02 - Normalization and clustering (simple)
Made by the scdrake pipeline
Document generated: 2023-12-02 18:51:17 UTC+0000
Note: some texts in this report are based on the book Orchestrating Single-Cell Analysis with Bioconductor published under CC BY 4.0
Project: PBMC 1k
- Institute: Example institute
- Laboratory: Example laboratory
- People: Example person 1, Example person 2
1000 peripheral blood mononuclear cells by 10x Genomics
- Organism: human
This is a simplified report without technical details. For the full report see 02_norm_clustering.html
Clustering
Graph-based
Graph-based clustering is commonly used for scRNA-seq, and often shows a good performance.
First, we used scran to generate the shared nearest
neighbor (SNN) graph using 10 nearest neighbors (cells) and ‘rank’
weighting scheme. The graph was then subjected to community detection
using algorithms implemented in the igraph package.
Show used functions ▾
Leiden algorithm
Leiden algorithm is an improved version of the Louvain algorithm that should prevent badly connected or even disconnected clusters.
It can be parametrized with different resolutions that determine how large communities are detected in the SNN graph. Generally, lower resolutions result in coarse-grained clusters, while higher ones in more fine-grained structures.
The relationships in cluster abundances under different resolutions
are visualized in the clustree plot below. Stable clusters
across different resolutions can be quickly find as straight or little
branched vertical lines.
Show used functions ▾
Louvain algorithm
Louvain
algorithm is perhaps the most popular clustering method for
scRNA-seq, and was popularized by Seurat.
It can be parametrized with different resolutions that determine how large communities are detected in the SNN graph. Generally, lower resolutions result in coarse-grained clusters, while higher ones in more fine-grained structures.
The relationships in cluster abundances under different resolutions
are visualized in the clustree plot below. Stable clusters
across different resolutions can be quickly find as straight or little
branched vertical lines.
Show used functions ▾
Walktrap algorithm
Walktrap algorithm uses random walks to find communities in the
graph, and it is the default
graph-based clustering method in scran.
Walktrap algorithm is not using resolutions.
Show used functions ▾
PCA
TSNE
UMAP
SC3
Single-Cell
Consensus Clustering (SC3) is a tool for unsupervised
clustering of scRNA-seq data. SC3 achieves high accuracy and robustness
by consistently integrating different clustering solutions through a
consensus approach (it calculates clusters for selected numbers of
target clusters).
Cluster stability index shows how stable each cluster is
across the selected range of ks. The stability index varies
between 0 and 1, where 1 means that the same cluster appears in every
solution for different k.
PDF with cluster stability plots
The relationships in cluster abundances under different
ks are visualized in the clustree plot below.
Stable clusters across different ks can be quickly find as
straight or little branched vertical lines.
Show used functions ▾
K-means
K-means is a generic clustering algorithm that has been used in many
application areas. In R, it can be applied via the
stats::kmeans() function. Typically, it is applied to a
reduced dimension representation of the expression data (most often PCA,
because of the interpretability of the low-dimensional distances). We
need to define the number of clusters in advance.
It is also possible to determine an optimal value of k.
One way to measure the goodness of clustering is to calculate
within-cluster sum of squares \(W\)
(i.e. sum of distances between each data point and cluster center). The
optimal k should have clusters with minimal \(W\). Here, we used a modified gap
statistic method described in OSCA.
The relationships in cluster abundances under different
ks are visualized in the clustree plot below.
Stable clusters across different ks can be quickly find as
straight or little branched vertical lines.
Show used functions ▾
Dimensionality reduction plots
PCA
cluster_graph_louvain_r0.4_annotated
cluster_sc3_k6_custom
detected
doublet_score
phase
total
TSNE
cluster_graph_louvain_r0.4_annotated
cluster_sc3_k6_custom
detected
doublet_score
phase
total
UMAP
cluster_graph_louvain_r0.4_annotated
cluster_sc3_k6_custom
detected
doublet_score
phase
total
Cell annotation
We used the SingleR
package to predict cell types in the dataset. Given a reference dataset
of samples (single-cell or bulk) with known labels,
SinglerR assigns those labels to new cells from a test
dataset based on similarities in their expression profiles. You can find
more information in the SingleR
book.
The used references are shown below in the tabs. Each have several diagnostic plots:
- Score heatmaps show distribution of predicted cell types in computed clusters (if any), along with per-cell annotation scores
- Marker heatmaps show genes that are markers for a given cell type in both the reference and current datasets, i.e. those markers have driven the decision to label cells by the chosen cell type
- Delta scores show poor-quality or ambiguous assignments based on the per-cell ‘delta’, i.e., the difference between the score for the assigned label and the median across all labels for each cell. See OSCA for more details
human_primary_cell_atlas_main
Microarray datasets derived from human primary cells (Mabbott et al. 2013). Most of the labels refer to blood subpopulations but cell types from other tissues are also available.
Score heatmaps PDF | Marker heatmaps PDF | Delta distribution PDF
PCA
TSNE
UMAP
monaco_immune_main
This is the human immune reference that best covers all of the bases for a typical PBMC sample.
