Contents

The ExperimentHub server provides easy R / Bioconductor access to large files of data.

1 ExperimentHub objects

The ExperimentHub package provides a client interface to resources stored at the ExperimentHub web service. It has similar functionality to AnnotationHub package.

library(ExperimentHub)

The ExperimentHub package is straightforward to use. Create an ExperiemntHub object

eh = ExperimentHub()
## snapshotDate(): 2019-07-10

Now at this point you have already done everything you need in order to start retrieving experiment data. For most operations, using the ExperimentHub object should feel a lot like working with a familiar list or data.frame and has all of the functionality of an Hub object like AnnotationHub package’s AnnotationHub object.

Lets take a minute to look at the show method for the hub object eh

eh
## ExperimentHub with 2377 records
## # snapshotDate(): 2019-07-10 
## # $dataprovider: Eli and Edythe L. Broad Institute of Harvard and MIT, NA...
## # $species: Homo sapiens, Mus musculus, Saccharomyces cerevisiae, human g...
## # $rdataclass: ExpressionSet, SummarizedExperiment, character, Summarized...
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH1"]]' 
## 
##            title                                                           
##   EH1    | RNA-Sequencing and clinical data for 7706 tumor samples from ...
##   EH166  | ERR188297                                                       
##   EH167  | ERR188088                                                       
##   EH168  | ERR188204                                                       
##   EH169  | ERR188317                                                       
##   ...      ...                                                             
##   EH2696 | Shekhar retina counts                                           
##   EH2697 | Shekhar retina colData                                          
##   EH2698 | Xin pancreas RPKMs                                              
##   EH2699 | Xin pancreas rowData                                            
##   EH2700 | Xin pancreas colData

You can see that it gives you an idea about the different types of data that are present inside the hub. You can see where the data is coming from (dataprovider), as well as what species have samples present (species), what kinds of R data objects could be returned (rdataclass). We can take a closer look at all the kinds of data providers that are available by simply looking at the contents of dataprovider as if it were the column of a data.frame object like this:

head(unique(eh$dataprovider))
## [1] "GEO"                                                                                                                                          
## [2] "GEUVADIS"                                                                                                                                     
## [3] "Allen Brain Atlas"                                                                                                                            
## [4] "ArrayExpress"                                                                                                                                 
## [5] "Department of Psychology, Abdul Haq Campus, Federal Urdu University for Arts, Science and Technology, Karachi, Pakistan. shahiq_psy@yahoo.com"
## [6] "Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden."

In the same way, you can also see data from different species inside the hub by looking at the contents of species like this:

head(unique(eh$species))
## [1] "Homo sapiens"            "Mus musculus"           
## [3] "Mus musculus (E18 mice)" NA                       
## [5] "Rattus norvegicus"       "human gut metagenome"

And this will also work for any of the other types of metadata present. You can learn which kinds of metadata are available by simply hitting the tab key after you type ‘eh$’. In this way you can explore for yourself what kinds of data are present in the hub right from the command line. This interface also allows you to access the hub programatically to extract data that matches a particular set of criteria.

Another valuable types of metadata to pay attention to is the rdataclass.

head(unique(eh$rdataclass))
## [1] "ExpressionSet"              "GAlignmentPairs"           
## [3] "CellMapperList"             "gds.class"                 
## [5] "RangedSummarizedExperiment" "GRanges"

The rdataclass allows you to see which kinds of R objects the hub will return to you. This kind of information is valuable both as a means to filter results and also as a means to explore and learn about some of the kinds of experimenthub objects that are widely available for the project. Right now this is a pretty short list, but over time it should grow as we support more of the different kinds of experimenthub objects via the hub.

Now lets try getting the data files associated with the r Biocpkg("alpineData") package using the query method. The query method lets you search rows for specific strings, returning an ExperimentHub instance with just the rows matching the query. The preparerclass column of metadata monitors which package is associated with the ExperimentHub data.

One can get chain files for Drosophila melanogaster from UCSC with:

apData <- query(eh, "alpineData")
apData
## ExperimentHub with 4 records
## # snapshotDate(): 2019-07-10 
## # $dataprovider: GEUVADIS
## # $species: Homo sapiens
## # $rdataclass: GAlignmentPairs
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH166"]]' 
## 
##           title    
##   EH166 | ERR188297
##   EH167 | ERR188088
##   EH168 | ERR188204
##   EH169 | ERR188317

Query has worked and you can now see that the only data present is provided by the “alpineData”.

