Brings transcriptomics to the tidyverse
Function | Description |
---|---|
identify_abundant |
identify the abundant genes |
aggregate_duplicates |
Aggregate abundance and annotation of duplicated transcripts in a robust way |
scale_abundance |
Scale (normalise) abundance for RNA sequencing depth |
reduce_dimensions |
Perform dimensionality reduction (PCA, MDS, tSNE) |
cluster_elements |
Labels elements with cluster identity (kmeans, SNN) |
remove_redundancy |
Filter out elements with highly correlated features |
adjust_abundance |
Remove known unwanted variation (Combat) |
test_differential_abundance |
Differential transcript abundance testing (DE) |
deconvolve_cellularity |
Estimated tissue composition (Cibersort or llsr) |
test_differential_cellularity |
Differential cell-type abundance testing |
keep_variable |
Filter for top variable features |
keep_abundant |
Filter out lowly abundant transcripts |
test_gene_enrichment |
Gene enrichment analyses (EGSEA) |
test_gene_overrepresentation |
Gene enrichment on list of transcript names (no rank) |
Utilities | Description |
---|---|
get_bibliography |
Get the bibliography of your workflow |
tidybulk |
add tidybulk attributes to a tibble object |
tidybulk_SAM_BAM |
Convert SAM BAM files into tidybulk tibble |
pivot_sample |
Select sample-wise columns/information |
pivot_transcript |
Select transcript-wise columns/information |
rotate_dimensions |
Rotate two dimensions of a degree |
ensembl_to_symbol |
Add gene symbol from ensembl IDs |
symbol_to_entrez |
Add entrez ID from gene symbol |
describe_transcript |
Add gene description from gene symbol |
impute_missing_abundance |
Impute abundance for missing data points using sample groupings |
fill_missing_abundance |
Fill abundance for missing data points using an arbitrary value |
All functions are directly compatibble with SummarizedExperiment
object.
From Bioconductor
BiocManager::install("tidybulk")
From Github
devtools::install_github("stemangiola/tidybulk")
We will use a SummarizedExperiment
object
se_mini
## # A SummarizedExperiment-tibble abstraction: 2,635 × 9
## # [90mFeatures=527 | Samples=5 | Assays=count[0m
## .feature .sample count Cell.type time condition days dead entrez
## <chr> <chr> <dbl> <chr> <chr> <lgl> <dbl> <dbl> <chr>
## 1 ABCB4 SRR1740034 1035 b_cell 0 d TRUE 1 1 5244
## 2 ABCB9 SRR1740034 45 b_cell 0 d TRUE 1 1 23457
## 3 ACAP1 SRR1740034 7151 b_cell 0 d TRUE 1 1 9744
## 4 ACHE SRR1740034 2 b_cell 0 d TRUE 1 1 43
## 5 ACP5 SRR1740034 2278 b_cell 0 d TRUE 1 1 54
## 6 ADAM28 SRR1740034 11156 b_cell 0 d TRUE 1 1 10863
## 7 ADAMDEC1 SRR1740034 72 b_cell 0 d TRUE 1 1 27299
## 8 ADAMTS3 SRR1740034 0 b_cell 0 d TRUE 1 1 9508
## 9 ADRB2 SRR1740034 298 b_cell 0 d TRUE 1 1 154
## 10 AIF1 SRR1740034 8 b_cell 0 d TRUE 1 1 199
## # ℹ 40 more rows
Loading tidySummarizedExperiment
will automatically abstract this object as tibble
, so we can display it and manipulate it with tidy tools. Although it looks different, and more tools (tidyverse) are available to us, this object is in fact a SummarizedExperiment
object.
class(se_mini)
## [1] "SummarizedExperiment"
## attr(,"package")
## [1] "SummarizedExperiment"
First of all, you can cite all articles utilised within your workflow automatically from any tidybulk tibble
se_mini |> get_bibliography()
transcripts
tidybulk provide the aggregate_duplicates
function to aggregate duplicated transcripts (e.g., isoforms, ensembl). For example, we often have to convert ensembl symbols to gene/transcript symbol, but in doing so we have to deal with duplicates. aggregate_duplicates
takes a tibble and column names (as symbols; for sample
, transcript
and count
) as arguments and returns a tibble with transcripts with the same name aggregated. All the rest of the columns are appended, and factors and boolean are appended as characters.