Score heatmaps PDF | Marker heatmaps PDF | Delta distribution PDF
PCA
TSNE
UMAP
Show input parameters
Main config
## $PROJECT_NAME
## [1] "PBMC 1k"
##
## $PROJECT_DESCRIPTION
## [1] "1000 peripheral blood mononuclear cells by 10x Genomics"
##
## $INSTITUTE
## [1] "Example institute"
##
## $LABORATORY
## [1] "Example laboratory"
##
## $PEOPLE
## [1] "Example person 1, Example person 2"
##
## $ORGANISM
## [1] "human"
##
## $ANNOTATION_LIST
## $ANNOTATION_LIST$human
## [1] "org.Hs.eg.db"
##
## $ANNOTATION_LIST$mouse
## [1] "org.Mm.eg.db"
##
##
## $ENSEMBL_SPECIES
## [1] "Homo_sapiens"
##
## $CSS_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/Rmd/common/stylesheet.css"
##
## $BASE_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output"
##
## $ANNOTATION_DB_FILE
## [1] "/usr/local/lib/R/site-library/org.Hs.eg.db/extdata/org.Hs.eg.sqlite"
##
## $ANNOTATION_PKG
## [1] "org.Hs.eg.db"
##
## attr(,"class")
## [1] "scdrake_list" "list"Normalization and clustering config
## $NORMALIZATION_TYPE
## [1] "scran"
##
## $SCRAN_USE_QUICKCLUSTER
## [1] TRUE
##
## $SCRAN_QUICKCLUSTER_METHOD
## [1] "igraph"
##
## $SCT_VARS_TO_REGRESS
## NULL
##
## $SCT_N_HVG
## [1] 3000
##
## $HVG_METRIC
## [1] "gene_var"
##
## $HVG_SELECTION
## [1] "top"
##
## $HVG_SELECTION_VALUE
## [1] 1000
##
## $HVG_RM_CC_GENES
## [1] FALSE
##
## $HVG_CC_GENES_VAR_EXPL_THRESHOLD
## [1] 5
##
## $MAX_DOUBLET_SCORE
## [1] 3.5
##
## $PCA_SELECTION_METHOD
## [1] "forced"
##
## $PCA_FORCED_PCS
## [1] 15
##
## $TSNE_PERP
## [1] 20
##
## $TSNE_MAX_ITER
## [1] 1000
##
## $CLUSTER_GRAPH_SNN_K
## [1] 10
##
## $CLUSTER_GRAPH_SNN_TYPE
## [1] "rank"
##
## $CLUSTER_GRAPH_LEIDEN_ENABLED
## [1] TRUE
##
## $CLUSTER_GRAPH_LEIDEN_RESOLUTIONS
## [1] 0.4 0.8
##
## $CLUSTER_GRAPH_LOUVAIN_ENABLED
## [1] TRUE
##
## $CLUSTER_GRAPH_LOUVAIN_RESOLUTIONS
## [1] 0.4 0.8
##
## $CLUSTER_GRAPH_WALKTRAP_ENABLED
## [1] TRUE
##
## $CLUSTER_KMEANS_K_ENABLED
## [1] TRUE
##
## $CLUSTER_KMEANS_K
## [1] 3 4 5 6
##
## $CLUSTER_KMEANS_KBEST_ENABLED
## [1] TRUE
##
## $CLUSTER_SC3_ENABLED
## [1] TRUE
##
## $CLUSTER_SC3_K
## [1] 5 6
##
## $CLUSTER_SC3_N_CORES
## [1] 8
##
## $CELL_ANNOTATION_SOURCES
## $CELL_ANNOTATION_SOURCES$human_primary_cell_atlas_main
## $reference_type
## [1] "celldex"
##
## $reference
## [1] "HumanPrimaryCellAtlasData"
##
## $description
## [1] "Microarray datasets derived from human primary cells (Mabbott et al. 2013). Most of the labels refer to blood subpopulations but cell types from other tissues are also available.\n"
##
## $label_column
## [1] "label.main"
##
## $label_subsets
## [1] NA
##
## $train_params
## $genes
## [1] "de"
##
## $sd_thresh
## [1] 1
##
## $de_method
## [1] "wilcox"
##
## $de_n
## [1] 30
##
## $assay_type
## [1] "logcounts"
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $name
## [1] "human_primary_cell_atlas_main"
##
## $classify_params
## $quantile
## [1] 0.8
##
## $tune_thresh
## [1] 0.05
##
## $assay_type
## [1] "logcounts"
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $prune_score_params
## $n_mads
## [1] 3
##
## $min_diff_med
## [1] -Inf
##
## $min_diff_next
## [1] 0
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $diagnostics_params
## $heatmap_n_top_markers
## [1] 20
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $CELL_ANNOTATION_SOURCES$monaco_immune_main