The metadata underlying this hub object can be retrieved by you

apData$preparerclass
## [1] "alpineData" "alpineData" "alpineData" "alpineData"
df <- mcols(apData)

By default the show method will only display the first 5 and last 5 rows. There are hundreds of records present in the hub.

length(eh)
## [1] 2377

Lets look at another example, where we pull down only data from the hub for species “mus musculus”.

mm <- query(eh, "mus musculus")
mm
## ExperimentHub with 254 records
## # snapshotDate(): 2019-07-10 
## # $dataprovider: Jonathan Griffiths, NCBI GEO, Robinson group (UZH), Sten...
## # $species: Mus musculus, Mus musculus (E18 mice)
## # $rdataclass: character, data.frame, SingleCellExperiment, SummarizedBen...
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH173"]]' 
## 
##            title                                                           
##   EH173  | Pre-processed microarray data from the Affymetrix MG-U74Av2 p...
##   EH552  | st100k                                                          
##   EH553  | st400k                                                          
##   EH557  | tasicST6                                                        
##   EH1039 | Brain scRNA-seq data, 'HDF5-based 10X Genomics' format          
##   ...      ...                                                             
##   EH2691 | Macosko retina colData                                          
##   EH2694 | Nestorowa HSC counts                                            
##   EH2695 | Nestorowa HSC colData                                           
##   EH2696 | Shekhar retina counts                                           
##   EH2697 | Shekhar retina colData

We can also look at the ExperimentHub object in a browser using the display() function. We can then filter the ExperimentHub object using the Global search field on the top right corner of the page or the in-column search fields.

d <- display(eh)

2 Using ExperimentHub to retrieve data

Looking back at our alpineData file example, if we are interested in the first file, we can gets its metadata using

apData
## ExperimentHub with 4 records
## # snapshotDate(): 2019-07-10 
## # $dataprovider: GEUVADIS
## # $species: Homo sapiens
## # $rdataclass: GAlignmentPairs
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH166"]]' 
## 
##           title    
##   EH166 | ERR188297
##   EH167 | ERR188088
##   EH168 | ERR188204
##   EH169 | ERR188317
apData["EH166"]
## ExperimentHub with 1 record
## # snapshotDate(): 2019-07-10 
## # names(): EH166
## # package(): alpineData
## # $dataprovider: GEUVADIS
## # $species: Homo sapiens
## # $rdataclass: GAlignmentPairs
## # $rdatadateadded: 2016-07-21
## # $title: ERR188297
## # $description: Subset of aligned reads from sample ERR188297
## # $taxonomyid: 9606
## # $genome: GRCh38
## # $sourcetype: FASTQ
## # $sourceurl: ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR188/ERR188297/ERR1882...
## # $sourcesize: NA
## # $tags: c("Sequencing", "RNASeq", "GeneExpression",
## #   "Transcription") 
## # retrieve record with 'object[["EH166"]]'

We can download the file using

apData[["EH166"]]
## see ?alpineData and browseVignettes('alpineData') for documentation
## downloading 0 resources
## loading from cache
## GAlignmentPairs object with 25531 pairs, strandMode=1, and 0 metadata columns:
##           seqnames strand   :              ranges  --              ranges
##              <Rle>  <Rle>   :           <IRanges>  --           <IRanges>
##       [1]        1      +   : 108560389-108560463  -- 108560454-108560528
##       [2]        1      -   : 108560454-108560528  -- 108560383-108560457
##       [3]        1      +   : 108560534-108600608  -- 108600626-108606236
##       [4]        1      -   : 108569920-108569994  -- 108569825-108569899
##       [5]        1      -   : 108587954-108588028  -- 108587881-108587955
##       ...      ...    ... ...                 ... ...                 ...
##   [25527]        X      +   : 119790596-119790670  -- 119790717-119790791
##   [25528]        X      +   : 119790988-119791062  -- 119791086-119791160
##   [25529]        X      +   : 119791037-119791111  -- 119791142-119791216
##   [25530]        X      +   : 119791348-119791422  -- 119791475-119791549
##   [25531]        X      +   : 119791376-119791450  -- 119791481-119791555
##   -------
##   seqinfo: 194 sequences from an unspecified genome

Each file is retrieved from the ExperimentHub server and the file is also cache locally, so that the next time you need to retrieve it, it should download much more quickly.

3 Configuring ExperimentHub objects

When you create the ExperimentHub object, it will set up the object for you with some default settings. See ?ExperimentHub for ways to customize the hub source, the local cache, and other instance-specific options, and ?getExperimentHubOption to get or set package-global options for use across sessions.

If you look at the object you will see some helpful information about it such as where the data is cached and where online the hub server is set to.

eh
## ExperimentHub with 2377 records
## # snapshotDate(): 2019-07-10 
## # $dataprovider: Eli and Edythe L. Broad Institute of Harvard and MIT, NA...
## # $species: Homo sapiens, Mus musculus, Saccharomyces cerevisiae, human g...
## # $rdataclass: ExpressionSet, SummarizedExperiment, character, Summarized...
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["EH1"]]' 
## 
##            title                                                           
##   EH1    | RNA-Sequencing and clinical data for 7706 tumor samples from ...
##   EH166  | ERR188297                                                       
##   EH167  | ERR188088                                                       
##   EH168  | ERR188204                                                       
##   EH169  | ERR188317                                                       
##   ...      ...                                                             
##   EH2696 | Shekhar retina counts                                           
##   EH2697 | Shekhar retina colData                                          
##   EH2698 | Xin pancreas RPKMs                                              
##   EH2699 | Xin pancreas rowData                                            
##   EH2700 | Xin pancreas colData

