SimBenchData 1.12.0
The SimBenchData package contains a total of 35 single-cell RNA-seq datasets covering a wide range of data characteristics, including major sequencing protocols, multiple tissue types, and both human and mouse sources. This package serves as a key resource for performance benchmark of single-cell simulation methods, and was used to comprehensively assess the performance of 12 single-cell simulation methods in retaining key data properties of single-cell sequencing data, including gene-wise and cell-wise properties, as well as biological signals such as differential expression and differential proportion of genes. This data package is a valuable resource for the single-cell community for future development and benchmarking of new single-cell simulation methods and other applications.
The data stored in this package can be retrieved using ExperimentHub.
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("ExperimentHub")
library(ExperimentHub)
eh <- ExperimentHub()
alldata <- query(eh, "SimBenchData")
alldata ## ExperimentHub with 35 records
## # snapshotDate(): 2024-04-29
## # $dataprovider: Broad Institute of MIT & Harvard, Cambridge, MA USA, Peking...
## # $species: Homo sapiens, Mus musculus
## # $rdataclass: SeuratObject
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH5384"]]'
##
## title
## EH5384 | 293T cell line
## EH5385 | Jurkat and 293T
## EH5386 | BC01 blood
## EH5387 | BC01 normal
## EH5388 | BC02 lymph
## ... ...
## EH5414 | Soumillon
## EH5415 | stem cell
## EH5416 | Tabula Muris
## EH5417 | Tung ipsc
## EH5418 | Yang liverEach dataset can be downloaded using its ID.
data_1 <- alldata[["EH5384"]] Information about each dataset such as its description and source URL can be found in the metadata files under the inst/extdata directory. It can also be explored using the function showMetaData. Additional details on each dataset can be explored using the function showAdditionalDetail(). The information on the first three datasets is shown as an example.
library(SimBenchData)
metadata <- showMetaData()
metadata[1:3, ]## Name Description BiocVersion
## 1 293T cell line 293T cell line 3.13
## 2 Jurkat and 293T mixture of Jurkat (human T lymphocyte) and 293T 3.13
## 3 BC01 blood PBMC of breast cancer patient ID BC01 3.13
## Genome SourceType
## 1 hg19 tar.gz
## 2 hg19 tar.gz
## 3 hg19 Zip
## SourceUrl
## 1 https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/293t
## 2 https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/jurkat
## 3 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114725
## SourceVersion Species TaxonomyId
## 1 293t_filtered_gene_bc_matrices.tar.gz Homo sapiens 9606
## 2 jurkat_filtered_gene_bc_matrices.tar.gz Homo sapiens 9606
## 3 GSE114725_RAW.tar Homo sapiens 9606
## Coordinate_1_based
## 1 NA
## 2 NA
## 3 NA
## DataProvider
## 1 10x genomics
## 2 10x genomics
## 3 Memorial Sloan Kettering Cancer Center,\tComputational and Systems Biology Program, SKI
## Maintainer RDataClass DispatchClass
## 1 Yue Cao <yue.cao@sydney.edu.au> SeuratObject Rds
## 2 Yue Cao <yue.cao@sydney.edu.au> SeuratObject Rds
## 3 Yue Cao <yue.cao@sydney.edu.au> SeuratObject Rds
## RDataPath ExperimentHub_ID
## 1 SimBenchData/293t_cellline.rds EH5384
## 2 SimBenchData/293t_jurkat.rds EH5385
## 3 SimBenchData/BC01_blood.rds EH5386additionaldetail <- showAdditionalDetail()
additionaldetail[1:3, ]## ExperimentHub_ID Name Species Protocol Number_of_cells
## 1 EH5384 293T cell line Human 10x Genomics 2885
## 2 EH5385 Jurkat and 293T Human 10x Genomics 6143
## 3 EH5386 BC01 blood Human inDrops 3034
## Multiple_celltypes_or_conditions
## 1 No
## 2 Yes
## 3 NoThe data processing script for each dataset can be found under the inst/scripts directory.
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] SimBenchData_1.12.0 ExperimentHub_2.12.0 AnnotationHub_3.12.0
## [4] BiocFileCache_2.12.0 dbplyr_2.5.0 BiocGenerics_0.50.0
## [7] BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.44.0 xfun_0.43 bslib_0.7.0
## [4] Biobase_2.64.0 vctrs_0.6.5 tools_4.4.0
## [7] generics_0.1.3 stats4_4.4.0 curl_5.2.1
## [10] tibble_3.2.1 fansi_1.0.6 AnnotationDbi_1.66.0
## [13] RSQLite_2.3.6 blob_1.2.4 pkgconfig_2.0.3
## [16] S4Vectors_0.42.0 lifecycle_1.0.4 GenomeInfoDbData_1.2.12
## [19] compiler_4.4.0 Biostrings_2.72.0 GenomeInfoDb_1.40.0
## [22] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.8
## [25] pillar_1.9.0 crayon_1.5.2 jquerylib_0.1.4
## [28] cachem_1.0.8 mime_0.12 tidyselect_1.2.1
## [31] digest_0.6.35 dplyr_1.1.4 purrr_1.0.2
## [34] bookdown_0.39 BiocVersion_3.19.1 fastmap_1.1.1
## [37] cli_3.6.2 magrittr_2.0.3 utf8_1.2.4
## [40] withr_3.0.0 filelock_1.0.3 UCSC.utils_1.0.0
## [43] rappdirs_0.3.3 bit64_4.0.5 rmarkdown_2.26
## [46] XVector_0.44.0 httr_1.4.7 bit_4.0.5
## [49] png_0.1-8 memoise_2.0.1 evaluate_0.23
## [52] knitr_1.46 IRanges_2.38.0 rlang_1.1.3
## [55] glue_1.7.0 DBI_1.2.2 BiocManager_1.30.22
## [58] jsonlite_1.8.8 R6_2.5.1 zlibbioc_1.50.0