An introduction to the SingleCellAlleleExperiment class
Installation
From Bioconductor:
Introduction to the workflow
Biological background and motivation
Immune molecules such as B and T cell receptors, human leukocyte antigens (HLAs) or killer Ig-like receptors (KIRs) are encoded in the genetically most diverse loci of the human genome. Many of these immune genes are hyperpolymorphic, showing high allelic diversity across human populations. In addition, typical immune molecules are polygenic, which means that multiple functionally similar genes encode the same protein subunit.
However, interactive single-cell methods commonly used to analyze immune cells in large patient cohorts do not consider this. This leads to erroneous quantification of important immune mediators and impaired inter-donor comparability.
Workflow for unraveling the immunogenetic diversity in scData
We have developed a workflow that allows quantification of expression and interactive exploration of donor-specific alleles of different immune genes. The workflow is divided into two software packages and one additional data package:
The scIGD software package consist of a Snakemake workflow designed to automate and streamline the genotyping process for immune genes, focusing on key targets such as HLAs and KIRs, and enabling allele-specific quantification from single-cell RNA-sequencing (scRNA-seq) data using donor-specific references. For detailed information of the performed steps and how to utilize this workflow, please refer to its documentation here: scIGD.
To harness the full analytical potential of the results, we’ve developed a dedicated
Rpackage,SingleCellAlleleExperimentpresented in this repository. This package provides a comprehensive multi-layer data structure, enabling the representation of immune genes at specific levels, including alleles, genes, and groups of functionally similar genes and thus, allows data analysis across these immunologically relevant, different layers of annotation.The scaeData is an
R/ExperimentHubdata package providing datasets generated and processed by the scIGD software package which can be used to explore the data and potential downstream analysis workflows using the here presented novelSingleCellAlleleExperimentdata structure. Refer to scaeData for more information regarding the available datasets and source of raw data.
This workflow is designed to support both 10x and BD Rhapsody data, encompassing amplicon/targeted sequencing as well as whole-transcriptome-based data, providing flexibility to users working with different experimental setups.
Figure 1: Overview of the scIGD workflow for unraveling
immunogenomic diversity in single-cell data, highlighting the
integration of the SingleCellAlleleExperiment package for comprehensive
data analysis.
Introduction to the SingleCellAlleleExperiment (SCAE)
class
The SingleCellAlleleExperiment (SCAE) class serves as a
comprehensive multi-layer data structure, enabling the representation of
immune genes at specific levels, including alleles, genes, and groups of
functionally similar genes and thus, allows data analysis across these
immunologically relevant, different layers of annotation. The
implemented data object is derived from the SingleCellExperiment
class and follows similar conventions, where rows should represent
features (genes, transcripts) and columns should represent cells.
Figure 2: Scheme of SingleCellAlleleExperiment object
structure with lookup table.
For the integration of the relevant additional data layers (see Figure 2), the quantification data for alleles, generated by the novel scIGD software package, is aggregated into two additional data layers via an ontology-based design principle using a lookup table during object generation.
For example, the counts of the alleles A*01:01:01:01 and
A*02:01:01:01 that are present in the raw input data will
be combined into the HLA-A immune gene layer (see
Table 1 below). Next, all counts of immune genes
corresponding to HLA-class I are combined into the
HLA-class I functional class layer. See the structure of
the used lookup table below.
Table 1: Scheme of the lookup table used to aggregate allele information into multiple data layers.
| Allele | Gene | Function |
|---|---|---|
| A*01:01:01 | HLA-A | HLA class I |
| A*01:01:01 | HLA-A | HLA class I |
| … | … | … |
| DRB1*01:01:01 | HLA-DRB1 | HLA class II |
The resulting SCAE data object can be used in
combination with established single cell analysis packages like scater and
scran to
perform downstream analysis on immune gene expression, allowing data
exploration on functional and allele level. See the vignette for further
information and insights on how to perform downstream analysis using
exemplary data from the accompanying R/Experimenthub
package scaeData.