rowData(se_mini)$gene_name = rownames(se_mini)
se_mini.aggr = se_mini |> aggregate_duplicates(.transcript = gene_name)
temp = data.frame(
symbol = dge_list$genes$symbol,
dge_list$counts
)
dge_list.nr <- by(temp, temp$symbol,
function(df)
if(length(df[1,1])>0)
matrixStats:::colSums(as.matrix(df[,-1]))
)
dge_list.nr <- do.call("rbind", dge_list.nr)
colnames(dge_list.nr) <- colnames(dge_list)
counts
We may want to compensate for sequencing depth, scaling the transcript abundance (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25). scale_abundance
takes a tibble, column names (as symbols; for sample
, transcript
and count
) and a method as arguments and returns a tibble with additional columns with scaled data as <NAME OF COUNT COLUMN>_scaled
.
se_mini.norm = se_mini.aggr |> identify_abundant(factor_of_interest = condition) |> scale_abundance()
## tidybulk says: the sample with largest library size SRR1740035 was chosen as reference for scaling
library(edgeR)
dgList <- DGEList(count_m=x,group=group)
keep <- filterByExpr(dgList)
dgList <- dgList[keep,,keep.lib.sizes=FALSE]
[...]
dgList <- calcNormFactors(dgList, method="TMM")
norm_counts.table <- cpm(dgList)
We can easily plot the scaled density to check the scaling outcome. On the x axis we have the log scaled counts, on the y axes we have the density, data is grouped by sample and coloured by cell type.
se_mini.norm |>
ggplot(aes(count_scaled + 1, group=.sample, color=`Cell.type`)) +
geom_density() +
scale_x_log10() +
my_theme
variable transcripts
We may want to identify and filter variable transcripts.
se_mini.norm.variable = se_mini.norm |> keep_variable()
## Getting the 393 most variable genes
library(edgeR)
x = norm_counts.table
s <- rowMeans((x-rowMeans(x))^2)
o <- order(s,decreasing=TRUE)
x <- x[o[1L:top],,drop=FALSE]
norm_counts.table = norm_counts.table[rownames(x)]
norm_counts.table$cell_type = tibble_counts[
match(
tibble_counts$sample,
rownames(norm_counts.table)
),
"Cell.type"
]
dimensions
We may want to reduce the dimensions of our data, for example using PCA or MDS algorithms. reduce_dimensions
takes a tibble, column names (as symbols; for sample
, transcript
and count
) and a method (e.g., MDS or PCA) as arguments and returns a tibble with additional columns for the reduced dimensions.
MDS (Robinson et al., 10.1093/bioinformatics/btp616)
se_mini.norm.MDS =
se_mini.norm |>
reduce_dimensions(method="MDS", .dims = 3)
## Getting the 393 most variable genes
## tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
library(limma)
count_m_log = log(count_m + 1)
cmds = limma::plotMDS(ndim = .dims, plot = FALSE)
cmds = cmds %$%
cmdscale.out |>
setNames(sprintf("Dim%s", 1:6))
cmds$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(cmds)),
"Cell.type"
]
On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.
se_mini.norm.MDS |> pivot_sample() |> select(contains("Dim"), everything())
se_mini.norm.MDS |>
pivot_sample() |>
GGally::ggpairs(columns = 9:11, ggplot2::aes(colour=`Cell.type`))
PCA
se_mini.norm.PCA =
se_mini.norm |>
reduce_dimensions(method="PCA", .dims = 3)
count_m_log = log(count_m + 1)
pc = count_m_log |> prcomp(scale = TRUE)
variance = pc$sdev^2
variance = (variance / sum(variance))[1:6]
pc$cell_type = counts[
match(counts$sample, rownames(pc)),
"Cell.type"
]
On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.
