The sevenbridges package only supports v2+ versions of the API, since versions prior to V2 are not compatible with the Common Workflow Language (CWL). This package provides a simple interface for accessing and trying out various methods.
There are two ways of constructing API calls. For instance, you can use low-level API calls which use arguments like path, query, and body. These are documented in the API reference libraries for the Seven Bridges Platform and the CGC. An example of a low-level request to “list all projects” is shown below. In this request, you can also pass query and body as a list.
Since we are constantly improving our API and client libraries, please also visit our GitHub repository for the most recent news and the latest version of the package.
If you do not have devtools
This installation requires that you have the devtools package. If you do not have this package, you can install it from CRAN.
You may get an error for missing system dependencies such as curl and openssl. For example, in Ubuntu, you probably need to do the following first to install devtools and to build vignettes since you need pandoc.
If you have trouble with pandoc and do not want to install it, set build_vignettes = FALSE to avoid the vignettes build.
@@ -193,7 +199,8 @@
Create Auth Object
Before you can access your account via the API, you have to provide your credentials. You can obtain your credentials in the form of an “authentication token” from the Developer Tab under Account Settings on the visual interface. Once you’ve obtained this, create an Auth object, so it remembers your authentication token and the path for the API. All subsequent requests will draw upon these two pieces of information.
Note that this change might be just temporary, please feel free to use the standard method to set persistent environment variables according to your operating system.
Note: API paths (base URLs) differ for each Seven Bridges environment. Be sure to provide the correct path for the environment you are using. API paths for some of the environments are:
This call returns information about the specified user. Note that currently you can view only your own user information, so this call is equivalent to the call to get information about your account.
Each project must have a Billing Group associated with it. This Billing Group pays for the storage and computation in the project.
For example, your first project(s) were created with the free funds from the Pilot Funds Billing Group assigned to each user at sign-up. To get information about billing:
-
# check your billing info
-a$billing()
-a$invoice()
+
+# check your billing info
+a$billing()
+a$invoice()
Projects are the core building blocks of the platform. Each project corresponds to a distinct scientific investigation, serving as a container for its data, analysis tools, results, and collaborators.
Create a new project called “api testing” with the billing group id obtained above.
-
# get billing group id
-bid<-a$billing()$id
+
+# get billing group id
+bid<-a$billing()$id# create new project
-(p<-a$project_new(name="api testing", bid, description="Just a test"))
+(p<-a$project_new(name ="api testing", bid, description ="Just a test"))
== Project ==
id : RFranklin/api-testing
name : api testing
@@ -365,14 +383,15 @@
Get Details about Existing Project
-
# list first 100
-a$project()
+
+# list first 100
+a$project()# list all
-a$project(complete=TRUE)
+a$project(complete =TRUE)# return all named match "demo"
-a$project(name="demo", complete=TRUE)
+a$project(name ="demo", complete =TRUE)# get the project you want by id
-p<-a$project(id="RFranklin/api-tutorial")
Browse them online. Check out the tutorial for the “Find apps” section.
You can use the sevenbridges package to find it, as shown below.
-
# search by name matching, complete = TRUE search all apps,
+
+# search by name matching, complete = TRUE search all apps,# not limited by offset or limit.
-a$public_app(name="STAR", complete=TRUE)
+a$public_app(name ="STAR", complete =TRUE)# search by id is accurate
-a$public_app(id="admin/sbg-public-data/rna-seq-alignment-star/5")
+a$public_app(id ="admin/sbg-public-data/rna-seq-alignment-star/5")# you can also get everything
-a$public_app(complete=TRUE)
+a$public_app(complete =TRUE)# default limit = 100, offset = 0 which means the first 100
-a$public_app()
+a$public_app()
Now, from your Auth object, you copy an App id into your project id with a new name, following this logic.
+# copy
+a$copy_app(
+ id ="admin/sbg-public-data/rna-seq-alignment-star/5",
+ project ="RFranklin/api-testing", name ="new copy of star"
+)# check if it is copied
-p<-a$project(id="RFranklin/api-testing")
+p<-a$project(id ="RFranklin/api-testing")# list apps your got in your project
-p$app()
+p$app()
The short name is changed to newcopyofstar.
== App ==
id : RFranklin/api-testing/newcopyofstar/0
@@ -412,11 +433,12 @@
project : RFranklin/api-testing-2
revision : 0
Alternatively, you can copy it from the app object.
-
app<-a$public_app(id="admin/sbg-public-data/rna-seq-alignment-star")
-app$copy_to(
- project="RFranklin/api-testing",
- name="copy of star"
-)
+
+app<-a$public_app(id ="admin/sbg-public-data/rna-seq-alignment-star")
+app$copy_to(
+ project ="RFranklin/api-testing",
+ name ="copy of star"
+)
You can also upload your own Common Workflow Language JSON file which describes your app to your project.
Note: Alternatively, you can directly describe your CWL tool in R with this package. Please read the vignette on “Describe CWL Tools/Workflows in R and Execution”.
-
# add an CWL file to your project
-f.star<-system.file("extdata/app", "flow_star.json", package="sevenbridges")
-app<-p$app_add("starlocal", fl.runif)
-(aid<-app$id)
+
+# add an CWL file to your project
+f.star<-system.file("extdata/app", "flow_star.json", package ="sevenbridges")
+app<-p$app_add("starlocal", fl.runif)
+(aid<-app$id)
You will get an app id, like the one below:
"RFranklin/api-testing/starlocal/0"
It’s composed of the following elements:
@@ -445,11 +468,12 @@
Alternatively, you can describe tools in R directly, as shown below:
The inputs for your workflow. In this case, the CWL app accepts four parameters: number, min, max, and seed.
You can always check the App details on the visual interface for task input requirements. To find the required inputs with R, you need to get an App object first.
In the response body, locate the names of the required inputs. Note that task inputs need to match the expected data type and name. In the above example, we see two required fields:
+fastqs<-c("SRR1039508_1.fastq", "SRR1039508_2.fastq")# get all 2 exact files
-fastq_in<-p$file(name=fastqs, exact=TRUE)
+fastq_in<-p$file(name =fastqs, exact =TRUE)# get a single file
-fasta_in<-p$file(
- name="Homo_sapiens.GRCh38.dna.primary_assembly.fa",
- exact=TRUE
-)
+fasta_in<-p$file(
+ name ="Homo_sapiens.GRCh38.dna.primary_assembly.fa",
+ exact =TRUE
+)# get all single file
-gtf_in<-p$file(
- name="Homo_sapiens.GRCh38.84.gtf",
- exact=TRUE
-)
+gtf_in<-p$file(
+ name ="Homo_sapiens.GRCh38.84.gtf",
+ exact =TRUE
+)
+f1<-p$file(name ="SRR1039508_1.fastq", exact =TRUE)
+f2<-p$file(name ="SRR1039508_2.fastq", exact =TRUE)# get all 2 exact files
-fastq_in<-list(f1, f2)
+fastq_in<-list(f1, f2)# or if you know you only have 2 files whose names match SRR924146*.fastq
-fastq_in<-p$file(name="SRR1039508*.fastq", complete=TRUE)
Use complete = TRUE when the number of items is over 100.
Draft a batch task
Now let’s do a batch with 8 files in 4 groups, which is batched by metadata sample_id and library_id. We will assume each file has these two metadata fields entered. Since these files can be evenly grouped into 4, we will have a single parent batch task with 4 child tasks.
Now you have a draft batch task. Please check it out in the visual interface. Your response body should inform you of any errors or warnings.
Note: you can also directly pass file id or file names as characters to inputs list, the package will first guess if the passed strings are file id (24-bit hexadecimal) or names, then convert it to Files or FilesList object. However, as a good practice, we recommend you construct your files object(e.g. p$file(id = ..., name = ...)) first, check the value, then pass it to task_add inputs. This is a safer approach.
@@ -703,20 +737,24 @@
Run a Task
Now, we are ready to run our task.
-
# run your task
-tsk$run()
+
+# run your task
+tsk$run()
Before you run your task, you can adjust your draft task if you have any final modifications. Alternatively, you can delete the draft task if you no longer wish to run it.
-
# # not run
-# tsk$delete()
+
+# # not run
+# tsk$delete()
After you run a task, you can abort it.
-
# abort your task
-tsk$abort()
+
+# abort your task
+tsk$abort()
If you want to update your task and then re-run it, follow the example below.
-
tsk$getInputs()
+
+tsk$getInputs()# missing number input, only update number
-tsk$update(inputs=list(sjdbGTFfile="some new file"))
+tsk$update(inputs =list(sjdbGTFfile ="some new file"))# double check
-tsk$getInputs()
Running tasks with spot instances could potentially reduce a considerable amount of computational cost. This option can be controlled on the project level or the task level on Seven Bridges platforms. Our package follows the same logic as our platform’s web interface (the current default setting for spot instances is on).
For example, when we create a project using project_new(), we can set use_interruptible = FALSE to use on-demand instances (non-interruptible but more expensive) instead of the spot instances (interruptible but cheaper):
Then all the new tasks created under this project will use on-demand instances to run by default, unless an argument use_interruptible_instances is specifically set to TRUE when drafting the new task using task_add().
For example, if p is the above spot disabled project, to draft a task that will use spot instances to run:
+tsk<-p$task_add(
+ name =paste0("spot enabled task in a spot disabled project"),
+ description ="spot enabled task",
+ app =...,
+ inputs =list(...),
+ use_interruptible_instances =TRUE
+)
Conversely, you can have a spot instance enabled project, but draft and run specific tasks using on-demand instances, by setting use_interruptible_instances = FALSE in task_add() explicitly.
@@ -746,39 +786,45 @@
Execution hints per task run
During workflow development and benchmarking, sometimes we need to view and make adjustments to the computational resources needed for a task to run more efficiently. Also, if a task fails due to resource deficiency, we often want to define a larger instance for the task re-run without editing the app itself. This is particularly important in cases where there is not enough disk space.
The Seven Bridges API allows setting specific task execution parameters by using execution_settings. It includes the instance type (instance_type) and the maximum number of parallel instances (max_parallel_instances):
To monitor your task as it runs, you can always request a task update to ask your task to report its status. Or, you can monitor a running task with a hook function, which triggers the function when that status is “completed”. Please check the details in section below.
Instead of the default task monitor action, you can use setTaskHook to connect a function call to the status of a task. When you run tsk$monitor(time = 30) it will check your task every 30 seconds to see if the current task status matches one of the following statuses: “queued”, “draft”, “running”, “completed”, “aborted”, and “failed”. When it finds a match for the task status, getTaskHook returns the function call for the specific status.
If you want to customize the monitor function, you can adjust the following requirement. Your function must return TRUE or FALSE in the end. When it is TRUE (or non-logical value) it means the monitoring will be terminated after it finds a status matched and the function executes, such as when the task is completed. When it is FALSE, it means the monitoring will continue for the next iteration of checking, e.g., when it is “running”, you want to keep tracking.
Follow the example below to set a new function to monitor the status “completed”. Then, when the task is completed, it will download all task output files to the local folder.
The above will use the specified url as the base URL for the API calls. In this example, the url points to the Seven Bridges Platform on Google Cloud Platform (GCP).
If you did not pass any parameters to api() from Auth, it would list all API calls. Any parameters you provide will be passed to the api() function, but you do not have to pass your input token and path once more since the Auth object already has this information. The following call from the Auth object will check the response as well.
limit defines the number of items you want to get.
By default, offset is set to 0 and limit is set to 50. As such, your API request returns the first 100 items when you list items or search for items by name. To search and list all items, use complete = TRUE in your API request.
Search by name returns all partial matches unless you specify exact = TRUE. This type of search will only search across current pulled content, so use complete = TRUE if you want to search across everything.
For example, to list all public apps, use visibility argument, but make sure you pass complete = TRUE to it, to show everything. This arguments generally works for items like “App”, “Project”, “Task”, “File”, etc.
-
# first, search by id is fast
-x<-a$app(visibility="public", id="admin/sbg-public-data/sbg-ucsc-b37-bed-converter/1")
+
+# first, search by id is fast
+x<-a$app(visibility ="public", id ="admin/sbg-public-data/sbg-ucsc-b37-bed-converter/1")# show 100 items from public
-x<-a$app(visibility="public")
-length(x) # 100
+x<-a$app(visibility ="public")
+length(x)# 100
-x<-a$app(visibility="public", complete=TRUE)
-length(x) # 272 by Nov 2016
+x<-a$app(visibility ="public", complete =TRUE)
+length(x)# 272 by Nov 2016# this return nothing, because it is not in the first 100 returned names
-a$app(visibility="public", name="bed converter")
+a$app(visibility ="public", name ="bed converter")# this return an app, because it pulls *all* app names and did search
-a$app(visibility="public", name="bed converter", complete=TRUE)
+a$app(visibility ="public", name ="bed converter", complete =TRUE)
Similar to offset and limit, every API call accepts an argument named advance_access. This argument was first introduced in August 2017 and controls if a special field in the HTTP request header will be sent, which can enable the access to the “Advance Access” features in the Seven Bridges API. Note that the Advance Access features in the API are not officially released yet, therefore the API usages are subject to change, so please use with discretion.
In addition to modifying each API call that uses Advance Access features, the option can also be set globally at the beginning of your API script. This offers a one-button switch for users who want to experiment with the Advance Access features. The option is disabled by default:
For example, the Markers API is in Advance Access as of November 2018. If we try to use the Markers API to list markers available on a BAM file with the advance_access option disabled, it will return an error message:
No fields parameter specified: calls return default fields. For calls that return complete details of a single resource, this is all their properties; for calls that list resources of a certain type, this is some default properties.
The fields parameter can be set to a list of fields: for example, to return the fields id, name and size for files in a project, you may issue the call p$file(fields = "id,name,size")
-
The fields parameter can be used to exclude a specific file: if you wish to omit certain field from the response, do so using the fields parameter with the prefix !. For example, to get the details of a file without listing its metadata, issue a call p$file(fields = '!metadata') The entire metadata field will be removed from the response.
+
The fields parameter can be used to exclude a specific file: if you wish to omit certain field from the response, do so using the fields parameter with the prefix !. For example, to get the details of a file without listing its metadata, issue a call p$file(fields = '!metadata') The entire metadata field will be removed from the response.
The fields parameter can be used to include or omit certain nested fields, in the same way as listed in 2 and 3 above: for example, you can use metadata.sample_id or origin.task for files.
To see all fields for a resource, specify fields="_all". This returns all fields for each resource returned. Note that if you are getting the details of a specific resource, the use of fields="_all" won’t return any more properties than would have been shown without this parameter — the use case is instead for when you are listing details of many resources. Please use with care if your resource has particularly large fields; for example, the raw field for an app resource contains the complete CWL specification of the app which can result in bulky response if listing many apps.
Negations and nesting can be combined freely, so, for example, you can issue p$file(fields="id,name,status,!metadata.library,!origin") or p$task(fields="!inputs,!outputs").
+## default fields id, name, project
+p$file()## return file(s) with id, name, siae information
-p$file(fields="id,name,size")
+p$file(fields ="id,name,size")## return file(s) with all available info
-p$file(detail=TRUE)
+p$file(detail =TRUE)## same as above
-p$file(fields="_all")
This call returns a list of the resources, such as projects, billing groups, and organizations, that are accessible to you. Currently, this call will only return a successful response if {username} is replaced with your own username. Be sure to capitalize your username in the same way as when you registered for an account.
If you did not provide a username, it will show your user information.
-
# return your information
-a$user()
+
+# return your information
+a$user()# return user RFranklin's information
-a$user("RFranklin")
If no billing group id is provided, this call returns a list of paths used to access billing information via the API. If a username is provided, this call lists all your billing groups, including groups that are pending or which have been disabled. If you specify breakdown = TRUE, the call below returns a breakdown of the spending per-project for the billing group specified by billing_group. Information is also displayed for each of the projects a particular billing group is associated with, including task executions, their initiating user, start and end time, and their cost.
-
# return a BillingList object
-(b<-a$billing())
-a$billing(id=b$id, breakdown=TRUE)
+
+# return a BillingList object
+(b<-a$billing())
+a$billing(id =b$id, breakdown =TRUE)
If no Billing Group id provided, This call returns a list of invoices, with information about each, including whether or not the invoice is pending and the billing period it covers. This call returns information about all your available invoices unless you use the query parameter bg_id to specify the ID of a particular Billing Group, in which case it will return the invoice incurred by that Billing Group only. If an invoice id is provided, this call retrieves information about the specified invoice, including the costs for analysis and storage and the invoice period.
-
a$invoice()
-a$invoice(id="your_id")
+
+a$invoice()
+a$invoice(id ="your_id")
Note: Currently, invoice is not an object. Instead, it just returns a regular R list.
The following call returns a list of all projects of which you are a member. Each project’s project_id and the path will be returned.
-
a$project()
+
+a$project()
If you want to list the projects owned by and accessible to a particular user, specify the owner argument as follows. Each project’s ID and path will be returned.
For a more friendly interface and convenient search, the sevenbridges package supports partial name matching. The first argument for the following request is name.
-
# want to return a project called
-a$project("hello")
+
+# want to return a project called
+a$project("hello")
Filter by project creation date, modification date, and creator
Project creation date, modification date, and creator information is useful for quickly locating the project you need, especially when you want to follow the life cycle of a large number of projects and distinguish recent projects from old ones. To facilitate such needs, the fields created_by, created_on, and modified_on are returned in the project query calls:
-
# return all projects matching the name "wgs"
-p<-a$project("wgs", complete=TRUE)
+
+# return all projects matching the name "wgs"
+p<-a$project("wgs", complete =TRUE)# filter by project creators
-creators<-sapply(p, "[[", "created_by")
-which(creator=="RFranklin")
+creators<-sapply(p, "[[", "created_by")
+which(creator=="RFranklin")# filter by project creation date
-create_date<-as.Date(sapply(p, "[[", "created_on"))
-which(as.Date(create_date) <as.Date("2019-01-01"))
+create_date<-as.Date(sapply(p, "[[", "created_on"))
+which(as.Date(create_date)<as.Date("2019-01-01"))# filter by project modification date
-modify_date<-as.Date(sapply(p, "[[", "modified_on"))
-which(as.Date(modify_date) <as.Date("2019-01-01"))
You can delete a single project by making the request to $delete(). Note that the returned object from a$project() sometimes returns list if you use partial matching by name. The $delete() request cannot operate on a list. If you want to operate on a list of object, read more about batch functions in the relevant section below.
-
# remove it, not run
-a$project("api_testing")$delete()
+
+# remove it, not run
+a$project("api_testing")$delete()# check# will delete all projects match the name
-delete(a$project("api_testing_donnot_delete_me"))
Requests to add a project member must include the key permissions. However, if you do not include a value, the member’s permissions will be set to false by default.
Set permission by passing the following values: copy, write, execute, admin, or read.
Note: read is implicit and set by default. You can not be a project member without having read permissions.
Second rule: understand the operation: When filtering on any resource, including the same field several times with different filtering criteria results in an implicit OR operation for that field and the different criteria.When filtering by different specified fields, an implicit AND is performed between those criteria." so which are different fields, metadata, origin.task, tags, those are different fields. Note different metadata fields are treated as different fields as well, so it will be an AND operation for different metadata fields.
As a quick example, the following code gives us all files in the project RFranklin/demo that with metadata sample_id “Sample1” OR “Sample2”, AND the library id has to be “EXAMPLE”, AND tags has either “hello” OR “world”.
There are two ways to return the exact file you want. One is by id, the other one is by exact name.
