This tutorial assume you have basic knowledge about Docker concept.
Note: Right now we are supporting CWL draft 2 with SBG extension, but we will support CWL v1.0 soon.
In our terminology, a workflow is composed of one or more tool, both of them are just app to users. You can imagine some raw input data go through a pipeline with many nodes that each step perform a function on the data in the flow, and in the end, you got want you want: a fully processed data or result (plot, report, action).
Here are some key ideas:
Looks like full of jargons and hard to understand. Here is an example. You have a csv table, full of missing value and you want to process it in 3 steps:
You can describe each step into a single module or tool then connect them one by one to form a flow. You can put everything into one single “tool”, then downside is that other user cannot use your step1 for missing value problem. So it’s both art and sciense to leverage between flexibility and efficiency.
Why we are using CWL? Imagine a single file represeting a tool or workflow, could be executed anywhere in a reproducible manner and you don’t have to install anything because Docker container is imaged, that’s going to change the world of computational scientific research and how we do research and publish results. In this package we are trying to hide CWL details as much as possible, so user can just use it like a typical R function.
Tool is the basic unit, and also your “lego brick” you usually start with. As developer you also want to provide those “lego” piecies to users to directly run it or make their own flow with it.
The main interface provided by sevenbridges package is Tool function, it’s much more straight forward to describe than composing your raw CWL JSON file from scratch. A “Tool” object in R could be exported into JSON or imported from a CWL JSON file.
I highly recommend going over documentation The Tool Editor chapter for the Cancer Genomics Cloud to understand how it works, and even try it on the platform with the GUI. This will help understand our R interface better.
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.
library("sevenbridges") t1 <- system.file("extdata/app", "tool_star.json", package = "sevenbridges") # # convert JSON file into a Tool object t1 <- convert_app(t1) # # try print it out # t1
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.
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$input_type())
reads readMatesLengthsIn readMapNumber limitOutSJoneRead
"File..." "enum" "int" "int"
limitOutSJcollapsed outReadsUnmapped
"int" "enum" # get output type information head(t1$output_type())
aligned_reads transcriptome_aligned_reads
"File" "File"
reads_per_gene log_files
"File" "File..."
splice_junctions chimeric_junctions
"File" "File" # return a input matrix with more information head(t1$input_matrix())
id label type required
1 #reads Read sequence File... TRUE
95 #sjdbGTFfile Splice junction file File... FALSE
102 #genome Genome files File TRUE
2 #readMatesLengthsIn Reads lengths enum FALSE
3 #readMapNumber Reads to map int FALSE
4 #limitOutSJoneRead Junctions max number int FALSE
prefix
1 <NA>
95 <NA>
102 <NA>
2 --readMatesLengthsIn
3 --readMapNumber
4 --limitOutSJoneRead
fileTypes
1 FASTA, FASTQ, FA, FQ, FASTQ.GZ, FQ.GZ, FASTQ.BZ2, FQ.BZ2
95 GTF, GFF, TXT
102 TAR
2 null
3 null
4 null id type required
1 #reads File... TRUE
95 #sjdbGTFfile File... FALSE
102 #genome File TRUE
2 #readMatesLengthsIn enum FALSE
3 #readMapNumber int FALSE
4 #limitOutSJoneRead int FALSE# return only required t1$input_matrix(required = TRUE)
id label type required prefix
1 #reads Read sequence File... TRUE <NA>
102 #genome Genome files File TRUE <NA>
fileTypes
1 FASTA, FASTQ, FA, FQ, FASTQ.GZ, FQ.GZ, FASTQ.BZ2, FQ.BZ2
102 TAR# return a output matrix with more information t1$output_matrix()
id label type fileTypes
1 #aligned_reads Aligned SAM/BAM File SAM, BAM
2 #transcriptome_aligned_reads Transcriptome alignments File BAM
3 #reads_per_gene Reads per gene File TAB
4 #log_files Log files File... OUT
5 #splice_junctions Splice junctions File TAB
6 #chimeric_junctions Chimeric junctions File JUNCTION
7 #unmapped_reads Unmapped reads File... FASTQ
8 #intermediate_genome Intermediate genome files File TAR
9 #chimeric_alignments Chimeric alignments File SAM# return only a few fields t1$output_matrix(c("id", "type"))
id type
1 #aligned_reads File
2 #transcriptome_aligned_reads File
3 #reads_per_gene File
4 #log_files File...
