clustifyrdatahub

clustifyrdatahub

clustifyrdatahub provides external reference data sets for cell-type assignment with clustifyr.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("clustifyrdatahub")

Available references include

knitr::kable(dplyr::select(
  read.csv(system.file("extdata", "metadata.csv", package = "clustifyrdatahub")),
  c(1, 9, 2:7)))
Title Species Description RDataPath BiocVersion Genome SourceType SourceUrl
ref_MCA Mus musculus Mouse Cell Atlas clustifyrdatahub/ref_MCA.rda 3.12 mm10 Zip https://ndownloader.figshare.com/files/10756795
ref_tabula_muris_drop Mus musculus Tabula Muris (10X) clustifyrdatahub/ref_tabula_muris_drop.rda 3.12 mm10 Zip https://ndownloader.figshare.com/articles/5821263
ref_tabula_muris_facs Mus musculus Tabula Muris (SmartSeq2) clustifyrdatahub/ref_tabula_muris_facs.rda 3.12 mm10 Zip https://ndownloader.figshare.com/articles/5821263
ref_mouse.rnaseq Mus musculus Mouse RNA-seq from 28 cell types clustifyrdatahub/ref_mouse.rnaseq.rda 3.12 mm10 RDA https://github.com/dviraran/SingleR/tree/master/data
ref_moca_main Mus musculus Mouse Organogenesis Cell Atlas (main cell types) clustifyrdatahub/ref_moca_main.rda 3.12 mm10 RDA https://oncoscape.v3.sttrcancer.org/atlas.gs.washington.edu.mouse.rna/downloads
ref_immgen Mus musculus Mouse sorted immune cells clustifyrdatahub/ref_immgen.rda 3.12 mm10 RDA https://github.com/dviraran/SingleR/tree/master/data
ref_hema_microarray Homo sapiens Human hematopoietic cell microarray clustifyrdatahub/ref_hema_microarray.rda 3.12 hg38 TXT https://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24759/matrix/GSE24759_series_matrix.txt.gz
ref_cortex_dev Homo sapiens Human cortex development scRNA-seq clustifyrdatahub/ref_cortex_dev.rda 3.12 hg38 TSV https://cells.ucsc.edu/cortex-dev/exprMatrix.tsv.gz
ref_pan_indrop Homo sapiens Human pancreatic cell scRNA-seq (inDrop) clustifyrdatahub/ref_pan_indrop.rda 3.12 hg38 RDA https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/baron-human.rds
ref_pan_smartseq2 Homo sapiens Human pancreatic cell scRNA-seq (SmartSeq2) clustifyrdatahub/ref_pan_smartseq2.rda 3.12 hg38 RDA https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/segerstolpe.rds
ref_mouse_atlas Mus musculus Mouse Atlas scRNA-seq from 321 cell types clustifyrdatahub/ref_mouse_atlas.rda 3.12 mm10 RDA https://github.com/rnabioco/scRNA-seq-Cell-Ref-Matrix/blob/master/atlas/musMusculus/MouseAtlas.rda

To use clustifyrdatahub

library(ExperimentHub)
eh <- ExperimentHub()

## query
refs <- query(eh, "clustifyrdatahub")
refs
#> ExperimentHub with 11 records
#> # snapshotDate(): 2026-10-07
#> # $dataprovider: figshare, S3, GitHub, GEO, washington.edu, UCSC
#> # $species: Mus musculus, Homo sapiens
#> # $rdataclass: data.frame
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["EH3444"]]' 
#> 
#>            title                
#>   EH3444 | ref_MCA              
#>   EH3445 | ref_tabula_muris_drop
#>   EH3446 | ref_tabula_muris_facs
#>   EH3447 | ref_mouse.rnaseq     
#>   EH3448 | ref_moca_main        
#>   ...      ...                  
#>   EH3450 | ref_hema_microarray  
#>   EH3451 | ref_cortex_dev       
#>   EH3452 | ref_pan_indrop       
#>   EH3453 | ref_pan_smartseq2    
#>   EH3779 | ref_mouse_atlas
## either by index or id
ref_hema_microarray <- refs[[7]]         ## load the first resource in the list
ref_hema_microarray <- refs[["EH3450"]]  ## load by EH id

## or list and load
refs <- listResources(eh, "clustifyrdatahub")
ref_hema_microarray <- loadResources(
    eh, 
    "clustifyrdatahub",
    "ref_hema_microarray"
    )[[1]]