se_mini.norm.PCA |> pivot_sample() |> select(contains("PC"), everything())
se_mini.norm.PCA |>
pivot_sample() |>
GGally::ggpairs(columns = 11:13, ggplot2::aes(colour=`Cell.type`))
tSNE
se_mini.norm.tSNE =
breast_tcga_mini_SE |>
identify_abundant() |>
reduce_dimensions(
method = "tSNE",
perplexity=10,
pca_scale =TRUE
)
count_m_log = log(count_m + 1)
tsne = Rtsne::Rtsne(
t(count_m_log),
perplexity=10,
pca_scale =TRUE
)$Y
tsne$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(tsne)),
"Cell.type"
]
Plot
se_mini.norm.tSNE |>
pivot_sample() |>
select(contains("tSNE"), everything())
## # A tibble: 251 × 4
## tSNE1 tSNE2 .sample Call
## <dbl> <dbl> <chr> <fct>
## 1 -2.79 9.69 TCGA-A1-A0SD-01A-11R-A115-07 LumA
## 2 2.46 -7.24 TCGA-A1-A0SF-01A-11R-A144-07 LumA
## 3 -8.15 11.2 TCGA-A1-A0SG-01A-11R-A144-07 LumA
## 4 5.29 5.74 TCGA-A1-A0SH-01A-11R-A084-07 LumA
## 5 4.86 9.02 TCGA-A1-A0SI-01A-11R-A144-07 LumB
## 6 1.44 5.88 TCGA-A1-A0SJ-01A-11R-A084-07 LumA
## 7 4.82 -31.7 TCGA-A1-A0SK-01A-12R-A084-07 Basal
## 8 -4.68 -10.1 TCGA-A1-A0SM-01A-11R-A084-07 LumA
## 9 -3.80 -8.92 TCGA-A1-A0SN-01A-11R-A144-07 LumB
## 10 -17.4 15.6 TCGA-A1-A0SQ-01A-21R-A144-07 LumA
## # ℹ 241 more rows
se_mini.norm.tSNE |>
pivot_sample() |>
ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=Call)) + geom_point() + my_theme
dimensions
We may want to rotate the reduced dimensions (or any two numeric columns really) of our data, of a set angle. rotate_dimensions
takes a tibble, column names (as symbols; for sample
, transcript
and count
) and an angle as arguments and returns a tibble with additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as <NAME OF DIMENSION> rotated <ANGLE>
by default, or as specified in the input arguments.
se_mini.norm.MDS.rotated =
se_mini.norm.MDS |>
rotate_dimensions(`Dim1`, `Dim2`, rotation_degrees = 45, action="get")
rotation = function(m, d) {
r = d * pi / 180
((bind_rows(
c(`1` = cos(r), `2` = -sin(r)),
c(`1` = sin(r), `2` = cos(r))
) |> as_matrix()) %*% m)
}
mds_r = pca |> rotation(rotation_degrees)
mds_r$cell_type = counts[
match(counts$sample, rownames(mds_r)),
"Cell.type"
]
Original On the x and y axes axis we have the first two reduced dimensions, data is coloured by cell type.
se_mini.norm.MDS.rotated |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type` )) +
geom_point() +
my_theme
Rotated On the x and y axes axis we have the first two reduced dimensions rotated of 45 degrees, data is coloured by cell type.
se_mini.norm.MDS.rotated |>
pivot_sample() |>
ggplot(aes(x=`Dim1_rotated_45`, y=`Dim2_rotated_45`, color=`Cell.type` )) +
geom_point() +
my_theme
differential abundance
We may want to test for differential transcription between sample-wise factors of interest (e.g., with edgeR). test_differential_abundance
takes a tibble, column names (as symbols; for sample
, transcript
and count
) and a formula representing the desired linear model as arguments and returns a tibble with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
se_mini.de =
se_mini |>
test_differential_abundance( ~ condition, action="get")
se_mini.de
library(edgeR)
dgList <- DGEList(counts=counts_m,group=group)
keep <- filterByExpr(dgList)
dgList <- dgList[keep,,keep.lib.sizes=FALSE]
dgList <- calcNormFactors(dgList)
design <- model.matrix(~group)
dgList <- estimateDisp(dgList,design)
fit <- glmQLFit(dgList,design)
qlf <- glmQLFTest(fit,coef=2)
topTags(qlf, n=Inf)
The functon test_differential_abundance
operated with contrasts too. The constrasts hve the name of the design matrix (generally <NAME_COLUMN_COVARIATE><VALUES_OF_COVARIATE>)
se_mini.de =
se_mini |>
identify_abundant(factor_of_interest = condition) |>
test_differential_abundance(
~ 0 + condition,
.contrasts = c( "conditionTRUE - conditionFALSE"),
action="get"
)
counts
We may want to adjust counts
for (known) unwanted variation. adjust_abundance
takes as arguments a tibble, column names (as symbols; for sample
, transcript
and count
) and a formula representing the desired linear model where the first covariate is the factor of interest and the second covariate is the unwanted variation, and returns a tibble with additional columns for the adjusted counts as <COUNT COLUMN>_adjusted
. At the moment just an unwanted covariates is allowed at a time.
se_mini.norm.adj =
se_mini.norm |> adjust_abundance( .factor_unwanted = time, .factor_of_interest = condition, method="combat")
library(sva)
count_m_log = log(count_m + 1)
design =
model.matrix(
object = ~ factor_of_interest + batch,
data = annotation
)
count_m_log.sva =
ComBat(
batch = design[,2],
mod = design,
...