## $reference_type
## [1] "celldex"
##
## $reference
## [1] "MonacoImmuneData"
##
## $description
## [1] "This is the human immune reference that best covers all of the bases for a typical PBMC sample."
##
## $label_column
## [1] "label.main"
##
## $label_subsets
## [1] NA
##
## $train_params
## $genes
## [1] "sd"
##
## $sd_thresh
## [1] 1
##
## $de_method
## [1] "classic"
##
## $de_n
## NULL
##
## $assay_type
## [1] "logcounts"
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $name
## [1] "monaco_immune_main"
##
## $classify_params
## $quantile
## [1] 0.8
##
## $tune_thresh
## [1] 0.05
##
## $assay_type
## [1] "logcounts"
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $prune_score_params
## $n_mads
## [1] 3
##
## $min_diff_med
## [1] -Inf
##
## $min_diff_next
## [1] 0
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $diagnostics_params
## $heatmap_n_top_markers
## [1] 20
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS
## $CELL_ANNOTATION_SOURCES_DEFAULTS$TRAIN_PARAMS
## $CELL_ANNOTATION_SOURCES_DEFAULTS$TRAIN_PARAMS$GENES
## [1] "de"
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$TRAIN_PARAMS$SD_THRESH
## [1] 1
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$TRAIN_PARAMS$DE_METHOD
## [1] "classic"
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$TRAIN_PARAMS$DE_N
## NULL
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$TRAIN_PARAMS$ASSAY_TYPE
## [1] "logcounts"
##
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$CLASSIFY_PARAMS
## $CELL_ANNOTATION_SOURCES_DEFAULTS$CLASSIFY_PARAMS$QUANTILE
## [1] 0.8
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$CLASSIFY_PARAMS$TUNE_THRESH
## [1] 0.05
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$CLASSIFY_PARAMS$ASSAY_TYPE
## [1] "logcounts"
##
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$PRUNE_SCORE_PARAMS
## $CELL_ANNOTATION_SOURCES_DEFAULTS$PRUNE_SCORE_PARAMS$N_MADS
## [1] 3
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$PRUNE_SCORE_PARAMS$MIN_DIFF_MED
## [1] -Inf
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$PRUNE_SCORE_PARAMS$MIN_DIFF_NEXT
## [1] 0
##
##
## $CELL_ANNOTATION_SOURCES_DEFAULTS$DIAGNOSTICS_PARAMS
## $CELL_ANNOTATION_SOURCES_DEFAULTS$DIAGNOSTICS_PARAMS$HEATMAP_N_TOP_MARKERS
## [1] 20
##
##
##
## $ADDITIONAL_CELL_DATA_FILE
## [1] "additional_cell_data.Rds"
##
## $CELL_GROUPINGS
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated$source_column
## [1] "cluster_graph_louvain_r0.4"
##
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated$description
## [1] "Graph-based clustering (Louvain alg.), annotated clusters"
##
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated$assignments
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated$assignments$`1`
## [1] "memory_CD4+"
##
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated$assignments$`2`
## [1] "B"
##
## $CELL_GROUPINGS$cluster_graph_louvain_r0.4_annotated$assignments$`3`
## [1] "memory_CD4+"
##
##
##
##
## $NORM_CLUSTERING_REPORT_DIMRED_NAMES
## [1] "umap" "pca" "tsne"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$phase
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$phase$name
## [1] "phase"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$phase$label
## [1] "Cell cycle phases"
##
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$doublet_score
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$doublet_score$name
## [1] "doublet_score"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$doublet_score$label
## [1] "Doublet score"
##
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$total
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$total$name
## [1] "total"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$total$label
## [1] "Total number of UMI"
##
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$detected
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$detected$name
## [1] "detected"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$detected$label
## [1] "Detected number of genes"
##
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$cluster_graph_louvain_r0.4_annotated
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$cluster_graph_louvain_r0.4_annotated$name
## [1] "cluster_graph_louvain_r0.4_annotated"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$cluster_graph_louvain_r0.4_annotated$label
## NULL
##
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$cluster_sc3_k6_custom