## use for classification of cell types
res <- clustifyr::clustify(
    input = clustifyr::pbmc_matrix_small,
    metadata = clustifyr::pbmc_meta$classified,
    ref_mat = ref_hema_microarray,
    query_genes = clustifyr::pbmc_vargenes
)
## or load refs by function name (after loading hub library)
library(clustifyrdatahub)
ref_hema_microarray()[1:5, 1:5]           ## data are loaded
#>        Basophils CD4+ Central Memory CD4+ Effector Memory CD8+ Central Memory
#> DDR1    6.084244            5.967502             5.933039            6.005278
#> RFC2    6.280044            6.028615             6.047005            5.992979
#> HSPA6   6.535444            5.811475             5.746326            5.928349
#> PAX8    6.669153            5.896401             6.118577            6.270870
#> GUCA1A  5.239230            5.232116             5.206960            5.227415
#>        CD8+ Effector Memory
#> DDR1               5.895926
#> RFC2               5.942426
#> HSPA6              5.942670
#> PAX8               6.323922
#> GUCA1A             5.090882
ref_hema_microarray(metadata = TRUE)      ## only metadata
#> ExperimentHub with 1 record
#> # snapshotDate(): 2026-10-07
#> # names(): EH3450
#> # package(): clustifyrdatahub
#> # $dataprovider: GEO
#> # $species: Homo sapiens
#> # $rdataclass: data.frame
#> # $rdatadateadded: 2020-05-14
#> # $title: ref_hema_microarray
#> # $description: Human hematopoietic cell microarray
#> # $taxonomyid: 9606
#> # $genome: hg38
#> # $sourcetype: TXT
#> # $sourceurl: https://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24759/matr...
#> # $sourcesize: NA
#> # $tags: c("SingleCellData", "SequencingData", "MicroarrayData",
#> #   "ExperimentHub") 
#> # retrieve record with 'object[["EH3450"]]'

session info

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 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-r0.3.32.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=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       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] clustifyrdatahub_1.23.0 ExperimentHub_3.3.2     AnnotationHub_4.3.2    
#> [4] BiocFileCache_3.3.0     dbplyr_2.6.0            BiocGenerics_0.59.12   
#> [7] generics_0.1.4          BiocStyle_2.41.0       
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1            dplyr_1.2.1                
#>  [3] farver_2.1.2                blob_1.3.0                 
#>  [5] filelock_1.0.3              Biostrings_2.81.9          
#>  [7] S7_0.2.2                    SingleCellExperiment_1.35.2
#>  [9] fastmap_1.2.0               dotCall64_1.2              
#> [11] digest_0.6.39               lifecycle_1.0.5            
#> [13] SeuratObject_5.4.0          KEGGREST_1.53.6            
#> [15] RSQLite_3.53.3              magrittr_2.0.5             
#> [17] clustifyr_1.25.0            compiler_4.6.1             
#> [19] rlang_1.3.0                 sass_0.4.10                
#> [21] tools_4.6.1                 yaml_2.3.12                
#> [23] data.table_1.18.6.1         knitr_1.52                 
#> [25] S4Arrays_1.13.2             bit_4.6.0                  
#> [27] sp_2.2-3                    curl_8.0.0                 
#> [29] DelayedArray_0.39.8         RColorBrewer_1.1-3         
#> [31] BiocParallel_1.47.0         abind_1.4-8                
#> [33] withr_3.0.3                 purrr_1.2.2                
#> [35] sys_3.4.3                   grid_4.6.1                 
#> [37] stats4_4.6.1                future_1.76.0              
#> [39] progressr_1.0.0             ggplot2_4.0.3              
#> [41] globals_0.19.1              scales_1.4.0               
#> [43] SummarizedExperiment_1.43.0 cli_3.6.6                  
#> [45] rmarkdown_2.32              crayon_1.5.3               
#> [47] otel_0.2.0                  future.apply_1.20.2        
#> [49] httr_1.4.9                  BiocBaseUtils_1.15.1       
#> [51] DBI_1.3.0                   cachem_1.1.0               
#> [53] parallel_4.6.1              AnnotationDbi_1.75.2       
#> [55] BiocManager_1.30.27         XVector_0.53.0             
#> [57] matrixStats_1.5.0           vctrs_0.7.3                
#> [59] Matrix_1.7-6                jsonlite_2.0.0             
#> [61] IRanges_2.47.5              S4Vectors_0.51.10          
#> [63] bit64_4.8.6                 listenv_1.1.0              
#> [65] maketools_1.3.2             tidyr_1.3.2                
#> [67] jquerylib_0.1.4             glue_1.8.1                 
#> [69] parallelly_1.48.0           spam_2.11-4                
#> [71] codetools_0.2-20            cowplot_1.2.0              
#> [73] gtable_0.3.6                BiocVersion_3.24.0         
#> [75] GenomicRanges_1.65.4        tibble_3.3.1               
#> [77] pillar_1.11.1               rappdirs_0.3.4             
#> [79] htmltools_0.5.9             Seqinfo_1.3.2              
#> [81] fgsea_1.39.2                entropy_1.3.2              
#> [83] R6_2.6.1                    httr2_1.3.0                
#> [85] evaluate_1.0.5              lattice_0.23-1             
#> [87] Biobase_2.73.2              png_0.1-9                  
#> [89] memoise_2.0.1               bslib_0.12.0               
#> [91] fastmatch_1.1-8             Rcpp_1.1.2                 
#> [93] SparseArray_1.13.4          xfun_0.61                  
#> [95] MatrixGenerics_1.25.0       buildtools_1.0.0           
#> [97] pkgconfig_2.0.3