For advanced users: When searching by name with exact = TRUE, it directly uses our public API call to match the exact name and return the object with a single query. When you turn off exact, it will grep all files or files defined with limit and offset first, then match by name, which could be slow.
To get the file id, you can check the URL on the web interface from the file details page.
-
# return single object id is "some_file_id"
-p$file(id="some_file_id")
+
+# return single object id is "some_file_id"
+p$file(id ="some_file_id")# return a single object named a.fastq
-p$file(name="a.fastq", exact=TRUE)
+p$file(name ="a.fastq", exact =TRUE)# public file search using Auth object
-a$public_file(name="ucsc.hg19.fasta.fai", exact=TRUE)
-a$public_file(id="578cf94a507c17681a3117e8")
To get more than one object, the arguments id and name both accept vectors, so you can pass more than one id or name to it.
-
# get two files
-p$file(name=c("test1.fastq", "test2.fastq"), exact=TRUE)
+
+# get two files
+p$file(name =c("test1.fastq", "test2.fastq"), exact =TRUE)# get two files from public files using shorthand
-a$public_file(
- name=c("ucsc.hg19.fasta.fai", "ucsc.hg19.fasta"),
- exact=TRUE
-)
+a$public_file(
+ name =c("ucsc.hg19.fasta.fai", "ucsc.hg19.fasta"),
+ exact =TRUE
+)
If you did not use exact = TRUE, the API would assume it is pattern grep and use partial matching.
-
# get matchd the pattern for searching first 100 files
-p$file(name=c("gz", "fastq"))
+
+# get matchd the pattern for searching first 100 files
+p$file(name =c("gz", "fastq"))# get all matched files from the project
-p$file(name=c("gz", "fastq"), complete=TRUE)
+p$file(name =c("gz", "fastq"), complete =TRUE)# get all files matched ucsc
-a$public_file(name="ucsc.hg19", complete=TRUE)
When you specify different metadata fields, the values use an AND operation, and when you specify the same metadata fields, the values use an OR operation.
To list all files in project RFranklin/demo, that have a sample_id of “Sample1” OR “Sample2” AND another metadata field library_id set to “EXAMPLE”, make the following request.
# list all outputs file from a task id
-a$task(id="53020538-6936-422f-80de-02fa65ae4b39")$file()
+
+# list all outputs file from a task id
+a$task(id ="53020538-6936-422f-80de-02fa65ae4b39")$file()# OR
-p<-a$project(id="RFranklin/demo")
-p$file(origin.task="53020538-6936-422f-80de-02fa65ae4b39")
NOTE: To copy a group of files, you need the Auth$copyFile() interface. The id of the files in your project will be different from their id in the Public Files repository.
Alternatively, you copy a single file as shown below.
Notably, if you have many files to copy between projects, we would always recommend using the Auth$bulk_file_copy() method described in the Actions API section below, to batch copy the files in a single API call and save the number of API calls.
As an alternative to API file uploading, we also offer an interface in the sevenbridges package for uploading (possibly large) files via the Java-based command line uploader.
The first step is to download the command line uploader. With get_uploader(), users can easily download the uploader to a local directory for a specified Seven Bridges platform, for example, the Cancer Genomics Cloud:
To list the available projects accessible from your account via the uploader, use cli_list_projects(), with your authentication token for that platform and the directory containing the uploader:
The file’s metadata. The metadata is in the form of a list. Note that as this call overwrites all the metadata for a given file, please supply a full list of all the metadata for the file. Conversely, you can use the set_meta() function to patch but not overwrite the metadata for a file.
If no parameters were provided, this call will get the details of that file and update the object itself.
Note that the name of a file is not the same as its ID. A file’s ID is a hexadecimal string automatically assigned to a file in a project. The file’s name is a human-readable string. For information, please see the API overview for the Seven Bridges Platform and for the CGC.
Get metadata
To get the metadata for a file, call the meta() function, as shown below.
-
# meta is pulling the latest information via API
-fl$meta()
+
+# meta is pulling the latest information via API
+fl$meta()# field metadata saved the previously saved one
-fl$metadata
+fl$metadata
Set metadata
Files from curated datasets on Seven Bridges environments have a defined set of metadata which is visible on the visual interface of each environment. However, you can also pass additional metadata for each file which is stored with your copy of the file in your project.
To set metadata but not overwrite all previously stored metadata, call the set_meta() function from the Files object. Unlike the update() function for the Files object, the set_meta() function doesn’t overwrite the metadata field. To overwrite the metadata field, use the update() function or pass the optional argument overwrite = TRUE for the set_meta() call.
-
fl$set_meta(new_item3="item3")
-fl
+
+fl$set_meta(new_item3 ="item3")
+fl# oops it removed rest of the meta
-fl$set_meta(new_item4="item4", overwrite=TRUE)
-fl
You can use the Metadata() constructor to check the details of each metadata field. Simply call the function (name of the metadata) to display descriptions and enumerated items. Please pay attention to the suggested_values field.
-
# check which schema we have
-Metadata()$show(full=TRUE)
+
You can use the Metadata() constructor to check the details of each metadata field. Simply call the function (name of the metadata) to display descriptions and enumerated items. Please pay attention to the suggested_values field.
As you can see, some metadata fields have suggested values. To construct the metadata, use Metadata() directly to pass the metadata fields and their values directly into the call. The call will perform the validation.
As you can see, some metadata fields have suggested values. To construct the metadata, use Metadata() directly to pass the metadata fields and their values directly into the call. The call will perform the validation.
Below, find convenient methods for FilesList. Let’s add the tag “s1” to a group files matching “Sample1” and add the tag “s2” to a group of files that match “s2”. Then, we will filter by these tags to obtain specific files.
-
# work on a group of files
+
+# work on a group of files# add tag "s2" to a group of files named with "Sample2" in it
-fl2<-p$file("Sample2")
-add_tag(fl2, "s2")
+fl2<-p$file("Sample2")
+add_tag(fl2, "s2")# add tag "s2" to a group of files named with "Sample1" in it
-fl1<-p$file("Sample1")
-add_tag(fl1, "s1")
+fl1<-p$file("Sample1")
+add_tag(fl1, "s1")# filter by tag s1 or s2
-p$file(tag="s1")
-p$file(tag="s2")
+p$file(tag ="s1")
+p$file(tag ="s2")# get files tagged with s2 and 1234
-p$file(tag=list("s2", "s1"))
To create new folders and manage files using folders in a project, we first need to get the information about the root folder of the project. For example
To search for a specific app by its name, pass a pattern for the name argument or provide a unique id as shown below.
-
# pattern match
-a$app(name="STAR")
+
+# pattern match
+a$app(name ="STAR")# unique id
-aid<-a$app()[[1]]$id
-aid
-a$app(id=aid)
+aid<-a$app()[[1]]$id
+aid
+a$app(id =aid)# get a specific revision from an app
-a$app(id=aid, revision=0)
+a$app(id =aid, revision =0)
List all apps in a project
To list all apps belonging to a specific project, use the project argument.
-
# my favorite, always
-a$project("demo")$app()
+
+# my favorite, always
+a$project("demo")$app()# or alternatviely
-pid<-a$project("demo")$id
-a$app(project=pid)
+pid<-a$project("demo")$id
+a$app(project =pid)
List all public apps
To list all public apps, use the visibility argument.
-
# show 100 items from public
-x<-a$app(visibility="public")
-length(x)
-x<-a$app(visibility="public", complete=TRUE)
-length(x)
-x<-a$app(project="RFranklin/helloworld", complete=TRUE)
-length(x)
-a$app(visibility="public", limit=5, offset=150)
+
+# show 100 items from public
+x<-a$app(visibility ="public")
+length(x)
+x<-a$app(visibility ="public", complete =TRUE)
+length(x)
+x<-a$app(project ="RFranklin/helloworld", complete =TRUE)
+length(x)
+a$app(visibility ="public", limit =5, offset =150)
Search through all public apps in all locations
To search for a public app cross all locations, make the following call. Note that this may take a bit of time.
This call returns the raw CWL of a specific app, including its raw CWL. The call differs from the call to GET details of an app in that it returns a JSON object that is the CWL.
The app should be one in a project that you can access, either an app which has been uploaded by a project member or a publicly available app which has been copied to the project.
To get a specific revision, pass the revision argument.
On Seven Bridges platforms, when you create or update your tools in the GUI, there is a test tab allow users to tweak the parameters and see what it looks like in your command line simulated terminal. To do this via R when you push your Tool object to your project, you need to provide "sbg:job" information like the example shown below
Or if you have created test info on the platform or previously pushed one, and you want to keep the previous test setup. We provide an argument named keep_test to allow you to keep the previous revision’s test information.
inputs list. Note that you can only update the items you provided.
-
# get the single task you want to update
-tsk<-a$project("demo")$task("Draft runif 3")
-tsk
-tsk$update(
- name="Draft runif update",
- description="draft 2",
- inputs=list(max=100)
-)
+
+# get the single task you want to update
+tsk<-a$project("demo")$task("Draft runif 3")
+tsk
+tsk$update(
+ name ="Draft runif update",
+ description ="draft 2",
+ inputs =list(max =100)
+)# alternative way to check all inputs
-tsk$getInputs()
Note that you can only delete DRAFT tasks, not running tasks.
-
tsklst<-a$task(status="draft")
+
+tsklst<-a$task(status ="draft")# delete a single task
-tsklst[[1]]$delete()
+tsklst[[1]]$delete()# confirm
-a$task(status="draft")
+a$task(status ="draft")# delete a list of tasks
-delete(tsklst)
+# list all volume
+a$volume()# get unique volume by id
-a$volume(id="RFranklin/RFranklin_demo")
+a$volume(id ="RFranklin/RFranklin_demo")# partial search by name
-a$volume(name="demo")
This call deletes a volume you’ve created to refer to storage on Amazon Web Services or Google Cloud Storage.
