The mpra User’s Guide
29 October 2024
Abstract
A comprehensive guide to using the mpra package for analyzing massively parallel reporter assays (MPRA).
Package
mpra 1.28.0
Contents
1 Introduction
The mpra package provides tools for the analysis of data from massively parallel reporter assays (MPRA). Specifically, it contains the functionality described in (Myint et al. 2019). The primary analysis purpose is to enable differential analysis of activity measures, but the package can also be used to generate precision weights useful in regression analyses of activity scores on sequence features. The main workhorse of the mpra package is the mpralm() function which draws on the previously proposed voom framework for RNA-seq analysis (Law et al. 2014). In this document, we will be looking at MPRA data from a study comparing episomal and lentiviral versions of MPRA (Inoue et al. 2017). We will also look at MPRA data from a study comparing the regulatory activity of different alleles of thousands of SNPs (Tewhey et al. 2016).
2 Dependencies
This document has the following dependencies
library(mpra)3 Creating an MPRASet object
In this package, MPRA data are contained in MPRASet objects. Because MPRA data do not have a common prescribed format, these objects must be created manually. In this section, we demonstrate how to do this.
MPRASet objects must contain DNA and RNA count information because this is the information used to quantify activity levels of the elements being assayed. DNA and RNA count information should be specified as \(K \times S\) integer count matrices where \(K\) is the total number of barcodes over all elements if barcode-level information is being supplied or the total number of putative regulatory elements (PREs) if element-level information is being supplied. \(S\) is the number of samples (typically, the number of independent transfections).
MPRASet objects must also contain element identification information. This should be supplied as a character vector of length \(K\), the number of rows in the DNA and RNA count matrices. These are any strings used to describe/identify the unique PREs being assayed.
Optionally, the barcode sequences and PRE sequences can be specified as length \(K\) character vectors.
In the next sections we provide specific examples for how to specify this information for two common differential analysis settings: tissue and allele comparisons. Although we show simulated data, this information would typically be read from text files.
3.1 Tissue comparison
In tissue comparison studies, the same set of PREs is assayed in two or more cell types. In the following example, the experiment looks at four PREs with three barcodes each. Two tissues (liver and kidney) are studied, and each tissue has four replicates (four independent transfections each).
RNA and DNA count matrices would look as below:
E <- 4 # Number of elements
B <- 3 # Number of barcodes
s <- 4 # Samples per tissue
nt <- 2 # Number of tissues
set.seed(434)
rna <- matrix(rpois(E*B*s*nt, lambda = 30), nrow = E*B, ncol = s*nt)
dna <- matrix(rpois(E*B*s*nt, lambda = 30), nrow = E*B, ncol = s*nt)
rn <- as.character(outer(paste0("barcode_", seq_len(B), "_"), paste0("elem_", seq_len(E)), FUN = "paste0"))
cn <- c(paste0("liver_", seq_len(s)), paste0("kidney_", seq_len(s)))
rownames(rna) <- rn
rownames(dna) <- rn
colnames(rna) <- cn
colnames(dna) <- cn
rna## liver_1 liver_2 liver_3 liver_4 kidney_1 kidney_2 kidney_3