5 #splice_junctions File
6 #chimeric_junctions File
7 #unmapped_reads File...
8 #intermediate_genome File
9 #chimeric_alignments File# get required input id t1$get_required()
reads genome
"File..." "File" # set new required input with ID, # or without # t1$set_required(c("#reads", "winFlankNbins"))
not implemented yet!t1$get_required()
reads genome
"File..." "File" # turn off requirements for input node #reads t1$set_required("reads", FALSE)
not implemented yet!t1$get_required()
reads genome
"File..." "File" # get input id head(t1$input_id())
#STAR #STAR #STAR
"#reads" "#readMatesLengthsIn" "#readMapNumber"
#STAR #STAR #STAR
"#limitOutSJoneRead" "#limitOutSJcollapsed" "#outReadsUnmapped" # get full input id with Tool name head(t1$input_id(TRUE))
File... enum
"#STAR.reads" "#STAR.readMatesLengthsIn"
int int
"#STAR.readMapNumber" "#STAR.limitOutSJoneRead"
int enum
"#STAR.limitOutSJcollapsed" "#STAR.outReadsUnmapped" # get output id head(t1$output_id())
#STAR #STAR
"#aligned_reads" "#transcriptome_aligned_reads"
#STAR #STAR
"#reads_per_gene" "#log_files"
#STAR #STAR
"#splice_junctions" "#chimeric_junctions" # get full output id head(t1$output_id(TRUE))
File File
"#STAR.aligned_reads" "#STAR.transcriptome_aligned_reads"
File File...
"#STAR.reads_per_gene" "#STAR.log_files"
File File
"#STAR.splice_junctions" "#STAR.chimeric_junctions" # get input and output object t1$get_input(id = "#winFlankNbins")
type:
- 'null'
- int
label: Flanking regions size
description: =log2(winFlank), where win Flank is the size of the left and right flanking
regions for each window (int>0).
streamable: no
id: '#winFlankNbins'
inputBinding:
position: 0
prefix: --winFlankNbins
separate: yes
shellQuote: no
sbg:cmdInclude: yes
streamable: no
separator: ' '
sbg:category: Windows, Anchors, Binning
sbg:toolDefaultValue: '4'
required: not1$get_input(name = "ins")
[[1]]
type:
- 'null'
- int
label: Max bins between anchors
description: Max number of bins between two anchors that allows aggregation of anchors
into one window (int>0).
streamable: no
id: '#winAnchorDistNbins'
inputBinding:
position: 0
prefix: --winAnchorDistNbins
separate: yes
shellQuote: no
sbg:cmdInclude: yes
streamable: no
separator: ' '
sbg:category: Windows, Anchors, Binning
sbg:toolDefaultValue: '9'
required: no
[[2]]
type:
- 'null'
- int
label: Max insert junctions
description: Maximum number of junction to be inserted to the genome on the fly at
the mapping stage, including those from annotations and those detected in the 1st
step of the 2-pass run.
streamable: no
id: '#limitSjdbInsertNsj'
inputBinding:
position: 0
prefix: --limitSjdbInsertNsj
separate: yes
shellQuote: no
sbg:cmdInclude: yes
streamable: no
separator: ' '
sbg:category: Limits
sbg:toolDefaultValue: '1000000'
required: not1$get_output(id = "#aligned_reads")
type:
- 'null'
- File
label: Aligned SAM/BAM
description: Aligned sequence in SAM/BAM format.
streamable: no
id: '#aligned_reads'
outputBinding:
glob:
engine: '#cwl-js-engine'
script: |-
{
if ($job.inputs.outSortingType == 'SortedByCoordinate') {
sort_name = '.sortedByCoord'
}
else {
sort_name = ''
}
if ($job.inputs.outSAMtype == 'BAM') {
sam_name = "*.Aligned".concat( sort_name, '.out.bam')
}
else {
sam_name = "*.Aligned.out.sam"
}
return sam_name
}
class: Expression
sbg:fileTypes: SAM, BAMt1$get_output(name = "gene")
type:
- 'null'
- File
label: Reads per gene
description: File with number of reads per gene. A read is counted if it overlaps
(1nt or more) one and only one gene.
streamable: no
id: '#reads_per_gene'
outputBinding:
glob: '*ReadsPerGene*'
sbg:fileTypes: TABBefore 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.
fl <- system.file("docker/rnaseqGene/rabix", "generator.R", package = "sevenbridges") cat(readLines(fl), sep = "\n")
library("sevenbridges")
rbx <- Tool(
id = "rnaseqGene",
label = "rnaseqgene",
description = "A RNA-seq Differiencial Expression Flow and Report",
hints = requirements(docker(pull = "tengfei/rnaseqgene"), cpu(1), mem(2000)),
baseCommand = "performDE.R",
inputs = list(
input(
id = "bamfiles", label = "bam files",
description = "a list of bam files",
type = "File...", ## or type = ItemArray("File")
prefix = "--bamfiles",
required = TRUE,
itemSeparator = ","
),
input(
id = "design", label = "design matrix",
type = "File",
required = TRUE,
prefix = "--design"
),
input(
id = "gtffile", label = "gene feature files",
type = "File",
stageInput = "copy",
required = TRUE,
prefix = "--gtffile"
),
input(
id = "format", label = "report foramt html or pdf",
type = enum("format", c("pdf", "html")),
prefix = "--format"
)
),
outputs = list(
output(
id = "report", label = "report",
description = "A reproducible report created by Rmarkdown",
glob = Expression(
engine = "#cwl-js-engine",
script = "x = $job[['inputs']][['format']]; if(x == 'undefined' || x == null){x = 'html';}; 'rnaseqGene.' + x"
)
),
output(
id = "heatmap", label = "heatmap",
description = "A heatmap plot to show the Euclidean distance between samples",
glob = "heatmap.pdf"
),
output(
id = "count", label = "count",
description = "Reads counts matrix",
glob = "count.csv"
),
output(
id = "de", label = "Differential expression table",
description = "Differential expression table",
glob = "de.csv"
)
)
)
fl <- "inst/docker/rnaseqGene/rabix/rnaseqGene.json"
write(rbx$toJSON(pretty = TRUE), fl)Now let’s break it down:
Some key arguments used in Tool function.
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.
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.
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.
library("sevenbridges") rbx <- Tool( id = "runif", label = "runif", hints = requirements(docker(pull = "rocker/r-base")), baseCommand = "Rscript -e 'runif(100)'", stdout = "output.txt", outputs = output(id = "random", glob = "*.txt") ) rbx
sbg:id: runif
id: '#runif'
inputs: []
outputs:
- type:
- 'null'
- File
label: ''
description: ''
streamable: no
default: ''
id: '#random'
outputBinding:
glob: '*.txt'
requirements: []
hints:
- class: DockerRequirement
dockerPull: rocker/r-base
label: runif
class: CommandLineTool
baseCommand:
- Rscript -e 'runif(100)'
arguments: []
stdout: output.txtrbx$toJSON()
{"sbg:id":"runif","id":"#runif","inputs":[],"outputs":[{"type":["null","File"],"label":"","description":"","streamable":false,"default":"","id":"#random","outputBinding":{"glob":"*.txt"}}],"requirements":[],"hints":[{"class":"DockerRequirement","dockerPull":"rocker/r-base"}],"label":"runif","class":"CommandLineTool","baseCommand":["Rscript -e 'runif(100)'"],"arguments":[],"stdout":"output.txt"} By default, the tool object shows YAML, but you can simply convert it to JSON and copy it to your seven bridges platform graphic editor by importing JSON.
rbx$toJSON()
{"sbg:id":"runif","id":"#runif","inputs":[],"outputs":[{"type":["null","File"],"label":"","description":"","streamable":false,"default":"","id":"#random","outputBinding":{"glob":"*.txt"}}],"requirements":[],"hints":[{"class":"DockerRequirement","dockerPull":"rocker/r-base"}],"label":"runif","class":"CommandLineTool","baseCommand":["Rscript -e 'runif(100)'"],"arguments":[],"stdout":"output.txt"} rbx$toJSON(pretty = TRUE)
{
"sbg:id": "runif",
"id": "#runif",
"inputs": [],
"outputs": [
{
"type": ["null", "File"],
"label": "",
"description": "",
"streamable": false,
"default": "",
"id": "#random",
"outputBinding": {
"glob": "*.txt"
}
}
],
"requirements": [],
"hints": [
{
"class": "DockerRequirement",
"dockerPull": "rocker/r-base"
}
],
"label": "runif",
"class": "CommandLineTool",
"baseCommand": [
"Rscript -e 'runif(100)'"
],
"arguments": [],
"stdout": "output.txt"
} rbx$toYAML()
[1] "sbg:id: runif\nid: '#runif'\ninputs: []\noutputs:\n- type:\n - 'null'\n - File\n label: ''\n description: ''\n streamable: no\n default: ''\n id: '#random'\n outputBinding:\n glob: '*.txt'\nrequirements: []\nhints:\n- class: DockerRequirement\n dockerPull: rocker/r-base\nlabel: runif\nclass: CommandLineTool\nbaseCommand:\n- Rscript -e 'runif(100)'\narguments: []\nstdout: output.txt\n"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)" ) # read via reader .srcfile <- system.file("docker/sevenbridges/src/runif.R", package = "sevenbridges") 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") )
How about multiple script?