Note that any files you’ve imported from your volume onto a Seven Bridges environment, known as an alias, will no longer be usable. If a new volume is created with the same volume_id as the deleted volume, aliases will point to files on the newly created volume instead (if those exist).
+v<-a$volume(id ="RFranklin/tutorial_volume")
+res<-v$import(
+ location ="A-RNA-File.bam.bai",
+ project ="RFranklin/s3tutorial",
+ name ="new.bam.bai",
+ overwrite =TRUE
+)# get job status update# state will be "COMPLETED" when it's finished, otherwise "PENDING"
-v$get_import_job(res$id)
-v
+v$get_import_job(res$id)
+v
@@ -2250,16 +2415,17 @@
If this call is successful, the original project file will become an alias to the newly exported object on the volume. The source file will be deleted from the Seven Bridges environment and, if no more copies of this file exist, it will no longer count towards your total storage price on the Seven Bridges environment.
When testing, please update your file in a project.
-
res<-v$export(
- file="579fb1c9e4b08370afe7903a",
- volume="RFranklin/tutorial_volume",
- location="", # when "" use old name
- sse_algorithm="AES256"
-)
+
+res<-v$export(
+ file ="579fb1c9e4b08370afe7903a",
+ volume ="RFranklin/tutorial_volume",
+ location ="", # when "" use old name
+ sse_algorithm ="AES256"
+)# get job status update# state will be "COMPLETED" when it's finished other wise "PENDING"
-v$get_export_job(res$id)
-v
+# list the first 100 files
+a$public_file()# list by offset and limit
-a$public_file(offset=100, limit=100)
+a$public_file(offset =100, limit =100)# simply list everything!
-a$public_file(complete=TRUE)
+a$public_file(complete =TRUE)# get exact file by id
-a$public_file(id="5772b6f0507c175267448700")
+a$public_file(id ="5772b6f0507c175267448700")# get exact file by name with exact = TRUE
-a$public_file(name="G20479.HCC1143.2.converted.pe_1_1Mreads.fastq", exact=TRUE)
+a$public_file(name ="G20479.HCC1143.2.converted.pe_1_1Mreads.fastq", exact =TRUE)# with exact = FALSE by default search by name pattern
-a$public_file(name="fastq")
-a$public_file(name="G20479.HCC1143.2.converted.pe_1_1Mreads.fastq")
+# list for 100 apps
+a$public_app()# list by offset and limit
-a$public_app(offset=100, limit=50)
+a$public_app(offset =100, limit =50)# search by id
-a$public_app(id="admin/sbg-public-data/control-freec-8-1/12")
+a$public_app(id ="admin/sbg-public-data/control-freec-8-1/12")# search by name in ALL apps
-a$public_app(name="STAR", complete=TRUE)
+a$public_app(name ="STAR", complete =TRUE)# search by name with exact match
-a$public_app(name="Control-FREEC", exact=TRUE, complete=TRUE)
This call allows you to copy files between projects. Unlike a$copyFile() or file(...)$copyTo(...) which only copy one file with each API call, this call lets you batch the copy operation and copy a list of files at a time.
+p<-a$project(id ="RFranklin/source-project")
+f<-p$file(complete =TRUE)# get all file IDs
-file_ids<-sapply(f, "[[", "id")
+file_ids<-sapply(f, "[[", "id")# bulk copy files to the target project
-req<-a$bulk_file_copy(file_ids, "RFranklin/target-project")
+req<-a$bulk_file_copy(file_ids, "RFranklin/target-project")# print the response list
-(req<-unname(req))
Note that a file’s ID is dependent on the project it is contained in. So, when you copy files from one project to another, their IDs will be different in the target project. The file names, however, do not change by copying, except where copying would yield non-unique file names in the target project.
@@ -2353,10 +2525,11 @@
Send a feedback item
Use this call to send feedbacks to Seven Bridges via the API. There are three feedback types available: "idea", "thought", or "problem". You can send one feedback item per minute.
-
a$send_feedback(
+
+a$send_feedback("This is a test for sending feedback via API. Please ignore this message.",
- type="thought"
-)
To check the details (ID and name) of the first team we just created, use
-
d$team(team1$id)
+
+d$team(team1$id)
== Team ==
id : 39f6cb44-9e3c-40e7-8639-3c0f3c1e4892
name : New Team 1
@@ -2433,10 +2610,11 @@
Add a team member
We can add division members to the teams we created. For example, let’s add yourself (with the division username the-division/your_username) and another division member (with the division username the-division/another_username) to the first team. Also, let’s add yourself to the second team:
The Markers API allows you to add, get, modify, and delete genetic markers on BAM files. These genetic markers can be then examined in the Seven Bridges Genome Browser.
As of November 2018, the Markers API is still an Advance Access feature. To use this API, set the advance_access option to TRUE:
# locate the project
-p<-a$project(id="RFranklin/api-markers")
+
+# locate the project
+p<-a$project(id ="RFranklin/api-markers")# search for files with `.bam` in their names
-f<-p$file(name=".bam")
+f<-p$file(name =".bam")# use the first BAM file
-f<-p$file(id=f[[1]]$id)
+f<-p$file(id =f[[1]]$id)# create two markers
-m1<-f$create_marker("The First Marker", start=21631232, end=21631232)
-m2<-f$create_marker("The Second Marker", start=21631156, end=21631158, chromosome="chr7", private=FALSE)
+m1<-f$create_marker("The First Marker", start =21631232, end =21631232)
+m2<-f$create_marker("The Second Marker", start =21631156, end =21631158, chromosome ="chr7", private =FALSE)
The possible parameters for creating the marker include
In the easy API, we return an object which contains the raw response from httr as a field. You can either call response() on that object or use the field as is.
+
In the easy API, we return an object which contains the raw response from httr as a field. You can either call response() on that object or use the field as is.
diff --git a/docs/articles/api_files/header-attrs-2.4/header-attrs.js b/docs/articles/api_files/header-attrs-2.4/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/api_files/header-attrs-2.4/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/articles/apps.html b/docs/articles/apps.html
index aeda395..766dd6f 100644
--- a/docs/articles/apps.html
+++ b/docs/articles/apps.html
@@ -44,7 +44,7 @@
sevenbridges-r
- 1.19.1
+ 1.19.2
Sometimes people share Tool in pure JSON text format. You can simply load it into R by using convert_app function, this will recognize your JSON file class (Tool or Workflow) automatically.
In this way, you can load it, revise it, use it with API or edit and export it back to JSON file. However, in this tutorial, the most important thing is that you learn how to desribe it directly in R.
@@ -172,22 +173,25 @@
Utilitites for Tool object
We provide couple utitlities to help construct your own CWL tool quickly in R. For all availale utiles please check out help("Tool")
Some utiles you will find it useful when you execute a task, you need to know what is the input type and what is the input id and if it’s required or not, so you can execute the task with parameters it need. Try play with input_matrix or input_type as shown below.
-
# get input type information
-head(t1$input_type())
+
+# get input type information
+head(t1$input_type())
# get full input id with Tool name
-head(t1$input_id(TRUE))
+
+# get full input id with Tool name
+head(t1$input_id(TRUE))
File... enum
"#STAR.reads" "#STAR.readMatesLengthsIn"
int int
"#STAR.readMapNumber" "#STAR.limitOutSJoneRead"
int enum
"#STAR.limitOutSJcollapsed" "#STAR.outReadsUnmapped"
# get input and output object
-t1$get_input(id="#winFlankNbins")
+
+# get input and output object
+t1$get_input(id ="#winFlankNbins")
type:
- 'null'
- int
@@ -317,7 +335,8 @@
sbg:category: Windows, Anchors, Binning
sbg:toolDefaultValue: '4'
required: no
-
t1$get_input(name="ins")
+
+t1$get_input(name ="ins")
[[1]]
type:
- 'null'
@@ -360,7 +379,8 @@
sbg:category: Limits
sbg:toolDefaultValue: '1000000'
required: no
-
t1$get_output(id="#aligned_reads")
+
+t1$get_output(id ="#aligned_reads")
type:
- 'null'
- File
@@ -389,7 +409,8 @@
}
class: Expression
sbg:fileTypes: SAM, BAM
-
t1$get_output(name="gene")
+
+t1$get_output(name ="gene")
type:
- 'null'
- File
@@ -409,9 +430,10 @@
Introduction
Before we continue, this is how it looks like for full tool description, you don’t always need to describe all those details, following section will walk you through simple examples to full examples like this one.
+Requirements and hints: in short, hints are not required for execution. We now accept following requirement items cpu, mem, docker, fileDef; and you can easily construct them via requirements() constructor. This is how you describe the resources you need to execute the tool, so the system knows what type of instances suit your case best.
-
To specify inputs and outpus, usually your command line interface accept extra arguments as input, for example, file(s), string, enum, int, float, boolean. So to specify that in your tool, you can use input function, then pass it to the inputs arguments as a list or single item. You can even construct them as data.frame with less flexibility. input() require arguments id and type. output() require arguments id because type by default is file.
+
To specify inputs and outpus, usually your command line interface accept extra arguments as input, for example, file(s), string, enum, int, float, boolean. So to specify that in your tool, you can use input function, then pass it to the inputs arguments as a list or single item. You can even construct them as data.frame with less flexibility. input() require arguments id and type. output() require arguments id because type by default is file.
There are some special type: ItemArray and enum. For ItemArray the type could be an array of single type, the most common case is that if your input is a list of files, you can do something like type = ItemArray("File") or as simple as type = "File..." to diffenciate from a single file input. When you add “…” suffix, R will know it’s an ItemArray.
We also provide an enum type, when you specify the enum, please pass the required name and symbols like this type = enum("format", c("pdf", "html")) then in the UI on the platform you will be poped with drop down when you execute the task.
Now let’s work though from simple case to most flexible case.
@@ -500,18 +522,19 @@
Using existing Docker images and command
If you already have a Docker image in mind that provide the functionality you need, you can just use it. The baseCommand is the command line you want to execute in that container. stdout specify the output file you want to capture the standard output and collect it on the platform.