Expected input and dataset description
The read in function of the SCAE package
read_allele_counts() expects specific files that are
generated by the previous steps of the workflow, performed in the scIGD package. One
input parameter needs to state the path to a directory containing all
the input files. The file identifiers can be specifically stated as
parameters. The default file identifiers, as they are outputted from
scIGD are listed below: The stated input directory
should contain the following files:
- cells_x_genes.barcodes.txt (list of barcodes/cell identifiers)
- cells_x_genes.features.txt (list of feature identifiers)
- cells_x_genes_mtx.mtx (Contains the quantification matrix)
- lookup_table.csv (Info for generating multiple data layers)
The dataset used for the here shown downstream analysis is taken from
the accompanying data package scaeData.
Specifically we are using the pbmc_5k dataset. Find more
information about the data, including the source of the raw data here:
scaeData.
Exemplary downstream analysis
Loading suggested packages
The following packages are abundant for performing the here stated downstream analysis and visualization. Make sure they are installed to use this vignette.
Generation of a SingleCellAlleleExperiment object
Download and save the “pbmc_10k” dataset from the
accompanying ExperimentHub/scaeData package.
## Retrieving barcode identifiers for **pbmc 10k** dataset...DONE## Retrieving feature identifiers for **pbmc 10k** dataset...DONE## Retrieving quantification matrix for **pbmc 10k** dataset...DONE## $dir
## [1] "/github/home/.cache/R/ExperimentHub/"
##
## $barcodes
## [1] "8d6818670012_9522"
##
## $features
## [1] "8d682645cc00_9523"
##
## $matrix
## [1] "8d6844caddb6_9524"The return value of the scaeDataGet() function is a list
containing four elements. The $dir slot contains the
directory where the files downloaded from ExperimentHub are
saved on your device. The remaining three slots $barcodes,
$features, $matrix contain the corresponding
file names (named by ExperimentHub). The list is then used to pass the
saved information to the related parameters of the
read_allele_counts() function of the here presented
package. See next section.
For the usage of the corresponding lookup table, specify a directory
containing the lookup table as seen in the next chunk. For the available
example datasets in scaeData, you can choose one of the
following names. This provides the lookup table to the corresponding
dataset:
- “pbmc_5k_lookup_table.csv” is used for the `pbmc_5k” dataset.
- “pbmc_10k_lookup_table.csv” is used for the `pbmc_10k” dataset.
- “pbmc_20k_lookup_table.csv” is used for the `pbmc_20k” dataset.
These are part of the scaeData package, but are not
fetched from ExperimentHub but rather read in as internal
files.
lookup_name <- "pbmc_10k_lookup_table.csv"
lookup <- utils::read.csv(system.file("extdata", lookup_name, package="scaeData"))## Allele Gene Function
## 1 A*02:01:01:01 HLA-A HLA_class_I
## 2 A*24:02:01:01 HLA-A HLA_class_I
## 3 B*15:01:01:01 HLA-B HLA_class_I
## 4 B*44:03:01:01 HLA-B HLA_class_I
## 5 C*03:03:01:01 HLA-C HLA_class_I
## 6 C*16:01:01:01 HLA-C HLA_class_IAfter the directories of the data files and the lookup table are
known and saved, proceed to generate a
SingleCellAlleleExperiment object.
This is an essential step. The read in function
read_allele_counts() is used to read in data and generate a
SCAE object. The SingleCellAlleleExperiment
Constructor is exported as well. However, as the raw data is processed
during the read in, we recommend to use
read_allele_counts(). Find information about the read in
using the Constructor in its function documentation
?SingleCellAlleleExperiment.
Description of Read in parameters
If you used the scIGD package to
generate your input files (RECOMMENDED AND EXPECTED),
then state the path containing all expected files to the
sample_dir parameter in the
read_allele_counts() function and the corresponding lookup
table. In case you renamed the files, specify the new file identifiers
in the corresponding parameters lookup_file,
barcode_file, gene_file,
matrix_file, otherwise leave it to the stated default
values.
The read_allele_counts() function also provides multiple
parameters to perform filtering steps on the cells contained in your
data. For this, multiple parameter combinations are possible and
showcased below. Advanced filtering can be performed based on a
knee
plot.