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$cluster_sc3_k6_custom$name
## [1] "cluster_sc3_k6_custom"
##
## $NORM_CLUSTERING_REPORT_DIMRED_PLOTS_OTHER$cluster_sc3_k6_custom$label
## [1] "From additional cell data"
##
##
##
## $SELECTED_MARKERS_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/selected_markers.csv"
##
## $NORM_CLUSTERING_REPORT_RMD_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/Rmd/single_sample/02_norm_clustering.Rmd"
##
## $NORM_CLUSTERING_REPORT_SIMPLE_RMD_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/Rmd/single_sample/02_norm_clustering_simple.Rmd"
##
## $NORM_CLUSTERING_BASE_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering"
##
## $NORM_CLUSTERING_SELECTED_MARKERS_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering/selected_markers"
##
## $NORM_CLUSTERING_DIMRED_PLOTS_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering/dimred_plots"
##
## $NORM_CLUSTERING_CELL_ANNOTATION_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering/cell_annotation"
##
## $NORM_CLUSTERING_OTHER_PLOTS_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering/other_plots"
##
## $NORM_CLUSTERING_REPORT_HTML_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering/02_norm_clustering.html"
##
## $NORM_CLUSTERING_REPORT_SIMPLE_HTML_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/output/02_norm_clustering/02_norm_clustering_simple.html"
##
## $NORM_CLUSTERING_KNITR_MESSAGE
## [1] FALSE
##
## $NORM_CLUSTERING_KNITR_WARNING
## [1] FALSE
##
## $NORM_CLUSTERING_KNITR_ECHO
## [1] FALSE
##
## attr(,"class")
## [1] "scdrake_list" "list"Show runtime info
drake cache directory
/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/pbmc1k/.drake
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## No traceback availableBioconductor version
3.15
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## ─ Packages ───────────────────────────────────────────────────────────────────
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## clustree 0.5.0 2022-06-25 [1] RSPM (R 4.2.0)
## codetools 0.2-18 2020-11-04 [2] CRAN (R 4.2.1)
## colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.2.0)
## conflicted * 1.1.0 2021-11-26 [1] RSPM (R 4.2.0)
## cowplot 1.1.1 2020-12-30 [1] RSPM (R 4.2.0)
## crayon 1.5.2 2022-09-29 [1] RSPM (R 4.2.0)
## crosstalk 1.2.0 2021-11-04 [1] CRAN (R 4.2.0)
## curl 4.3.3 2022-10-06 [1] RSPM (R 4.2.0)
## data.table 1.14.6 2022-11-16 [1] RSPM (R 4.2.0)
## DBI 1.1.3 2022-06-18 [1] RSPM (R 4.2.0)
## dbplyr 2.2.1 2022-06-27 [1] RSPM (R 4.2.0)
## DelayedArray * 0.22.0 2022-04-26 [1] Bioconductor
## DelayedMatrixStats 1.18.2 2022-10-13 [1] Bioconductor
## deldir 1.0-6 2021-10-23 [1] RSPM (R 4.2.0)
## DEoptimR 1.0-11 2022-04-03 [1] CRAN (R 4.2.0)
## desc 1.4.2 2022-09-08 [1] RSPM (R 4.2.0)
## devtools 2.4.4 2022-07-20 [1] RSPM (R 4.2.0)
## digest 0.6.30 2022-10-18 [1] RSPM (R 4.2.0)
## downlit 0.4.2 2022-07-05 [1] RSPM (R 4.2.0)
## dplyr 1.0.10 2022-09-01 [1] RSPM (R 4.2.0)
## dqrng 0.3.0 2021-05-01 [1] RSPM (R 4.2.0)
## drake * 7.13.4 2022-08-19 [1] RSPM (R 4.2.0)
## DropletUtils 1.16.0 2022-04-26 [1] Bioconductor
## DT 0.26 2022-10-19 [1] RSPM (R 4.2.0)
## e1071 1.7-12 2022-10-24 [1] RSPM (R 4.2.0)
## edgeR 3.38.4 2022-08-07 [1] Bioconductor
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## ensembldb * 2.20.2 2022-06-16 [1] Bioconductor
## evaluate 0.18 2022-11-07 [1] RSPM (R 4.2.0)
## ExperimentHub 2.4.0 2022-04-26 [1] Bioconductor
## fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0)
## farver 2.1.1 2022-07-06 [1] RSPM (R 4.2.0)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
## filelock 1.0.2 2018-10-05 [1] CRAN (R 4.2.0)
## fitdistrplus 1.1-8 2022-03-10 [1] RSPM (R 4.2.0)
## fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0)
## future 1.29.0 2022-11-06 [1] RSPM (R 4.2.0)
## future.apply 1.10.0 2022-11-05 [1] RSPM (R 4.2.0)
## generics 0.1.3 2022-07-05 [1] RSPM (R 4.2.0)
## GenomeInfoDb * 1.32.4 2022-09-06 [1] Bioconductor
## GenomeInfoDbData 1.2.8 2022-05-02 [1] Bioconductor
## GenomicAlignments 1.32.1 2022-07-24 [1] Bioconductor
## GenomicFeatures * 1.48.4 2022-09-20 [1] Bioconductor
## GenomicRanges * 1.48.0 2022-04-26 [1] Bioconductor
## ggbeeswarm 0.6.0 2017-08-07 [1] RSPM (R 4.2.0)
## ggforce 0.4.1 2022-10-04 [1] RSPM (R 4.2.0)
## ggplot2 3.4.0 2022-11-04 [1] RSPM (R 4.2.0)
