgseaRes)
#> pathway pval padj log2err ES NES size
#> 1: GO:0006399 3.543000e-32 7.976586e-29 1.474563 -0.7006414 -2.967155 192
#> 2: GO:0140053 3.694574e-32 7.976586e-29 1.474563 -0.7275861 -2.997353 163
#> 3: GO:0009451 1.363682e-30 1.962792e-27 1.439026 -0.7118139 -2.960285 172
#> 4: GO:0032543 1.442761e-26 1.557460e-23 1.334497 -0.7341839 -2.933556 132
#> 5: GO:0042274 3.590575e-25 3.100821e-22 1.303093 -0.8221723 -3.002545 77
#> 6: GO:0008033 1.062547e-23 7.646799e-21 1.262740 -0.7094165 -2.843095 134
#> leadingEdge
#> 1: QTRT2,MARS2,RARS1,YRDC,ELAC2,GTF3C2,...
#> 2: TBRG4,FASTKD2,MRPL38,TWNK,SUPV3L1,ELAC2,...
#> 3: QTRT2,NSUN4,NOP2,YRDC,METTL16,DUS3L,...
#> 4: FASTKD2,MRPL38,EARS2,MRPS30,MRPS12,TRMT10C,...
#> 5: KRI1,MPHOSPH10,NOB1,UTP23,DHX37,HEATR1,...
#> 6: QTRT2,YRDC,ELAC2,DUS3L,PUS1,POP1,...
## Embed pathway analysis results in the SCE object
sce <- embedPathwaysResults(fgseaRes,
sce,
name = "IFNgTRUE.SL1344TRUE.DESeq2.fgsea",
class = "fgsea",
pathwayType = "GO",
pathwaysList = pathways,
featuresStats = feature_stats)
sce
#> class: SingleCellExperiment
#> dim: 29060 24
#> metadata(9): tximetaInfo quantInfo ... version iSEEpathways
#> assays(4): counts abundance avgTxLength vst
#> rownames(29060): TSPAN6 DPM1 ... ENSG00000285992 ENSG00000285994
#> rowData names(5): gene_id tx_ids SYMBOL GOALL iSEEde
#> colnames(24): SAMEA103885102 SAMEA103885347 ... SAMEA103885308
#> SAMEA103884949
#> colData names(6): names sample_id ... IFNg SL1344
#> reducedDimNames(1): PCA
#> mainExpName: NULL
#> altExpNames(0):
## Again: where's the data stored?
metadata(sce)$iSEEpathways
#> $IFNgTRUE.SL1344TRUE.DESeq2.fgsea
#> iSEEfgseaResults with 4318 rows and 7 columns
#> pval padj log2err ES NES size
#> <numeric> <numeric> <numeric> <numeric> <numeric> <integer>
#> GO:0006399 3.54300e-32 7.97659e-29 1.47456 -0.700641 -2.96715 192
#> GO:0140053 3.69457e-32 7.97659e-29 1.47456 -0.727586 -2.99735 163
#> GO:0009451 1.36368e-30 1.96279e-27 1.43903 -0.711814 -2.96028 172
#> GO:0032543 1.44276e-26 1.55746e-23 1.33450 -0.734184 -2.93356 132
#> GO:0042274 3.59057e-25 3.10082e-22 1.30309 -0.822172 -3.00255 77
#> ... ... ... ... ... ... ...
#> GO:0003382 1 1 0.0426734 -0.147476 -0.443623 30
#> GO:0006636 1 1 0.0426734 -0.140740 -0.468816 46
#> GO:0007156 1 1 0.0496901 0.142194 0.597536 162
#> GO:0044062 1 1 0.0438002 -0.159312 -0.425874 18
#> GO:1900273 1 1 0.0438002 -0.142301 -0.403754 23
#> leadingEdge
#> <list>
#> GO:0006399 QTRT2,MARS2,RARS1,...
#> GO:0140053 TBRG4,FASTKD2,MRPL38,...
#> GO:0009451 QTRT2,NSUN4,NOP2,...
#> GO:0032543 FASTKD2,MRPL38,EARS2,...
#> GO:0042274 KRI1,MPHOSPH10,NOB1,...
#> ... ...
#> GO:0003382 CCDC88C,PKHD1,VSIG1,...
#> GO:0006636 PLA2G4A,DECR2,ELOVL1,...
#> GO:0007156 PCDH12,FAT2,CD84,...
#> GO:0044062 SNX5,NPR1,SPX,...
#> GO:1900273 CRTC1,NPTN,RELN,...