The first parameter, where you state which filter
mode you want to use, gives the following valid options:
filter_mode=c("yes", "no", "custom").
filter_mode = "no": Default filter mode. Filtering based on threshold=0, filtering out columns (cells) with a count sum of 0 over all features.filter_mode = "yes": Advanced filtering is performed on the computed inflection point of a knee plot based on barcode ranks. This is an optional functionality.filter_mode = "custom": Custom filter mode. Filtering performed on a threshold stated in thefilter_thresholdparameter. (Useful for example after you looked at the knee plot after using filter_mode=“yes” and decide to use a different filter threshold)
Additionally, the verbose parameter gives you an option
to toggle runtime-information for the different steps during object
generation. Messages regarding the filtering process (used thresholds)
will not be toggled off if verbose = FALSE.
Exemplary read in using all described filter modes generating a SCAE object are shown in the following code-chunks:
filter_mode="no"
Default filter mode. Filtering based on threshold=0, filtering out columns (cells) with a count sum of 0 over all features.
scae <- read_allele_counts(example_data_10k$dir,
sample_names="example_data",
filter_mode="no",
lookup_file=lookup,
barcode_file=example_data_10k$barcodes,
gene_file=example_data_10k$features,
matrix_file=example_data_10k$matrix,
filter_threshold=NULL,
verbose=FALSE)filter_mode="yes"
Advanced filtering is performed on the computed inflection point of a knee plot based on barcode ranks. This is an optional functionality. To be able to use this filter mode, the DropletUtils package needs to be installed on your machine.
scae <- read_allele_counts(example_data_10k$dir,
sample_names="example_data",
filter_mode="yes",
lookup_file=lookup,
barcode_file=example_data_10k$barcodes,
gene_file=example_data_10k$features,
matrix_file=example_data_10k$matrix,
log=TRUE)## Filtering performed based on the inflection point at: 729 UMI counts.## class: SingleCellAlleleExperiment
## dim: 62766 11494
## metadata(1): knee_info
## assays(2): counts logcounts
## rownames(62766): ENSG00000279928.2 ENSG00000228037.1 ... HLA_class_I
## HLA_class_II
## rowData names(3): Ensembl_ID NI_I Quant_type
## colnames(11494): AAACCCAAGGTAGTCG AAACCCACAATCCAGT ... TTTGTTGTCTGTACAG
## TTTGTTGTCTTCTAAC
## colData names(3): Sample Barcode sizeFactor
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## ---------------
## Including a total of 23 immune related features:
## Allele-level information (13): A*02:01:01:01 A*24:02:01:01 ...
## DPB1*03:01:01:01 DPB1*11:01:01:01
## Immune genes (8): HLA-A HLA-B ... HLA-DPA1 HLA-DPB1
## Functional level information (2): HLA_class_I HLA_class_IIIf this filter mode is chosen, the information to the corresponding
plot including the inflection point used for filtering is stored in
metadata(scae)[["knee_info"]]:
## $knee_df
## DataFrame with 1363483 rows and 3 columns
## rank total fitted
## <numeric> <integer> <numeric>
## AAACCCAAGAAACCCA 885249.0 1 NA
## AAACCCAAGAAACTCA 290624.0 2 NA
## AAACCCAAGAAATTCG 95797.5 4 NA
## AAACCCAAGAACAAGG 885249.0 1 NA
## AAACCCAAGAACAGGA 885249.0 1 NA
## ... ... ... ...
## TTTGTTGTCTTTCAGC 885249 1 NA
## TTTGTTGTCTTTCTAG 885249 1 NA
## TTTGTTGTCTTTGATC 885249 1 NA
## TTTGTTGTCTTTGCGC 885249 1 NA
## TTTGTTGTCTTTGCTA 885249 1 NA
##
## $knee_point
## [1] 4253
##
## $inflection_point
## [1] 729This can be used to plot the corresponding knee plot and potentially
infer a new threshold for filtering (then used with
filter_mode=“custom”). Note that only using
filter_mode="yes" computes the information for the knee
plot. See corresponding code chunks for further information about the
other filter modes.