# or simply readLines .srcfile <- system.file("docker/sevenbridges/src/runif.R", package = "sevenbridges") fd1 <- fileDef( name = "runif.R", content = readr::read_file(.srcfile) ) fd2 <- fileDef( name = "runif2.R", content = "set.seed(1); runif(100)" ) rbx <- Tool( id = "runif_twoscript", label = "runif_twoscript", hints = requirements(docker(pull = "rocker/r-base")), requirements = requirements(fd1, fd2), baseCommand = "Rscript runif.R", stdout = "output.txt", outputs = output(id = "random", glob = "*.txt") )
All those examples above, many parameters are hard-coded in your script, you don’t have flexiblity to control how many numbers to generate. Most often, your tools or command line tools expose some inputs arguments to users. You need a better way to describe a command line with input/output.
Now we bring the example to next level. For example, we prepare a Docker image called RFranklin/runif on Docker Hub. This container has a exeutable command called runif.R, you don’t have to know what is inside, you only have to know when you run the command line in that container it looks like this
runif.R --n=100 --max=100 --min=1 --seed=123This command outpus two files directly, so you don’t need standard output to capture random number.
So the goal here is to describe this command and expose all input parameters and collect all two files.
To define input, you can specify
id : unique identifier to this input node.description: description, also visible on UI.type: required to specify input types, files, integer, or character.label: human readable label for this input node.prefix: the prefix in command line for this input parameter.default: default value for this input.required: is this input parameter required or not. If required, when you execte the tool you have to provide a value for the parameter.cmdInclude: included in command line or not.Output is similar, espeicaly when you want to collect file, you can use glob for pattern matching.
# pass a input list in.lst <- list( input( id = "number", description = "number of observations", type = "integer", label = "number", prefix = "--n", default = 1, required = TRUE, cmdInclude = TRUE ), input( id = "min", description = "lower limits of the distribution", type = "float", label = "min", prefix = "--min", default = 0 ), input( id = "max", description = "upper limits of the distribution", type = "float", label = "max", prefix = "--max", default = 1 ), input( id = "seed", description = "seed with set.seed", type = "float", label = "seed", prefix = "--seed", default = 1 ) ) # the same method for outputs out.lst <- list( output( id = "random", type = "file", label = "output", description = "random number file", glob = "*.txt" ), output( id = "report", type = "file", label = "report", glob = "*.html" ) ) rbx <- Tool( id = "runif", label = "Random number generator", hints = requirements(docker(pull = "RFranklin/runif")), baseCommand = "runif.R", inputs = in.lst, # or ins.df outputs = out.lst )
Alternatively you can use data.frame as example for input and output, but it’s less flexible.
in.df <- data.frame( id = c("number", "min", "max", "seed"), description = c( "number of observation", "lower limits of the distribution", "upper limits of the distribution", "seed with set.seed" ), type = c("integer", "float", "float", "float"), label = c("number", "min", "max", "seed"), prefix = c("--n", "--min", "--max", "--seed"), default = c(1, 0, 10, 123), required = c(TRUE, FALSE, FALSE, FALSE) ) out.df <- data.frame( id = c("random", "report"), type = c("file", "file"), glob = c("*.txt", "*.html") ) rbx <- Tool( id = "runif", label = "Random number generator", hints = requirements(docker(pull = "RFranklin/runif"), cpu(1), mem(2000)), baseCommand = "runif.R", inputs = in.df, # or ins.df outputs = out.df )
commandArgs (position and named args)Now you must be wondering, I have a Docker container with R, but I don’t have any existing command line that I could directly use. Can I provide a script with a formal and quick command line interface to make an App for existing container. The anwser is yes. When you add script to your tool, you can always use some trick to do so, one popular one you may already head of is commandArgs. More formal one is called “docopt” which I will show you later.
