FieldEffectCrc 1.2.0
Data included in this package was generated by harmonizing all publicly
available bulk RNA-seq samples from human primary colorectal tissue available
through NCBI’s Sequence Read Archive and
database of Genotypes and Phenotypes, NCI’s
Genomic Data Commons, and the NIH-funded
BarcUVa-Seq project. FASTQ files were downloaded
directly or generated from BAM files using
biobambam2. Gene expression
estimates were generated from FASTQ files using
Salmon and aggregated from quant.sf
files using
tximport.
The original processed data are stored as tab-delimited text on
Synapse under Synapse ID
syn22237139 and
packaged as SummarizedExperiment objects on ExperimentHub to facilitate
reproduction and extension of results published in Dampier et al. (PMCID:
PMC7386360, PMID: 32764205).
The following example demonstrates how to download the dataset from
ExperimentHub.
First, make sure the Bioconductor packages BiocManager,
SummarizedExperiment, and ExperimentHub are installed:
if (!requireNamespace("BiocManager", quietly=TRUE)) {
install.packages("BiocManager")
}
BiocManager::install(c("SummarizedExperiment", "ExperimentHub", "FieldEffectCrc"))Second, load the SummarizedExperiment and ExperimentHub packages:
library(SummarizedExperiment)## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:base':
##
## I, expand.grid, unname## Loading required package: IRanges## Loading required package: GenomeInfoDb## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedianslibrary(ExperimentHub)## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyr##
## Attaching package: 'AnnotationHub'## The following object is masked from 'package:Biobase':
##
## cacheThird, find the FieldEffectCrc data objects and look at their descriptions:
hub = ExperimentHub()## snapshotDate(): 2021-05-05## simply list the resource names
ns <- listResources(hub, "FieldEffectCrc")
ns## [1] "cohort A from Dampier et al." "cohort B from Dampier et al."
## [3] "cohort C from Dampier et al."## query the hub for full metadata
crcData <- query(hub, "FieldEffectCrc")
crcData## ExperimentHub with 3 records
## # snapshotDate(): 2021-05-05
## # $dataprovider: Synapse
## # $species: Homo sapiens
## # $rdataclass: SummarizedExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH3524"]]'
##
## title
## EH3524 | cohort A from Dampier et al.
## EH3525 | cohort B from Dampier et al.
## EH3526 | cohort C from Dampier et al.## extract metadata
df <- mcols(crcData)
df## DataFrame with 3 rows and 15 columns
## title dataprovider species taxonomyid genome
## <character> <character> <character> <integer> <character>
## EH3524 cohort A from Dampie.. Synapse Homo sapiens 9606 GRCh38
## EH3525 cohort B from Dampie.. Synapse Homo sapiens 9606 GRCh38
## EH3526 cohort C from Dampie.. Synapse Homo sapiens 9606 GRCh38
## description coordinate_1_based maintainer
## <character> <integer> <character>
## EH3524 Salmon-generated tra.. 1 Chris Dampier <chd5n..
## EH3525 Salmon-generated tra.. 1 Chris Dampier <chd5n..
## EH3526 Salmon-generated tra.. 1 Chris Dampier <chd5n..
## rdatadateadded preparerclass
## <character> <character>
## EH3524 2020-07-10 FieldEffectCrc
## EH3525 2020-07-10 FieldEffectCrc
## EH3526 2020-07-10 FieldEffectCrc
## tags rdataclass
## <list> <character>
## EH3524 ExperimentData,ReproducibleResearch,Tissue,... SummarizedExperiment
## EH3525 ExperimentData,ReproducibleResearch,Tissue,... SummarizedExperiment
## EH3526 ExperimentData,ReproducibleResearch,Tissue,... SummarizedExperiment
## rdatapath sourceurl sourcetype
## <character> <character> <character>
## EH3524 FieldEffectCrc/Field.. https://www.synapse... tar.gz
## EH3525 FieldEffectCrc/Field.. https://www.synapse... tar.gz
## EH3526 FieldEffectCrc/Field.. https://www.synapse... tar.gz## see where the cache is stored
hc <- hubCache(crcData)
hc## [1] "/home/biocbuild/.cache/R/ExperimentHub"Fourth, explore the metadata of a particular file. Let’s look at the data for cohort A:
md <- hub["EH3524"]