By default the ExperimentHub object is set to the latest snapshotData and a snapshot version that matches the version of Bioconductor that you are using. You can also learn about these data with the appropriate methods.

snapshotDate(eh)
## [1] "2019-07-10"

If you are interested in using an older version of a snapshot, you can list previous versions with the possibleDates() like this:

pd <- possibleDates(eh)
pd
##  [1] "2016-02-23" "2016-06-07" "2016-07-14" "2016-07-21" "2016-08-08"
##  [6] "2016-10-01" "2017-06-09" "2017-08-25" "2017-10-06" "2017-10-10"
## [11] "2017-10-12" "2017-10-16" "2017-10-19" "2017-10-26" "2017-10-30"
## [16] "2017-10-29" "2018-01-08" "2018-02-02" "2018-02-09" "2018-02-22"
## [21] "2018-03-16" "2018-03-30" "2018-04-02" "2018-04-10" "2018-04-20"
## [26] "2018-04-25" "2018-04-26" "2018-04-27" "2018-05-02" "2018-05-08"
## [31] "2018-06-29" "2018-07-30" "2018-08-02" "2018-08-03" "2018-08-27"
## [36] "2018-08-29" "2018-09-07" "2018-09-11" "2018-09-19" "2018-09-20"
## [41] "2018-10-30" "2018-11-02" "2018-11-05" "2018-11-13" "2018-12-12"
## [46] "2018-12-13" "2018-12-19" "2018-12-20" "2019-01-02" "2019-01-09"
## [51] "2019-01-15" "2019-01-25" "2019-03-21" "2019-04-01" "2019-04-15"
## [56] "2019-04-23" "2019-04-25" "2019-04-26" "2019-04-29" "2019-05-28"
## [61] "2019-05-29" "2019-06-11" "2019-06-18" "2019-06-20" "2019-06-28"
## [66] "2019-07-01" "2019-07-02" "2019-07-10" "2019-07-10"

Set the dates like this:

snapshotDate(ah) <- pd[1]

4 ExperimentHub

5 Session info

sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] alpineData_1.11.0           GenomicAlignments_1.21.4   
##  [3] Rsamtools_2.1.3             Biostrings_2.53.2          
##  [5] XVector_0.25.0              SummarizedExperiment_1.15.5
##  [7] DelayedArray_0.11.4         BiocParallel_1.19.0        
##  [9] matrixStats_0.54.0          Biobase_2.45.0             
## [11] GenomicRanges_1.37.14       GenomeInfoDb_1.21.1        
## [13] IRanges_2.19.10             S4Vectors_0.23.17          
## [15] ExperimentHub_1.11.3        AnnotationHub_2.17.6       
## [17] BiocFileCache_1.9.1         dbplyr_1.4.2               
## [19] BiocGenerics_0.31.5         BiocStyle_2.13.2           
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.1                    lattice_0.20-38              
##  [3] assertthat_0.2.1              zeallot_0.1.0                
##  [5] digest_0.6.20                 mime_0.7                     
##  [7] R6_2.4.0                      backports_1.1.4              
##  [9] RSQLite_2.1.1                 evaluate_0.14                
## [11] httr_1.4.0                    pillar_1.4.2                 
## [13] zlibbioc_1.31.0               rlang_0.4.0                  
## [15] curl_3.3                      blob_1.2.0                   
## [17] Matrix_1.2-17                 rmarkdown_1.14               
## [19] stringr_1.4.0                 RCurl_1.95-4.12              
## [21] bit_1.1-14                    shiny_1.3.2                  
## [23] compiler_3.6.1                httpuv_1.5.1                 
## [25] xfun_0.8                      pkgconfig_2.0.2              
## [27] htmltools_0.3.6               tidyselect_0.2.5             
## [29] tibble_2.1.3                  GenomeInfoDbData_1.2.1       
## [31] interactiveDisplayBase_1.23.0 bookdown_0.12                
## [33] crayon_1.3.4                  dplyr_0.8.3                  
## [35] later_0.8.0                   bitops_1.0-6                 
## [37] rappdirs_0.3.1                grid_3.6.1                   
## [39] xtable_1.8-4                  DBI_1.0.0                    
## [41] magrittr_1.5                  stringi_1.4.3                
## [43] promises_1.0.1                vctrs_0.2.0                  
## [45] tools_3.6.1                   bit64_0.9-7                  
## [47] glue_1.3.1                    purrr_0.3.2                  
## [49] yaml_2.2.0                    AnnotationDbi_1.47.0         
## [51] BiocManager_1.30.4            memoise_1.1.0                
## [53] knitr_1.23