HDCytoData 1.8.0
The HDCytoData package is an extensible resource containing a set of publicly available high-dimensional flow cytometry and mass cytometry (CyTOF) benchmark datasets, which have been formatted into SummarizedExperiment and flowSet Bioconductor object formats. The data objects are hosted on Bioconductor’s ExperimentHub platform.
The objects each contain one or more tables of cell-level expression values, as well as all required metadata. Row metadata includes sample IDs, group IDs, patient IDs, reference cell population or cluster labels (where available), and labels identifying ‘spiked in’ cells (where available). Column metadata includes channel names, protein marker names, and protein marker classes (cell type, cell state, as well as non protein marker columns).
Note that raw expression values should be transformed prior to any downstream analyses (see below).
Currently, the package includes benchmark datasets used in our previous work to evaluate methods for clustering and differential analyses. The datasets are provided here in SummarizedExperiment and flowSet formats in order to make them easier to access and integrate into R/Bioconductor workflows.
For more details, see our paper describing the HDCytoData package:
The package contains the following datasets, which can be grouped into datasets useful for benchmarking methods for (i) clustering, and (ii) differential analyses.
Extensive documentation is available in the help files for the objects. For each dataset, this includes a description of the dataset (e.g. biological context, number of samples and conditions, number of cells, number of reference cell populations, number and classes of protein markers, etc.), as well as an explanation of the object structures, details on accessor functions required to access the expression tables and metadata, and references to original data sources.
File sizes are listed in the help files for the datasets. The removeCache function from the ExperimentHub package can be used to clear the local download cache (see ExperimentHub documentation).
The help files can be accessed by the dataset names, e.g. ?Bodenmiller_BCR_XL or help(Bodenmiller_BCR_XL).
An updated list of all available datasets can also be obtained programmatically using the ExperimentHub accessor functions, as follows. This retrieves a table of metadata from the ExperimentHub database, which includes information such as the ExperimentHub ID, title, and description for each dataset.
suppressPackageStartupMessages(library(ExperimentHub))
# Create ExperimentHub instance
ehub <- ExperimentHub()## snapshotDate(): 2020-04-27# Find HDCytoData datasets
ehub <- query(ehub, "HDCytoData")
ehub## ExperimentHub with 56 records
## # snapshotDate(): 2020-04-27
## # $dataprovider: NA
## # $species: Homo sapiens, Mus musculus
## # $rdataclass: flowSet, SummarizedExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH2240"]]'
##
## title
## EH2240 | Levine_32dim_SE
## EH2241 | Levine_32dim_flowSet
## EH2242 | Levine_13dim_SE
## EH2243 | Levine_13dim_flowSet
## EH2244 | Samusik_01_SE
## ... ...
## EH3060 | Weber_BCR_XL_sim_random_seeds_rep3_flowSet
## EH3061 | Weber_BCR_XL_sim_less_distinct_less_50pc_SE
## EH3062 | Weber_BCR_XL_sim_less_distinct_less_50pc_flowSet
## EH3063 | Weber_BCR_XL_sim_less_distinct_less_75pc_SE
## EH3064 | Weber_BCR_XL_sim_less_distinct_less_75pc_flowSet# Retrieve metadata table
md <- as.data.frame(mcols(ehub))
head(md, 2)## title dataprovider species taxonomyid genome
## EH2240 Levine_32dim_SE NA Homo sapiens 9606 <NA>
## EH2241 Levine_32dim_flowSet NA Homo sapiens 9606 <NA>
## description
## EH2240 Mass cytometry (CyTOF) dataset from Levine et al. (2015), containing 32 dimensions (surface protein markers). Manually gated cell population labels are available for 14 populations. Cells are human bone marrow cells from 2 healthy donors. This dataset can be used to benchmark clustering algorithms.
## EH2241 Mass cytometry (CyTOF) dataset from Levine et al. (2015), containing 32 dimensions (surface protein markers). Manually gated cell population labels are available for 14 populations. Cells are human bone marrow cells from 2 healthy donors. This dataset can be used to benchmark clustering algorithms.