## ggplotify 0.1.0 2021-09-02 [1] RSPM (R 4.2.0)
## ggraph 2.1.0 2022-10-09 [1] RSPM (R 4.2.0)
## ggrepel 0.9.2 2022-11-06 [1] RSPM (R 4.2.0)
## ggridges 0.5.4 2022-09-26 [1] RSPM (R 4.2.0)
## glmGamPoi 1.8.0 2022-04-26 [1] Bioconductor
## globals 0.16.2 2022-11-21 [1] RSPM (R 4.2.0)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
## goftest 1.2-3 2021-10-07 [1] RSPM (R 4.2.0)
## graphlayouts 0.8.4 2022-11-24 [1] RSPM (R 4.2.0)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.0)
## gridGraphics 0.5-1 2020-12-13 [1] RSPM (R 4.2.0)
## gtable 0.3.1 2022-09-01 [1] RSPM (R 4.2.0)
## harmony 0.1.1 2022-11-14 [1] RSPM (R 4.2.0)
## HDF5Array * 1.24.2 2022-08-02 [1] Bioconductor
## here * 1.0.1 2020-12-13 [1] RSPM (R 4.2.0)
## highr 0.9 2021-04-16 [1] CRAN (R 4.2.0)
## hms 1.1.2 2022-08-19 [1] RSPM (R 4.2.0)
## htmltools 0.5.3 2022-07-18 [1] RSPM (R 4.2.0)
## htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0)
## httpuv 1.6.6 2022-09-08 [1] RSPM (R 4.2.0)
## httr 1.4.4 2022-08-17 [1] RSPM (R 4.2.0)
## ica 1.0-3 2022-07-08 [1] RSPM (R 4.2.0)
## igraph 1.3.5 2022-09-22 [1] RSPM (R 4.2.0)
## interactiveDisplayBase 1.34.0 2022-04-26 [1] Bioconductor
## IRanges * 2.30.1 2022-08-18 [1] Bioconductor
## irlba 2.3.5.1 2022-10-03 [1] RSPM (R 4.2.0)
## janitor 2.1.0 2021-01-05 [1] RSPM (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
## jsonlite 1.8.4 2022-12-06 [1] RSPM (R 4.2.0)
## kableExtra 1.3.4 2021-02-20 [1] RSPM (R 4.2.0)
## KEGGREST 1.36.3 2022-07-12 [1] Bioconductor
## KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.1)
## knitr 1.41 2022-11-18 [1] RSPM (R 4.2.0)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0)
## lattice 0.20-45 2021-09-22 [2] CRAN (R 4.2.1)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.2.0)
## leiden 0.4.3 2022-09-10 [1] RSPM (R 4.2.0)
## lifecycle 1.0.3 2022-10-07 [1] RSPM (R 4.2.0)
## limma 3.52.4 2022-09-27 [1] Bioconductor
## listenv 0.8.0 2019-12-05 [1] RSPM (R 4.2.0)
## littler 0.3.17 2023-05-26 [1] Github (eddelbuettel/littler@31aa160)
## lmtest 0.9-40 2022-03-21 [1] RSPM (R 4.2.0)
## locfit 1.5-9.6 2022-07-11 [1] RSPM (R 4.2.0)
## lubridate 1.9.0 2022-11-06 [1] RSPM (R 4.2.0)
## magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## MASS 7.3-58.1 2022-08-03 [2] RSPM (R 4.2.0)
## Matrix * 1.5-3 2022-11-11 [1] RSPM (R 4.2.0)
## MatrixGenerics * 1.8.1 2022-06-26 [1] Bioconductor
## matrixStats * 0.63.0 2022-11-18 [1] RSPM (R 4.2.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## metapod 1.4.0 2022-04-26 [1] Bioconductor
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.2.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
## mvtnorm 1.1-3 2021-10-08 [1] CRAN (R 4.2.0)
## nlme 3.1-158 2022-06-15 [2] RSPM (R 4.2.0)
## org.Hs.eg.db 3.15.0 2022-04-11 [1] Bioconductor
## parallelly 1.32.1 2022-07-21 [1] RSPM (R 4.2.0)
## patchwork 1.1.2 2022-08-19 [1] RSPM (R 4.2.0)
## pbapply 1.6-0 2022-11-16 [1] RSPM (R 4.2.0)
## pcaPP 2.0-3 2022-10-24 [1] RSPM (R 4.2.0)
## PCAtools 2.8.0 2022-04-26 [1] Bioconductor
## pheatmap 1.0.12 2019-01-04 [1] RSPM (R 4.2.0)
## pillar 1.8.1 2022-08-19 [1] RSPM (R 4.2.0)
## pkgbuild 1.3.1 2021-12-20 [1] RSPM (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.2 2022-11-16 [1] RSPM (R 4.2.0)
## plotly 4.10.1 2022-11-07 [1] RSPM (R 4.2.0)
## plyr 1.8.8 2022-11-11 [1] RSPM (R 4.2.0)
## png 0.1-8 2022-11-29 [1] RSPM (R 4.2.0)
## polyclip 1.10-4 2022-10-20 [1] RSPM (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0)
## processx 3.8.0 2022-10-26 [1] RSPM (R 4.2.0)
## profvis 0.3.7 2020-11-02 [1] RSPM (R 4.2.0)
## progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0)
## progressr 0.11.0 2022-09-02 [1] RSPM (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
## ProtGenerics 1.28.0 2022-04-26 [1] Bioconductor
## proxy 0.4-27 2022-06-09 [1] RSPM (R 4.2.0)
## ps 1.7.2 2022-10-26 [1] RSPM (R 4.2.0)
## purrr 0.3.5 2022-10-06 [1] RSPM (R 4.2.0)
## qs 0.25.4 2022-08-09 [1] RSPM (R 4.2.0)
## R.methodsS3 1.8.2 2022-06-13 [1] RSPM (R 4.2.0)
## R.oo 1.25.0 2022-06-12 [1] RSPM (R 4.2.0)
## R.utils 2.12.2 2022-11-11 [1] RSPM (R 4.2.0)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
## ragg 1.2.4 2022-10-24 [1] RSPM (R 4.2.0)
## RANN 2.6.1 2019-01-08 [1] RSPM (R 4.2.0)
## RApiSerialize 0.1.2 2022-08-25 [1] RSPM (R 4.2.0)
## rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
## Rcpp 1.0.9 2022-07-08 [1] RSPM (R 4.2.0)
## RcppAnnoy 