In bioinfocz/scdrake: A pipeline for droplet-based single-cell RNA-seq data secondary analysis implemented in the drake Make-like toolkit for R language
Pipeline config is stored in the config/pipeline.yaml file. Directory with this file is read from SCDRAKE_PIPELINE_CONFIG_DIR
environment variable upon {scdrake} load or attach, and saved as scdrake_pipeline_config_dir option.
This option is used as the default argument value in several {scdrake} functions.
Pipeline parameters don't have impact on analysis (except SEED, but that should be reproducibly treated by {drake}).
Parameters starting with DRAKE_ are generally passed to drake::make() or drake::drake_config().
General parameters
DRAKE_TARGETS: null
Type: character vector or null
Array of target names to make. Setting to null will make all targets.
Example for single-sample pipeline / stage 02_norm_clustering reports:
DRAKE_TARGETS: ["report_norm_clustering", "report_norm_clustering_simple"]
DRAKE_CACHE_DIR: ".drake"
Type: character scalar or null
A name of directory to store drake's cache in.
If null, the default directory ".drake" will be used.
DRAKE_KEEP_GOING: False
Type: logical scalar
If True, let the pipeline continue even if some target fails.
DRAKE_VERBOSITY: 1
Type: integer scalar (1 | 2 | 3)
Verbosity of {drake}:
0: print nothing.1: print target-by-target messages as make() progresses.2: show a progress bar to track how many targets are done so far.
DRAKE_LOCK_ENVIR: True
Type: logical scalar
{drake} locks R global environment to avoid its unwanted modifications by targets.
However, in some cases is needed to keep it unlocked.
DRAKE_UNLOCK_CACHE: True
Type: logical scalar
Don't wait for {drake} to discover locked cache after pipeline is run and unlock it immediately.
DRAKE_FORMAT: "rds"
Type: character scalar
A file format used to store intermediate results in DRAKE_CACHE_DIR.
See https://books.ropensci.org/drake/plans.html#special-data-formats-for-targets for more details.
By default, R's Rds format is used (see ?saveRds), but we recommend to use DRAKE_FORMAT: "qs"
(see https://github.com/traversc/qs) which offers better performance, but sometimes doesn't work correctly
(drake throws untraceable errors).
DRAKE_REBUILD: null
Type: character scalar ("all" | "current") or null
Instruct {drake} to rebuild targets although they are considered finished.
- For
"all", the pipeline is run from scratch (drake::trigger(condition = TRUE) is passed as trigger argument to
drake::make() or drake::drake_config()).
- For
"current", drake::clean() is run for targets specified in DRAKE_TARGETS.
DRAKE_CACHING: "worker"
Type: character scalar ("worker" | "main")
How to collect data from parallel workers. See the caching parameter in drake::drake_config().
DRAKE_MEMORY_STRATEGY: "speed"
Type: character scalar ("speed" | "autoclean" | "preclean" | "lookahead" | "unload" | "none")
How to manage target objects in memory during runtime. See the memory_strategy parameter in drake::drake_config().
You can consider "autoclean", "preclean" or "lookahead" to conserve memory, but at the expense of speed.
DRAKE_LOG_BUILD_TIMES: False
Type: logical scalar
Whether to record build times for targets. Mac users may notice a 20% speedup in drake::make() with
DRAKE_LOG_BUILD_TIMES: False.
BLAS_N_THREADS: null
Type: positive integer scalar or null
A maximum number of threads for BLAS operations, passed to RhpcBLASctl::blas_set_num_threads().
Prevents "BLAS : Program is Terminated. Because you tried to allocate too many memory regions" when a massive target
parallelism is used. Set to null if you want to keep BLAS defaults.
RSTUDIO_PANDOC: null
Type: character scalar or null
A path to directory with pandoc's binary which is required for rendering of HTML reports.
You can ignore this if:
Scdrake is run in its Docker container.- You are running
scdrake from RStudio (it has pandoc bundled).
pandoc is available in the PATH environment variable. You can check this by calling system("pandoc -v").
In {rmarkdown}, the used pandocs binary is then resolved by rmarkdown::find_pandoc().
SEED: 100
Type: integer scalar
An initial seed for random number generator.
Parallelism
DRAKE_PARALLELISM: "loop"
Type: character scalar ("loop" | "future" | "clustermq")
Type of {drake} paralellism.
DRAKE_CLUSTERMQ_SCHEDULER: "multicore"
Type: character scalar
Which scheduler to use if DRAKE_PARALLELISM is "clustermq".
See https://mschubert.github.io/clustermq/articles/userguide.html#configuration for possible values.
DRAKE_N_JOBS: 4
Type: positive integer scalar
A number of parallel jobs for drake.
DRAKE_N_JOBS_PREPROCESS: 4
Type: positive integer scalar
A number of parallel jobs for processing the imports and doing other preprocessing tasks.
WITHIN_TARGET_PARALLELISM: False
Type: logical scalar
Allow or disable within-target parallelism through
the BiocParallel package.
Only possible when DRAKE_PARALLELISM is "loop".
N_CORES: 4
Type: positive integer scalar
A number of cores for within-target parallelism.
Targets
An informative plan is binded with every other plan, and contains targets with useful runtime information.
