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 ExperimentHub object

eh = ExperimentHub()
## snapshotDate(): 2021-10-18

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 6062 records
## # snapshotDate(): 2021-10-18
## # $dataprovider: Eli and Edythe L. Broad Institute of Harvard and MIT, NCBI,...
## # $species: Homo sapiens, Mus musculus, Saccharomyces cerevisiae, human gut ...
## # $rdataclass: SummarizedExperiment, ExpressionSet, character, matrix, DFram...
## # 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 The...
##   EH166  | ERR188297                                                          
##   EH167  | ERR188088                                                          
##   EH168  | ERR188204                                                          
##   EH169  | ERR188317                                                          
##   ...      ...                                                                
##   EH7256 | 2021-10-14.YachidaS_2019.marker_presence                           
##   EH7257 | 2021-10-14.YachidaS_2019.pathway_abundance                         
##   EH7258 | 2021-10-14.YachidaS_2019.pathway_coverage                          
##   EH7259 | 2021-10-14.YachidaS_2019.relative_abundance                        
##   EH7260 | Hs_GRCh38_chr22_1_25409234_fasta

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 programmatically 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 let’s 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 ExperimentHub::package() function indicates the package responsible for original preparation and documentation of the ExperimentHub data, and is a useful resource to find out more about the data. This package may also depend, import or suggest other packages required for manipulating the data, and for this reason the package may need to be installed and loaded to use the resource. The package is also available in the (cryptically named) preparerclass column of the metadata of the ExperimentHub object.

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

apData <- query(eh, "alpineData")
apData
## ExperimentHub with 4 records
## # snapshotDate(): 2021-10-18
## # $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 package.

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] 6062

Let’s 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 1397 records
## # snapshotDate(): 2021-10-18
## # $dataprovider: Jonathan Griffiths, The Tabula Muris Consortium, GEO, Mike ...
## # $species: Mus musculus, Mus musculus (E18 mice)
## # $rdataclass: character, matrix, DFrame, H5File, SummarizedExperiment, data...
## # 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 plat...
##   EH552  | st100k                                                             
##   EH553  | st400k                                                             
##   EH557  | tasicST6                                                           
##   EH1039 | Brain scRNA-seq data, 'HDF5-based 10X Genomics' format             
##   ...      ...                                                                
##   EH6925 | EM_id0007_Mouse_Kidney_MultiScale_All_Low_Uriniferous_Tubule_102...
##   EH6926 | EM_id0007_Mouse_Kidney_MultiScale_All_Low_Glomerulus_1024_4dTens...
##   EH6927 | EM_id0007_Mouse_Kidney_MultiScale_All_Low_Glomerulus_1024_4dTens...
##   EH6941 | EM_id0009_MurineBMMC_All_512_4dTensor.Rds                          
##   EH6942 | EM_id0009_MurineBMMC_All_512_4dTensor_dataset.gif

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 get its metadata using

apData
## ExperimentHub with 4 records
## # snapshotDate(): 2021-10-18
## # $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(): 2021-10-18
## # 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/ERR188297_...
## # $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
## 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 cached 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 6062 records
## # snapshotDate(): 2021-10-18
## # $dataprovider: Eli and Edythe L. Broad Institute of Harvard and MIT, NCBI,...
## # $species: Homo sapiens, Mus musculus, Saccharomyces cerevisiae, human gut ...
## # $rdataclass: SummarizedExperiment, ExpressionSet, character, matrix, DFram...
## # 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 The...
##   EH166  | ERR188297                                                          
##   EH167  | ERR188088                                                          
##   EH168  | ERR188204                                                          
##   EH169  | ERR188317                                                          
##   ...      ...                                                                
##   EH7256 | 2021-10-14.YachidaS_2019.marker_presence                           
##   EH7257 | 2021-10-14.YachidaS_2019.pathway_abundance                         
##   EH7258 | 2021-10-14.YachidaS_2019.pathway_coverage                          
##   EH7259 | 2021-10-14.YachidaS_2019.relative_abundance                        
##   EH7260 | Hs_GRCh38_chr22_1_25409234_fasta

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] "2021-10-18"