## coordinate_1_based maintainer rdatadateadded
## EH2240 1 Lukas M. Weber <lukmweber@gmail.com> 2019-01-15
## EH2241 1 Lukas M. Weber <lukmweber@gmail.com> 2019-01-15
## preparerclass tags rdataclass
## EH2240 HDCytoData Experime.... SummarizedExperiment
## EH2241 HDCytoData Experime.... flowSet
## rdatapath
## EH2240 HDCytoData/Levine_32dim/Levine_32dim_SE.rda
## EH2241 HDCytoData/Levine_32dim/Levine_32dim_flowSet.rda
## sourceurl sourcetype
## EH2240 http://imlspenticton.uzh.ch/robinson_lab/HDCytoData/ FCS
## EH2241 http://imlspenticton.uzh.ch/robinson_lab/HDCytoData/ FCSThis section shows how to load the datasets, using one of the datasets (Bodenmiller_BCR_XL) as an example.
The datasets can be loaded by either (i) referring to named functions for each dataset, or (ii) creating an ExperimentHub instance and referring to the dataset IDs. Both methods are demonstrated below.
See the help files (e.g. ?Bodenmiller_BCR_XL) for details about the structure of the SummarizedExperiment or flowSet objects.
Load the datasets using named functions:
suppressPackageStartupMessages(library(HDCytoData))
# Load 'SummarizedExperiment' object using named function
Bodenmiller_BCR_XL_SE()## snapshotDate(): 2020-04-27## see ?HDCytoData and browseVignettes('HDCytoData') for documentation## loading from cache## class: SummarizedExperiment
## dim: 172791 35
## metadata(2): experiment_info n_cells
## assays(1): exprs
## rownames: NULL
## rowData names(4): group_id patient_id sample_id population_id
## colnames(35): Time Cell_length ... DNA-1 DNA-2
## colData names(3): channel_name marker_name marker_class# Load 'flowSet' object using named function
Bodenmiller_BCR_XL_flowSet()## snapshotDate(): 2020-04-27## see ?HDCytoData and browseVignettes('HDCytoData') for documentation## loading from cache## A flowSet with 16 experiments.
##
## column names:
## Time Cell_length CD3(110:114)Dd CD45(In115)Dd BC1(La139)Dd BC2(Pr141)Dd pNFkB(Nd142)Dd pp38(Nd144)Dd CD4(Nd145)Dd BC3(Nd146)Dd CD20(Sm147)Dd CD33(Nd148)Dd pStat5(Nd150)Dd CD123(Eu151)Dd pAkt(Sm152)Dd pStat1(Eu153)Dd pSHP2(Sm154)Dd pZap70(Gd156)Dd pStat3(Gd158)Dd BC4(Tb159)Dd CD14(Gd160)Dd pSlp76(Dy164)Dd BC5(Ho165)Dd pBtk(Er166)Dd pPlcg2(Er167)Dd pErk(Er168)Dd BC6(Tm169)Dd pLat(Er170)Dd IgM(Yb171)Dd pS6(Yb172)Dd HLA-DR(Yb174)Dd BC7(Lu175)Dd CD7(Yb176)Dd DNA-1(Ir191)Dd DNA-2(Ir193)Dd group_id patient_id sample_id population_idAlternatively, load the datasets by creating an ExperimentHub instance:
# Create ExperimentHub instance
ehub <- ExperimentHub()## snapshotDate(): 2020-04-27# Find HDCytoData datasets
query(ehub, "HDCytoData")## ExperimentHub with 56 records
## # snapshotDate(): 2020-04-27
## # $dataprovider: NA
## # $species: Homo sapiens, Mus musculus
## # $rdataclass: flowSet, SummarizedExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH2240"]]'
##
## title
## EH2240 | Levine_32dim_SE
## EH2241 | Levine_32dim_flowSet
## EH2242 | Levine_13dim_SE
## EH2243 | Levine_13dim_flowSet
## EH2244 | Samusik_01_SE
## ... ...
## EH3060 | Weber_BCR_XL_sim_random_seeds_rep3_flowSet
## EH3061 | Weber_BCR_XL_sim_less_distinct_less_50pc_SE
## EH3062 | Weber_BCR_XL_sim_less_distinct_less_50pc_flowSet
## EH3063 | Weber_BCR_XL_sim_less_distinct_less_75pc_SE
## EH3064 | Weber_BCR_XL_sim_less_distinct_less_75pc_flowSet# Load 'SummarizedExperiment' object using dataset ID
ehub[["EH2254"]]## see ?HDCytoData and browseVignettes('HDCytoData') for documentation## loading from cache## class: SummarizedExperiment
## dim: 172791 35
## metadata(2): experiment_info n_cells
## assays(1): exprs
## rownames: NULL
## rowData names(4): group_id patient_id sample_id population_id
## colnames(35): Time Cell_length ... DNA-1 DNA-2
## colData names(3): channel_name marker_name marker_class# Load 'flowSet' object using dataset ID
ehub[["EH2255"]]## see ?HDCytoData and browseVignettes('HDCytoData') for documentation
## loading from cache## A flowSet with 16 experiments.
##
## column names:
## Time Cell_length CD3(110:114)Dd CD45(In115)Dd BC1(La139)Dd BC2(Pr141)Dd pNFkB(Nd142)Dd pp38(Nd144)Dd CD4(Nd145)Dd BC3(Nd146)Dd CD20(Sm147)Dd CD33(Nd148)Dd pStat5(Nd150)Dd CD123(Eu151)Dd pAkt(Sm152)Dd pStat1(Eu153)Dd pSHP2(Sm154)Dd pZap70(Gd156)Dd pStat3(Gd158)Dd BC4(Tb159)Dd CD14(Gd160)Dd pSlp76(Dy164)Dd BC5(Ho165)Dd pBtk(Er166)Dd pPlcg2(Er167)Dd pErk(Er168)Dd BC6(Tm169)Dd pLat(Er170)Dd IgM(Yb171)Dd pS6(Yb172)Dd HLA-DR(Yb174)Dd BC7(Lu175)Dd CD7(Yb176)Dd DNA-1(Ir191)Dd DNA-2(Ir193)Dd group_id patient_id sample_id population_idOnce the datasets have been loaded from ExperimentHub, they can be used as normal within an R session. For example, using the SummarizedExperiment form of the dataset loaded above:
# Load dataset in 'SummarizedExperiment' format
d_SE <- Bodenmiller_BCR_XL_SE()## snapshotDate(): 2020-04-27## see ?HDCytoData and browseVignettes('HDCytoData') for documentation## loading from cache# Inspect object
d_SE## class: SummarizedExperiment
## dim: 172791 35
## metadata(2): experiment_info n_cells
## assays(1): exprs
## rownames: NULL
## rowData names(4): group_id patient_id sample_id population_id
## colnames(35): Time Cell_length ... DNA-1 DNA-2
## colData names(3): channel_name marker_name marker_classlength(assays(d_SE))## [1] 1assay(d_SE)[1:6, 1:6]## Time Cell_length CD3 CD45 BC1 BC2