See the Targets section in vignette("config_main").
bioinfocz/scdrake documentation built on Sept. 19, 2024, 4:43 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Pipeline config is stored in the config/pipeline.yaml file. Directory with this file is read from SCDRAKE_PIPELINE_CONFIG_DIR
environment variable upon {scdrake} load or attach, and saved as scdrake_pipeline_config_dir option.
This option is used as the default argument value in several {scdrake} functions.
Pipeline parameters don't have impact on analysis (except SEED, but that should be reproducibly treated by {drake}).
Parameters starting with DRAKE_ are generally passed to drake::make() or drake::drake_config().
General parameters
DRAKE_TARGETS: null
Type: character vector or null
Array of target names to make. Setting to null will make all targets.
Example for single-sample pipeline / stage 02_norm_clustering reports:
DRAKE_TARGETS: ["report_norm_clustering", "report_norm_clustering_simple"]
DRAKE_CACHE_DIR: ".drake"
Type: character scalar or null
A name of directory to store drake's cache in.
If null, the default directory ".drake" will be used.
DRAKE_KEEP_GOING: False
Type: logical scalar
If True, let the pipeline continue even if some target fails.
DRAKE_VERBOSITY: 1
Type: integer scalar (1 | 2 | 3)
Verbosity of {drake}:
0: print nothing.1: print target-by-target messages asmake()progresses.2: show a progress bar to track how many targets are done so far.
DRAKE_LOCK_ENVIR: True
Type: logical scalar
{drake} locks R global environment to avoid its unwanted modifications by targets.
However, in some cases is needed to keep it unlocked.
DRAKE_UNLOCK_CACHE: True
Type: logical scalar
Don't wait for {drake} to discover locked cache after pipeline is run and unlock it immediately.
DRAKE_FORMAT: "rds"
Type: character scalar
A file format used to store intermediate results in DRAKE_CACHE_DIR.
See https://books.ropensci.org/drake/plans.html#special-data-formats-for-targets for more details.
By default, R's Rds format is used (see ?saveRds), but we recommend to use DRAKE_FORMAT: "qs"
(see https://github.com/traversc/qs) which offers better performance, but sometimes doesn't work correctly
(drake throws untraceable errors).
DRAKE_REBUILD: null
Type: character scalar ("all" | "current") or null
Instruct {drake} to rebuild targets although they are considered finished.
- For
"all", the pipeline is run from scratch (drake::trigger(condition = TRUE)is passed astriggerargument todrake::make()ordrake::drake_config()). - For
"current",drake::clean()is run for targets specified inDRAKE_TARGETS.
DRAKE_CACHING: "worker"
Type: character scalar ("worker" | "main")
How to collect data from parallel workers. See the caching parameter in drake::drake_config().
DRAKE_MEMORY_STRATEGY: "speed"
Type: character scalar ("speed" | "autoclean" | "preclean" | "lookahead" | "unload" | "none")
How to manage target objects in memory during runtime. See the memory_strategy parameter in drake::drake_config().
You can consider "autoclean", "preclean" or "lookahead" to conserve memory, but at the expense of speed.
DRAKE_LOG_BUILD_TIMES: False
Type: logical scalar
Whether to record build times for targets. Mac users may notice a 20% speedup in drake::make() with
DRAKE_LOG_BUILD_TIMES: False.
BLAS_N_THREADS: null
Type: positive integer scalar or null
A maximum number of threads for BLAS operations, passed to RhpcBLASctl::blas_set_num_threads().
Prevents "BLAS : Program is Terminated. Because you tried to allocate too many memory regions" when a massive target
parallelism is used. Set to null if you want to keep BLAS defaults.
RSTUDIO_PANDOC: null
Type: character scalar or null
A path to directory with pandoc's binary which is required for rendering of HTML reports.
You can ignore this if:
Scdrakeis run in its Docker container.- You are running
scdrakefrom RStudio (it haspandocbundled). pandocis available in thePATHenvironment variable. You can check this by callingsystem("pandoc -v").
In {rmarkdown}, the used pandocs binary is then resolved by rmarkdown::find_pandoc().
SEED: 100
Type: integer scalar
An initial seed for random number generator.
Parallelism
DRAKE_PARALLELISM: "loop"
Type: character scalar ("loop" | "future" | "clustermq")
Type of {drake} paralellism.
DRAKE_CLUSTERMQ_SCHEDULER: "multicore"
Type: character scalar
Which scheduler to use if DRAKE_PARALLELISM is "clustermq".
See https://mschubert.github.io/clustermq/articles/userguide.html#configuration for possible values.
DRAKE_N_JOBS: 4
Type: positive integer scalar
A number of parallel jobs for drake.
DRAKE_N_JOBS_PREPROCESS: 4
Type: positive integer scalar
A number of parallel jobs for processing the imports and doing other preprocessing tasks.
WITHIN_TARGET_PARALLELISM: False
Type: logical scalar
Allow or disable within-target parallelism through
the BiocParallel package.
Only possible when DRAKE_PARALLELISM is "loop".
N_CORES: 4
Type: positive integer scalar
A number of cores for within-target parallelism.
Targets
An informative plan is binded with every other plan, and contains targets with useful runtime information.
See the Targets section in vignette("config_main").
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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