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" "2019-06-20" "2019-06-28" "2019-07-01"
##  [66] "2019-07-02" "2019-07-10" "2019-08-01" "2019-08-02" "2019-08-06"
##  [71] "2019-08-07" "2019-08-13" "2019-09-04" "2019-09-09" "2019-09-11"
##  [76] "2019-09-25" "2019-10-17" "2019-10-18" "2019-10-22" "2019-12-17"
##  [81] "2019-12-27" "2020-01-08" "2020-02-11" "2020-02-12" "2020-03-09"
##  [86] "2020-03-12" "2020-04-03" "2020-04-27" "2020-05-11" "2020-05-14"
##  [91] "2020-05-19" "2020-05-29" "2020-06-11" "2020-06-26" "2020-07-02"
##  [96] "2020-07-10" "2020-07-29" "2020-08-03" "2020-08-04" "2020-08-05"
## [101] "2020-08-17" "2020-08-24" "2020-08-26" "2020-08-31" "2020-09-03"
## [106] "2020-09-04" "2020-09-08" "2020-09-09" "2020-09-10" "2020-09-14"
## [111] "2020-09-23" "2020-09-24" "2020-10-02" "2020-10-30" "2020-11-05"
## [116] "2020-11-06" "2020-11-18" "2020-11-25" "2020-12-02" "2020-12-03"
## [121] "2020-12-07" "2020-12-09" "2020-12-14" "2021-01-04" "2020-10-27"
## [126] "2021-01-12" "2021-01-19" "2021-01-20" "2021-01-27" "2021-01-28"
## [131] "2021-02-08" "2021-02-11" "2021-02-19" "2021-03-02" "2021-03-04"
## [136] "2021-03-10" "2021-03-18" "2021-03-22" "2021-03-23" "2021-03-24"
## [141] "2021-03-30" "2021-04-06" "2021-04-08" "2021-04-27" "2021-05-04"
## [146] "2021-05-05" "2021-05-18" "2021-06-14" "2021-06-16" "2021-06-21"
## [151] "2021-07-15" "2021-07-16" "2021-07-27" "2021-07-30" "2021-08-03"
## [156] "2021-08-09" "2021-04-26" "2021-08-18" "2021-08-26" "2021-08-27"
## [161] "2021-09-13" "2021-09-23" "2021-09-24" "2021-10-05" "2021-10-06"
## [166] "2021-10-15" "2021-10-18" "2021-10-18"

Set the dates like this:

snapshotDate(ah) <- pd[1]

4 Creating an ExperimentHub Package or Converting to an ExperimentHub Package

Please see HubPub Vignette “CreateAHubPackage”.

vignette("CreateAHubPackage", package="HubPub")

5 Default Caching Location Update

As of ExperimentHub version > 1.17.2, the default caching location has changed. The default cache is now controlled by the function tools::R_user_dir instead of rappdirs::user_cache_dir. Users who have utilized the default ExperimentHub location, to continue using the created cache, must move the cache and its files to the new default location, create a system environment variable to point to the old location, or delete and start a new cache.

5.1 Option 1: Move cache and files to new location

The following steps can be used to move the files to the new location:

  1. Determine the old location by running the following in R rappdirs::user_cache_dir(appname="ExperimentHub")

  2. Determine the new location by running the following in R tools::R_user_dir("ExperimentHub", which="cache")

  3. Move the files to the new location. You can do this manually or do the following steps in R. Remember if you have a lot of cached files, this may take awhile.

       # make sure you have permissions on the cache/files
       # use at own risk

       moveFiles<-function(package){
       olddir <- path.expand(rappdirs::user_cache_dir(appname=package))
       newdir <- tools::R_user_dir(package, which="cache")
       dir.create(path=newdir, recursive=TRUE)
       files <- list.files(olddir, full.names =TRUE)
       moveres <- vapply(files,
           FUN=function(fl){
           filename = basename(fl)
           newname = file.path(newdir, filename)
           file.rename(fl, newname)
           },
           FUN.VALUE = logical(1))
       if(all(moveres)) unlink(olddir, recursive=TRUE)
       }

       package="ExperimentHub"
       moveFiles(package)

5.2 Option 2: Create a system environment variable

A user may set the system environment variable EXPERIMENT_HUB_CACHE to control the default location of the cache. Setting system environment variables can vary depending on the operating system; we suggest using google to find appropriate instructions per your operating system.