knee_df <- metadata(scae)[["knee_info"]]$knee_df
knee_point <- metadata(scae)[["knee_info"]]$knee_point
inflection_point <- metadata(scae)[["knee_info"]]$inflection_point
ggplot(knee_df, aes(x=rank, y=total)) +
geom_point() +
geom_line(aes(y=fitted), color="red") +
scale_x_log10() +
scale_y_log10() +
annotation_logticks() +
labs(x="Barcode rank", y="Total UMI count") +
geom_hline(yintercept=S4Vectors::metadata(knee_df)$knee,
color="dodgerblue", linetype="dashed") +
geom_hline(yintercept=S4Vectors::metadata(knee_df)$inflection,
color="forestgreen", linetype="dashed") +
annotate("text", x=2, y=S4Vectors::metadata(knee_df)$knee * 1.2,
label="knee", color="dodgerblue") +
annotate("text", x=2.25, y=S4Vectors::metadata(knee_df)$inflection * 1.2,
label="inflection", color="forestgreen") + theme_bw()
filter_mode="custom"
Custom filter mode. Filtering performed on a threshold stated in the
filter_threshold parameter.
#this is the object used in the further workflow
scae <- read_allele_counts(example_data_10k$dir,
sample_names="example_data",
filter_mode="custom",
lookup_file=lookup,
barcode_file=example_data_10k$barcodes,
gene_file=example_data_10k$features,
matrix_file=example_data_10k$matrix,
filter_threshold=282)
scaeDescription of optional functionalities
Three optional functionalities can be toggled on or off during read in:
- Optional filtering performed on the inflection point of a computed
knee plot based on barcode ranks. Use
filter_mode="yes"for this optional filter mode. Find more information about the different filter modes in the prior section. - Computation of a
logcountsassay using Library Factors. Uselog=TRUE/FALSEto toggle on or off accordingly. Turned on by default. If you want to compute the logcounts using a different method, be aware that the count matrix is extended (adding additional rows) during the object generation, its abundant to compute scaling factors only on data layers present in the raw data (non-immune and alleles). Otherwise the scaling factors would not be correct. Find information about subsetting the object in sections further below. - Computation of additional gene symbols in case the raw data only
contains Ensembl gene identifiers. Use
gene_symbols=TRUE/FALSEto toggle on or off accordingly. Turned off by default. Uses the org.HS package.
Showcasing content of object slots
RowData
Two new classification columns are introduced in the
rowData slot. Namely the NI_I column
(classification of each row as NI = non_immune or
I = immune) and Quant_type column
(classification of each row to which data layer it is corresponding to).
Both columns are used jointly to identify each row of the object to its
corresponding data layer (see figure 1).
## DataFrame with 62766 rows and 3 columns
## Ensembl_ID NI_I Quant_type
## <character> <character> <character>
## ENSG00000279928.2 ENSG00000279928.2 NI G
## ENSG00000228037.1 ENSG00000228037.1 NI G
## ENSG00000142611.17 ENSG00000142611.17 NI G
## ENSG00000284616.1 ENSG00000284616.1 NI G
## ENSG00000157911.11 ENSG00000157911.11 NI G
## ... ... ... ...
## HLA-DQA1 HLA-DQA1 I G
## HLA-DPA1 HLA-DPA1 I G
## HLA-DPB1 HLA-DPB1 I G
## HLA_class_I HLA_class_I I F
## HLA_class_II HLA_class_II I FColData
Contains sample and barcode information. If the
logcounts assay is computed, find another column containing
the sizeFactors here.
## DataFrame with 6 rows and 3 columns
## Sample Barcode sizeFactor
## <character> <character> <numeric>
## AAACCCAAGGTAGTCG example_data AAACCCAAGGTAGTCG 0.839061
## AAACCCACAATCCAGT example_data AAACCCACAATCCAGT 2.100158
## AAACCCACACCGTCTT example_data AAACCCACACCGTCTT 1.126572
## AAACCCACATAGATCC example_data AAACCCACATAGATCC 0.601640
## AAACCCACATCTCATT example_data AAACCCACATCTCATT 1.442275
## AAACCCACATTGACTG example_data AAACCCACATTGACTG 1.037986Metadata
Only contains information if filter_mode="yes" is
used.