)
count_m_log.sva = ceiling(exp(count_m_log.sva) -1)
count_m_log.sva$cell_type = counts[
match(counts$sample, rownames(count_m_log.sva)),
"Cell.type"
]
Cell type composition
We may want to infer the cell type composition of our samples (with the algorithm Cibersort; Newman et al., 10.1038/nmeth.3337). deconvolve_cellularity
takes as arguments a tibble, column names (as symbols; for sample
, transcript
and count
) and returns a tibble with additional columns for the adjusted cell type proportions.
se_mini.cibersort =
se_mini |>
deconvolve_cellularity(action="get", cores=1, prefix = "cibersort__")
source(‘CIBERSORT.R’)
count_m |> write.table("mixture_file.txt")
results <- CIBERSORT(
"sig_matrix_file.txt",
"mixture_file.txt",
perm=100, QN=TRUE
)
results$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(results)),
"Cell.type"
]
With the new annotated data frame, we can plot the distributions of cell types across samples, and compare them with the nominal cell type labels to check for the purity of isolation. On the x axis we have the cell types inferred by Cibersort, on the y axis we have the inferred proportions. The data is facetted and coloured by nominal cell types (annotation given by the researcher after FACS sorting).
se_mini.cibersort |>
pivot_longer(
names_to= "Cell_type_inferred",
values_to = "proportion",
names_prefix ="cibersort__",
cols=contains("cibersort__")
) |>
ggplot(aes(x=Cell_type_inferred, y=proportion, fill=`Cell.type`)) +
geom_boxplot() +
facet_wrap(~`Cell.type`) +
my_theme +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), aspect.ratio=1/5)
We can also perform a statistical test on the differential cell-type abundance across conditions
se_mini |>
test_differential_cellularity(. ~ condition )
We can also perform regression analysis with censored data (coxph).
se_mini |>
test_differential_cellularity(survival::Surv(time, dead) ~ .)
samples
We may want to cluster our data (e.g., using k-means sample-wise). cluster_elements
takes as arguments a tibble, column names (as symbols; for sample
, transcript
and count
) and returns a tibble with additional columns for the cluster annotation. At the moment only k-means clustering is supported, the plan is to introduce more clustering methods.
k-means
se_mini.norm.cluster = se_mini.norm.MDS |>
cluster_elements(method="kmeans", centers = 2, action="get" )
count_m_log = log(count_m + 1)
k = kmeans(count_m_log, iter.max = 1000, ...)
cluster = k$cluster
cluster$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(cluster)),
c("Cell.type", "Dim1", "Dim2")
]
We can add cluster annotation to the MDS dimension reduced data set and plot.
se_mini.norm.cluster |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`cluster_kmeans`)) +
geom_point() +
my_theme
SNN
Matrix package (v1.3-3) causes an error with Seurat::FindNeighbors used in this method. We are trying to solve this issue. At the moment this option in unaviable.
se_mini.norm.SNN =
se_mini.norm.tSNE |>
cluster_elements(method = "SNN")
library(Seurat)
snn = CreateSeuratObject(count_m)
snn = ScaleData(
snn, display.progress = TRUE,
num.cores=4, do.par = TRUE
)
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = RunPCA(snn, npcs = 30)
snn = FindNeighbors(snn)
snn = FindClusters(snn, method = "igraph", ...)