## Add details about the GO terms
## These will be displayed in the application
go_details <- function(x) {
info <- select(GO.db, x, c("TERM", "ONTOLOGY", "DEFINITION"), "GOID")
html <- list(p(strong(info$GOID), ":", info$TERM, paste0("(", info$ONTOLOGY, ")")))
if (!is.na(info$DEFINITION)) {
html <- append(html, list(p(info$DEFINITION)))
}
tagList(html)
}
## Define the mapping from GO terms to gene IDs
map_GO <- function(pathway_id, se) {
pathway_symbol <- mapIds(org.Hs.eg.db, pathway_id, "SYMBOL",
keytype = "GOALL", multiVals = "CharacterList")[[pathway_id]]
pathway_rownames <- rownames(se)[rowData(se)$SYMBOL %in% pathway_symbol]
pathway_rownames
}
## Register the pathway mapping information into the SCE object
sce <- registerAppOptions(sce, Pathways.map.functions = list(GO = map_GO))
sce <- registerAppOptions(sce, PathwaysTable.select.details = go_details)
metadata(sce)$iSEE
#> $options
#> $options$Pathways.map.functions
#> $options$Pathways.map.functions$GO
#> function(pathway_id, se) {
#> pathway_symbol <- mapIds(org.Hs.eg.db, pathway_id, "SYMBOL",
#> keytype = "GOALL", multiVals = "CharacterList")[[pathway_id]]
#> pathway_rownames <- rownames(se)[rowData(se)$SYMBOL %in% pathway_symbol]
#> pathway_rownames
#> }
#>
#>
#> $options$PathwaysTable.select.details
#> function(x) {
#> info <- select(GO.db, x, c("TERM", "ONTOLOGY", "DEFINITION"), "GOID")
#> html <- list(p(strong(info$GOID), ":", info$TERM, paste0("(", info$ONTOLOGY, ")")))
#> if (!is.na(info$DEFINITION)) {
#> html <- append(html, list(p(info$DEFINITION)))
#> }
#> tagList(html)
#> }This concludes the preparation of the dataset for this
workshop.
We can conveniently store that as a binary RDS file which we can load
afterwards, anytime.
saveRDS(sce, "sce_macrophage_readytouse.RDS")iSEE
iSEE is a
Bioconductor package, based on shiny, that
allows the user to create an interactive interface for exploring their
data. The input should be an object of the class
SummarizedExperiment, or any class extending
SummarizedExperiment (e.g.,
SingleCellExperiment, DESeqDataSet). Launching
an application is as simple as:
iSEE(sce)For a more extensive description of the different parts of the interface, we refer to the overview vignette in a previous, extended workshop on iSEE.
iSEEde and
iSEEpathways
are two new Bioconductor packages that provide iSEE panels
specifically aimed towards exploration of differential expression and
pathway analysis results. More precisely, iSEEde
provides the VolcanoPlot, MAPlot,
LogFCLogFCPlot and DETable panels. These
panels can be configured to extract data that was added via the
embedContrastResults() function above. Let’s look at an
example:
app <- iSEE(sce, initial = list(
DETable(ContrastName = "IFNgTRUE.SL1344TRUE.DESeq2",
HiddenColumns = c("baseMean", "lfcSE", "stat")),
VolcanoPlot(ContrastName = "IFNgTRUE.SL1344TRUE.DESeq2"),
MAPlot(ContrastName = "IFNgTRUE.SL1344TRUE.DESeq2")
))
appNote how it is easy to switch to a different contrast in any of the panels.
app <- iSEE(sce, initial = list(
DETable(ContrastName = "IFNgTRUE.SL1344TRUE.DESeq2",
HiddenColumns = c("baseMean", "lfcSE", "stat")),
VolcanoPlot(ContrastName = "IFNgTRUE.SL1344TRUE.DESeq2"),
MAPlot(ContrastName = "IFNgTRUE.SL1344TRUE.DESeq2"),
PathwaysTable(ResultName = "IFNgTRUE.SL1344TRUE.limma.fgsea",
Selected = "GO:0046324"),
ComplexHeatmapPlot(RowSelectionSource = "PathwaysTable1",
CustomRows = FALSE, ColumnData = "condition_name",
ClusterRows = TRUE, Assay = "vst"),
FgseaEnrichmentPlot(ResultName = "IFNgTRUE.SL1344TRUE.limma.fgsea",
PathwayId = "GO:0046324")
))
appiSEEing the ExperimentHub datasets
The iSEEhub package provides a custom landing page for an iSEE application interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into an iSEE app.
ehub <- ExperimentHub()
app <- iSEEhub(ehub)
appiSEE to explore them
all
iSEEindex
provides an interface to any collection of data sets
within a single iSEE web-application.