md## ExperimentHub with 1 record
## # snapshotDate(): 2021-05-05
## # names(): EH3524
## # package(): FieldEffectCrc
## # $dataprovider: Synapse
## # $species: Homo sapiens
## # $rdataclass: SummarizedExperiment
## # $rdatadateadded: 2020-07-10
## # $title: cohort A from Dampier et al.
## # $description: Salmon-generated transcript-level abundance estimates summar...
## # $taxonomyid: 9606
## # $genome: GRCh38
## # $sourcetype: tar.gz
## # $sourceurl: https://www.synapse.org/#!Synapse:syn22237139/files/
## # $sourcesize: NA
## # $tags: c("ExperimentData", "ReproducibleResearch", "Tissue",
## # "Homo_sapiens_Data", "ColonCancerData", "RNASeqData",
## # "ExpressionData", "ExperimentHub")
## # retrieve record with 'object[["EH3524"]]'Fifth, download the data object itself as a SummarizedExperiment:
## using resource ID
data1 <- hub[["EH3524"]]## snapshotDate(): 2021-05-05## see ?FieldEffectCrc and browseVignettes('FieldEffectCrc') for documentation## loading from cachedata1## class: SummarizedExperiment
## dim: 37361 834
## metadata(4): cohort build assay feature
## assays(3): abundance counts length
## rownames(37361): ENSG00000000003 ENSG00000000005 ... ENSG00000283698
## ENSG00000283700
## rowData names(0):
## colnames(834): AMC_10 AMC_12 ... VM1421 VM1422
## colData names(27): dirName projId ... percMap data## using loadResources()
data2 <- loadResources(hub, "FieldEffectCrc", "cohort A")## snapshotDate(): 2021-05-05## see ?FieldEffectCrc and browseVignettes('FieldEffectCrc') for documentation## loading from cachedata2## [[1]]
## class: SummarizedExperiment
## dim: 37361 834
## metadata(4): cohort build assay feature
## assays(3): abundance counts length
## rownames(37361): ENSG00000000003 ENSG00000000005 ... ENSG00000283698
## ENSG00000283700
## rowData names(0):
## colnames(834): AMC_10 AMC_12 ... VM1421 VM1422
## colData names(27): dirName projId ... percMap dataSince the data is a SummarizedExperiment, the different phenotypes can be
accessed using colData(). The phenotype of interest in the field effect
analysis is referred to as sampType:
summary(colData(data1)$sampType)## CRC HLT NAT
## 311 462 61To immediately access the count data for the 834 samples in cohort A, load the
SummarizedExperiment object for cohort A as demonstrated in the
Installation section immediately above. Then use the assays()
accessor function to extract the counts assay, which is the second assay in
the SummarizedExperiment:
se <- data1
counts1 <- assays(se)[["counts"]]Note that assay(), the singular as opposed to plural, will by default extract
the first assay element from a SummarizedExperiment but will extract the ’i’th
assay element with the assay(se, i) syntax:
counts2 <- assay(se, 2)The two alternatives are equivalent:
all(counts1==counts2)## [1] TRUEWe are interested in conducting a differential expression analysis using
DESeq2 to identify genes over- and under-expressed in different colorectal
tissue phenotypes. Due to substantial technical artifacts between single-end and
paired-end library formats observed in the full FieldEffectCrc data set,
samples are grouped according to library format, with paired-end samples in
cohorts A and B and single-end samples in cohort C. Cohort A is the primary
analysis cohort and the one we want to use to discover differentially expressed
genes. To begin, load cohort A as demonstrated in the
Installation section immediately above if not already loaded.
In the following steps, we will use DESeq2 to perform differential expression
analysis on expression data stored in the SummarizedExperiment object for
cohort A. We will adjust for latent batch effects with surrogate variable
analysis as implemented in the sva package. This analysis would typically
consume excess memory and time, so for the purpose of demonstration, we perform
the analysis on a small subset of the full data.
In order to take advantage of the feature lengths preserved by tximport, we
will recreate a tximport object from the SummarizedExperiment object
downloaded from ExperimentHub and use the DESeqDataSetFromTximport()
function to create the DESeqDataSet object. Alternatively, the
SummarizedExperiment object itself could be used to create the DESeqDataSet
object using the DESeqDataSet() function, but the order of the assays would
need to be re-arranged and the counts would need to be rounded, as the
DESeqDataSet() function expects counts with integer values to be the first
assay in a SummarizedExperiment object, and abundance with floating-point
values is the first assay in the FieldEffectCrc objects to facilitate
conversion to tximport objects. Such a re-ordering is facilitated by the FieldEffectCrc::reorder_assays() function. Yet another option is to extract
the counts assay as a matrix and use the DESeqDataSetFromMatrix() function
to create the DESeqDataSet object. Since there are probably some benefits to
feature length normalization as implemented in DESeq2 when such information is
available, we demonstrate creation of the tximport object.