0.0.20 2022-10-27 [1] RSPM (R 4.2.0)
## RcppParallel 5.1.5 2022-01-05 [1] RSPM (R 4.2.0)
## RCurl 1.98-1.9 2022-10-03 [1] RSPM (R 4.2.0)
## readr 2.1.3 2022-10-01 [1] RSPM (R 4.2.0)
## remotes 2.4.2 2021-11-30 [1] RSPM (R 4.2.0)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.2.0)
## ResidualMatrix 1.6.1 2022-08-16 [1] Bioconductor
## restfulr 0.0.15 2022-06-16 [1] RSPM (R 4.2.0)
## reticulate 1.26 2022-08-31 [1] RSPM (R 4.2.0)
## rhdf5 * 2.40.0 2022-04-26 [1] Bioconductor
## rhdf5filters 1.8.0 2022-04-26 [1] Bioconductor
## Rhdf5lib 1.18.2 2022-05-15 [1] Bioconductor
## RhpcBLASctl 0.21-247.1 2021-11-05 [1] RSPM (R 4.2.0)
## rjson 0.2.21 2022-01-09 [1] CRAN (R 4.2.0)
## rlang * 1.0.6 2022-09-24 [1] RSPM (R 4.2.0)
## rmarkdown 2.18 2022-11-09 [1] RSPM (R 4.2.0)
## robustbase 0.95-0 2022-04-02 [1] CRAN (R 4.2.0)
## ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.2.0)
## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0)
## rrcov 1.7-2 2022-10-24 [1] RSPM (R 4.2.0)
## Rsamtools 2.12.0 2022-04-26 [1] Bioconductor
## RSQLite 2.2.19 2022-11-24 [1] RSPM (R 4.2.0)
## rstudioapi 0.14 2022-08-22 [1] RSPM (R 4.2.0)
## rsvd 1.0.5 2021-04-16 [1] RSPM (R 4.2.0)
## rtracklayer 1.56.1 2022-06-23 [1] Bioconductor
## Rtsne 0.16 2022-04-17 [1] RSPM (R 4.2.0)
## rvest 1.0.3 2022-08-19 [1] RSPM (R 4.2.0)
## rzmq 0.9.8 2021-05-04 [1] RSPM (R 4.2.0)
## S4Vectors * 0.34.0 2022-04-26 [1] Bioconductor
## sass 0.4.4 2022-11-24 [1] RSPM (R 4.2.0)
## SC3 1.15.1 2023-05-26 [1] Github (gorgitko/SC3@58d73fb)
## ScaledMatrix 1.4.1 2022-09-11 [1] Bioconductor
## scales 1.2.1 2022-08-20 [1] RSPM (R 4.2.0)
## scater 1.24.0 2022-04-26 [1] Bioconductor
## scattermore 0.8 2022-02-14 [1] RSPM (R 4.2.0)
## scDblFinder 1.10.0 2022-04-26 [1] Bioconductor
## VP scdrake * 1.5.1 2023-06-15 [?] Bioconductor (on disk 1.5.0)
## scran 1.24.1 2022-09-11 [1] Bioconductor
## sctransform 0.3.5 2022-09-21 [1] RSPM (R 4.2.0)
## scuttle 1.6.3 2022-08-23 [1] Bioconductor
## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.2.0)
## Seurat 4.3.0 2022-11-18 [1] RSPM (R 4.2.0)
## SeuratObject 4.1.3 2022-11-07 [1] RSPM (R 4.2.0)
## shiny 1.7.3 2022-10-25 [1] RSPM (R 4.2.0)
## SingleCellExperiment 1.18.1 2022-10-02 [1] Bioconductor
## SingleR 1.10.0 2022-04-26 [1] Bioconductor
## snakecase 0.11.0 2019-05-25 [1] RSPM (R 4.2.0)
## sp 1.5-1 2022-11-07 [1] RSPM (R 4.2.0)
## sparseMatrixStats 1.8.0 2022-04-26 [1] Bioconductor
## spatstat.data 3.0-0 2022-10-21 [1] RSPM (R 4.2.0)
## spatstat.explore 3.0-5 2022-11-10 [1] RSPM (R 4.2.0)
## spatstat.geom 3.0-3 2022-10-25 [1] RSPM (R 4.2.0)
## spatstat.random 3.0-1 2022-11-03 [1] RSPM (R 4.2.0)
## spatstat.sparse 3.0-0 2022-10-21 [1] RSPM (R 4.2.0)
## spatstat.utils 3.0-1 2022-10-19 [1] RSPM (R 4.2.0)
## statmod 1.4.37 2022-08-12 [1] RSPM (R 4.2.0)
## storr 1.2.5 2020-12-01 [1] RSPM (R 4.2.0)
## stringfish 0.15.7 2022-04-13 [1] RSPM (R 4.2.0)
## stringi 1.7.8 2022-07-11 [1] RSPM (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] RSPM (R 4.2.0)
## SummarizedExperiment 1.26.1 2022-04-29 [1] Bioconductor
## survival 3.3-1 2022-03-03 [2] CRAN (R 4.2.1)
## svglite 2.1.0 2022-02-03 [1] RSPM (R 4.2.0)
## systemfonts 1.0.4 2022-02-11 [1] RSPM (R 4.2.0)
## tensor 1.5 2012-05-05 [1] RSPM (R 4.2.0)
## testthat * 3.1.5 2022-10-08 [1] RSPM (R 4.2.0)
## textshaping 0.3.6 2021-10-13 [1] RSPM (R 4.2.0)
## tibble 3.1.8 2022-07-22 [1] RSPM (R 4.2.0)
## tidygraph 1.2.3 2023-02-01 [1] RSPM (R 4.2.0)
## tidyr 1.2.1 2022-09-08 [1] RSPM (R 4.2.0)
## tidyselect 1.1.2 2022-02-21 [1] RSPM (R 4.2.0)
## timechange 0.1.1 2022-11-04 [1] RSPM (R 4.2.0)
## tweenr 2.0.2 2022-09-06 [1] RSPM (R 4.2.0)
## txtq 0.2.4 2021-03-27 [1] RSPM (R 4.2.0)
## tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] RSPM (R 4.2.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] RSPM (R 4.2.0)
## vctrs 0.5.1 2022-11-16 [1] RSPM (R 4.2.0)
## vipor 0.4.5 2017-03-22 [1] RSPM (R 4.2.0)
## viridis 0.6.2 2021-10-13 [1] CRAN (R 4.2.0)
## viridisLite 0.4.1 2022-08-22 [1] RSPM (R 4.2.0)
## webshot 0.5.4 2022-09-26 [1] RSPM (R 4.2.0)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
## WriteXLS 6.4.0 2022-02-24 [1] RSPM (R 4.2.0)
## xfun 0.35 2022-11-16 [1] RSPM (R 4.2.0)
## xgboost 1.6.0.1 2022-04-16 [1] RSPM (R 4.2.0)
## XML 3.99-0.13 2022-12-04 [1] RSPM (R 4.2.0)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## XVector 0.36.0 2022-04-26 [1] Bioconductor