The main functionality of this package is to define a custom landing
page allowing app maintainers to list a custom collection of data sets
that users can select from and directly load objects into an iSEE web
application. To see how to configure such an app, we will create a small
example:
bfc <- BiocFileCache(cache = tempdir())
dataset_fun <- function() {
x <- yaml::read_yaml(system.file(package = "iSEEindex", "example.yaml"))
x$datasets
}
initial_fun <- function() {
x <- yaml::read_yaml(system.file(package = "iSEEindex", "example.yaml"))
x$initial
}
app <- iSEEindex(bfc, dataset_fun, initial_fun)
appA more elaborate example is available at https://rehwinkellab.shinyapps.io/ifnresource/. The source can be found at https://github.com/kevinrue/IFNresource.
Potential use cases can include:
sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 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-r0.3.20.so; LAPACK version 3.10.0
#>
#> 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] GenomicFeatures_1.53.2 yaml_2.3.7
#> [3] scater_1.29.4 ggplot2_3.4.3
#> [5] scuttle_1.11.2 DuoClustering2018_1.19.0
#> [7] GO.db_3.17.0 fgsea_1.27.1
#> [9] iSEEindex_0.99.13 iSEEhub_1.3.2
#> [11] ExperimentHub_2.9.1 AnnotationHub_3.9.2
#> [13] BiocFileCache_2.9.1 dbplyr_2.3.3
#> [15] iSEEpathways_0.99.0 iSEEde_0.99.0
#> [17] iSEE_2.13.5 SingleCellExperiment_1.23.0
#> [19] stringr_1.5.0 org.Hs.eg.db_3.17.0
#> [21] AnnotationDbi_1.63.2 tximeta_1.19.5
#> [23] edgeR_3.43.8 limma_3.57.7
#> [25] DESeq2_1.41.10 SummarizedExperiment_1.31.1
#> [27] Biobase_2.61.0 MatrixGenerics_1.13.1
#> [29] matrixStats_1.0.0 GenomicRanges_1.53.1
#> [31] GenomeInfoDb_1.37.4 IRanges_2.35.2
#> [33] S4Vectors_0.39.1 BiocGenerics_0.47.0
#> [35] macrophage_1.17.0 BiocStyle_2.29.1
#>
#> loaded via a namespace (and not attached):
#> [1] fs_1.6.3 ProtGenerics_1.33.1
#> [3] bitops_1.0-7 httr_1.4.7
#> [5] RColorBrewer_1.1-3 doParallel_1.0.17
#> [7] tools_4.3.1 utf8_1.2.3
#> [9] R6_2.5.1 DT_0.29
#> [11] lazyeval_0.2.2 mgcv_1.9-0
#> [13] GetoptLong_1.0.5 withr_2.5.0
#> [15] prettyunits_1.1.1 gridExtra_2.3
#> [17] cli_3.6.1 textshaping_0.3.6
#> [19] shinyjs_2.1.0 sass_0.4.7
#> [21] pkgdown_2.0.7 Rsamtools_2.17.0
#> [23] systemfonts_1.0.4 RSQLite_2.3.1
#> [25] generics_0.1.3 shape_1.4.6
#> [27] BiocIO_1.11.0 dplyr_1.1.3
#> [29] Matrix_1.6-1 ggbeeswarm_0.7.2
#> [31] fansi_1.0.4 abind_1.4-5
#> [33] lifecycle_1.0.3 SparseArray_1.1.12
#> [35] grid_4.3.1 blob_1.2.4
#> [37] promises_1.2.1 crayon_1.5.2
#> [39] shinydashboard_0.7.2 miniUI_0.1.1.1
#> [41] lattice_0.21-8 beachmat_2.17.16
#> [43] cowplot_1.1.1 KEGGREST_1.41.0
#> [45] pillar_1.9.0 knitr_1.44
#> [47] ComplexHeatmap_2.17.0 rjson_0.2.21
#> [49] codetools_0.2-19 fastmatch_1.1-4
#> [51] glue_1.6.2 data.table_1.14.8
#> [53] vctrs_0.6.3 png_0.1-8
#> [55] urltools_1.7.3 gtable_0.3.4
#> [57] cachem_1.0.8 xfun_0.40
#> [59] S4Arrays_1.1.6 mime_0.12
#> [61] iterators_1.0.14 statmod_1.5.0
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