## [1,] 33073 30 120.823265 454.6009 576.8983 10.0057297
## [2,] 36963 35 135.106171 624.6824 564.6299 5.5991135
## [3,] 37892 30 -1.664619 601.0125 3077.2668 1.7105789
## [4,] 41345 58 115.290245 820.7125 6088.5967 22.5641403
## [5,] 42475 35 14.373802 326.6405 4606.6929 -0.6584854
## [6,] 44620 31 37.737877 557.0137 4854.1519 -0.4517288rowData(d_SE)## DataFrame with 172791 rows and 4 columns
## group_id patient_id sample_id population_id
## <factor> <factor> <factor> <factor>
## 1 BCR-XL patient1 patient1_BCR-XL CD4 T-cells
## 2 BCR-XL patient1 patient1_BCR-XL CD4 T-cells
## 3 BCR-XL patient1 patient1_BCR-XL NK cells
## 4 BCR-XL patient1 patient1_BCR-XL CD4 T-cells
## 5 BCR-XL patient1 patient1_BCR-XL CD8 T-cells
## ... ... ... ... ...
## 172787 Reference patient8 patient8_Reference CD8 T-cells
## 172788 Reference patient8 patient8_Reference CD4 T-cells
## 172789 Reference patient8 patient8_Reference CD4 T-cells
## 172790 Reference patient8 patient8_Reference CD4 T-cells
## 172791 Reference patient8 patient8_Reference CD8 T-cellscolData(d_SE)## DataFrame with 35 rows and 3 columns
## channel_name marker_name marker_class
## <character> <character> <factor>
## Time Time Time none
## Cell_length Cell_length Cell_length none
## CD3 CD3(110:114)Dd CD3 type
## CD45 CD45(In115)Dd CD45 type
## BC1 BC1(La139)Dd BC1 none
## ... ... ... ...
## HLA-DR HLA-DR(Yb174)Dd HLA-DR type
## BC7 BC7(Lu175)Dd BC7 none
## CD7 CD7(Yb176)Dd CD7 type
## DNA-1 DNA-1(Ir191)Dd DNA-1 none
## DNA-2 DNA-2(Ir193)Dd DNA-2 nonemetadata(d_SE)## $experiment_info
## group_id patient_id sample_id
## 1 BCR-XL patient1 patient1_BCR-XL
## 2 Reference patient1 patient1_Reference
## 3 BCR-XL patient2 patient2_BCR-XL
## 4 Reference patient2 patient2_Reference
## 5 BCR-XL patient3 patient3_BCR-XL
## 6 Reference patient3 patient3_Reference
## 7 BCR-XL patient4 patient4_BCR-XL
## 8 Reference patient4 patient4_Reference
## 9 BCR-XL patient5 patient5_BCR-XL
## 10 Reference patient5 patient5_Reference
## 11 BCR-XL patient6 patient6_BCR-XL
## 12 Reference patient6 patient6_Reference
## 13 BCR-XL patient7 patient7_BCR-XL
## 14 Reference patient7 patient7_Reference
## 15 BCR-XL patient8 patient8_BCR-XL
## 16 Reference patient8 patient8_Reference
##
## $n_cells
## patient1_BCR-XL patient1_Reference patient2_BCR-XL patient2_Reference
## 2838 2739 16675 16725
## patient3_BCR-XL patient3_Reference patient4_BCR-XL patient4_Reference
## 12252 9434 8990 6906
## patient5_BCR-XL patient5_Reference patient6_BCR-XL patient6_Reference
## 8543 11962 8622 11038
## patient7_BCR-XL patient7_Reference patient8_BCR-XL patient8_Reference
## 14770 15974 11653 13670Note that flow and mass cytometry data should be transformed prior to performing any downstream analyses, such as clustering. Standard transformations include the asinh with cofactor parameter equal to 5 for mass cytometry (CyTOF) data, or 150 for flow cytometry data (see Bendall et al. 2011, Supplementary Figure S2).
Interactive visualizations to explore the datasets can be generated from the SummarizedExperiment objects using the iSEE (“Interactive SummarizedExperiment Explorer”) package, available from Bioconductor (Soneson, Lun, Marini, and Rue-Albrecht, 2018), which provides a Shiny-based graphical user interface to explore single-cell datasets stored in the SummarizedExperiment format. For more details, see the iSEE package vignettes.
We welcome contributions or suggestions for new datasets to include in the HDCytoData package. Contribution guidelines are provided in the Contribution guidelines vignette, available from Bioconductor.
If the HDCytoData package is useful in your work, please cite the following paper: