AnVILGCP 1.0.0
av*() to work with AnVIL tables and dataavnotebooks*() for notebook managementavworkflows_*() for workflowsavworkspace_*() for workspacesInstall the AnVILGCP package from Bioconductor with:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("AnVILGCP")Once installed, load the package with
library(AnVILGCP)For reproducibility, it is advisable to install packages into libraries on a project-specific basis, e.g., to create a ‘snapshot’ of packages for reproducible analysis. Use
add_libpaths("~/my/project")as a convenient way to prepend a project-specific library path to
.libPaths(). New packages will be installed into this library.
In the AnVIL cloud environment, clone or create a new workspace. Click
on the Cloud Environment button at the top right of the
screen. Choose the R / Bioconductor runtime to use in a Jupyter
notebook, or RStudio to use in RStudio. When creating a Jupyter
notebook, choose R as the engine.
A new layout is being introduced in Fall of 2022. If the workspace has
an ‘Analyses’ tab, navigate to it and look for the ‘Environment
Configuration’ button to the right of the screen. For a Jupyter
notebook-based environment, select jupyter ‘Environment Settings’
followed by Customize and the R / Bioconductor application
configuration. RStudio is available by clicking on the RStudio / Bioconductor ‘Environment Settings’ button.
For tasks more complicated than manipulation and visualization of tabular data (e.g., performing steps of a single-cell work flow) the default Jupyter notebook configuration of 1 CPU and 3.75 GB of memory will be insufficient; the RStudio image defaults to 4 CPU and 15 GB of memory.
Local use requires that the gcloud SDK is installed, and that the billing account used by AnVIL can be authenticated with the user. These requirements are satisfied when using the AnVIL compute cloud. For local use, one must
[Install][install-gcloud-sdk] the gcloud sdk (for Linux and Windows,
cloudml::gcloud_install() provides an alternative way to install
gcloud).
Define an environment variable or option() named GCLOUD_SDK_PATH
pointing to the root of the SDK installation, e.g,
dir(file.path(Sys.getenv("GCLOUD_SDK_PATH"), "bin"), "^(gcloud|gsutil)$")
## [1] "gcloud" "gsutil"Test the installation with gcloud_exists()
## the code chunks in this vignette are fully evaluated when
## gcloud_exists() returns TRUE
gcloud_exists()
## [1] FALSESeveral commonly used functions have an additional ‘gadget’ interface,
allowing selection of workspaces (avworkspace_gadget(), DATA tables
(avtable_gadget()) and workflows avworkflow_gadget() using a
simple tabular graphical user interface. The browse_workspace()
function allows selection of a workspace to be opened as a browser
tab.
The AnVIL package implements functions to facilitate access to Google cloud resources.
gcloud_*() for account managementThe gcloud_*() family of functions provide access to Google cloud
functions implemented by the gcloud binary. gcloud_project()
returns the current billing account.
gcloud_account() # authentication account
gcloud_project() # billing project informationA convenient way to access any gcloud SDK command is to use
gcloud_cmd(), e.g.,
gcloud_cmd("projects", "list") |>
readr::read_table() |>
filter(startsWith(PROJECT_ID, "anvil"))This translates into the command line gcloud projects list. Help is
also available within R, e.g.,
gcloud_help("projects")Use gcloud_help() (with no arguments) for an overview of available
commands.
gsutil_*() for file and bucket managementThe gsutil_*() family of functions provides an interface to google
bucket manipulation. The following refers to publicly available 1000
genomes data available in Google Cloud Storage.
src <- "gs://genomics-public-data/1000-genomes/"gsutil_ls() lists bucket content; gsutil_stat() additional detail
about fully-specified buckets.
avlist(src)
other <- paste0(src, "other")
avlist(other, recursive = TRUE)
sample_info <- paste0(src, "other/sample_info/sample_info.csv")
gsutil_stat(sample_info)gsutil_cp() copies buckets from or to Google cloud storage; copying
to cloud storage requires write permission, of course. One or both of
the arguments can be cloud endpoints.
fl <- tempfile()
avcopy(sample_info, fl)
csv <- readr::read_csv(fl, guess_max = 5000L, col_types = readr::cols())
csvgsutil_pipe() provides a streaming interface that does not require
intermediate disk storage.
pipe <- gsutil_pipe(fl, "rb")
readr::read_csv(pipe, guess_max = 5000L, col_types = readr::cols()) |>
dplyr::select("Sample", "Family_ID", "Population", "Gender")gsutil_rsync() synchronizes a local file hierarchy with a remote
bucket. This can be a powerful operation when delete = TRUE
(removing local or remote files), and has default option dry = TRUE
to indicate the consequences of the sync.
