SingleCellAlleleExperiment 1.2.0
From Bioconductor:
if (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellAlleleExperiment")
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.
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 R
package, SingleCellAlleleExperiment
presented 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/ExperimentHub
data 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 novel SingleCellAlleleExperiment
data 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.
SingleCellAlleleExperiment (SCAE)
classThe 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.
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:
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.
The following packages are abundant for performing the here stated downstream analysis and visualization. Make sure they are installed to use this vignette.
library(SingleCellAlleleExperiment)
library(scaeData)
library(scater)
library(scran)
library(scuttle)
library(DropletUtils)
library(ggplot2)
library(patchwork)
library(org.Hs.eg.db)
library(AnnotationDbi)
Download and save the “pbmc_10k” dataset from the accompanying ExperimentHub/scaeData
package.
example_data_10k <- scaeData::scaeDataGet(dataset="pbmc_10k")
## Retrieving barcode identifiers for **pbmc 10k** dataset...DONE
## Retrieving feature identifiers for **pbmc 10k** dataset...DONE
## Retrieving quantification matrix for **pbmc 10k** dataset...DONE
example_data_10k
## $dir
## [1] "/home/biocbuild/.cache/R/ExperimentHub/"
##
## $barcodes
## [1] "25628b15c2e299_9522"
##
## $features
## [1] "25628b61f015bf_9523"
##
## $matrix
## [1] "25628b71d6c028_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:
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"))
head(lookup)
## 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_I
After 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
.
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 the filter_threshold
parameter. (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.
scae
## 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_II
If 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"]]
:
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
##
## $inflection_point
## [1] 729
This can be used to plot the corresponding knee plot and potentially infer a new threshold for filtering (then used with filter_mode=“custom”). Note that only using filter_mode="yes"
computes the information for the knee plot. See corresponding code chunks for further information about the other filter modes.
knee_df <- metadata(scae)[["knee_info"]]$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)
scae
Three optional functionalities can be toggled on or off during read in:
filter_mode="yes"
for this optional filter mode. Find more information about the different filter modes in the prior section.logcounts
assay using Library Factors. Use log=TRUE/FALSE
to 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.gene_symbols=TRUE/FALSE
to toggle on or off accordingly. Turned off by default. Uses the org.HS package.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).
rowData(scae)
## 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 F
Contains sample and barcode information. If the logcounts
assay is computed, find another column containing the sizeFactors
here.
head(colData(scae))
## 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 1.037986
Only contains information if filter_mode="yes"
is used.
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] 729
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.
scae_nonimmune_subset <- scae_subset(scae, "nonimmune")
head(rownames(scae_nonimmune_subset))
## [1] "ENSG00000279928.2" "ENSG00000228037.1" "ENSG00000142611.17"
## [4] "ENSG00000284616.1" "ENSG00000157911.11" "ENSG00000269896.2"
scae_alleles_subset <- scae_subset(scae, "alleles")
head(rownames(scae_alleles_subset))
## [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"
scae_immune_genes_subset <- scae_subset(scae, "immune_genes")
head(rownames(scae_immune_genes_subset))
## [1] "HLA-A" "HLA-B" "HLA-C" "HLA-DRA" "HLA-DRB1" "HLA-DQA1"
scae_functional_groups_subset <- scae_subset(scae, "functional_groups")
head(rownames(scae_functional_groups_subset))
## [1] "HLA_class_I" "HLA_class_II"
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)
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.
The non-immune genes are combined with each of the integrated immune gene allele-aware layers to determine three different subsets.
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):
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):
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_II
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
.
df_ni_a <- modelGeneVar(scae_nonimmune__allels_subset)
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
top_ni_a <- getTopHVGs(df_ni_a, prop=0.1)
df_ni_g <- modelGeneVar(scae_nonimmune__immune)
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
top_ni_g <- getTopHVGs(df_ni_g, prop=0.1)
df_ni_f <- modelGeneVar(scae_nonimmune__functional)
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
top_ni_f <- getTopHVGs(df_ni_f, prop=0.1)
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")
reducedDimNames(scae)
## [1] "PCA_a" "PCA_g" "PCA_f"
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:
set.seed(18)
scae <- runTSNE(scae, dimred="PCA_g", name="TSNE_g")
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:
set.seed(18)
scae <- runTSNE(scae, dimred="PCA_a", name="TSNE_a")
set.seed(18)
scae <- runTSNE(scae, dimred="PCA_f", name="TSNE_f")
List of results from the performed reduced dimension analysis.
reducedDimNames(scae)
## [1] "PCA_a" "PCA_g" "PCA_f" "TSNE_g"
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.
