The ExperimentHub server provides easy R / Bioconductor access to
large files of data.
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(): 2019-10-22Now 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 2770 records
## # snapshotDate(): 2019-10-22
## # $dataprovider: Eli and Edythe L. Broad Institute of Harvard and MIT, NA...
## # $species: Homo sapiens, Mus musculus, Saccharomyces cerevisiae, human g...
## # $rdataclass: ExpressionSet, SummarizedExperiment, character, Summarized...
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## # tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH1"]]'
##
## title
## EH1 | RNA-Sequencing and clinical data for 7706 tumor samples from ...
## EH166 | ERR188297
## EH167 | ERR188088
## EH168 | ERR188204
## EH169 | ERR188317
## ... ...
## EH3230 | goAnno_drug
## EH3231 | GO_DATA
## EH3232 | GO_DATA_drug
## EH3233 | taurefList
## EH3234 | ES_NULLYou can see that it gives you an idea about the different types of data that are present inside the hub. You can see where the data is coming from (dataprovider), as well as what species have samples present (species), what kinds of R data objects could be returned (rdataclass). We can take a closer look at all the kinds of data providers that are available by simply looking at the contents of dataprovider as if it were the column of a data.frame object like this:
head(unique(eh$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(): 2019-10-22
## # $dataprovider: GEUVADIS
## # $species: Homo sapiens
## # $rdataclass: GAlignmentPairs
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## # tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH166"]]'
##
## title
## EH166 | ERR188297
## EH167 | ERR188088
## EH168 | ERR188204
## EH169 | ERR188317Query has worked and you can now see that the only data present is provided by the “alpineData”.
The metadata underlying this hub object can be retrieved by you
apData$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] 2770Lets 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 471 records
## # snapshotDate(): 2019-10-22
## # $dataprovider: Jonathan Griffiths, GEO, NCBI GEO, Robinson group (UZH),...
## # $species: Mus musculus, Mus musculus (E18 mice)
## # $rdataclass: character, data.frame, DataFrame, SingleCellExperiment, ma...
## # 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
## ... ...
## EH3136 | mm10.generanges
## EH3137 | mm10.grt
## EH3214 | Buettner ESC counts
## EH3215 | Buettner ESC rowData
## EH3216 | Buettner ESC colDataWe 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)ExperimentHub to retrieve dataLooking 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(): 2019-10-22
## # $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 | ERR188317apData["EH166"]## ExperimentHub with 1 record
## # snapshotDate(): 2019-10-22
## # 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## 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 genomeEach 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.
ExperimentHub objectsWhen 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 2770 records
## # snapshotDate(): 2019-10-22
## # $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
## ... ...
## EH3230 | goAnno_drug
## EH3231 | GO_DATA
## EH3232 | GO_DATA_drug
## EH3233 | taurefList
## EH3234 | ES_NULLBy 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] "2019-10-22"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" "2019-10-22"Set the dates like this:
snapshotDate(ah) <- pd[1]sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 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_1.22.0
## [3] Rsamtools_2.2.0 Biostrings_2.54.0
## [5] XVector_0.26.0 SummarizedExperiment_1.16.0
## [7] DelayedArray_0.12.0 BiocParallel_1.20.0
## [9] matrixStats_0.55.0 Biobase_2.46.0
## [11] GenomicRanges_1.38.0 GenomeInfoDb_1.22.0
## [13] IRanges_2.20.0 S4Vectors_0.24.0
## [15] ExperimentHub_1.12.0 AnnotationHub_2.18.0
## [17] BiocFileCache_1.10.0 dbplyr_1.4.2
## [19] BiocGenerics_0.32.0 BiocStyle_2.14.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.2 lattice_0.20-38
## [3] assertthat_0.2.1 zeallot_0.1.0
## [5] digest_0.6.22 mime_0.7
## [7] R6_2.4.0 backports_1.1.5
## [9] RSQLite_2.1.2 evaluate_0.14
## [11] httr_1.4.1 pillar_1.4.2
## [13] zlibbioc_1.32.0 rlang_0.4.1
## [15] curl_4.2 blob_1.2.0
## [17] Matrix_1.2-17 rmarkdown_1.16
## [19] stringr_1.4.0 RCurl_1.95-4.12
## [21] bit_1.1-14 shiny_1.4.0
## [23] compiler_3.6.1 httpuv_1.5.2
## [25] xfun_0.10 pkgconfig_2.0.3
## [27] htmltools_0.4.0 tidyselect_0.2.5
## [29] tibble_2.1.3 GenomeInfoDbData_1.2.2
## [31] interactiveDisplayBase_1.24.0 bookdown_0.14
## [33] crayon_1.3.4 dplyr_0.8.3
## [35] later_1.0.0 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_1.1.0 vctrs_0.2.0
## [45] tools_3.6.1 bit64_0.9-7
## [47] glue_1.3.1 purrr_0.3.3
## [49] BiocVersion_3.10.1 fastmap_1.0.1
## [51] yaml_2.2.0 AnnotationDbi_1.48.0
## [53] BiocManager_1.30.9 memoise_1.1.0
## [55] knitr_1.25