snn = snn[["seurat_clusters"]]
snn$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(snn)),
c("Cell.type", "Dim1", "Dim2")
]
se_mini.norm.SNN |>
pivot_sample() |>
select(contains("tSNE"), everything())
se_mini.norm.SNN |>
pivot_sample() |>
gather(source, Call, c("cluster_SNN", "Call")) |>
distinct() |>
ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=Call)) + geom_point() + facet_grid(~source) + my_theme
# Do differential transcription between clusters
se_mini.norm.SNN |>
mutate(factor_of_interest = `cluster_SNN` == 3) |>
test_differential_abundance(
~ factor_of_interest,
action="get"
)
redundant
transcriptsWe may want to remove redundant elements from the original data set (e.g., samples or transcripts), for example if we want to define cell-type specific signatures with low sample redundancy. remove_redundancy
takes as arguments a tibble, column names (as symbols; for sample
, transcript
and count
) and returns a tibble with redundant elements removed (e.g., samples). Two redundancy estimation approaches are supported:
Approach 1
se_mini.norm.non_redundant =
se_mini.norm.MDS |>
remove_redundancy( method = "correlation" )
## Getting the 393 most variable genes
library(widyr)
.data.correlated =
pairwise_cor(
counts,
sample,
transcript,
rc,
sort = TRUE,
diag = FALSE,
upper = FALSE
) |>
filter(correlation > correlation_threshold) |>
distinct(item1) |>
rename(!!.element := item1)
# Return non redudant data frame
counts |> anti_join(.data.correlated) |>
spread(sample, rc, - transcript) |>
left_join(annotation)
We can visualise how the reduced redundancy with the reduced dimentions look like
se_mini.norm.non_redundant |>
pivot_sample() |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type`)) +
geom_point() +
my_theme
Approach 2
se_mini.norm.non_redundant =
se_mini.norm.MDS |>
remove_redundancy(
method = "reduced_dimensions",
Dim_a_column = `Dim1`,
Dim_b_column = `Dim2`
)
We can visualise MDS reduced dimensions of the samples with the closest pair removed.
se_mini.norm.non_redundant |>
pivot_sample() |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type`)) +
geom_point() +
my_theme
The above wrapper streamline the most common processing of bulk RNA sequencing data. Other useful wrappers are listed above.
We can calculate gene counts (using FeatureCounts; Liao Y et al., 10.1093/nar/gkz114) from a list of BAM/SAM files and format them into a tidy structure (similar to counts).
counts = tidybulk_SAM_BAM(
file_names,
genome = "hg38",
isPairedEnd = TRUE,
requireBothEndsMapped = TRUE,
checkFragLength = FALSE,
useMetaFeatures = TRUE
)
We can add gene symbols from ensembl identifiers. This is useful since different resources use ensembl IDs while others use gene symbol IDs. This currently works for human and mouse.
counts_ensembl |> ensembl_to_symbol(ens)
## # A tibble: 119 × 8
## ens iso `read count` sample cases_0_project_dise…¹ cases_0_samples_0_sa…²
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 ENSG… 13 144 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 2 ENSG… 13 72 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 3 ENSG… 13 0 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 4 ENSG… 13 1099 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 5 ENSG… 13 11 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 6 ENSG… 13 2 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 7 ENSG… 13 3 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 8 ENSG… 13 2678 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 9 ENSG… 13 751 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 10 ENSG… 13 1 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## # ℹ 109 more rows
## # ℹ abbreviated names: ¹cases_0_project_disease_type,
## # ²cases_0_samples_0_sample_type
## # ℹ 2 more variables: transcript <chr>, ref_genome <chr>
We can add gene full name (and in future description) from symbol identifiers. This currently works for human and mouse.