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
As the SCAE object is extending the SCE object, it is also compatible with the Biocpkg("iSEE")
package for interactive data exploration.
sessionInfo()
## 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
##
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## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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## 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_1.68.0
## [3] patchwork_1.3.0 DropletUtils_1.26.0
## [5] scran_1.34.0 scater_1.34.0
## [7] ggplot2_3.5.1 scuttle_1.16.0
## [9] scaeData_1.1.2 SingleCellAlleleExperiment_1.2.0
## [11] SingleCellExperiment_1.28.0 SummarizedExperiment_1.36.0
## [13] Biobase_2.66.0 GenomicRanges_1.58.0
## [15] GenomeInfoDb_1.42.0 IRanges_2.40.0
## [17] S4Vectors_0.44.0 BiocGenerics_0.52.0
## [19] MatrixGenerics_1.18.0 matrixStats_1.4.1
## [21] BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 magrittr_2.0.3
## [3] ggbeeswarm_0.7.2 farver_2.1.2
## [5] rmarkdown_2.28 zlibbioc_1.52.0
## [7] vctrs_0.6.5 memoise_2.0.1
## [9] DelayedMatrixStats_1.28.0 htmltools_0.5.8.1
## [11] S4Arrays_1.6.0 AnnotationHub_3.14.0
## [13] curl_5.2.3 BiocNeighbors_2.0.0
## [15] Rhdf5lib_1.28.0 SparseArray_1.6.0
## [17] rhdf5_2.50.0 sass_0.4.9
## [19] bslib_0.8.0 cachem_1.1.0
## [21] igraph_2.1.1 mime_0.12
## [23] lifecycle_1.0.4 pkgconfig_2.0.3
## [25] rsvd_1.0.5 Matrix_1.7-1
## [27] R6_2.5.1 fastmap_1.2.0
## [29] GenomeInfoDbData_1.2.13 digest_0.6.37
## [31] colorspace_2.1-1 dqrng_0.4.1
## [33] irlba_2.3.5.1 ExperimentHub_2.14.0
## [35] RSQLite_2.3.7 beachmat_2.22.0
## [37] labeling_0.4.3 filelock_1.0.3
## [39] fansi_1.0.6 httr_1.4.7
## [41] abind_1.4-8 compiler_4.4.1
## [43] bit64_4.5.2 withr_3.0.2
## [45] BiocParallel_1.40.0 viridis_0.6.5
## [47] DBI_1.2.3 highr_0.11
## [49] HDF5Array_1.34.0 R.utils_2.12.3
## [51] rappdirs_0.3.3 DelayedArray_0.32.0
## [53] bluster_1.16.0 tools_4.4.1
## [55] vipor_0.4.7 beeswarm_0.4.0
## [57] R.oo_1.26.0 glue_1.8.0
## [59] rhdf5filters_1.18.0 grid_4.4.1
## [61] Rtsne_0.17 cluster_2.1.6
## [63] generics_0.1.3 gtable_0.3.6
## [65] R.methodsS3_1.8.2 BiocSingular_1.22.0
## [67] ScaledMatrix_1.14.0 metapod_1.14.0
## [69] utf8_1.2.4 XVector_0.46.0
## [71] ggrepel_0.9.6 BiocVersion_3.20.0
## [73] pillar_1.9.0 limma_3.62.0
## [75] dplyr_1.1.4 BiocFileCache_2.14.0
## [77] lattice_0.22-6 bit_4.5.0
## [79] tidyselect_1.2.1 locfit_1.5-9.10
## [81] Biostrings_2.74.0 knitr_1.48
## [83] gridExtra_2.3 bookdown_0.41
## [85] edgeR_4.4.0 xfun_0.48
## [87] statmod_1.5.0 UCSC.utils_1.2.0
## [89] yaml_2.3.10 evaluate_1.0.1
## [91] codetools_0.2-20 tibble_3.2.1
## [93] BiocManager_1.30.25 cli_3.6.3
## [95] munsell_0.5.1 jquerylib_0.1.4
## [97] Rcpp_1.0.13 dbplyr_2.5.0
## [99] png_0.1-8 parallel_4.4.1
## [101] blob_1.2.4 sparseMatrixStats_1.18.0
## [103] viridisLite_0.4.2 scales_1.3.0
## [105] purrr_1.0.2 crayon_1.5.3
## [107] rlang_1.1.4 cowplot_1.1.3
## [109] KEGGREST_1.46.0