First, make the tximport object, which is just an object of class list from
base R:
## manual construction
txi <- as.list(assays(se))
txi$countsFromAbundance <- "no"
## convenience function construction
txi <- make_txi(se)Next, we could assign the clinical annotations from colData(se) to an
S4Vectors::DataFrame or a base::data.frame object, since that is what
DESeqDataSetFromTximport() expects for the colData argument. However, the
colData slot of a SummarizedExperiment object already stores the clinical
annotations as an S4Vectors::DataFrame, so we are ready to create the
DESeqDataSet object:
if (!requireNamespace("DESeq2", quietly=TRUE)) {
BiocManager::install("DESeq2")
}
library(DESeq2)
dds <- DESeqDataSetFromTximport(txi, colData(se), ~ sampType)## using counts and average transcript lengths from tximportNote that we are interested in expression differences between phenotypes, which
are coded in the sampType field, so we include sampType in our preliminary
design.
Normally, we would pre-filter genes to select genes whose quantities are likely to be estimated accurately. We could use something like the following code, which selects for genes with a count greater than 10 RNA molecules in at least a third of all samples:
countLimit <- 10
sampleLimit <- (1/3) * ncol(dds)
keep <- rowSums( counts(dds) > countLimit ) >= sampleLimit
dds <- dds[keep, ]However, to expedite subsequent computational steps in this demonstration, we will select a random subset of genes and samples for analysis and then filter out any gene with a count less than 10 across all samples:
r <- sample(seq_len(nrow(dds)), 6000)
c <- sample(seq_len(ncol(dds)), 250)
dds <- dds[r, c]
r <- rowSums(counts(dds)) >= 10
dds <- dds[r, ]Amazingly, DESeq2 estimates size factors (i.e. normalization factors based on
library size and feature length) and dispersions for every gene, fits negative
binomial models for every gene, and performs a Wald test for every gene in a
single function:
dds <- 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## -- replacing outliers and refitting for 214 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingPretty incredible. Unfortunately, we have to do some more work before we can fit
useful models. The size factor estimates are the key result of the DESeq()
function at this stage.
If we trusted our high-throughput assays to be perfect representations of the
underlying biology they were designed to measure, we would be ready to test for
differential expression. However, our dataset is composed of samples from
several studies collected and processed in various ways over several years, so
we have reason to suspect there are latent factors driving non-biological
variation in the expression data. Fortunately, we can use sva to estimate the
latent factors and include them in our DESeq2 design.
For a full tutorial on how to use the sva package, see the package
vignette.
For this demonstration, we will provide only cursory motivations for the code.
The sva functions will take the normalized count data as well as model
matrices as inputs. The full model matrix should include the variables of
interest (e.g. sampType) as well as adjustment variables (e.g. sex, age),
which we do not use here. The null model matrix should include adjustment
variables only (i.e. no variables of interest). Without any adjustment
variables, the null model includes an intercept term alone.
dat <- counts(dds, normalized=TRUE) ## extract the normalized counts
mod <- model.matrix(~sampType, data=colData(dds)) ## set the full model
mod0 <- model.matrix(~1, data=colData(dds)) ## set the null modelThe sva package offers two approaches for estimating the number of surrogate
variables that should be included in a differential expression model, the Buja
Eyuboglu and Leek approaches. Buja Eyuboglu is the default approach.
if (!requireNamespace("sva", quietly=TRUE)) {
BiocManager::install("sva")
}
library(sva)## Loading required package: mgcv## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:IRanges':
##
## collapse## This is mgcv 1.8-35. For overview type 'help("mgcv-package")'.## Loading required package: genefilter##
## Attaching package: 'genefilter'## The following objects are masked from 'package:MatrixGenerics':
##
## rowSds, rowVars## The following objects are masked from 'package:matrixStats':
##
## rowSds, rowVars## Loading required package: BiocParallelnsv <- num.sv(dat, mod, method=c("be"), B=20, seed=1) ## Buja Eyuboglu methodThe crucial function for estimating surrogate variables in RNA-seq data is
svaseq():
svs <- svaseq(dat, mod, mod0, n.sv=nsv)## Number of significant surrogate variables is: 1
## Iteration (out of 5 ):1 2 3 4 5The output is a list of 4 elements, the first of which is the latent factor
estimate with a vector for each factor. The name of this element is sv.