se_mini |>
describe_transcript() |>
select(feature, description, everything())
## Warning in is_sample_feature_deprecated_used(.data, .cols):
## tidySummarizedExperiment says: from version 1.3.1, the special columns
## including sample/feature id (colnames(se), rownames(se)) has changed to
## ".sample" and ".feature". This dataset is returned with the old-style
## vocabulary (feature and sample), however we suggest to update your workflow to
## reflect the new vocabulary (.feature, .sample)
## # A SummarizedExperiment-tibble abstraction: 2,635 × 11
## # [90mFeatures=527 | Samples=5 | Assays=count[0m
## feature sample count Cell.type time condition days dead description entrez
## <chr> <chr> <dbl> <chr> <chr> <lgl> <dbl> <dbl> <chr> <chr>
## 1 ABCB4 SRR17… 1035 b_cell 0 d TRUE 1 1 ATP bindin… 5244
## 2 ABCB9 SRR17… 45 b_cell 0 d TRUE 1 1 ATP bindin… 23457
## 3 ACAP1 SRR17… 7151 b_cell 0 d TRUE 1 1 ArfGAP wit… 9744
## 4 ACHE SRR17… 2 b_cell 0 d TRUE 1 1 acetylchol… 43
## 5 ACP5 SRR17… 2278 b_cell 0 d TRUE 1 1 acid phosp… 54
## 6 ADAM28 SRR17… 11156 b_cell 0 d TRUE 1 1 ADAM metal… 10863
## 7 ADAMDE… SRR17… 72 b_cell 0 d TRUE 1 1 ADAM like … 27299
## 8 ADAMTS3 SRR17… 0 b_cell 0 d TRUE 1 1 ADAM metal… 9508
## 9 ADRB2 SRR17… 298 b_cell 0 d TRUE 1 1 adrenocept… 154
## 10 AIF1 SRR17… 8 b_cell 0 d TRUE 1 1 allograft … 199
## # ℹ 40 more rows
## # ℹ 1 more variable: gene_name <chr>
sessionInfo()
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 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.3.10.0
##
## 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] tidySummarizedExperiment_1.15.0 SummarizedExperiment_1.35.0
## [3] Biobase_2.65.0 GenomicRanges_1.57.0
## [5] GenomeInfoDb_1.41.0 IRanges_2.39.0
## [7] S4Vectors_0.43.0 BiocGenerics_0.51.0
## [9] MatrixGenerics_1.17.0 matrixStats_1.3.0
## [11] tidybulk_1.17.0 ttservice_0.4.0
## [13] ggrepel_0.9.5 ggplot2_3.5.1
## [15] magrittr_2.0.3 tibble_3.2.1
## [17] tidyr_1.3.1 dplyr_1.1.4
## [19] knitr_1.46
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.2 widyr_0.1.5 rlang_1.1.3
## [4] tidytext_0.4.2 e1071_1.7-14 compiler_4.4.0
## [7] RSQLite_2.3.6 mgcv_1.9-1 reshape2_1.4.4
## [10] png_0.1-8 vctrs_0.6.5 sva_3.53.0
## [13] stringr_1.5.1 pkgconfig_2.0.3 crayon_1.5.2
## [16] fastmap_1.1.1 backports_1.4.1 XVector_0.45.0
## [19] ellipsis_0.3.2 labeling_0.4.3 utf8_1.2.4
## [22] tzdb_0.4.0 UCSC.utils_1.1.0 preprocessCore_1.67.0
## [25] purrr_1.0.2 bit_4.0.5 xfun_0.43
## [28] zlibbioc_1.51.0 cachem_1.0.8 jsonlite_1.8.8
## [31] blob_1.2.4 SnowballC_0.7.1 highr_0.10
## [34] DelayedArray_0.31.0 BiocParallel_1.39.0 broom_1.0.5
## [37] parallel_4.4.0 R6_2.5.1 stringi_1.8.3
## [40] limma_3.61.0 genefilter_1.87.0 Rcpp_1.0.12
## [43] org.Mm.eg.db_3.19.1 readr_2.1.5 Matrix_1.7-0
## [46] splines_4.4.0 tidyselect_1.2.1 abind_1.4-5
## [49] codetools_0.2-20 plyr_1.8.9 lattice_0.22-6
## [52] withr_3.0.0 KEGGREST_1.45.0 evaluate_0.23
## [55] Rtsne_0.17 survival_3.6-4 proxy_0.4-27
## [58] Biostrings_2.73.0 pillar_1.9.0 janeaustenr_1.0.0
## [61] plotly_4.10.4 generics_0.1.3 hms_1.1.3
## [64] munsell_0.5.1 scales_1.3.0 xtable_1.8-4
## [67] class_7.3-22 glue_1.7.0 lazyeval_0.2.2
## [70] tools_4.4.0 tokenizers_0.3.0 data.table_1.15.4
## [73] annotate_1.83.0 locfit_1.5-9.9 XML_3.99-0.16.1
## [76] grid_4.4.0 AnnotationDbi_1.67.0 edgeR_4.3.0
## [79] colorspace_2.1-0 nlme_3.1-164 GenomeInfoDbData_1.2.12
## [82] cli_3.6.2 fansi_1.0.6 S4Arrays_1.5.0
## [85] viridisLite_0.4.2 gtable_0.3.5 digest_0.6.35
## [88] SparseArray_1.5.0 org.Hs.eg.db_3.19.1 htmlwidgets_1.6.4
## [91] farver_2.1.1 memoise_2.0.1 htmltools_0.5.8.1
## [94] lifecycle_1.0.4 httr_1.4.7 statmod_1.5.0
## [97] bit64_4.0.5