## barcode_1_elem_1 31 26 36 18 27 33 28
## barcode_2_elem_1 33 28 29 32 28 33 34
## barcode_3_elem_1 22 43 25 41 28 34 30
## barcode_1_elem_2 28 27 31 20 37 33 24
## barcode_2_elem_2 32 31 32 21 38 22 27
## barcode_3_elem_2 35 36 28 38 26 23 30
## barcode_1_elem_3 30 34 26 34 40 26 26
## barcode_2_elem_3 37 27 37 29 25 29 38
## barcode_3_elem_3 34 30 20 26 30 35 33
## barcode_1_elem_4 28 27 34 26 29 30 25
## barcode_2_elem_4 31 32 28 36 31 24 29
## barcode_3_elem_4 24 28 34 31 37 29 34
## kidney_4
## barcode_1_elem_1 30
## barcode_2_elem_1 30
## barcode_3_elem_1 37
## barcode_1_elem_2 29
## barcode_2_elem_2 29
## barcode_3_elem_2 29
## barcode_1_elem_3 22
## barcode_2_elem_3 28
## barcode_3_elem_3 38
## barcode_1_elem_4 30
## barcode_2_elem_4 32
## barcode_3_elem_4 36dna## liver_1 liver_2 liver_3 liver_4 kidney_1 kidney_2 kidney_3
## barcode_1_elem_1 29 34 28 27 27 25 30
## barcode_2_elem_1 43 37 34 33 43 26 26
## barcode_3_elem_1 35 23 32 27 19 29 29
## barcode_1_elem_2 23 30 34 27 32 18 24
## barcode_2_elem_2 32 29 42 21 37 34 32
## barcode_3_elem_2 26 23 22 24 32 41 24
## barcode_1_elem_3 29 38 34 27 31 27 31
## barcode_2_elem_3 30 36 32 21 34 29 26
## barcode_3_elem_3 34 28 42 35 26 32 32
## barcode_1_elem_4 34 25 29 30 44 26 23
## barcode_2_elem_4 29 26 37 37 28 37 34
## barcode_3_elem_4 24 28 33 40 31 30 19
## kidney_4
## barcode_1_elem_1 31
## barcode_2_elem_1 33
## barcode_3_elem_1 25
## barcode_1_elem_2 25
## barcode_2_elem_2 31
## barcode_3_elem_2 23
## barcode_1_elem_3 33
## barcode_2_elem_3 39
## barcode_3_elem_3 26
## barcode_1_elem_4 33
## barcode_2_elem_4 31
## barcode_3_elem_4 26PRE identification strings would look as below. When counts are provided at the barcode level, the eid character vector will have repeated elements.
eid <- rep(paste0("elem_", seq_len(E)), each = B)
eid## [1] "elem_1" "elem_1" "elem_1" "elem_2" "elem_2" "elem_2" "elem_3" "elem_3"
## [9] "elem_3" "elem_4" "elem_4" "elem_4"We may also have PRE sequences as below. These sequences must be specified in a character vector of the same length as eid and the same number of rows as rna and dna.
eseq <- replicate(E, paste(sample(c("A", "T", "C", "G"), 10, replace = TRUE), collapse = ""))
eseq <- rep(eseq, each = B)
eseq## [1] "CAACGTTTGT" "CAACGTTTGT" "CAACGTTTGT" "TTCTCTTTGA" "TTCTCTTTGA"
## [6] "TTCTCTTTGA" "ACCTACCTCA" "ACCTACCTCA" "ACCTACCTCA" "CCGTACTACT"
## [11] "CCGTACTACT" "CCGTACTACT"The above pieces (rna, dna, eid, and eseq) can be supplied as arguments to the MPRASet constructor function as below. If barcode (barcode sequences) or eseq (PRE sequences) is not supplied, it must be specified as NULL.
mpraset_example <- MPRASet(DNA = dna, RNA = rna, eid = eid, eseq = eseq, barcode = NULL)
mpraset_example## class: MPRASet
## dim: 12 8
## metadata(0):
## assays(2): DNA RNA
## rownames(12): barcode_1_elem_1 barcode_2_elem_1 ... barcode_2_elem_4
## barcode_3_elem_4
## rowData names(2): eid eseq
## colnames(8): liver_1 liver_2 ... kidney_3 kidney_4
## colData names(0):## (no barcodes present)3.2 Allele comparison
In allele comparison studies, PREs that exist with two or more alleles are assayed. All allelic-versions of the PREs are assayed in the same sample. Because activity comparisons between alleles is desired, these counts must be separated into different columns. In the following example, the experiment looks at four PREs with three barcodes each. There are two alleles per PRE, and there are four replicates (four independent transfections total).
Note that because the different alleles of a single PRE are linked to different barcodes, there is not a natural way to construct the RNA and DNA count matrices as above, where a particular barcode is in a row. Further, sometimes each PRE-allele combination is paired with varying numbers of barcodes. This is yet another reason that DNA and RNA count matrices should not look as above for allelic studies. In the example below, the count matrices shown might look as follows before they are ready to be made into an MPRASet object.