We want to include the latent factors as adjustment variables in our statistical
testing. Here, we add the surrogate variables to colData and update the
DESeqDataSet design formula:
for (i in seq_len(svs$n.sv)) {
newvar <- paste0("sv", i)
colData(dds)[ , newvar] <- svs$sv[, i]
}
nvidx <- (ncol(colData(dds)) - i + 1):ncol(colData(dds))
newvars <- colnames(colData(dds))[nvidx]
d <- formula(
paste0("~", paste(paste(newvars, collapse="+"), "sampType", sep="+"))
)
design(dds) <- dNow that we have the surrogate variables in the DESeqDataSet, we can perform
the differential expression analysis we set out to do:
dds <- DESeq(dds)## using pre-existing normalization factors## estimating dispersions## found already estimated dispersions, replacing these## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingWe want to extract results from each of the following 3 comparisons:
With three categories under consideration, the DESeq() function call tests the
first level against the other two, but the second and third levels are not
tested against each other. (Note that the order of the levels is simply
alphabetical if they are not explicitly set.) Furthermore, the results reported
with the results() function are for the comparison of the first level against
the last by default. Therefore, in order to extract all the results of interest,
we set contrasts as follows:
cons <- list()
m <- combn(levels(colData(dds)$sampType), 2)
for (i in seq_len(ncol(m))) {
cons[[i]] <- c("sampType", rev(m[, i]))
names(cons) <- c(
names(cons)[seq_len(length(cons) - 1)], paste(rev(m[, i]), collapse="v")
)
}
res <- list()
for (i in seq_len(length(cons))) {
res[[i]] <- results(dds, contrast=cons[[i]], alpha=0.05) ## default alpha is 0.1
}
names(res) <- names(cons)Let’s take a look at the results:
lapply(res, head)## $HLTvCRC
## log2 fold change (MLE): sampType HLT vs CRC
## Wald test p-value: sampType HLT vs CRC
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSG00000175471 342.5100 1.330127 0.1416500 9.39024 5.98659e-21
## ENSG00000118564 2215.2529 0.340145 0.0609250 5.58302 2.36380e-08
## ENSG00000081923 5355.0658 0.268508 0.0969413 2.76980 5.60906e-03
## ENSG00000156925 14.1221 0.768913 0.1863081 4.12711 3.67357e-05
## ENSG00000122912 848.2637 0.960704 0.0572027 16.79474 2.66691e-63
## ENSG00000141424 913.8983 -0.901744 0.0686755 -13.13050 2.20195e-39
## padj
## <numeric>
## ENSG00000175471 1.39548e-20
## ENSG00000118564 3.71666e-08
## ENSG00000081923 6.94907e-03
## ENSG00000156925 5.08218e-05
## ENSG00000122912 1.87811e-62
## ENSG00000141424 8.23673e-39
##
## $NATvCRC
## log2 fold change (MLE): sampType NAT vs CRC
## Wald test p-value: sampType NAT vs CRC
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSG00000175471 342.5100 -0.585322 0.280190 -2.089019 0.0367060
## ENSG00000118564 2215.2529 -0.111348 0.120333 -0.925329 0.3547947
## ENSG00000081923 5355.0658 -0.127619 0.191220 -0.667392 0.5045217
## ENSG00000156925 14.1221 0.620001 0.360737 1.718708 0.0856675
## ENSG00000122912 848.2637 0.208428 0.112962 1.845124 0.0650194
## ENSG00000141424 913.8983 -0.119179 0.135322 -0.880708 0.3784759
## padj
## <numeric>
## ENSG00000175471 0.310395
## ENSG00000118564 0.751735
## ENSG00000081923 0.843682
## ENSG00000156925 0.447350
## ENSG00000122912 0.396460
## ENSG00000141424 0.765582
##
## $NATvHLT
## log2 fold change (MLE): sampType NAT vs HLT
## Wald test p-value: sampType NAT vs HLT
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSG00000175471 342.5100 -1.915449 0.273510 -7.003207 2.50168e-12