## $knee_df
## DataFrame with 1363483 rows and 3 columns
## rank total fitted
## <numeric> <integer> <numeric>
## AAACCCAAGAAACCCA 885249.0 1 NA
## AAACCCAAGAAACTCA 290624.0 2 NA
## AAACCCAAGAAATTCG 95797.5 4 NA
## AAACCCAAGAACAAGG 885249.0 1 NA
## AAACCCAAGAACAGGA 885249.0 1 NA
## ... ... ... ...
## TTTGTTGTCTTTCAGC 885249 1 NA
## TTTGTTGTCTTTCTAG 885249 1 NA
## TTTGTTGTCTTTGATC 885249 1 NA
## TTTGTTGTCTTTGCGC 885249 1 NA
## TTTGTTGTCTTTGCTA 885249 1 NA
##
## $knee_point
## [1] 4253
##
## $inflection_point
## [1] 729Utilize layer specific getter-functions()
On top of the established getters from the SCE package,
new getters are implemented to retrieve the different data layers
integrated in the SCAE object.
Non-immune genes
## [1] "ENSG00000279928.2" "ENSG00000228037.1" "ENSG00000142611.17"
## [4] "ENSG00000284616.1" "ENSG00000157911.11" "ENSG00000269896.2"Alleles
## [1] "A*02:01:01:01" "A*24:02:01:01" "B*15:01:01:01" "B*44:03:01:01"
## [5] "C*03:03:01:01" "C*16:01:01:01"Expression evaluation
Checking the expression for the allele-layer, immune gene layer and
functional class layer. Allele identifiers are in the form of
A*02:01:01:01. The immune genes are in the form of
HLA-A and the functional classes HLA_class_I.
Here we see that HLA_class_I and HLA-C are the
most abundant functional class and immune gene respectively, given the
underlying dataset.
scae_immune_layers_subset <- c(rownames(scae_subset(scae, "alleles")),
rownames(scae_subset(scae, "immune_genes")),
rownames(scae_subset(scae, "functional_groups")))
scater::plotExpression(scae, scae_immune_layers_subset)
Downstream analysis on immune genes
In the following sections, main steps for dimensional reduction are performed, offering insights into the different data layers of the SCAE object as well as giving an idea on how to perform immune gene expression analysis.
Subsetting the different layers
The non-immune genes are combined with each of the integrated immune gene allele-aware layers to determine three different subsets.
Non-immune genes + alleles
rn_scae_ni <- rownames(scae_subset(scae, "nonimmune"))
rn_scae_alleles <- rownames(scae_subset(scae, "alleles"))
scae_nonimmune__allels_subset <- scae[c(rn_scae_ni, rn_scae_alleles), ]