Suppose you have a R script “runif2spin.R” with three arguments using position mapping
numbersminmaxMy base command will be somethine like
Rscript runif2spin.R 10 30 50This is how you do in your R script
fl <- system.file("docker/sevenbridges/src", "runif2spin.R", package = "sevenbridges" ) cat(readLines(fl), sep = "\n")
#' ---
#' title: "Uniform random number generator example"
#' output:
#' html_document:
#' toc: true
#' number_sections: true
#' ---
#' # summary report
#'
#' This is a random number generator
#+
args = commandArgs(TRUE)
r = runif(n = as.integer(args[1]),
min = as.numeric(args[2]),
max = as.numeric(args[3]))
head(r)
summary(r)
hist(r)Ignore the comment part, I will introduce spin/stich later.
Then just describe my tool in this way, add your script as you learned in previous sections.
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", position = 1 ), input( id = "min", type = "float", position = 2 ), input( id = "max", type = "float", position = 3 ) ), outputs = output(id = "random", glob = "output.txt") )
How about named argumentments? I will still recommend use “docopt” package, but for simple way. You want command line looks like this
Rscript runif_args.R --n=10 --min=30 --max=50Here is how you do in R script.
fl <- system.file("docker/sevenbridges/src", "runif_args.R", package = "sevenbridges") cat(readLines(fl), sep = "\n")
#' ---
#' title: "Uniform random number generator example"
#' output:
#' html_document:
#' toc: true
#' number_sections: true
#' ---
#' # summary report
#'
#' This is a random number generator
#+
args <- commandArgs(TRUE)
## quick hack to split named arguments
splitArgs <- function(x) {
res <- do.call(rbind, lapply(x, function(i){
res <- strsplit(i, "=")[[1]]
nm <- gsub("-+", "",res[1])
c(nm, res[2])
}))
.r <- res[,2]
names(.r) <- res[,1]
.r
}
args <- splitArgs(args)
#+
r <- runif(n = as.integer(args["n"]),
min = as.numeric(args["min"]),
max = as.numeric(args["max"]))
summary(r)
hist(r)
write.csv(r, file = "out.csv")Then 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
You can use spin/stich from knitr to generate report directly from a Rscript with special format. For example, let’s use above example
fl <- system.file("docker/sevenbridges/src", "runif_args.R", package = "sevenbridges") cat(readLines(fl), sep = "\n")
#' ---
#' title: "Uniform random number generator example"
#' output:
#' html_document:
#' toc: true
#' number_sections: true
#' ---
#' # summary report
#'
#' This is a random number generator
#+
args <- commandArgs(TRUE)
## quick hack to split named arguments
splitArgs <- function(x) {
res <- do.call(rbind, lapply(x, function(i){
res <- strsplit(i, "=")[[1]]
nm <- gsub("-+", "",res[1])
c(nm, res[2])
}))
.r <- res[,2]
names(.r) <- res[,1]
.r
}
args <- splitArgs(args)
#+
r <- runif(n = as.integer(args["n"]),
min = as.numeric(args["min"]),
max = as.numeric(args["max"]))
summary(r)
hist(r)
write.csv(r, file = "out.csv")You command is something like this
Rscript -e "rmarkdown::render(knitr::spin('runif_args.R', FALSE))" --args --n=100 --min=30 --max=50And so I describe my tool like this with Docker image rocker/tidyverse which contians the knitr and rmarkdown packages.
fd <- fileDef( name = "runif.R", content = readr::read_file(fl) ) 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.
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.
out.lst <- list( output( id = "random", type = "file", label = "output", description = "random number file", glob = "*.txt" ), output( id = "report", type = "file", label = "report", glob = "*.html", inheritMetadataFrom = "bam_file", metadata = list( author = "RFranklin", sample = "random" ) ) ) out.lst
[[1]]
type:
- 'null'
- File
label: output
description: random number file
streamable: no
default: ''
id: '#random'
outputBinding:
glob: '*.txt'
[[2]]
type:
- 'null'
- File
label: report
description: ''
streamable: no
default: ''
id: '#report'
outputBinding:
glob: '*.html'
sbg:inheritMetadataFrom: '#bam_file'
sbg:metadata:
author: RFranklin
sample: randomExample with file/files as input node
fl <- system.file("docker/rnaseqGene/rabix", "generator.R", package = "sevenbridges") cat(readLines(fl), sep = "\n")
library("sevenbridges")
rbx <- Tool(
id = "rnaseqGene",
label = "rnaseqgene",
description = "A RNA-seq Differiencial Expression Flow and Report",
hints = requirements(docker(pull = "tengfei/rnaseqgene"), cpu(1), mem(2000)),
baseCommand = "performDE.R",
inputs = list(
input(
id = "bamfiles", label = "bam files",
description = "a list of bam files",
type = "File...", ## or type = ItemArray("File")
prefix = "--bamfiles",
required = TRUE,
itemSeparator = ","
),
input(
id = "design", label = "design matrix",
type = "File",
required = TRUE,
prefix = "--design"
),
input(
id = "gtffile", label = "gene feature files",
type = "File",
stageInput = "copy",
required = TRUE,
prefix = "--gtffile"
),
input(
id = "format", label = "report foramt html or pdf",
type = enum("format", c("pdf", "html")),
prefix = "--format"
)
),
outputs = list(
output(
id = "report", label = "report",
description = "A reproducible report created by Rmarkdown",
glob = Expression(
engine = "#cwl-js-engine",
script = "x = $job[['inputs']][['format']]; if(x == 'undefined' || x == null){x = 'html';}; 'rnaseqGene.' + x"
)
),
output(
id = "heatmap", label = "heatmap",
description = "A heatmap plot to show the Euclidean distance between samples",
glob = "heatmap.pdf"
),
output(
id = "count", label = "count",
description = "Reads counts matrix",
glob = "count.csv"
),
output(
id = "de", label = "Differential expression table",
description = "Differential expression table",
glob = "de.csv"
)
)
)
fl <- "inst/docker/rnaseqGene/rabix/rnaseqGene.json"
write(rbx$toJSON(pretty = TRUE), fl)Note the stageInput example in the above script, you can set it to “copy” or “link”.
Input node batch mode
Batch by File (the long output has been omitted here):
f1 <- system.file("extdata/app", "flow_star.json", package = "sevenbridges") f1 <- convert_app(f1) f1$set_batch("sjdbGTFfile", type = "ITEM")
Batch by other critieria such as metadta, following example, is using sample_id and library_id (the long output has been omitted here):
f1 <- system.file("extdata/app", "flow_star.json", package = "sevenbridges") f1 <- convert_app(f1) f1$set_batch("sjdbGTFfile", c("metadata.sample_id", "metadata.library_id"))
criteria provided, convert type from ITEM to CRITERIAWhen you push your app to the platform, you will see the batch available at task page or workflow editor.
Note: The GUI Tool Editor on Seven Bridges Platform is more conventient for this purpose.
Yes, you could use the same function convert_app to import JSON files.
f1 <- system.file("extdata/app", "flow_star.json", package = "sevenbridges") f1 <- convert_app(f1) # show it # f1
Flow objectsJust like Tool object, you also have convenient utils for it, especially useful when you execute task.
f1 <- system.file("extdata/app", "flow_star.json", package = "sevenbridges") f1 <- convert_app(f1) # input matrix head(f1$input_matrix())
id label type required
1 #sjdbGTFfile sjdbGTFfile File... FALSE
2 #fastq fastq File... TRUE
3 #genomeFastaFiles genomeFastaFiles File TRUE
4 #sjdbGTFtagExonParentTranscript Exons' parents name string FALSE
5 #sjdbGTFtagExonParentGene Gene name string FALSE
6 #winAnchorMultimapNmax Max loci anchors int FALSE
fileTypes
1 null
2 null
3 null
4 null
5 null
6 null id type required
1 #sjdbGTFfile File... FALSE
2 #fastq File... TRUE
3 #genomeFastaFiles File TRUE
4 #sjdbGTFtagExonParentTranscript string FALSE
5 #sjdbGTFtagExonParentGene string FALSE
6 #winAnchorMultimapNmax int FALSE# return only required head(f1$input_matrix(required = TRUE))
id label type required fileTypes
2 #fastq fastq File... TRUE null
3 #genomeFastaFiles genomeFastaFiles File TRUE null# return everything head(f1$input_matrix(NULL))