## ENSG00000118564 2215.2529 -0.451493 0.117468 -3.843528 1.21278e-04
## ENSG00000081923 5355.0658 -0.396127 0.186712 -2.121587 3.38724e-02
## ENSG00000156925 14.1221 -0.148912 0.352736 -0.422162 6.72907e-01
## ENSG00000122912 848.2637 -0.752276 0.110273 -6.821944 8.98168e-12
## ENSG00000141424 913.8983 0.782565 0.132189 5.920057 3.21831e-09
## padj
## <numeric>
## ENSG00000175471 1.15998e-11
## ENSG00000118564 2.42637e-04
## ENSG00000081923 4.77077e-02
## ENSG00000156925 7.19943e-01
## ENSG00000122912 3.90791e-11
## ENSG00000141424 1.07217e-08lapply(res, summary)##
## out of 6000 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 2465, 41%
## LFC < 0 (down) : 2647, 44%
## outliers [1] : 0, 0%
## low counts [2] : 0, 0%
## (mean count < 6)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
##
## out of 6000 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 32, 0.53%
## LFC < 0 (down) : 83, 1.4%
## outliers [1] : 0, 0%
## low counts [2] : 0, 0%
## (mean count < 6)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
##
## out of 6000 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 2190, 36%
## LFC < 0 (down) : 2083, 35%
## outliers [1] : 0, 0%
## low counts [2] : 0, 0%
## (mean count < 6)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results## $HLTvCRC
## NULL
##
## $NATvCRC
## NULL
##
## $NATvHLT
## NULLsessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [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
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] sva_3.40.0 BiocParallel_1.26.0
## [3] genefilter_1.74.0 mgcv_1.8-35
## [5] nlme_3.1-152 DESeq2_1.32.0
## [7] FieldEffectCrc_1.2.0 ExperimentHub_2.0.0
## [9] AnnotationHub_3.0.0 BiocFileCache_2.0.0
## [11] dbplyr_2.1.1 SummarizedExperiment_1.22.0
## [13] Biobase_2.52.0 GenomicRanges_1.44.0
## [15] GenomeInfoDb_1.28.0 IRanges_2.26.0
## [17] S4Vectors_0.30.0 BiocGenerics_0.38.0
## [19] MatrixGenerics_1.4.0 matrixStats_0.58.0
## [21] BiocStyle_2.20.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5
## [3] filelock_1.0.2 RColorBrewer_1.1-2
## [5] httr_1.4.2 tools_4.1.0
## [7] bslib_0.2.5.1 utf8_1.2.1
## [9] R6_2.5.0 colorspace_2.0-1
## [11] DBI_1.1.1 withr_2.4.2
## [13] tidyselect_1.1.1 bit_4.0.4
## [15] curl_4.3.1 compiler_4.1.0
## [17] DelayedArray_0.18.0 bookdown_0.22
## [19] sass_0.4.0 scales_1.1.1
## [21] rappdirs_0.3.3 stringr_1.4.0
## [23] digest_0.6.27 rmarkdown_2.8
## [25] XVector_0.32.0 pkgconfig_2.0.3
## [27] htmltools_0.5.1.1 limma_3.48.0
## [29] fastmap_1.1.0 rlang_0.4.11
## [31] RSQLite_2.2.7 shiny_1.6.0
## [33] jquerylib_0.1.4 generics_0.1.0
## [35] jsonlite_1.7.2 dplyr_1.0.6
## [37] RCurl_1.98-1.3 magrittr_2.0.1
## [39] GenomeInfoDbData_1.2.6 Matrix_1.3-3
## [41] munsell_0.5.0 Rcpp_1.0.6
## [43] fansi_0.4.2 lifecycle_1.0.0
## [45] edgeR_3.34.0 stringi_1.6.2
## [47] yaml_2.2.1 zlibbioc_1.38.0
## [49] grid_4.1.0 blob_1.2.1
## [51] promises_1.2.0.1 crayon_1.4.1
## [53] lattice_0.20-44 Biostrings_2.60.0
## [55] splines_4.1.0 annotate_1.70.0
## [57] KEGGREST_1.32.0 locfit_1.5-9.4
## [59] knitr_1.33 pillar_1.6.1
## [61] RUnit_0.4.32 geneplotter_1.70.0
## [63] XML_3.99-0.6 glue_1.4.2
## [65] BiocVersion_3.13.1 evaluate_0.14
## [67] BiocManager_1.30.15 png_0.1-7
## [69] vctrs_0.3.8 httpuv_1.6.1
## [71] gtable_0.3.0 purrr_0.3.4
## [73] assertthat_0.2.1 cachem_1.0.5
## [75] ggplot2_3.3.3 xfun_0.23
## [77] mime_0.10 xtable_1.8-4
## [79] later_1.2.0 survival_3.2-11
## [81] tibble_3.1.2 AnnotationDbi_1.54.0
## [83] memoise_2.0.0 ellipsis_0.3.2
## [85] interactiveDisplayBase_1.30.0