## yaml 2.3.6 2022-10-18 [1] RSPM (R 4.2.0)
## yulab.utils 0.0.5 2022-06-30 [1] RSPM (R 4.2.0)
## zlibbioc 1.42.0 2022-04-26 [1] Bioconductor
## zoo 1.8-11 2022-09-17 [1] RSPM (R 4.2.0)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library
##
## V ── Loaded and on-disk version mismatch.
## P ── Loaded and on-disk path mismatch.
##
## ──────────────────────────────────────────────────────────────────────────────Session info (base)
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics stats4 utils methods base
##
## other attached packages:
## [1] ensembldb_2.20.2 AnnotationFilter_1.20.0 GenomicFeatures_1.48.4
## [4] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4 HDF5Array_1.24.2
## [7] rhdf5_2.40.0 DelayedArray_0.22.0 MatrixGenerics_1.8.1
## [10] matrixStats_0.63.0 Matrix_1.5-3 drake_7.13.4
## [13] AnnotationDbi_1.58.0 IRanges_2.30.1 S4Vectors_0.34.0
## [16] Biobase_2.56.0 BiocGenerics_0.42.0 scdrake_1.5.1
## [19] testthat_3.1.5 magrittr_2.0.3 here_1.0.1
## [22] cli_3.4.1 rlang_1.0.6 conflicted_1.1.0
##
## loaded via a namespace (and not attached):
## [1] rsvd_1.0.5 ica_1.0-3
## [3] svglite_2.1.0 class_7.3-20
## [5] ps_1.7.2 Rsamtools_2.12.0
## [7] lmtest_0.9-40 rprojroot_2.0.3
## [9] crayon_1.5.2 MASS_7.3-58.1
## [11] rhdf5filters_1.8.0 nlme_3.1-158
## [13] WriteXLS_6.4.0 backports_1.4.1
## [15] XVector_0.36.0 ROCR_1.0-11
## [17] irlba_2.3.5.1 callr_3.7.3
## [19] limma_3.52.4 scater_1.24.0
## [21] filelock_1.0.2 stringfish_0.15.7
## [23] xgboost_1.6.0.1 qs_0.25.4
## [25] BiocParallel_1.30.4 rjson_0.2.21
## [27] bit64_4.0.5 glue_1.6.2
## [29] harmony_0.1.1 scDblFinder_1.10.0
## [31] pheatmap_1.0.12 sctransform_0.3.5
## [33] parallel_4.2.1 processx_3.8.0
## [35] vipor_0.4.5 spatstat.sparse_3.0-0
## [37] base64url_1.4 spatstat.geom_3.0-3
## [39] tidyselect_1.1.2 SummarizedExperiment_1.26.1
## [41] usethis_2.1.6 argparser_0.7.1
## [43] SeuratObject_4.1.3 fitdistrplus_1.1-8
## [45] XML_3.99-0.13 tidyr_1.2.1
## [47] zoo_1.8-11 GenomicAlignments_1.32.1
## [49] xtable_1.8-4 evaluate_0.18
## [51] ggplot2_3.4.0 scuttle_1.6.3
## [53] zlibbioc_1.42.0 rstudioapi_0.14
## [55] miniUI_0.1.1.1 sp_1.5-1
## [57] bslib_0.4.1 shiny_1.7.3
## [59] BiocSingular_1.12.0 xfun_0.35
## [61] pkgbuild_1.3.1 cluster_2.1.3
## [63] tidygraph_1.2.3 KEGGREST_1.36.3
## [65] clustermq_0.8.8 tibble_3.1.8
## [67] interactiveDisplayBase_1.34.0 ggrepel_0.9.2
## [69] listenv_0.8.0 Biostrings_2.64.1
## [71] png_0.1-8 future_1.29.0
## [73] withr_2.5.0 bitops_1.0-7
## [75] ggforce_0.4.1 plyr_1.8.8
## [77] pcaPP_2.0-3 e1071_1.7-12
## [79] dqrng_0.3.0 RcppParallel_5.1.5
## [81] pillar_1.8.1 cachem_1.0.6
## [83] fs_1.5.2 DelayedMatrixStats_1.18.2
## [85] vctrs_0.5.1 ellipsis_0.3.2
## [87] generics_0.1.3 RApiSerialize_0.1.2
## [89] devtools_2.4.4 tools_4.2.1
## [91] beeswarm_0.4.0 munsell_0.5.0
## [93] tweenr_2.0.2 proxy_0.4-27
## [95] fastmap_1.1.0 compiler_4.2.1
## [97] pkgload_1.3.2 abind_1.4-5
## [99] httpuv_1.6.6 rtracklayer_1.56.1
## [101] ExperimentHub_2.4.0 sessioninfo_1.2.2
## [103] plotly_4.10.1 GenomeInfoDbData_1.2.8