destination <- tempfile()
stopifnot(dir.create(destination))
source <- paste0(src, "other/sample_info")
## dry run
gsutil_rsync(source, destination)
gsutil_rsync(source, destination, dry = FALSE)
dir(destination, recursive = TRUE)
## nothing to synchronize
gsutil_rsync(source, destination, dry = FALSE)
## one file requires synchronization
unlink(file.path(destination, "README"))
gsutil_rsync(source, destination, dry = FALSE)localize() and delocalize() provide ‘one-way’
synchronization. localize() moves the content of the gs://
source to the local file system. localize() could be used at the
start of an analysis to retrieve data stored in the google cloud to
the local compute instance. delocalize() performs the complementary
operation, copying local files to a gs:// destination. The unlink = TRUE option to delocalize() unlinks local source files
recursively. It could be used at the end of an analysis to move
results to the cloud for long-term persistent storage.
av*() to work with AnVIL tables and dataAnVIL organizes data and analysis environments into ‘workspaces’. AnVIL-provided data resources in a workspace are managed under the ‘DATA’ tab as ‘TABLES’, ‘REFERENCE DATA’, and ‘OTHER DATA’; the latter includes ‘’Workspace Data’ and ‘Files’, with ‘Files’ corresponding to a Google Cloud Bucket associated with the workspace. These components of the graphical user interface are illustrated in the figure below.
The AnVIL package provides programmatic tools to access different components of the data workspace, as summarized in the following table.
| Workspace | AnVIL function |
|---|---|
| TABLES | avtables() |
| REFERENCE DATA | None |
| OTHER DATA | avstorage() |
| Workspace Data | avdata() |
| Files | avlist(), avbackup(), avrestore() |
Data tables in a workspace are available by specifying the namespace
(billing account) and name (workspace name) of the workspace. When
on the AnVIL in a Jupyter notebook or RStudio, this information can be
discovered with
avworkspace_namespace()
avworkspace_name()It is also possible to specify, when not in the AnVIL compute environment, the data resource to work with.
## N.B.: IT MAY NOT BE NECESSARY TO SET THESE WHEN ON ANVIL
avworkspace_namespace("pathogen-genomic-surveillance")
avworkspace_name("COVID-19")avtable*() for accessing tablesAccessing data tables use the av*() functions. Use avtables() to
discover available tables, and avtable() to retrieve a particular
table
avtables()
sample <- avtable("sample_set")
sampleThe data in the table can then be manipulated using standard R commands, e.g., to identify SRA samples for which a final assembly fasta file is available.
sample |>
dplyr::select("sample_set_id", contains("fasta")) |>
dplyr::filter(!is.na("Successful_Assembly_group"))Users can easily add tables to their own workspace using
avtable_import(), perhaps as the final stage of a pipe
my_cars <-
mtcars |>
as_tibble(rownames = "model") |>
mutate(model = gsub(" ", "_", model))
job_status <- avtable_import(my_cars)Tables are imported ‘asynchronously’, and large tables (more than 1.5
million elements; see the pageSize argument) are uploaded in
pages. The job status is a tibble summarizing each page; the status
of the upload can be checked with
avtable_import_status(job_status)The transcript of a session where page size is set intentionally small for illustration is
(job_status <- avtable_import(my_cars, pageSize = 10))
## pageSize = 10 rows (4 pages)
## |======================================================================| 100%
## # A tibble: 4 × 5
## page from_row to_row job_id status
## <int> <int> <int> <chr> <chr>
## 1 1 1 10 a32e9706-f63c-49ed-9620-b214746b9392 Uploaded
## 2 2 11 20 f2910ac2-0954-4fb9-b36c-970845a266b7 Uploaded
## 3 3 21 30 e18adc5b-d26f-4a8a-a0d7-a232e17ac8d2 Uploaded
## 4 4 31 32 d14efb89-e2dd-4937-b80a-169520b5f563 Uploaded
(job_status <- avtable_import_status(job_status))
## checking status of 4 avtable import jobs
## |======================================================================| 100%
## # A tibble: 4 × 5
## page from_row to_row job_id status
## <int> <int> <int> <chr> <chr>
## 1 1 1 10 a32e9706-f63c-49ed-9620-b214746b9392 Done
## 2 2 11 20 f2910ac2-0954-4fb9-b36c-970845a266b7 Done
## 3 3 21 30 e18adc5b-d26f-4a8a-a0d7-a232e17ac8d2 ReadyForUpsert
## 4 4 31 32 d14efb89-e2dd-4937-b80a-169520b5f563 ReadyForUpsert
(job_status <- avtable_import_status(job_status))
## checking status of 4 avtable import jobs
## |======================================================================| 100%
## # A tibble: 4 × 5
## page from_row to_row job_id status
## <int> <int> <int> <chr> <chr>
## 1 1 1 10 a32e9706-f63c-49ed-9620-b214746b9392 Done
## 2 2 11 20 f2910ac2-0954-4fb9-b36c-970845a266b7 Done
## 3 3 21 30 e18adc5b-d26f-4a8a-a0d7-a232e17ac8d2 Done
## 4 4 31 32 d14efb89-e2dd-4937-b80a-169520b5f563 DoneThe Terra data model allows for tables that represent samples of other
tables. The following create or add rows to participant_set and
sample_set tables. Each row represents a sample from the
corresponding ‘origin’ table.