scae_nonimmune__allels_subset## class: SingleCellAlleleExperiment
## dim: 62756 11494
## metadata(1): knee_info
## assays(2): counts logcounts
## rownames(62756): ENSG00000279928.2 ENSG00000228037.1 ...
## DPB1*03:01:01:01 DPB1*11:01:01:01
## rowData names(3): Ensembl_ID NI_I Quant_type
## colnames(11494): AAACCCAAGGTAGTCG AAACCCACAATCCAGT ... TTTGTTGTCTGTACAG
## TTTGTTGTCTTCTAAC
## colData names(3): Sample Barcode sizeFactor
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## ---------------
## Including a total of 13 immune related features:
## Allele-level information (13): A*02:01:01:01 A*24:02:01:01 ...
## DPB1*03:01:01:01 DPB1*11:01:01:01
## Immune genes (0):
## Functional level information (0):Non-immune genes + immune genes
rn_scae_i <- rownames(scae_subset(scae, "immune_genes"))
scae_nonimmune__immune <- scae[c(rn_scae_ni, rn_scae_i), ]
scae_nonimmune__immune## class: SingleCellAlleleExperiment
## dim: 62751 11494
## metadata(1): knee_info
## assays(2): counts logcounts
## rownames(62751): ENSG00000279928.2 ENSG00000228037.1 ... HLA-DPA1
## HLA-DPB1
## rowData names(3): Ensembl_ID NI_I Quant_type
## colnames(11494): AAACCCAAGGTAGTCG AAACCCACAATCCAGT ... TTTGTTGTCTGTACAG
## TTTGTTGTCTTCTAAC
## colData names(3): Sample Barcode sizeFactor
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## ---------------
## Including a total of 8 immune related features:
## Allele-level information (0):
## Immune genes (8): HLA-A HLA-B ... HLA-DPA1 HLA-DPB1
## Functional level information (0):Non-immune genes + functional class
rn_scae_functional <- rownames(scae_subset(scae, "functional_groups"))
scae_nonimmune__functional <- scae[c(rn_scae_ni, rn_scae_functional), ]
scae_nonimmune__functional## class: SingleCellAlleleExperiment
## dim: 62745 11494
## metadata(1): knee_info
## assays(2): counts logcounts
## rownames(62745): ENSG00000279928.2 ENSG00000228037.1 ... HLA_class_I
## HLA_class_II
## rowData names(3): Ensembl_ID NI_I Quant_type
## colnames(11494): AAACCCAAGGTAGTCG AAACCCACAATCCAGT ... TTTGTTGTCTGTACAG
## TTTGTTGTCTTCTAAC
## colData names(3): Sample Barcode sizeFactor
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## ---------------
## Including a total of 2 immune related features:
## Allele-level information (0):
## Immune genes (0):
## Functional level information (2): HLA_class_I HLA_class_IIDimensional Reduction
Model variance and HVGs for all data layers
Using the modelGeneVar() function prior to
getTopHVGs. Both functions are part from the
Biocpkg("scran") package. Compute a list of HVGs for each
data layer. Return the top 0.1 % HVGs per layer using
getTopHVGs.
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' valuesPCA
Compute PCA for each layer and store the results in the object. Its
Important to make unique identifiers for each layer/run or the results
will be overwritten and just saved as PCA. Here, the
runPCA functions from the Biocpkg("scater")
package is used.
assay <- "logcounts"
scae <- runPCA(scae, ncomponents=10, subset_row=top_ni_a,
exprs_values=assay, name="PCA_a")
scae <- runPCA(scae, ncomponents=10, subset_row=top_ni_g,
exprs_values=assay, name="PCA_g")
scae <- runPCA(scae, ncomponents=10, subset_row=top_ni_f,
exprs_values=assay, name="PCA_f")## [1] "PCA_a" "PCA_g" "PCA_f"t-SNE
The same goes for running t-SNE with the runTSNE
function from the Biocpkg("scater") package. Unique
identifiers are stated here for each layer as well. For simplicity, we
only compute the t-SNE on the gene layer. Information
regarding how this process is conducted on the other additional layers,
can be seen in the non evaluated code chunks below:
The following two chunks show how the t-SNE could be computed on the
allele and functional_group layer. These
chunks are not run by default:
List of results from the performed reduced dimension analysis.
## [1] "PCA_a" "PCA_g" "PCA_f" "TSNE_g"Visualization of results
Exemplary visualization for the t-SNE results on gene level for
immune genes that relate to HLA-class I. In the given dataset, it is the
immune genes HLA-DRB1, plotted alongside their alleles.
This allows for insights into potential genetic differences shown on
allele-level.
HLA-A immune gene and alleles
tsne <- "TSNE_g"
tsne_g_a <- plotReducedDim(scae, dimred=tsne, colour_by="HLA-DRB1") +
ggtitle("HLA-DRB1 gene")
tsne_g_a1 <- plotReducedDim(scae, dimred=tsne, colour_by="DRB1*07:01:01:01") +
ggtitle("Allele DRB1*07:01:01:01")
tsne_g_a2 <- plotReducedDim(scae, dimred=tsne, colour_by="DRB1*13:01:01:01") +
ggtitle("Allele DRB1*13:01:01:01")
p2 <- tsne_g_a + tsne_g_a1 + tsne_g_a2
p2
Additional
As the SCAE object is extending the SCE object, it is also compatible
with the Biocpkg("iSEE") package for interactive data
exploration.