E <- 2 # Number of elements
B <- 3 # Number of barcodes
s <- 4 # Total number of samples
nalleles <- 2 # Number of alleles
set.seed(434)
rna <- matrix(rpois(E*B*s*nalleles, lambda = 30), nrow = E*B*nalleles, ncol = s)
dna <- matrix(rpois(E*B*s*nalleles, lambda = 30), nrow = E*B*nalleles, ncol = s)
rn <- expand.grid(barcode = seq_len(B), allele = seq_len(nalleles), elem = seq_len(E))
rn <- paste0("barcode", rn$barcode, "_elem", rn$elem, "_allele", rn$allele)
cn <- paste0("sample", seq_len(s))
rownames(rna) <- rn
rownames(dna) <- rn
colnames(rna) <- cn
colnames(dna) <- cn
rna## sample1 sample2 sample3 sample4
## barcode1_elem1_allele1 31 26 36 18
## barcode2_elem1_allele1 33 28 29 32
## barcode3_elem1_allele1 22 43 25 41
## barcode1_elem1_allele2 28 27 31 20
## barcode2_elem1_allele2 32 31 32 21
## barcode3_elem1_allele2 35 36 28 38
## barcode1_elem2_allele1 30 34 26 34
## barcode2_elem2_allele1 37 27 37 29
## barcode3_elem2_allele1 34 30 20 26
## barcode1_elem2_allele2 28 27 34 26
## barcode2_elem2_allele2 31 32 28 36
## barcode3_elem2_allele2 24 28 34 31dna## sample1 sample2 sample3 sample4
## barcode1_elem1_allele1 27 33 28 30
## barcode2_elem1_allele1 28 33 34 30
## barcode3_elem1_allele1 28 34 30 37
## barcode1_elem1_allele2 37 33 24 29
## barcode2_elem1_allele2 38 22 27 29
## barcode3_elem1_allele2 26 23 30 29
## barcode1_elem2_allele1 40 26 26 22
## barcode2_elem2_allele1 25 29 38 28
## barcode3_elem2_allele1 30 35 33 38
## barcode1_elem2_allele2 29 30 25 30
## barcode2_elem2_allele2 31 24 29 32
## barcode3_elem2_allele2 37 29 34 36Most often with allelic studies, we will want to aggregate counts over barcodes to have summarized counts for each PRE-allele combination in each sample as below:
agg_output <- lapply(seq_len(E), function(elem_id) {
pattern1 <- paste0(paste0("elem", elem_id), "_allele1")
bool_rna_allele1 <- grepl(pattern1, rownames(rna))
pattern2 <- paste0(paste0("elem", elem_id), "_allele2")
bool_rna_allele2 <- grepl(pattern2, rownames(rna))
agg_rna <- c(
colSums(rna[bool_rna_allele1,]),
colSums(rna[bool_rna_allele2,])
)
names(agg_rna) <- paste0(rep(c("allele1", "allele2"), each = s), "_", names(agg_rna))
bool_dna_allele1 <- grepl(pattern1, rownames(dna))
bool_dna_allele2 <- grepl(pattern2, rownames(dna))
agg_dna <- c(
colSums(dna[bool_dna_allele1,]),
colSums(dna[bool_dna_allele2,])
)
names(agg_dna) <- paste0(rep(c("allele1", "allele2"), each = s), "_", names(agg_dna))
list(rna = agg_rna, dna = agg_dna)
})
agg_rna <- do.call(rbind, lapply(agg_output, "[[", "rna"))
agg_dna <- do.call(rbind, lapply(agg_output, "[[", "dna"))
eid <- paste0("elem", seq_len(E))
rownames(agg_rna) <- eid
rownames(agg_dna) <- eid
eseq <- replicate(E, paste(sample(c("A", "T", "C", "G"), 10, replace = TRUE), collapse = ""))With the relevant information defined, we can use the MPRASet constructor function as in the first example:
mpraset_example2 <- MPRASet(DNA = agg_dna, RNA = agg_rna, eid = eid, eseq = eseq, barcode = NULL)
mpraset_example2## class: MPRASet
## dim: 2 8
## metadata(0):
## assays(2): DNA RNA
## rownames(2): elem1 elem2
## rowData names(2): eid eseq
## colnames(8): allele1_sample1 allele1_sample2 ... allele2_sample3
## allele2_sample4
## colData names(0):## (no barcodes present)4 Analysis
4.1 Tissue comparison
While the above section demonstrated how to create MPRASet objects, we will use preconstructed objects containing data from a comparison of episomal and lentiviral versions of MPRA (Inoue et al. 2017).