In this simple example, we know Docker image rocker/r-base has a function called runif we can directly call in the command line with Rscript -e. Then we want the ouput to be collected in stdout and ask the file system to capture the output files that matches the pattern *.txt. Please pay attention to this, your tool may produce many intermediate files in the current folder, if you don’t tell which output you need, they will all be ignored, so make sure you collect those files via the outputs parameter.
By default, the tool object shows YAML, but you can simply convert it to JSON and copy it to your seven bridges platform graphic editor by importing JSON.
Now you may want to run your own R script, but you still don’t want to create new command line and a new Docker image. You just want to run your script with new input files in existing container, it’s time to introduce fileDef. You can either directly write script as string or just import a R file to content. And provided as requirements.
-
# Make a new file
-fd<-fileDef(
- name="runif.R",
- content="set.seed(1); runif(100)"
-)
+
+# Make a new file
+fd<-fileDef(
+ name ="runif.R",
+ content ="set.seed(1); runif(100)"
+)# read via reader
-.srcfile<-system.file("docker/sevenbridges/src/runif.R", package="sevenbridges")
+.srcfile<-system.file("docker/sevenbridges/src/runif.R", package ="sevenbridges")
-fd<-fileDef(
- name="runif.R",
- content=readr::read_file(.srcfile)
-)
+fd<-fileDef(
+ name ="runif.R",
+ content =readr::read_file(.srcfile)
+)# add script to your tool
-rbx<-Tool(
- id="runif",
- label="runif",
- hints=requirements(docker(pull="rocker/r-base")),
- requirements=requirements(fd),
- baseCommand="Rscript runif.R",
- stdout="output.txt",
- outputs=output(id="random", glob="*.txt")
-)
+rbx<-Tool(
+ id ="runif",
+ label ="runif",
+ hints =requirements(docker(pull ="rocker/tidyverse"), cpu(1), mem(2000)),
+ requirements =requirements(fd),
+ baseCommand ="Rscript -e \"rmarkdown::render(knitr::spin('runif.R', FALSE))\" --args",
+ stdout ="output.txt",
+ inputs =list(
+ input(
+ id ="number",
+ type ="integer",
+ separate =FALSE,
+ prefix ="--n="
+ ),
+ input(
+ id ="min",
+ type ="float",
+ separate =FALSE,
+ prefix ="--min="
+ ),
+ input(
+ id ="max",
+ type ="float",
+ separate =FALSE,
+ prefix ="--max="
+ )
+ ),
+ outputs =list(
+ output(id ="stdout", type ="file", glob ="output.txt"),
+ output(id ="random", type ="file", glob ="*.csv"),
+ output(id ="report", type ="file", glob ="*.html")
+ )
+)
You will get a report in the end.
@@ -995,27 +1032,28 @@
Misc
Inherit metadata and additional metadata
Sometimes if you want your output files inherit from particular input file, just use inheritMetadataFrom in your output() call and pass the input file id. If you want to add additional metadata, you could pass metadata a list in your output() function call. For example, I want my output report inherit all metadata from my “bam_file” input node (which I don’t have in this example though) with two additional metadata fields.
Note: this workflow contains tools that do not exist on the platform, so if you copy and paste the JSON directly into the GUI, it won’t work properly. However, a simple way is to push your app to the platform via API. This will add new tools one by one to your project before add your workflow app on the platform. Alternatively, if you connect two tools you know that exist on the platform, you don’t need to do so.
-
# auto-check tool info and push new tools
-p$app_add("new_flow_log", f)
+
+# auto-check tool info and push new tools
+p$app_add("new_flow_log", f)
@@ -1422,16 +1479,17 @@
Checking potential mapping is easy with function link_what, it will print matched input and outputs. Then the generic function link will allow you to connect two Tool objects
If you don’t specify which input/ouput to expose at flow level for new Flow object, it will expose all availabl ones and print the message, otherwise, please provide parameters for flow_input and flow_output with full id.
diff --git a/docs/articles/apps_files/header-attrs-2.4/header-attrs.js b/docs/articles/apps_files/header-attrs-2.4/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/apps_files/header-attrs-2.4/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/articles/bioc-workflow.html b/docs/articles/bioc-workflow.html
index 0afc600..10aa249 100644
--- a/docs/articles/bioc-workflow.html
+++ b/docs/articles/bioc-workflow.html
@@ -44,7 +44,7 @@
sevenbridges-r
- 1.19.1
+ 1.19.2
First download the R Markdown source of this page, so you can load it to your RStudio or your favorite IDE to run through all examples and tweak setup.
Now let’s do some simple steps, first thing to do is to create an Auth object, almost everything started from this object. Our API client follow a style like this Auth$properties$action. On the platform, Auth is your account, and it contains projects, billing groups, users, project contains tasks, apps, files etc, so it’s easy to imagine your API call.
To create an Auth object, simply pass the token and platform name (or alternatively, API base URL) to the Auth function. The default platform is set to CGC. Good news you can use the sevenbridges package to access any Seven Bridges platform with API (v2).
This is the main way to create an Auth object, just replace your_token with your own authentication token from the CGC:
Alternatively, you can save your authentication cross different platforms in a user configuration file named credentials under the directory $HOME/.sevenbridges/. This allows you to manage multiple user profiles registered on multiple Seven Bridges environments. An example user configuration file looks like this:
When you have this user configuration file ready at this default location, all you need to do is setting from = "file" and choose the profile_name to use. For example:
The third way to save authentication information is by setting system environment variables. To set the two environment variables (SB_API_ENDPOINT and SB_AUTH_TOKEN) in your system, you could use the function sbg_set_env(). For example:
To create a new project, you need to know your billing group id, cost related to this project will be charged from this billing group, now play with your free credit.
-
(b<-a$billing())
-# a single billing group is showing
+
+(b<-a$billing())
+# a single billing group is showing
Now let’s create a new project called “hackathon”, save it to an object named p for convenient usage for any call related to this project.
-
(p<-a$project_new("hackathon",
- billing_group_id=b$id,
- description="This project is for CGC hackathon"
-))
+
+(p<-a$project_new("hackathon",
+ billing_group_id =b$id,
+ description ="This project is for CGC hackathon"
+))
Now check it on CGC, you will see a fresh new project is created.
To delete it, just call, but I will suggest you keep it for following tutorial : )
-
# p$delete()
+
+# p$delete()
@@ -363,15 +372,16 @@
For example, you know there is an runif function in the rocker/r-base container, you can just do something like this. Please read another tutorial called “Describe and Execute CWL Tools/Workflows in R”, it introduced a simpler example with random number generator.
If you don’t want to make a new container with command line interface and you can simply insert script temporarily for existing container. You can do things as quick as this, by provide a script in the ‘content’ of ‘fileDef’.
-
# provide scripts
+
+# provide scripts# make a new script file
-fd<-fileDef(
- name="runif.R",
- content="set.seed(1); runif(100)"
-)
-rbx<-Tool(
- id="runif",
- label="runif",
- hints=requirements(docker(pull="rocker/r-base"), cpu(1), mem(2000)),
- requirements=requirements(fd),
- baseCommand="Rscript runif.R", # run script you created.
- stdout="output.txt",
- outputs=output(id="random", glob="*.txt")
-)
Note: in the above example, I made a mistake on purpose, so try to debug on the platform if the task fails : )
I will introduce “Tool” function, in the later section, don’t worry.
For advanced developer/users: if you think a cool command line interface is worth doing, and convenient in any case, then go ahead and make one and create our own container will always be a better idea. this allow you to provide much formal interface at both command line level or container level.
Here is Dockerfile I used to generate the workflow I need
As a developer, if you always have a fixed report template provided for you tool, you can hard-code your template into your Docker container, like you noticed in the Dockerfile I created, I insert a report R Markdown template. And in the command line interface, in the end, I pass it to rmarkdown::render function as parameters ‘param’. In this way, you define your input in the header of R Markdown. Examples like this, this template I used here, is exactly the same report on Bioconductor’s RNA-Seq workflow website.
---
title: "Uniform random number generator example"
date: "`r Sys.Date()`"
@@ -631,64 +648,66 @@
Expression to specify the javascript expression (note:it’s convenient to do it with graphic user interface, because you can directly see the result of the expression)
{"sbg:id":"rnaseqGene","id":"#rnaseqGene","inputs":[{"type":["null",{"items":"File","type":"array"}],"label":"bam files","description":"a list of bam files","streamable":false,"default":"","id":"#bamfiles","inputBinding":{"position":0,"prefix":"--bamfiles","separate":true,"itemSeparator":",","shellQuote":false,"sbg:cmdInclude":true,"streamable":false,"separator":" "},"required":false},{"type":["null","File"],"label":"design matrix","description":"","streamable":false,"default":"","id":"#design","inputBinding":{"position":0,"prefix":"--design","separate":true,"shellQuote":false,"sbg:cmdInclude":true,"streamable":false,"separator":" "},"required":false},{"type":["null","File"],"label":"gene feature files","description":"","streamable":false,"default":"","id":"#gtffile","inputBinding":{"position":0,"prefix":"--gtffile","separate":true,"shellQuote":false,"sbg:cmdInclude":true,"streamable":false,"separator":" "},"required":false},{"type":["null",{"name":"format","symbols":["pdf","html"],"type":"enum"}],"label":"report foramt html or pdf","description":"","streamable":false,"default":"","id":"#format","inputBinding":{"position":0,"prefix":"--format","separate":true,"shellQuote":false,"sbg:cmdInclude":true,"streamable":false,"separator":" "},"required":false}],"outputs":[{"type":["null","File"],"label":"report","description":"A reproducible report created by Rmarkdown","streamable":false,"default":"","id":"#report","outputBinding":{"glob":{"engine":"#cwl-js-engine","script":"x = $job[['inputs']][['format']]; if(x == 'undefined' || x == null){x = 'html';};'rnaseqGene.' + x","class":"Expression"}}},{"type":["null","File"],"label":"heatmap","description":"A heatmap plot to show the Euclidean distance between samples","streamable":false,"default":"","id":"#heatmap","outputBinding":{"glob":"heatmap.pdf"}},{"type":["null","File"],"label":"count","description":"Reads counts matrix","streamable":false,"default":"","id":"#count","outputBinding":{"glob":"count.csv"}},{"type":["null","File"],"label":"Differential expression table","description":"Differential expression table","streamable":false,"default":"","id":"#de","outputBinding":{"glob":"de.csv"}}],"requirements":[],"hints":[{"class":"DockerRequirement","dockerPull":"tengfei/rnaseqgene"},{"class":"sbg:CPURequirement","value":1},{"class":"sbg:MemRequirement","value":2000}],"label":"rnaseqgene","description":"A RNA-seq Differiencial Expression Flow and Report","class":"CommandLineTool","baseCommand":["performDE.R"],"arguments":[]}
-
# # or write to an external file
-# fl <- "~/Downloads/rnaseqGene.json"
-# write(rbx$toJSON(pretty = TRUE), fl)
+
+# # or write to an external file
+# fl <- "~/Downloads/rnaseqGene.json"
+# write(rbx$toJSON(pretty = TRUE), fl)
Now you want to add app to your project p, by calling app_add method, the first argument is name, the second is either a CWL JSON file, Tool object, or Workflow object.
