CuratedAtlasQueryR ================ <!-- badges: start --> [](https://www.tidyverse.org/lifecycle/#maturing) <!-- badges: end --> `CuratedAtlasQuery` is a query interface that allow the programmatic exploration and retrieval of the harmonised, curated and reannotated CELLxGENE single-cell human cell atlas. Data can be retrieved at cell, sample, or dataset levels based on filtering criteria. Harmonised data is stored in the ARDC Nectar Research Cloud, and most `CuratedAtlasQuery` functions interact with Nectar via web requests, so a network connection is required for most functionality. <img src="man/figures/logo.png" width="120x" height="139px" /> <img src="man/figures/svcf_logo.jpeg" width="155x" height="58px" /><img src="man/figures/czi_logo.png" width="129px" height="58px" /><img src="man/figures/bioconductor_logo.jpg" width="202px" height="58px" /><img src="man/figures/vca_logo.png" width="219px" height="58px" /><img src="man/figures/nectar_logo.png" width="180px" height="58px" /> # Query interface ## Installation ``` r devtools::install_github("stemangiola/CuratedAtlasQueryR") ``` ## Load the package ``` r library(CuratedAtlasQueryR) ``` ## Load and explore the metadata ### Load the metadata ``` r # Note: in real applications you should use the default value of remote_url metadata <- get_metadata(remote_url = METADATA_URL) metadata #> # Source: table</vast/scratch/users/milton.m/cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet> [?? x 56] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> cell_ sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ _samp…⁸ #> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> #> 1 8387… 7bd7b8… natura… immune… 5 cd8 tem gmp natura… 842ce7… Q59___… #> 2 1768… 7bd7b8… natura… immune… 5 cd8 tem cd8 tcm natura… 842ce7… Q59___… #> 3 6329… 7bd7b8… natura… immune… 5 cd8 tem clp termin… 842ce7… Q59___… #> 4 5027… 7bd7b8… natura… immune… 5 cd8 tem clp natura… 842ce7… Q59___… #> 5 7956… 7bd7b8… natura… immune… 5 cd8 tem clp natura… 842ce7… Q59___… #> 6 4305… 7bd7b8… natura… immune… 5 cd8 tem clp termin… 842ce7… Q59___… #> 7 2126… 933f96… natura… ilc 1 nk nk natura… c250bf… AML3__… #> 8 3114… 933f96… natura… immune… 5 mait nk natura… c250bf… AML3__… #> 9 1407… 933f96… natura… immune… 5 mait clp natura… c250bf… AML3__… #> 10 2911… 933f96… natura… nk 5 nk clp natura… c250bf… AML3__… #> # … with more rows, 46 more variables: assay <chr>, #> # assay_ontology_term_id <chr>, file_id_db <chr>, #> # cell_type_ontology_term_id <chr>, development_stage <chr>, #> # development_stage_ontology_term_id <chr>, disease <chr>, #> # disease_ontology_term_id <chr>, ethnicity <chr>, #> # ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>, #> # is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, … ``` The `metadata` variable can then be re-used for all subsequent queries. ### Explore the tissue ``` r metadata |> dplyr::distinct(tissue, file_id) #> # Source: SQL [10 x 2] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> tissue file_id #> <chr> <chr> #> 1 bone marrow 1ff5cbda-4d41-4f50-8c7e-cbe4a90e38db #> 2 lung parenchyma 6661ab3a-792a-4682-b58c-4afb98b2c016 #> 3 respiratory airway 6661ab3a-792a-4682-b58c-4afb98b2c016 #> 4 nose 6661ab3a-792a-4682-b58c-4afb98b2c016 #> 5 renal pelvis dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 #> 6 kidney dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 #> 7 renal medulla dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 #> 8 cortex of kidney dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 #> 9 kidney blood vessel dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 #> 10 lung a2796032-d015-40c4-b9db-835207e5bd5b ``` ## Download single-cell RNA sequencing counts ### Query raw counts ``` r single_cell_counts = metadata |> dplyr::filter( ethnicity == "African" & stringr::str_like(assay, "%10x%") & tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> get_single_cell_experiment() #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Downloading 0 files, totalling 0 GB #> ℹ Reading files. #> ℹ Compiling Single Cell Experiment. single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 #> # Features=36229 | Cells=1571 | Assays=counts #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> #> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… #> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… #> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… #> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… #> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… #> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… #> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… #> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… #> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay <chr>, #> # assay_ontology_term_id <chr>, file_id_db <chr>, #> # cell_type_ontology_term_id <chr>, development_stage <chr>, #> # development_stage_ontology_term_id <chr>, disease <chr>, #> # disease_ontology_term_id <chr>, ethnicity <chr>, #> # ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>, #> # is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, … ``` ### Query counts scaled per million This is helpful if just few genes are of interest, as they can be compared across samples. ``` r single_cell_counts = metadata |> dplyr::filter( ethnicity == "African" & stringr::str_like(assay, "%10x%") & tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> get_single_cell_experiment(assays = "cpm") #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Downloading 0 files, totalling 0 GB #> ℹ Reading files. #> ℹ Compiling Single Cell Experiment. single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 #> # Features=36229 | Cells=1571 | Assays=cpm #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> #> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… #> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… #> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… #> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… #> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… #> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… #> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… #> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… #> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay <chr>, #> # assay_ontology_term_id <chr>, file_id_db <chr>, #> # cell_type_ontology_term_id <chr>, development_stage <chr>, #> # development_stage_ontology_term_id <chr>, disease <chr>, #> # disease_ontology_term_id <chr>, ethnicity <chr>, #> # ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>, #> # is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, … ``` ### Extract only a subset of genes ``` r single_cell_counts = metadata |> dplyr::filter( ethnicity == "African" & stringr::str_like(assay, "%10x%") & tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> get_single_cell_experiment(assays = "cpm", features = "PUM1") #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Downloading 0 files, totalling 0 GB #> ℹ Reading files. #> ℹ Compiling Single Cell Experiment. single_cell_counts #> # A SingleCellExperiment-tibble abstraction: 1,571 × 57 #> # Features=1 | Cells=1571 | Assays=cpm #> .