You will want to set the variable to the results of running the following in R:

path.expand(rappdirs::user_cache_dir(appname="ExperimentHub"))

NOTE: R has Sys.setenv however that will only set the variable for that R session. It would not be available or recognized in future R sessions. It is important to set the variable as a global user-wide or system-wide environment variable so it persists in all future logins to your system.

5.3 Option 3: Delete the old cache

Lastly, if a user does not care about the already existing default cache, the old location may be deleted to move forward with the new default location. This option should be used with caution. Once deleted, old cached resources will no longer be available and have to be re-downloaded.

One can do this manually by navigating to the location indicated in the ERROR message as Problematic cache: and deleting the folder and all its content.

The following can be done to delete through R code:

CAUTION This will remove the old cache and all downloaded resources. All resources will have to be re-downloaded after executing this code.

library(ExperimentHub)
oldcache = path.expand(rappdirs::user_cache_dir(appname="ExperimentHub"))
setExperimentHubOption("CACHE", oldcache)
eh = ExperimentHub(localHub=TRUE)

## removes old location and all resources
removeCache(eh, ask=FALSE)

## create the new default caching location
newcache = tools::R_user_dir("ExperimentHub", which="cache")
setExperimentHubOption("CACHE", newcache)
eh = ExperimentHub()

6 Session info

sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] alpineData_1.19.0           GenomicAlignments_1.30.0   
##  [3] Rsamtools_2.10.0            Biostrings_2.62.0          
##  [5] XVector_0.34.0              SummarizedExperiment_1.24.0
##  [7] Biobase_2.54.0              MatrixGenerics_1.6.0       
##  [9] matrixStats_0.61.0          GenomicRanges_1.46.0       
## [11] GenomeInfoDb_1.30.0         IRanges_2.28.0             
## [13] S4Vectors_0.32.0            ExperimentHub_2.2.0        
## [15] AnnotationHub_3.2.0         BiocFileCache_2.2.0        
## [17] dbplyr_2.1.1                BiocGenerics_0.40.0        
## [19] BiocStyle_2.22.0           
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.2                    sass_0.4.0                   
##  [3] bit64_4.0.5                   jsonlite_1.7.2               
##  [5] bslib_0.3.1                   shiny_1.7.1                  
##  [7] assertthat_0.2.1              interactiveDisplayBase_1.32.0
##  [9] BiocManager_1.30.16           blob_1.2.2                   
## [11] GenomeInfoDbData_1.2.7        yaml_2.2.1                   
## [13] BiocVersion_3.14.0            pillar_1.6.4                 
## [15] RSQLite_2.2.8                 lattice_0.20-45              
## [17] glue_1.4.2                    digest_0.6.28                
## [19] promises_1.2.0.1              htmltools_0.5.2              
## [21] httpuv_1.6.3                  Matrix_1.3-4                 
## [23] pkgconfig_2.0.3               bookdown_0.24                
## [25] zlibbioc_1.40.0               purrr_0.3.4                  
## [27] xtable_1.8-4                  later_1.3.0                  
## [29] BiocParallel_1.28.0           tibble_3.1.5                 
## [31] KEGGREST_1.34.0               generics_0.1.1               
## [33] ellipsis_0.3.2                cachem_1.0.6                 
## [35] withr_2.4.2                   magrittr_2.0.1               
## [37] crayon_1.4.1                  mime_0.12                    
## [39] memoise_2.0.0                 evaluate_0.14                
## [41] fansi_0.5.0                   tools_4.1.1                  
## [43] lifecycle_1.0.1               stringr_1.4.0                
## [45] DelayedArray_0.20.0           AnnotationDbi_1.56.0         
## [47] compiler_4.1.1                jquerylib_0.1.4              
## [49] rlang_0.4.12                  grid_4.1.1                   
## [51] RCurl_1.98-1.5                rappdirs_0.3.3               
## [53] bitops_1.0-7                  rmarkdown_2.11               
## [55] DBI_1.1.1                     curl_4.3.2                   
## [57] R6_2.5.1                      knitr_1.36                   
## [59] dplyr_1.0.7                   fastmap_1.1.0                
## [61] bit_4.0.4                     utf8_1.2.2                   
## [63] filelock_1.0.2                stringi_1.7.5                
## [65] parallel_4.1.1                Rcpp_1.0.7                   
## [67] vctrs_0.3.8                   png_0.1-7                    
## [69] tidyselect_1.1.1              xfun_0.27