-
# add App you just created
-(rna.app<-p$app_add("rnaseqgene", rbx))
+
+# add App you just created
+(rna.app<-p$app_add("rnaseqgene", rbx))
Please go check your app in your project, check input output and how it maps to the UI.
@@ -1015,30 +1038,32 @@
metadata: a list of meta otherwise search for the same file name ended with “.meta”
Copy this list, and then in your project, click “add files”, and choose “import from ftp” (tutorial).
When it’s finished, refresh your file page, you will be able to see all of them. The cool thing is that you can search file by ‘name’ not by id, it support fuzzy pattern match.
-
# get file id you need as inout
-(bamfiles.in<-p$file(".bam"))
-(design.in<-p$file("sample_table.csv"))
-(gtf.in<-p$file("Homo_sapiens.GRCh37.75_subset.gtf"))
+
+# get file id you need as inout
+(bamfiles.in<-p$file(".bam"))
+(design.in<-p$file("sample_table.csv"))
+(gtf.in<-p$file("Homo_sapiens.GRCh37.75_subset.gtf"))
Note: you can also passed a list of files like this, to give exact file names
Now create a new draft task in your project, don’t forget to pass input.
-
# add a new Task
-(tsk<-p$task_add(
- name="RNA DE report new",
- description="RNA DE analysis report",
- app=rna.app$id,
- inputs=list(
- bamfiles=bamfiles.in,
- design=design.in,
- gtffile=gtf.in
- )
-))
+
+# add a new Task
+(tsk<-p$task_add(
+ name ="RNA DE report new",
+ description ="RNA DE analysis report",
+ app =rna.app$id,
+ inputs =list(
+ bamfiles =bamfiles.in,
+ design =design.in,
+ gtffile =gtf.in
+ )
+))# don't forget to run a draft task
-tsk$run()
+tsk$run()
To monitor the task, run following command, it will tell you when it’s finished, but this is not running in the background now.
-
# # monitor the task
-# tsk$monitor()
+
+# # monitor the task
+# tsk$monitor()
A better way is to use the Task hook function, it’s flexible, you can hook any function to a task status. For example, when it’s complete download the files. Now try to send your self a text message : )
Even though in R with sevenbridges package you can use %>>% to connect two Tool object, but this only works for linear simple flow. For a complicated flow, we recommend you to use graphic user interface, it’s a lot of fun.
+
Even though in R with sevenbridges package you can use %>>% to connect two Tool object, but this only works for linear simple flow. For a complicated flow, we recommend you to use graphic user interface, it’s a lot of fun.
Now connect your RNA-seq tool with the report tool you just add it to your project, follow the tutorial here, then make a workflow like this:
And next run a task on the platform via UI like this:
diff --git a/docs/articles/bioc-workflow_files/header-attrs-2.4/header-attrs.js b/docs/articles/bioc-workflow_files/header-attrs-2.4/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/bioc-workflow_files/header-attrs-2.4/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/articles/cgc-datasets.html b/docs/articles/cgc-datasets.html
index 902c807..ba638ca 100644
--- a/docs/articles/cgc-datasets.html
+++ b/docs/articles/cgc-datasets.html
@@ -44,7 +44,7 @@
sevenbridges-r
- 1.19.1
+ 1.19.2
@@ -107,12 +107,12 @@
-
+
Find Data on CGC via Data Browser and Datasets API
Get file id from Datasets API, then use public API to copy files. Make sure your project is “TCGA” compatible, otherwise if you are trying to copy controlled access data to your non-TCGA project, you will get
"HTTP Status 403: Insufficient privileges to access the requested file."
Suppose you want to see all cases for which the age at diagnosis is between 10 and 50. Then, you use the following query.
Note that the value of the metadata field hasAgeAtDiagnosis is a dictionary containing the keyfilter, whose value is a further dictionary with keysgt(greater than) and lt (less than) for the upper and lower bounds to filter by.
Find cases with a given age at diagnosis and disease
Suppose you want to see all cases that, as in the example, (Find cases with given age at diagnosis)(doc:find-all-cases-with-a-given-age-at-diagnosis), have an age at diagnosis of between 10 and 50, but that also have the disease “Kidney Chromophobe”. Then, use the following query:
+(res<-a$api(path ="files", body =body))
+get_id(res)
@@ -318,11 +331,11 @@
diff --git a/docs/articles/cgc-datasets_files/header-attrs-2.4/header-attrs.js b/docs/articles/cgc-datasets_files/header-attrs-2.4/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/cgc-datasets_files/header-attrs-2.4/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/articles/docker.html b/docs/articles/docker.html
index 9080a0f..76e0b9b 100644
--- a/docs/articles/docker.html
+++ b/docs/articles/docker.html
@@ -44,7 +44,7 @@
sevenbridges-r
- 1.19.1
+ 1.19.2
@@ -107,12 +107,12 @@
-
+
Creating Your Docker Container and Command Line Interface (with docopt)
lower and upper limits of the distribution. Must be finite.
I will add one more parameter to set seed, here is the R script file called runif.R.
At the beginning of the command line script, I use the docopt standard to write my tool help.
-
"usage: runif.R [--n=<int> --min=<float> --max=<float> --seed=<float>]\n\noptions:\n --n=<int> number of observations. If length(n) > 1, the length is taken to be the number required [default: 1].\n --min=<float> lower limits of the distribution. Must be finite [default: 0].\n --max=<float> upper limits of the distribution. Must be finite [default: 1].\n --seed=<float> seed for set.seed() function [default: 1]"->doc
+
+"usage: runif.R [--n=<int> --min=<float> --max=<float> --seed=<float>]\n\noptions:\n --n=<int> number of observations. If length(n) > 1, the length is taken to be the number required [default: 1].\n --min=<float> lower limits of the distribution. Must be finite [default: 0].\n --max=<float> upper limits of the distribution. Must be finite [default: 1].\n --seed=<float> seed for set.seed() function [default: 1]"->doc
-library("docopt")
Add Shebang at the top of the file, this is a complete example for runif.R command line will be like this
-
#!/usr/bin/Rscript
-"usage: runif.R [--n=<int> --min=<float> --max=<float> --seed=<float>]\n\noptions:\n --n=<int> number of observations. If length(n) > 1, the length is taken to be the number required [default: 1].\n --min=<float> lower limits of the distribution. Must be finite [default: 0].\n --max=<float> upper limits of the distribution. Must be finite [default: 1].\n --seed=<float> seed for set.seed() function [default: 1]"->doc
-
-library("docopt")
-opts<-docopt(doc)
-set.seed(opts$seed)
-runif(
- n=as.integer(opts$n),
- min=as.numeric(opts$min),
- max=as.numeric(opts$max)
-)
+
+#!/usr/bin/Rscript
+"usage: runif.R [--n=<int> --min=<float> --max=<float> --seed=<float>]\n\noptions:\n --n=<int> number of observations. If length(n) > 1, the length is taken to be the number required [default: 1].\n --min=<float> lower limits of the distribution. Must be finite [default: 0].\n --max=<float> upper limits of the distribution. Must be finite [default: 1].\n --seed=<float> seed for set.seed() function [default: 1]"->doc
+
+library("docopt")
+opts<-docopt(doc)
+set.seed(opts$seed)
+runif(
+ n =as.integer(opts$n),
+ min =as.numeric(opts$min),
+ max =as.numeric(opts$max)
+)
I just describe my tool in this way, note, I use separate=FALSE and add = to my prefix as a hack.