data(mpraSetExample)We create the design matrix with an indicator for the episomal (mutant integrase) samples and fit the precision-weighted linear model with mpralm. In MPRA experiments, activity measures are quantified as the log2 ratio of RNA counts over DNA counts. When there is barcode level information (as in this experiment), there are various ways to summarize information over barcodes to compute the final element- and sample-specific log ratios that are used for subsequent statistical modeling.
We have specified aggregate = "mean" to indicate that the element- and sample-specific log ratios will be computed by first computing the log ratio of RNA counts over DNA counts for each barcode, then taking the mean over barcodes in a particular element and sample. This is termed the “average estimator” in (Myint et al. 2019). In contrast, specifying aggregate = "sum" would indicate the use of the “aggregate estimator”, in which counts are first summed over barcodes to create total RNA and DNA counts, and the log ratio activity measure is the ratio of these total counts.
We have specifed normalize = TRUE to perform total count normalization on the RNA and DNA libraries. This scales all libraries to have a common size of 10 million reads.
Because this experiment looks at a set of PREs in two different cellular conditions, the different samples (columns of the MPRASet object) are independent. Thus we specify model_type = "indep_groups" to perform an unpaired analysis. In contrast, if we were performing an allele comparison, we would specify model_type = "corr_groups" to performed a paired analysis (indicating that different columns of the MPRASet object are linked).
Finally, we specify plot = TRUE to plot the relationship between log ratio variability versus element copy number.
design <- data.frame(intcpt = 1, episomal = grepl("MT", colnames(mpraSetExample)))
mpralm_fit <- mpralm(object = mpraSetExample, design = design, aggregate = "mean", normalize = TRUE, model_type = "indep_groups", plot = TRUE)
The resulting fit object can be used with topTable from the limma package.
toptab <- topTable(mpralm_fit, coef = 2, number = Inf)
toptab6 <- head(toptab)Because the element codes are rather long for this dataset, we do some tricks to print the top differential elements:
rownames(toptab6)## [1] "C:SLEA_hg18:chr2:210861483-210861650|5:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;26:V_HNF1_C:AGTTAATGATTAACCAA;45:V_HNF1_C:AGTTAATGATTAACCAA;64:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;85:V_HNF1_C:AGTTAATGATTAACCAA;104:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;125:V_HNF1_C:AGTTAATGATTAACCAA;144:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC"
## [2] "R:EP300-NoMod_chr9:12814543-12814714_[chr9:12814543-12814714]"
## [3] "C:SLEA_hg18:chr2:210861483-210861650|4:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;25:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;46:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;67:V_HNF1_C:AGTTAATGATTAACCAA;86:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;107:V_HNF1_C:AGTTAATGATTAACCAA;126:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;147:V_HNF1_C:AGTTAATGATTAACCAA"
## [4] "C:SLEA_hg18:chr2:210861483-210861650|6:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;27:V_HNF1_C:AGTTAATGATTAACCAA;46:V_HNF1_C:AGTTAATGATTAACCAA;65:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC;86:V_HNF1_C:AGTTAATGATTAACCAA;105:V_HNF1_C:AGTTAATGATTAACCAA;124:V_HNF1_C:AGTTAATGATTAACCAA;143:V_AHRARNT_02:GGGGATCGCGTGCCAGCCC"
## [5] "R:FOXA1_FOXA2-ChMod_chr1:38000211-38000353_[chr1:38000196-38000367]"
## [6] "R:EP300-NoMod_chr16:3070958-3071129_[chr16:3070958-3071129]"rownames(toptab6) <- NULL
toptab6## logFC AveExpr t P.Value adj.P.Val B
## 1 -1.2076627 1.284363 -26.99214 1.450176e-36 3.538429e-33 73.05825
## 2 -1.7316415 1.668562 -23.50738 4.357972e-33 5.316726e-30 64.87553
## 3 -1.1603766 1.497371 -23.11093 1.150733e-32 9.359292e-30 64.12459
## 4 -0.9560848 1.016586 -18.57491 2.136470e-27 1.303247e-24 52.09394
## 5 -1.1713718 1.730330 -18.25135 5.494116e-27 2.681128e-24 51.09648
## 6 -0.7639104 0.733524 -17.71599 2.689472e-26 1.093719e-23 49.548884.2 Allelic comparison
We will also demonstrate an allelic comparison analysis using the data in (Tewhey et al. 2016). In this study, the investigators compare reference and alternate allele versions of sequences containing SNPs. These sequences were believed to be eQTLs based on previous work.