## [105] gridExtra_2.3 edgeR_3.38.4
## [107] lattice_0.20-45 deldir_1.0-6
## [109] utf8_1.2.2 later_1.3.0
## [111] dplyr_1.0.10 BiocFileCache_2.4.0
## [113] jsonlite_1.8.4 storr_1.2.5
## [115] scales_1.2.1 datasets_4.2.1
## [117] ScaledMatrix_1.4.1 pbapply_1.6-0
## [119] sparseMatrixStats_1.8.0 lazyeval_0.2.2
## [121] promises_1.2.0.1 R.utils_2.12.2
## [123] goftest_1.2-3 spatstat.utils_3.0-1
## [125] reticulate_1.26 rmarkdown_2.18
## [127] cowplot_1.1.1 textshaping_0.3.6
## [129] statmod_1.4.37 webshot_0.5.4
## [131] Rtsne_0.16 glmGamPoi_1.8.0
## [133] uwot_0.1.14 igraph_1.3.5
## [135] survival_3.3-1 ResidualMatrix_1.6.1
## [137] yaml_2.3.6 systemfonts_1.0.4
## [139] htmltools_0.5.3 memoise_2.0.1
## [141] profvis_0.3.7 BiocIO_1.6.0
## [143] Seurat_4.3.0 locfit_1.5-9.6
## [145] graphlayouts_0.8.4 PCAtools_2.8.0
## [147] viridisLite_0.4.1 digest_0.6.30
## [149] rrcov_1.7-2 assertthat_0.2.1
## [151] RhpcBLASctl_0.21-247.1 mime_0.12
## [153] rappdirs_0.3.3 SingleR_1.10.0
## [155] RSQLite_2.2.19 yulab.utils_0.0.5
## [157] future.apply_1.10.0 remotes_2.4.2
## [159] data.table_1.14.6 urlchecker_1.0.1
## [161] blob_1.2.3 R.oo_1.25.0
## [163] ragg_1.2.4 labeling_0.4.2
## [165] splines_4.2.1 Rhdf5lib_1.18.2
## [167] AnnotationHub_3.4.0 ProtGenerics_1.28.0
## [169] RCurl_1.98-1.9 hms_1.1.2
## [171] colorspace_2.0-3 DropletUtils_1.16.0
## [173] BiocManager_1.30.19 ggbeeswarm_0.6.0
## [175] littler_0.3.17 sass_0.4.4
## [177] Rcpp_1.0.9 RANN_2.6.1
## [179] mvtnorm_1.1-3 txtq_0.2.4
## [181] fansi_1.0.3 tzdb_0.3.0
## [183] brio_1.1.3 parallelly_1.32.1
## [185] R6_2.5.1 grid_4.2.1
## [187] ggridges_0.5.4 lifecycle_1.0.3
## [189] bluster_1.6.0 curl_4.3.3
## [191] jquerylib_0.1.4 leiden_0.4.3
## [193] snakecase_0.11.0 robustbase_0.95-0
## [195] desc_1.4.2 RcppAnnoy_0.0.20
## [197] org.Hs.eg.db_3.15.0 RColorBrewer_1.1-3
## [199] spatstat.explore_3.0-5 stringr_1.5.0
## [201] htmlwidgets_1.5.4 beachmat_2.12.0
## [203] polyclip_1.10-4 biomaRt_2.52.0
## [205] purrr_0.3.5 crosstalk_1.2.0
## [207] timechange_0.1.1 gridGraphics_0.5-1
## [209] rvest_1.0.3 globals_0.16.2
## [211] spatstat.random_3.0-1 patchwork_1.1.2
## [213] progressr_0.11.0 batchelor_1.12.3
## [215] codetools_0.2-18 grDevices_4.2.1
## [217] lubridate_1.9.0 metapod_1.4.0
## [219] prettyunits_1.1.1 SingleCellExperiment_1.18.1
## [221] dbplyr_2.2.1 R.methodsS3_1.8.2
## [223] celldex_1.6.0 gtable_0.3.1
## [225] DBI_1.1.3 highr_0.9
## [227] tensor_1.5 httr_1.4.4
## [229] KernSmooth_2.23-20 stringi_1.7.8
## [231] progress_1.2.2 reshape2_1.4.4
## [233] farver_2.1.1 viridis_0.6.2
## [235] DT_0.26 xml2_1.3.3
## [237] BiocNeighbors_1.14.0 kableExtra_1.3.4
## [239] restfulr_0.0.15 readr_2.1.3
## [241] ggplotify_0.1.0 scattermore_0.8
## [243] BiocVersion_3.15.2 scran_1.24.1
## [245] DEoptimR_1.0-11 bit_4.0.5
## [247] clustree_0.5.0 spatstat.data_3.0-0
## [249] ggraph_2.1.0 janitor_2.1.0
## [251] pkgconfig_2.0.3 rzmq_0.9.8
## [253] knitr_1.41 downlit_0.4.2
## [255] SC3_1.15.1Page generated on 2023-12-02 18:51:26
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