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸ #> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> #> 1 AGCG… 11a7dc… CD4-po… cd4 th1 3 cd4 tcm cd8 t th1 10b339… Donor_… #> 2 TCAG… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 10b339… Donor_… #> 3 TTTA… 11a7dc… CD4-po… cd4 th… 3 cd4 tcm cd4 tcm th17 10b339… Donor_… #> 4 ACAC… 11a7dc… CD4-po… immune… 5 cd4 tcm plasma th1/th… 10b339… Donor_… #> 5 CAAG… 11a7dc… CD4-po… immune… 1 cd4 tcm cd4 tcm mait 10b339… Donor_… #> 6 CTGT… 14a078… CD4-po… cd4 th… 3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85… #> 7 ACGT… 14a078… CD4-po… treg 5 cd4 tcm tregs t regu… 8f71c5… VUHD85… #> 8 CATA… 14a078… CD4-po… immune… 5 nk cd8 tem mait 8f71c5… VUHD85… #> 9 ACTT… 14a078… CD4-po… mait 5 mait cd8 tem mait 8f71c5… VUHD85… #> 10 TGCG… 14a078… CD4-po… cd4 th1 3 cd4 tcm cd4 tem th1 8f71c5… VUHD85… #> # … with 1,561 more rows, 47 more variables: assay <chr>, #> # assay_ontology_term_id <chr>, file_id_db <chr>, #> # cell_type_ontology_term_id <chr>, development_stage <chr>, #> # development_stage_ontology_term_id <chr>, disease <chr>, #> # disease_ontology_term_id <chr>, ethnicity <chr>, #> # ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>, #> # is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, … ``` ### Extract the counts as a Seurat object This convert the H5 SingleCellExperiment to Seurat so it might take long time and occupy a lot of memory depending on how many cells you are requesting. ``` r single_cell_counts_seurat = metadata |> dplyr::filter( ethnicity == "African" & stringr::str_like(assay, "%10x%") & tissue == "lung parenchyma" & stringr::str_like(cell_type, "%CD4%") ) |> get_seurat() #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Downloading 0 files, totalling 0 GB #> ℹ Reading files. #> ℹ Compiling Single Cell Experiment. single_cell_counts_seurat #> An object of class Seurat #> 36229 features across 1571 samples within 1 assay #> Active assay: originalexp (36229 features, 0 variable features) ``` ## Save your `SingleCellExperiment` The returned `SingleCellExperiment` can be saved with two modalities, as `.rds` or as `HDF5`. ### Saving as RDS (fast saving, slow reading) Saving as `.rds` has the advantage of being fast, andd the `.rds` file occupies very little disk space as it only stores the links to the files in your cache. However it has the disadvantage that for big `SingleCellExperiment` objects, which merge a lot of HDF5 from your `get_single_cell_experiment`, the display and manipulation is going to be slow. In addition, an `.rds` saved in this way is not portable: you will not be able to share it with other users. ``` r single_cell_counts |> saveRDS("single_cell_counts.rds") ``` ### Saving as HDF5 (slow saving, fast reading) Saving as `.hdf5` executes any computation on the `SingleCellExperiment` and writes it to disk as a monolithic `HDF5`. Once this is done, operations on the `SingleCellExperiment` will be comparatively very fast. The resulting `.hdf5` file will also be totally portable and sharable. However this `.hdf5` has the disadvantage of being larger than the corresponding `.rds` as it includes a copy of the count information, and the saving process is going to be slow for large objects. ``` r single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_counts", replace = TRUE) ``` ## Visualise gene transcription We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Downloading 0 files, totalling 0 GB #> ℹ Reading files. #> ℹ Compiling Single Cell Experiment. #> Warning: Transformation introduced infinite values in continuous y-axis #> Warning in min(x): no non-missing arguments to min; returning Inf #> Warning in max(x): no non-missing arguments to max; returning -Inf <!-- --> #> Warning: Transformation introduced infinite values in continuous y-axis #> Warning in min(x): no non-missing arguments to min; returning Inf #> Warning in max(x): no non-missing arguments to max; returning -Inf <!-- --> ``` r library(tidySingleCellExperiment) library(ggplot2) counts |> ggplot(aes( disease, `HLA.A`,color = file_id)) + geom_jitter(shape=".") ``` <!-- --> <!