Session Information
## 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: /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=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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] org.Hs.eg.db_3.20.0 AnnotationDbi_1.69.0
## [3] patchwork_1.3.0 DropletUtils_1.27.0
## [5] scran_1.34.0 scater_1.34.0
## [7] ggplot2_3.5.1 scuttle_1.16.0
## [9] scaeData_1.1.2 SingleCellAlleleExperiment_1.3.0
## [11] SingleCellExperiment_1.28.0 SummarizedExperiment_1.36.0
## [13] Biobase_2.67.0 GenomicRanges_1.59.0
## [15] GenomeInfoDb_1.43.0 IRanges_2.41.0
## [17] S4Vectors_0.44.0 BiocGenerics_0.53.0
## [19] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [21] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] sys_3.4.3 jsonlite_1.8.9
## [3] magrittr_2.0.3 ggbeeswarm_0.7.2
## [5] farver_2.1.2 rmarkdown_2.28
## [7] zlibbioc_1.52.0 vctrs_0.6.5
## [9] memoise_2.0.1 DelayedMatrixStats_1.29.0
## [11] htmltools_0.5.8.1 S4Arrays_1.6.0
## [13] AnnotationHub_3.15.0 curl_5.2.3
## [15] BiocNeighbors_2.1.0 Rhdf5lib_1.28.0
## [17] SparseArray_1.6.0 rhdf5_2.50.0
## [19] sass_0.4.9 bslib_0.8.0
## [21] cachem_1.1.0 buildtools_1.0.0
## [23] igraph_2.1.1 mime_0.12
## [25] lifecycle_1.0.4 pkgconfig_2.0.3
## [27] rsvd_1.0.5 Matrix_1.7-1
## [29] R6_2.5.1 fastmap_1.2.0
## [31] GenomeInfoDbData_1.2.13 digest_0.6.37
## [33] colorspace_2.1-1 dqrng_0.4.1
## [35] irlba_2.3.5.1 ExperimentHub_2.15.0
## [37] RSQLite_2.3.7 beachmat_2.23.0
## [39] labeling_0.4.3 filelock_1.0.3
## [41] fansi_1.0.6 httr_1.4.7
## [43] abind_1.4-8 compiler_4.4.1
## [45] bit64_4.5.2 withr_3.0.2
## [47] BiocParallel_1.41.0 viridis_0.6.5
## [49] DBI_1.2.3 highr_0.11
## [51] HDF5Array_1.35.0 R.utils_2.12.3
## [53] rappdirs_0.3.3 DelayedArray_0.33.1
## [55] bluster_1.17.0 tools_4.4.1
## [57] vipor_0.4.7 beeswarm_0.4.0
## [59] R.oo_1.26.0 glue_1.8.0
## [61] rhdf5filters_1.18.0 grid_4.4.1
## [63] Rtsne_0.17 cluster_2.1.6
## [65] generics_0.1.3 gtable_0.3.6
## [67] R.methodsS3_1.8.2 BiocSingular_1.23.0
## [69] ScaledMatrix_1.14.0 metapod_1.14.0
## [71] utf8_1.2.4 XVector_0.46.0
## [73] ggrepel_0.9.6 BiocVersion_3.21.1
## [75] pillar_1.9.0 limma_3.63.0
## [77] dplyr_1.1.4 BiocFileCache_2.15.0
## [79] lattice_0.22-6 bit_4.5.0
## [81] tidyselect_1.2.1 locfit_1.5-9.10
## [83] maketools_1.3.1 Biostrings_2.75.0
## [85] knitr_1.48 gridExtra_2.3
## [87] edgeR_4.4.0 xfun_0.48
## [89] statmod_1.5.0 UCSC.utils_1.2.0
## [91] yaml_2.3.10 evaluate_1.0.1
## [93] codetools_0.2-20 tibble_3.2.1
## [95] BiocManager_1.30.25 cli_3.6.3
## [97] munsell_0.5.1 jquerylib_0.1.4
## [99] Rcpp_1.0.13 dbplyr_2.5.0
## [101] png_0.1-8 parallel_4.4.1
## [103] blob_1.2.4 sparseMatrixStats_1.18.0
## [105] viridisLite_0.4.2 scales_1.3.0
## [107] purrr_1.0.2 crayon_1.5.3
## [109] rlang_1.1.4 KEGGREST_1.47.0