SimBenchData 1.13.0
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 liver
Each 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 EH5386
additionaldetail <- 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 No
The data processing script for each dataset can be found under the inst/scripts
directory.
## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 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.3.10.0
##
## 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_1.13.0 ExperimentHub_2.13.0 AnnotationHub_3.13.0
## [4] BiocFileCache_2.13.0 dbplyr_2.5.0 BiocGenerics_0.51.0
## [7] BiocStyle_2.33.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.45.0 xfun_0.43 bslib_0.7.0
## [4] Biobase_2.65.0 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_1.67.0
## [13] RSQLite_2.3.6 blob_1.2.4 pkgconfig_2.0.3
## [16] S4Vectors_0.43.0 lifecycle_1.0.4 GenomeInfoDbData_1.2.12
## [19] compiler_4.4.0 Biostrings_2.73.0 GenomeInfoDb_1.41.0
## [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_3.20.0 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_1.1.0
## [43] rappdirs_0.3.3 bit64_4.0.5 rmarkdown_2.26
## [46] XVector_0.45.0 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_2.39.0 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_1.51.0