We create a design matrix with an indicator for whether the counts come from the “B” allele (as opposed to the “A” allele). We also create an integer block_vector to indicate the actual sample that each column in the DNA and RNA count matrices comes from. In this case, columns 1 and 6 of the DNA and RNA count matrices come from sample 1 (the A and B alleles measured in transfection 1), columns 2 and 7 from sample 2, and so on. This information is needed to accurately model the within-sample correlation for a paired analysis.
data(mpraSetAllelicExample)
design <- data.frame(intcpt = 1, alleleB = grepl("allele_B", colnames(mpraSetAllelicExample)))
block_vector <- rep(1:5, 2)
mpralm_allele_fit <- mpralm(object = mpraSetAllelicExample, design = design, aggregate = "none", normalize = TRUE, block = block_vector, model_type = "corr_groups", plot = TRUE)
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values## Warning: Zero sample variances detected, have been offset away from zerotoptab_allele <- topTable(mpralm_allele_fit, coef = 2, number = Inf)
head(toptab_allele)## logFC AveExpr t P.Value adj.P.Val B
## rs11080327 3.106346 1.694029 79.77254 3.962616e-16 1.484396e-12 26.25693
## rs9661285 -2.116121 1.299072 -50.01525 5.630061e-14 1.054510e-10 22.30238
## rs71535706 -1.305818 1.201037 -38.18341 9.812946e-13 1.225310e-09 19.64486
## rs7257930 1.698574 1.850857 34.93271 2.512428e-12 1.911361e-09 18.79437
## rs112372623 -1.836127 2.457856 -34.88207 2.551203e-12 1.911361e-09 18.78015
## rs2191501 1.884512 1.436538 31.93908 6.465886e-12 3.689913e-09 17.996174.3 Returning an MPRASet
The last section notes an option endomorphic = TRUE that changes the
type of object returned by mpralm from an MArrayLM object to the
original object, an MPRASet with the statistical results attached as
rowData to the object. “Endomorphic” refers to the fact that the
same type of object that is passed into the function is returned, but
with additional information added.
We can demonstrate with the example from above:
design <- data.frame(intcpt = 1,
episomal = grepl("MT", colnames(mpraSetExample)))
efit <- mpralm(object = mpraSetExample,
design = design,
aggregate = "sum",
normalize = TRUE,
model_type = "indep_groups",
plot = FALSE,
endomorphic = TRUE, coef = 2)
# for ease of printing, because 'eid' are long here
rownames(efit) <- paste0("elem_", seq_len(nrow(efit)))