## editable copy of '1000G-high-coverage-2019' workspace
avworkspace("anvil-datastorage/1000G-high-coverage-2019")
sample <-
avtable("sample") |> # existing table
mutate(set = sample(head(LETTERS), nrow(.), TRUE)) # arbitrary groups
sample |> # new 'participant_set' table
avtable_import_set("participant", "set", "participant")
sample |> # new 'sample_set' table
avtable_import_set("sample", "set", "name")The TABLES data in a workspace are usually provided as curated
results from AnVIL. Nonetheless, it can sometimes be useful to delete
individual rows from a table. Use avtable_delete_values().
avdata() for accessing Workspace DataThe ‘Workspace Data’ is accessible through avdata() (the example
below shows that some additional parsing may be necessary).
avdata()avstorage() and workspace filesEach workspace is associated with a google bucket, with the content summarized in the ‘Files’ portion of the workspace. The location of the files is
bucket <- avstorage()
bucketThe content of the bucket can be viewed with (if permissions allow)
avlist()If the workspace is owned by the user, then persistent data can be written to the bucket.
## requires workspace ownership
uri <- avstorage() # discover bucket
bucket <- file.path(uri, "mtcars.tab")
write.table(mtcars, gsutil_pipe(bucket, "w")) # write to bucketA particularly convenient operation is to back up files or directories from the compute node to the bucket
## backup all files and folders in the current working directory
avbackup(getwd(), recursive = TRUE)
## backup all files in the current directory
avbackup(dir())
## backup all files to gs://<avstorage()>/scratch/
avbackup(dir, paste0(avstorage(), "/scratch"))Note that the backup operations have file naming behavior like the
Linux cp command; details are described in the help page
gsutil_help("cp").
Use avrestore() to restore files or directories from the
workspace bucket to the compute node.
avnotebooks*() for notebook managementPython (.ipynb) or R (.Rmd) notebooks are associated with
individual workspaces under the DATA tab, Files/notebooks
location.
Jupyter notebooks are exposed through the Terra interface under the NOTEBOOKS tab, and are automatically synchronized between the workspace and the current runtime.
R markdown documents may also be associated with the workspace (under
DATA Files/notebooks) but are not automatically synchronized with
the current runtime. The functions in this section help manage R
markdown documents.
Available notebooks in the workspace are listed with
avnotebooks(). Copies of the notebooks on the current runtime are
listed with avnotebooks(local = TRUE). The default location of the
notebooks is ~/<avworkspace_name()>/notebooks/.
Use avnotebooks_localize() to synchronize the version of the
notebooks in the workspace to the current runtime. This operation
might be used when a new runtime is created, and one wishes to start
with the notebooks found in the workspace. If a newer version of the
notebook exists in the workspace, this will overwrite the older
version on the runtime, potentially causing data loss. For this
reason, avnotebooks_localize() by default reports the actions that
will be performed, without actually performing them. Use
avnotebooks_localize(dry = FALSE) to perform the localization.
Use avnotebooks_delocalize() to synchronize local versions of the
notebooks on the current runtime to the workspace. This operation
might be used when developing a workspace, and wishing to update the
definitive notebook in the workspace. When dry = FALSE, this
operation also overwrites older workspace notebook files with their
runtime version.
avworkflows_*() for workflowsSee the vignette “Running an AnVIL workflow within R”, in this package, for details on running workflows and managing output.
avworkspace_*() for workspacesavworkspace() is used to define or return the ‘namespace’ (billing
project) and ‘name’ of the workspace on which operations are to
act. avworkspace_namespace() and avworkspace_name() can be used to
set individual elements of the workspace.
avworkspace_clone() clones a workspace to a new location. The clone
includes the ‘DATA’, ‘NOTEBOOK’, and ‘WORKFLOWS’ elements of the
workspace.
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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.3.12.0
##
## 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] AnVILGCP_1.0.0 BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.5 httr_1.4.7 cli_3.6.3
## [4] knitr_1.48 rlang_1.1.4 xfun_0.48
## [7] purrr_1.0.2 generics_0.1.3 jsonlite_1.8.9
## [10] glue_1.8.0 htmltools_0.5.8.1 BiocBaseUtils_1.8.0
## [13] sass_0.4.9 fansi_1.0.6 rmarkdown_2.28
## [16] rappdirs_0.3.3 evaluate_1.0.1 jquerylib_0.1.4
## [19] tibble_3.2.1 fastmap_1.2.0 yaml_2.3.10
## [22] lifecycle_1.0.4 httr2_1.0.5 bookdown_0.41
## [25] BiocManager_1.30.25 compiler_4.4.1 codetools_0.2-20
## [28] dplyr_1.1.4 pkgconfig_2.0.3 tidyr_1.3.1
## [31] digest_0.6.37 R6_2.5.1 tidyselect_1.2.1
## [34] utf8_1.2.4 parallel_4.4.1 pillar_1.9.0
## [37] magrittr_2.0.3 bslib_0.8.0 tools_4.4.1
## [40] AnVILBase_1.0.0 cachem_1.1.0