+
+fd<-fileDef(
+ name ="runif.R",
+ content =readr::read_file(fl)
+)
+
+rbx<-Tool(
+ id ="runif",
+ label ="runif",
+ hints =requirements(docker(pull ="rocker/r-base"), cpu(1), mem(2000)),
+ requirements =requirements(fd),
+ baseCommand ="Rscript runif.R",
+ stdout ="output.txt",
+ inputs =list(
+ input(
+ id ="number",
+ type ="integer",
+ separate =FALSE,
+ prefix ="--n="
+ ),
+ input(
+ id ="min",
+ type ="float",
+ separate =FALSE,
+ prefix ="--min="
+ ),
+ input(
+ id ="max",
+ type ="float",
+ separate =FALSE,
+ prefix ="--max="
+ )
+ ),
+ outputs =output(id ="random", glob ="output.txt")
+)
Quick Report: Spin and Stich
Alternative, you can use spin/stich from knitr to generate report directly from an R script with special format. For example, let’s use the above example
+fd<-fileDef(
+ name ="runif.R",
+ content =readr::read_file(fl)
+)
+
+rbx<-Tool(
+ id ="runif",
+ label ="runif",
+ hints =requirements(docker(pull ="rocker/hadleyverse"), cpu(1), mem(2000)),
+ requirements =requirements(fd),
+ baseCommand ="Rscript -e \"rmarkdown::render(knitr::spin('runif.R', FALSE))\" --args",
+ stdout ="output.txt",
+ inputs =list(
+ input(
+ id ="number",
+ type ="integer",
+ separate =FALSE,
+ prefix ="--n="
+ ),
+ input(
+ id ="min",
+ type ="float",
+ separate =FALSE,
+ prefix ="--min="
+ ),
+ input(
+ id ="max",
+ type ="float",
+ separate =FALSE,
+ prefix ="--max="
+ )
+ ),
+ outputs =list(
+ output(id ="stdout", type ="file", glob ="output.txt"),
+ output(id ="random", type ="file", glob ="*.csv"),
+ output(id ="report", type ="file", glob ="*.html")
+ )
+)
You will get a report in the end.
@@ -582,7 +595,8 @@
Of course, we can figure out a way to do it in liftr or knitr. But R Markdown allow you to pass parameters to your R Markdown template, please read this tutorial Parameterized Reports. This doesn’t solve my problem that I want to directly describe command line interface in the markdown template. However, here is alternative method:
Create an command line interface to pass params from docopt into rmarkdown::render() function. In this way, we can pass as many as possible parameters from command line interface into our R Markdown template.
So here we go, here is updated methods and it’s also what I use for another tutorial about RNA-seq workflow.
diff --git a/docs/articles/docker_files/header-attrs-2.4/header-attrs.js b/docs/articles/docker_files/header-attrs-2.4/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/docker_files/header-attrs-2.4/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 8bc82d7..b9d1269 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -90,7 +90,7 @@
sevenbridges-r
- 1.19.1
+ 1.19.2
FROM rocker/tidyverse
-LABEL maintainer="nan.xiao@sevenbridges.com"
+LABEL maintainer="soner.koc@sevenbridges.com"
## Install common dependencies
RUN apt-get update && apt-get install -y \
@@ -299,11 +300,12 @@
In your browser, http://<url>:8787/ for RStudio Server, for example, if 192.168.99.100 is what returned, visit http://192.168.99.100:8787/ for RStudio Server.
For Shiny Server, per user app is hosted http://<url>:3838/users/<username of rstudio>/<app_dir>, for example, for user rstudio (a default user) and an app called 01_hello, it will be http://<url>:3838/users/rstudio/01_hello/. To develop your Shiny app from RStudio Server, you can log into your RStudio Server with your username, and create a fold at home folder called ~/ShinyApps and develop Shiny apps under that folder, for example, you can create an app called 02_text at ~/ShinyApps/02_text/.
Let’s try this, please log into your RStudio at http://<url>:8787 now, then try to copy some example over to your home folder under ~/ShinyApps/
If you logged in with the username rstudio, then visit http://192.168.99.100:3838/rstudio/01_hello you should be able to see the hello example.
Note: Generic Shiny apps can also be hosted http://<url>:3838/ or for particular app, http://<url>:3838/<app_dir> and inside the Docker container, it is hosted under /srv/shiny-server/.
@@ -321,11 +323,11 @@
diff --git a/docs/articles/rstudio_files/header-attrs-2.4/header-attrs.js b/docs/articles/rstudio_files/header-attrs-2.4/header-attrs.js
new file mode 100644
index 0000000..dd57d92
--- /dev/null
+++ b/docs/articles/rstudio_files/header-attrs-2.4/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/docs/authors.html b/docs/authors.html
index 8d983ac..9d7d856 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -90,7 +90,7 @@
sevenbridges-r
- 1.19.1
+ 1.19.2
First, check the version of R you are using with the following command (in R):
-
R.version.string
+
+R.version.string
If you are not running the latest release version of R, install or upgrade with these instructions. If you are using RStudio, restart RStudio after installing R. RStudio will detect the new installation.
@@ -176,34 +177,38 @@
Bioconductor - Release Branch
This is recommended for most users as it is the most stable version.
You can install the package from the release branch on Bioconductor using BiocManager:
If you are developing tools under the devel branch or use the development version of R and Bioconductor, install the package from the Bioconductor devel branch. You probably also want to install R-devel first by following the directions in “Using the ‘Devel’ Version of Bioconductor”.
To install the sevenbridges package from the devel branch, use
You may get an error for missing system dependecies such as curl and ssl. You probably need to do the following first in order to install devtools and to build vignettes since you need pandoc under Ubuntu.
If you have trouble with pandoc and do not want to install it, set build_vignettes = FALSE to avoid building the vignettes.
@@ -218,80 +223,86 @@
Direct authentication:
-
# Direct authentication
-a<-Auth(token="your_token", platform="cgc")
+
+# Direct authentication
+a<-Auth(token ="your_token", platform ="cgc")# or use base url
-a<-Auth(token="your_token", url="https://cgc-api.sbgenomics.com/v2")
A complete API R client with a user-friendly, object-oriented API with printing and support operations for API requests relating to users, billing, projects, files, apps, and tasks. Short examples are also included, as shown below:
-
# Get a project by pattern-matching its name
-p<-a$project("demo")
+
+# Get a project by pattern-matching its name
+p<-a$project("demo")# Get a project by its id
-p<-a$project(id="username/demo")
+p<-a$project(id ="username/demo")# Delete files from a project
-p$file("sample.tz")$delete()
+p$file("sample.tz")$delete()# Upload fies from a folder to a project and include file metadata
-p$upload("folder_path", metadata=list(platform="Illumina"))
A task monitoring hook which allows you to add a hook function to specific task statuses as you monitor a task. For example, you can opt to receive an email when the task is completed or specify to download all files produced by the task, as shown below:
A Common Workflow Language (CWL) Tool interface to directly describe your tool in R, export it to JSON or YAML, or add it to your online project. This package defines a complete set of CWL object, so you can describe tools as follows:
+library("sevenbridges")# convert a SBG CWL JSON file
-t1<-system.file("extdata/app", "tool_star.json", package="sevenbridges")
+t1<-system.file("extdata/app", "tool_star.json", package ="sevenbridges")# convert json file into a Tool object
-t1<-convert_app(t1)
+t1<-convert_app(t1)# shows all input matrix
-t1$input_matrix()
+t1$input_matrix()
@@ -406,12 +419,13 @@
In your browser, you can see where the RStudio server is located from the path http://<url>:8787/. For example, if 192.168.99.100 is returned, visit http://192.168.99.100:8787/ for Rstudio.
For the Shiny server, each app__ is hosted at http://<url>:3838/users/<username of rstudio>/<app_dir> for the Shiny server. For example, an app called 01_hello owned by user rstudio (a default user) has the path http://<url>:3838/users/rstudio/01_hello/. To develop your Shiny apps as an Rstudio user, you can login your RStudio server and create a folder in your home folder called ~/ShinyApps. There, you can develop shiny apps in that folder. For example, you can create an app called 02_text at ~/ShinyApps/02_text/.
Log into your RStudio at http://<url>:8787. Then, try to copy an app to your home folder, as follows:
If you are logged in as user rstudio, visit http://192.168.99.100:3838/rstudio/01_hello. You should be able to see the “hello” example.
Note: Generic Shiny apps can also be hosted at http://<url>:3838/ or, for a particular app, at http://<url>:3838/<app_dir>. Inside the Docker container, it’s hosted under /srv/shiny-server/.
@@ -420,7 +434,6 @@
FAQ
The best place to ask questions about the sevenbridges package is the mailing list.
-
Q: Does this package support Seven Bridges’ API v1 which was not CWL compatible? A: No. This package only supports API v2 +. For API v1, please check out the sbgr package. Note that API v1 and associated legacy project types will be deprecated eventually.
Q: Which version of the Common Workflow Language (CWL) is supported? A: We support draft 2 and are making progress on supporting draft 3.
Q: Is there a Python binding for the API? A: Yes, the official Python client is here. Recipes and tutorials using the Python bindings are here.
Q: Why do I get warning messages when I use the API R client? A: The warning only exists in Rstudio and is potentially a bug in Rstudio. To ignore, it use options(warn = -1)
Added a new argument authorization in Auth and api() to allow specifying the token as the access token from Seven Bridges single sign-on (SSO).
@@ -204,9 +215,9 @@
sevenbridges 1.15.1
-
+
-Improvements
+Improvements
Added new fields created_by, created_on, and modified_on to the Project class following the recent API improvements. This enables better project filtering when querying projects. See the vignette for details.
@@ -215,9 +226,9 @@
sevenbridges 1.13.5
-
+
-Improvements
+Improvements
Added new platform options for the recently introduced environments. Now we can choose from "aws-us", "aws-eu", "ali-cn", "cgc", "cavatica", and "f4c" in Auth() calls.
Added floating TOC and changed the vignette theme for the HTML vignettes available on Bioconductor, to improve the browsing experience for long vignettes.
@@ -323,9 +334,9 @@
sevenbridges 1.11.3
-
+
-Improvements
+Improvements
Added better support for the spot instance feature, by introducing the new argument use_interruptible for project_new() and use_interruptible_instances for task_add(). This will allow users to enable/disable the spot instance feature on both the project level and individual task level. See the new section “Run tasks using spot instances” in the API vignette for details.
@@ -334,9 +345,9 @@
sevenbridges 1.11.2
-
+
-Improvements
+Improvements
Further remove all words related to the previous installation method per Bioconductor’s request.
@@ -345,9 +356,9 @@
sevenbridges 1.11.1
-
+
-Improvements
+Improvements
Use the new Bioconductor package manager BiocManager for installation instructions.