-- --> ``` r metadata |> # Filter and subset dplyr::filter(cell_type_harmonised=="nk") |> # Get counts per million for HCA-A gene get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> # Plot (styling code have been omitted) tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> ggplot(aes(tissue_harmonised, `HLA.A`,color = file_id)) + geom_jitter(shape=".") #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Downloading 0 files, totalling 0 GB #> ℹ Reading files. #> ℹ Compiling Single Cell Experiment. ``` <!-- --> <!-- --> ## Obtain Unharmonised Metadata Various metadata fields are *not* common between datasets, so it does not make sense for these to live in the main metadata table. However, we can obtain it using the `get_unharmonised_metadata()` function. This function returns a data frame with one row per dataset, including the `unharmonised` column which contains unharmnised metadata as a nested data frame. ``` r harmonised <- metadata |> dplyr::filter(tissue == "kidney blood vessel") unharmonised <- get_unharmonised_metadata(harmonised) unharmonised #> # A tibble: 1 × 2 #> file_id unharmonised #> <chr> <list> #> 1 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 <tbl_dck_[,14]> ``` Notice that the columns differ between each dataset’s data frame: ``` r dplyr::pull(unharmonised) |> head(2) #> [[1]] #> # Source: SQL [?? x 14] #> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:] #> cell_ file_id donor…¹ donor…² libra…³ mappe…⁴ sampl…⁵ suspe…⁶ suspe…⁷ autho…⁸ #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 2 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 3 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 4 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 5 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 6 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 7 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 8 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 9 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> 10 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic… #> # … with more rows, 4 more variables: reported_diseases <chr>, #> # Experiment <chr>, Project <chr>, broad_celltype <chr>, and abbreviated #> # variable names ¹​donor_age, ²​donor_uuid, ³​library_uuid, #> # ⁴​mapped_reference_annotation, ⁵​sample_uuid, ⁶​suspension_type, #> # ⁷​suspension_uuid, ⁸​author_cell_type ``` # Cell metadata Dataset-specific columns (definitions available at cellxgene.cziscience.com) `cell_count`, `collection_id`, `created_at.x`, `created_at.y`, `dataset_deployments`, `dataset_id`, `file_id`, `filename`, `filetype`, `is_primary_data.y`, `is_valid`, `linked_genesets`, `mean_genes_per_cell`, `name`, `published`, `published_at`, `revised_at`, `revision`, `s3_uri`, `schema_version`, `tombstone`, `updated_at.x`, `updated_at.y`, `user_submitted`, `x_normalization` Sample-specific columns (definitions available at cellxgene.cziscience.com) `sample_`, `sample_name`, `age_days`, `assay`, `assay_ontology_term_id`, `development_stage`, `development_stage_ontology_term_id`, `ethnicity`, `ethnicity_ontology_term_id`, `experiment___`, `organism`, `organism_ontology_term_id`, `sample_placeholder`, `sex`, `sex_ontology_term_id`, `tissue`, `tissue_harmonised`, `tissue_ontology_term_id`, `disease`, `disease_ontology_term_id`, `is_primary_data.x` Cell-specific columns (definitions available at cellxgene.cziscience.com) `cell_`, `cell_type`, `cell_type_ontology_term_idm`, `cell_type_harmonised`, `confidence_class`, `cell_annotation_azimuth_l2`, `cell_annotation_blueprint_singler` Through harmonisation and curation we introduced custom column, not present in the original CELLxGENE metadata - `tissue_harmonised`: a coarser tissue name for better filtering - `age_days`: the number of days corresponding to the age - `cell_type_harmonised`: the consensus call identity (for immune cells) using the original and three novel annotations using Seurat Azimuth and SingleR - `confidence_class`: an ordinal class of how confident `cell_type_harmonised` is. 1 is complete consensus, 2 is 3 out of four and so on. - `cell_annotation_azimuth_l2`: Azimuth cell annotation - `cell_annotation_blueprint_singler`: SingleR cell annotation using Blueprint reference - `cell_annotation_blueprint_monaco`: SingleR cell annotation using Monaco reference - `sample_id_db`: Sample subdivision for internal use - `file_id_db`: File subdivision for internal use - `sample_`: Sample ID - `.sample_name`: How samples were defined # RNA abundance The `raw` assay includes RNA abundance in the positive real scale (not transformed with non-linear functions, e.g. log sqrt). Originally CELLxGENE include a mix of scales and transformations specified in the `x_normalization` column. The `cpm` assay includes counts per million. # Session Info ``` r sessionInfo() #> R version 4.2.1 (2022-06-23) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: CentOS Linux 7 (Core) #> #> Matrix products: default #> BLAS: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRblas.so #> LAPACK: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRlapack.so #> #> locale: #> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 #> [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] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] tidySingleCellExperiment_1.6.3 SingleCellExperiment_1.18.1 #> [3] SummarizedExperiment_1.26.1 Biobase_2.56.0 #> [5] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4 #> [7] IRanges_2.30.1 S4Vectors_0.34.0 #> [9] BiocGenerics_0.42.0 MatrixGenerics_1.8.1 #> [11] matrixStats_0.63.0 ttservice_0.2.2 #> [13] ggplot2_3.4.1 CuratedAtlasQueryR_0.99.1 #> #> loaded via a namespace (and not attached): #> [1] plyr_1.8.8 igraph_1.4.1 lazyeval_0.2.2 #> [4] sp_1.5-1 splines_4.2.1 listenv_0.9.0 #> [7] scattermore_0.8 digest_0.6.31 htmltools_0.5.4 #> [10] fansi_1.0.3 magrittr_2.0.3 tensor_1.5 #> [13] cluster_2.1.3 ROCR_1.0-11 globals_0.16.2 #> [16] duckdb_0.7.1-1 spatstat.sparse_3.0-0 colorspace_2.0-3 #> [19] blob_1.2.3 ggrepel_0.9.2 xfun_0.36 #> [22] dplyr_1.1.0 RCurl_1.98-1.9 jsonlite_1.8.4 #> [25] progressr_0.13.0 spatstat.data_3.0-0 survival_3.3-1 #> [28] zoo_1.8-11 glue_1.6.2 polyclip_1.10-4 #> [31] gtable_0.3.1 zlibbioc_1.42.0 XVector_0.36.0 #> [34] leiden_0.4.3 DelayedArray_0.22.0 Rhdf5lib_1.18.2 #> [37] future.apply_1.10.0 HDF5Array_1.24.2 abind_1.4-5 #> [40] scales_1.2.1 DBI_1.1.3 spatstat.random_3.0-1 #> [43] miniUI_0.1.1.1 Rcpp_1.0.10 viridisLite_0.4.1 #> [46] xtable_1.8-4 reticulate_1.26 htmlwidgets_1.6.0 #> [49] httr_1.4.4 RColorBrewer_1.1-3 ellipsis_0.3.2 #> [52] Seurat_4.3.0 ica_1.0-3 farver_2.1.1 #> [55] pkgconfig_2.0.3 dbplyr_2.3.0 sass_0.4.4 #> [58] uwot_0.1.14 deldir_1.0-6 utf8_1.2.2 #> [61] labeling_0.4.2 tidyselect_1.2.0 rlang_1.0.6 #> [64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0 #> [67] tools_4.2.1 cachem_1.0.6 cli_3.6.0 #> [70] generics_0.1.3 ggridges_0.5.4 evaluate_0.19 #> [73] stringr_1.5.0 fastmap_1.1.0 yaml_2.3.6 #> [76] goftest_1.2-3 knitr_1.42 fitdistrplus_1.1-8 #> [79] purrr_1.0.1 RANN_2.6.1 pbapply_1.6-0 #> [82] future_1.30.0 nlme_3.1-157 mime_0.12 #> [85] compiler_4.2.1 rstudioapi_0.14 curl_4.3.3 #> [88] plotly_4.10.1 png_0.1-8 spatstat.utils_3.0-1 #> [91] tibble_3.1.8 bslib_0.4.2 stringi_1.7.12 #> [94] highr_0.10 forcats_1.0.0 lattice_0.20-45 #> [97] Matrix_1.5-3 vctrs_0.5.2 pillar_1.8.1 #> [100] lifecycle_1.0.3 rhdf5filters_1.8.0 spatstat.geom_3.0-3 #> [103] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20 #> [106] data.table_1.14.6 cowplot_1.1.1 bitops_1.0-7 #> [109] irlba_2.3.5.1 httpuv_1.6.7 patchwork_1.1.2 #> [112] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 #> [115] gridExtra_2.3 parallelly_1.33.0 codetools_0.2-18 #> [118] assertthat_0.2.1 MASS_7.3-57 rhdf5_2.40.0 #> [121] rprojroot_2.0.3 withr_2.5.0 SeuratObject_4.1.3 #> [124] sctransform_0.3.5 GenomeInfoDbData_1.2.8 parallel_4.2.1 #> [127] grid_4.2.1 tidyr_1.3.0 rmarkdown_2.20 #> [130] Rtsne_0.16 spatstat.explore_3.0-5 shiny_1.7.4 ```