rowData(efit)## DataFrame with 2440 rows and 7 columns
## eid logFC AveExpr t P.Value
## <character> <numeric> <numeric> <numeric> <numeric>
## elem_1 A:HNF4A-ChMod_chr1:1.. -0.0372331 0.3286243 -0.680443 5.00258e-01
## elem_2 A:HNF4A-ChMod_chr1:1.. 0.2194297 0.2537635 2.642458 1.18004e-02
## elem_3 A:HNF4A-ChMod_chr1:1.. -0.0371697 -0.1062811 -0.751938 4.56618e-01
## elem_4 A:HNF4A-ChMod_chr1:1.. 0.3260329 -0.0201576 3.223334 2.56696e-03
## elem_5 A:HNF4A-ChMod_chr1:1.. 0.3322387 -0.4469826 5.142783 8.04223e-06
## ... ... ... ... ... ...
## elem_2436 R:HNF4A-NoMod_chr9:7.. 0.2652956 -0.4381840 5.701967 1.35858e-06
## elem_2437 R:HNF4A-NoMod_chr9:7.. 0.2034860 -0.2556073 4.089514 2.10283e-04
## elem_2438 R:HNF4A-NoMod_chr9:8.. 0.0834214 -0.0859028 0.681994 4.99288e-01
## elem_2439 R:HNF4A-NoMod_chr9:9.. 0.2157308 -0.1195789 2.542841 1.50916e-02
## elem_2440 R:HNF4A-NoMod_chrY:1.. 0.1015237 -0.1142534 1.866165 6.95783e-02
## adj.P.Val B
## <numeric> <numeric>
## elem_1 5.53146e-01 -6.95583
## elem_2 1.83161e-02 -3.55794
## elem_3 5.11547e-01 -6.99384
## elem_4 4.48025e-03 -2.02609
## elem_5 2.22881e-05 3.21987
## ... ... ...
## elem_2436 4.33892e-06 4.671009
## elem_2437 4.47334e-04 -0.252453
## elem_2438 5.52650e-01 -6.211201
## elem_2439 2.29145e-02 -3.721659
## elem_2440 9.28726e-02 -5.477030The coef = 2 is passed to topTable which is run within mpralm.
This option also returns scaledDNA and scaledRNA that were used to
compute log ratios within mpralm. This can facilitate plotting
statistics alongside original or scaled count data.
Just to demonstrate that the scaled counts were in fact the ones used for statistics, we can compute the raw LFC and compare to the LFC computed by limma-voom using precision weights.
sdna <- assay(efit, "scaledDNA")
srna <- assay(efit, "scaledRNA")
mt <- rowMeans(log2(srna[,1:3] + 1) - log2(sdna[,1:3] + 1))
wt <- rowMeans(log2(srna[,4:6] + 1) - log2(sdna[,4:6] + 1))
raw_lfc <- mt - wt
# very similar, precision weights modify LFC from limma a bit
lm(raw_lfc ~ rowData(efit)$logFC)##
## Call:
## lm(formula = raw_lfc ~ rowData(efit)$logFC)
##
## Coefficients:
## (Intercept) rowData(efit)$logFC
## -0.0007747 0.99999405 Session Info
R version 4.4.1 (2024-06-14) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 24.04.1 LTS
Matrix products: default BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB 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
time zone: America/New_York tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] mpra_1.28.0 limma_3.62.0
[3] SummarizedExperiment_1.36.0 Biobase_2.66.0
[5] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0
[7] IRanges_2.40.0 S4Vectors_0.44.0
[9] MatrixGenerics_1.18.0 matrixStats_1.4.1
[11] BiocGenerics_0.52.0 BiocStyle_2.34.0
loaded via a namespace (and not attached):
[1] sass_0.4.9 SparseArray_1.6.0 lattice_0.22-6
[4] magrittr_2.0.3 digest_0.6.37 evaluate_1.0.1
[7] grid_4.4.1 bookdown_0.41 fastmap_1.2.0
[10] jsonlite_1.8.9 Matrix_1.7-1 tinytex_0.53
[13] BiocManager_1.30.25 httr_1.4.7 UCSC.utils_1.2.0
[16] scales_1.3.0 jquerylib_0.1.4 abind_1.4-8
[19] cli_3.6.3 rlang_1.1.4 crayon_1.5.3
[22] XVector_0.46.0 munsell_0.5.1 cachem_1.1.0
[25] DelayedArray_0.32.0 yaml_2.3.10 S4Arrays_1.6.0
[28] tools_4.4.1 colorspace_2.1-1 GenomeInfoDbData_1.2.13
[31] R6_2.5.1 lifecycle_1.0.4 magick_2.8.5
[34] zlibbioc_1.52.0 bslib_0.8.0 Rcpp_1.0.13
[37] glue_1.8.0 statmod_1.5.0 xfun_0.48
[40] highr_0.11 knitr_1.48 farver_2.1.2
[43] htmltools_0.5.8.1 rmarkdown_2.28 compiler_4.4.1
References
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