@@ -367,9 +378,9 @@
sevenbridges 1.7.5
-
+
-Improvements
+Improvements
Update rabix-cli (Bunny) to 1.0.2 for the Docker container.
@@ -389,9 +400,9 @@
sevenbridges 1.7.3
-
+
-Improvements
+Improvements
Use system font stack instead of Google Fonts in vignettes to avoid pandoc SSL issue.
@@ -444,13 +455,13 @@
Fixed IDE Docker image build issues; removed the libssl-dev dependency; updated Dockerfile (#50).
-
+
-Improvements
+Improvements
New function get_token() for getting the authentication token for different Seven Bridges platforms. The old function misc_get_token() is deprecated.
Added fields as query default in API calls, the same with limit, offset; now requests on file details will use fields = "_all" directly so that only one request is issued. The same applies to updating a upload logic for folder/multiple files (54488bc). Thanks: Raunaq Malhotra.
-
Added functions input_matrix() and output_matrix() (2ec7c84) to extract input/output matrix from CWL JSON files directly, without converting CWL JSON to Tool or Flow objects. This is a faster implementation compared to the old method, and more stable to custom fields.
+
Added functions input_matrix() and output_matrix() (2ec7c84) to extract input/output matrix from CWL JSON files directly, without converting CWL JSON to Tool or Flow objects. This is a faster implementation compared to the old method, and more stable to custom fields.
if (FALSE) {
-a<-Auth(url="https://api.sbgenomics.com/v2/", token="your_token")
+
if(FALSE){
+a<-Auth(url ="https://api.sbgenomics.com/v2/", token ="your_token")# get a public app
-app<-a$public_app(id="admin/sbg-public-data/rna-seq-alignment-star")
-app$input_matrix()
-app$output_matrix()
+app<-a$public_app(id ="admin/sbg-public-data/rna-seq-alignment-star")
+app$input_matrix()
+app$output_matrix()# get a public app
-app<-a$public_app(id="admin/sbg-public-data/star")
-app$input_matrix()
-app$output_matrix()}
[character] Authentication method. Could be "direct"
(pass the credential information to the arguments directly),
"env" (read from pre-set system environment variables),
@@ -223,7 +223,7 @@
# Direct authentication (default)# replace with your auth token
-token<-"your_token"
-a<-Auth(platform="cgc", token=token)
#> Using platform: cgc
if (FALSE) {
+token<-"your_token"
+a<-Auth(platform ="cgc", token =token)
+
#> Using platform: cgc
if(FALSE){# Authentication with environment variables# This will read system environments variables# `SB_API_ENDPOINT` and `SB_AUTH_TOKEN` by default
-a<-Auth(from="env")
+a<-Auth(from ="env")# Authentication with user configuration file# This will load profile `default` from config# file `~/.sevenbridges/credentials` by default
-a<-Auth(from="file")
-}
+a<-Auth(from ="file")
+}
+
@@ -172,7 +172,7 @@
Fields
+
loadContents
[logical] Only applies when type is File. Read
up to the first 64 KiB of text from the file and place it in the
"contents" field of the file object for manipulation by
@@ -192,7 +192,8 @@
(character) Specifies the program to execute. If
the value is an array, the first element is the program to execute,
and subsequent elements are placed at the beginning of the command
@@ -331,27 +331,28 @@
[characterORExpression] Find files relative to the
output directory, using POSIX glob(3) pathname matching. If
provided an array, match all patterns in the array. If provided an
@@ -195,7 +195,8 @@
[CommandOutputBinding] Describes how to handle
the concrete outputs of a process step (such as files created by a
program) and describe them in the process output parameter.
[CommandOutputBinding] Describes how to handle
the concrete outputs of a process step (such as files created by a
program) and describe them in the process output parameter.
This return a matrix of input parameters, by default, following the order id, label, type, required, prefix, fileTypes. new.order accept names of column you want to print, but it has to be a field of inputs. When its set to NULL, it prints all fields. When required = TRUE, only print required field.
This return a matrix of output parameters, by default, following the order id, label, type, fileTypes. new.order accept names of column you want to print, but it has to be a field of outputs. When its set to NULL, it prints all fields. When required = TRUE, only print required field.
This return a matrix of output parameters, by default, following the order id, label, type, fileTypes. new.order accept names of column you want to print, but it has to be a field of outputs. When its set to NULL, it prints all fields. When required = TRUE, only print required field.
output_type()
Show a vector of flow output type, names of them are output id.
This return a matrix of input parameters, by default, following the order id, label, type, required, prefix, fileTypes. new.order accept names of column you want to print, but it has to be a field of inputs. When its set to NULL, it prints all fields. When required = TRUE, only print required field.
@@ -218,7 +218,7 @@
Methods
output_id(full = FALSE)
Get output id from a Tool, when full = TRUE, connect tool id with input id.
This return a matrix of output parameters, by default, following the order id, label, type, fileTypes. new.order accept names of column you want to print, but it has to be a field of outputs. When its set to NULL, it prints all fields. when required = TRUE, only print required field.
This return a matrix of output parameters, by default, following the order id, label, type, fileTypes. new.order accept names of column you want to print, but it has to be a field of outputs. When its set to NULL, it prints all fields. when required = TRUE, only print required field.
output_type()
this return a vector of types, names of them are output id
fields. A workflow executes as described in execution model.
-
WorkflowOutputParameterList(...)
+
WorkflowOutputParameterList(...)
Arguments
@@ -188,7 +188,7 @@
Fields
+
outputs
(WorkflowOutputParameterList) Defines the parameters
representing the output of the process. May be used to generate
and/or validate the output object. Inherited from Process
Convert a object slots/fields to a list, json, or yaml file
prepare a object to be conveted to YAML/JSON.
-
asList(object, ...)
+
asList(object, ...)# S4 method for ANY
-asList(object, ...)
+asList(object, ...)# S4 method for CWL
-asList(object, ...)
+asList(object, ...)# S4 method for SingleEnum
-asList(object, ...)
+asList(object, ...)# S4 method for SimpleList
-asList(object, ...)
+asList(object, ...)# S4 method for DSCList
-asList(object, ...)
+asList(object, ...)
Arguments
@@ -205,37 +205,42 @@
Value
Examples
# define a S4 object
-A<-setClass("A", slots=list(a="character", b="numeric"))
+A<-setClass("A", slots =list(a ="character", b ="numeric"))# define a reference object which extends 'CWL' class
-B<-setRefClass("B", fields=list(x="character", y="A"), contains="CWL")
+B<-setRefClass("B", fields =list(x ="character", y ="A"), contains ="CWL")# new instances
-a<-A(a="hello", b=123)
-b<-B(x="world", y=a)
+a<-A(a ="hello", b =123)
+b<-B(x ="world", y =a)# show
-b
[Integer] for CPU default is 1L, if 0L, use all
CPU. For mem, default is 1000L. Note: for CPU, 0L means
multi-tread, and non-zero value will be converted to 1L, which
@@ -272,13 +272,17 @@
response(x)
-response(x) <-value
+response(x)<-value# S4 method for ANY
-response(x)
+response(x)# S4 method for ANY
-response(x) <-value
+response(x)<-value# S4 method for Item
-response(x)
+response(x)# S4 method for Item
-response(x) <-value
+response(x)<-value# S4 method for SimpleList
-response(x)
+response(x)# S4 method for SimpleList
-response(x) <-value
+response(x)<-value
Arguments
@@ -206,8 +206,9 @@
Value
a raw response from httr
Examples
-
if (FALSE) {
-response(x)}
+
if(FALSE){
+response(x)}
+
@@ -166,7 +166,7 @@
Set authentication environment variables for Seven Bridges API
Set authentication environment variables for Seven Bridges API
-
sbg_get_env(x)
+
sbg_get_env(x)
Arguments
@@ -183,11 +183,12 @@
Value
Examples
# set and get two environment variables for CGC
-token<-"your_token"
-if (FALSE) {
-sbg_set_env("https://cgc-api.sbgenomics.com/v2", token)
-sbg_get_env("SB_API_ENDPOINT")
-sbg_get_env("SB_AUTH_TOKEN")}
Set authentication environment variables for Seven Bridges API
Set authentication environment variables for Seven Bridges API
-
sbg_set_env(url=NULL, token=NULL)
+
sbg_set_env(url =NULL, token =NULL)
Arguments
@@ -187,11 +187,12 @@
Value
Examples
# set and get environment variables for CGC
-token<-"your_token"
-if (FALSE) {
-sbg_set_env("https://cgc-api.sbgenomics.com/v2", token)
-sbg_get_env("SB_API_ENDPOINT")
-sbg_get_env("SB_AUTH_TOKEN")}
set_tag(obj, ...)# S4 method for FilesList
-set_tag(obj, ...)
+set_tag(obj, ...)# S4 method for Files
-set_tag(obj, ...)
+set_tag(obj, ...)
-add_tag(obj, ...)
+add_tag(obj, ...)# S4 method for FilesList
-add_tag(obj, ...)
+add_tag(obj, ...)# S4 method for Files
-add_tag(obj, ...)
+add_tag(obj, ...)
Arguments
@@ -203,14 +203,15 @@
Value
tag list
Examples
-
if (FALSE) {
-fl<-a$project("demo")$file("omni")
-set_tag(fl, "new tag")
-set_tag(fl, list("new tag", "new tag 2"))}
-if (FALSE) {
-fl<-a$project("demo")$file("omni")
-add_tag(fl, "new tag")
-add_tag(fl, list("new tag", "new tag 2"))}
+
if(FALSE){
+fl<-a$project("demo")$file("omni")
+set_tag(fl, "new tag")
+set_tag(fl, list("new tag", "new tag 2"))}
+if(FALSE){
+fl<-a$project("demo")$file("omni")
+add_tag(fl, "new tag")
+add_tag(fl, list("new tag", "new tag 2"))}
+
@@ -166,7 +166,7 @@
Test tools in rabix/bunny
Test tools locally in rabix/bunny inside docker container