id type required fileTypes
1 #sjdbGTFfile File... FALSE null
2 #fastq File... TRUE null
3 #genomeFastaFiles File TRUE null
4 #sjdbGTFtagExonParentTranscript string FALSE null
5 #sjdbGTFtagExonParentGene string FALSE null
6 #winAnchorMultimapNmax int FALSE null
label category stageInput streamable
1 sjdbGTFfile null null FALSE
2 fastq null null FALSE
3 genomeFastaFiles null null FALSE
4 Exons' parents name Splice junctions db parameters null FALSE
5 Gene name Splice junctions db parameters null FALSE
6 Max loci anchors Windows, Anchors, Binning null FALSE
sbg.x sbg.y sbg.includeInPorts
1 160.50 195.0833 NA
2 164.25 323.7500 TRUE
3 167.75 469.9999 NA
4 200.00 350.0000 NA
5 200.00 400.0000 NA
6 200.00 450.0000 NA
description
1 <NA>
2 <NA>
3 <NA>
4 Tag name to be used as exons' transcript-parents.
5 Tag name to be used as exons' gene-parents.
6 Max number of loci anchors are allowed to map to (int>0).
sbg.toolDefaultValue link_to
1 <NA> #STAR_Genome_Generate.sjdbGTFfile | #STAR.sjdbGTFfile
2 <NA> #SBG_FASTQ_Quality_Detector.fastq
3 <NA> #STAR_Genome_Generate.genomeFastaFiles
4 transcript_id #STAR_Genome_Generate.sjdbGTFtagExonParentTranscript
5 gene_id #STAR_Genome_Generate.sjdbGTFtagExonParentGene
6 50 #STAR.winAnchorMultimapNmax# return a output matrix with more information head(f1$output_matrix())
id label type fileTypes
1 #unmapped_reads unmapped_reads File... null
2 #transcriptome_aligned_reads transcriptome_aligned_reads File null
3 #splice_junctions splice_junctions File null
4 #reads_per_gene reads_per_gene File null
5 #log_files log_files File... null
6 #chimeric_junctions chimeric_junctions File null id type
1 #unmapped_reads File...
2 #transcriptome_aligned_reads File
3 #splice_junctions File
4 #reads_per_gene File
5 #log_files File...
6 #chimeric_junctions File# return everything head(f1$output_matrix(NULL))
id label type fileTypes
1 #unmapped_reads unmapped_reads File... null
2 #transcriptome_aligned_reads transcriptome_aligned_reads File null
3 #splice_junctions splice_junctions File null
4 #reads_per_gene reads_per_gene File null
5 #log_files log_files File... null
6 #chimeric_junctions chimeric_junctions File null
required source streamable sbg.includeInPorts
1 FALSE #STAR.unmapped_reads FALSE TRUE
2 FALSE #STAR.transcriptome_aligned_reads FALSE TRUE
3 FALSE #STAR.splice_junctions FALSE TRUE
4 FALSE #STAR.reads_per_gene FALSE TRUE
5 FALSE #STAR.log_files FALSE TRUE
6 FALSE #STAR.chimeric_junctions FALSE TRUE
sbg.x sbg.y link_to
1 766.2498 159.58331 #STAR.unmapped_reads
2 1118.9998 86.58332 #STAR.transcriptome_aligned_reads
3 1282.3330 167.49998 #STAR.splice_junctions
4 1394.4164 245.74996 #STAR.reads_per_gene
5 1505.0830 322.99995 #STAR.log_files
6 1278.7498 446.74996 #STAR.chimeric_junctions# flow inputs f1$input_type()
sjdbGTFfile fastq
"File..." "File..."
genomeFastaFiles sjdbGTFtagExonParentTranscript
"File" "string"
sjdbGTFtagExonParentGene winAnchorMultimapNmax
"string" "int"
winAnchorDistNbins
"int" # flow outouts f1$output_type()
unmapped_reads transcriptome_aligned_reads
"File..." "File"
splice_junctions reads_per_gene
"File" "File"
log_files chimeric_junctions
"File..." "File"
intermediate_genome chimeric_alignments
"File" "File"
sorted_bam result
"File" "File" # list tools f1$list_tool()
label
1 STAR Genome Generate
2 SBG FASTQ Quality Detector
3 Picard SortSam
4 STAR
sbgid
1 sevenbridges/public-apps/star-genome-generate/1
2 sevenbridges/public-apps/sbg-fastq-quality-detector/3
3 sevenbridges/public-apps/picard-sortsam-1-140/2
4 sevenbridges/public-apps/star/4
id
1 #STAR_Genome_Generate
2 #SBG_FASTQ_Quality_Detector
3 #Picard_SortSam
4 #STAR
# f1$get_tool("STAR")There are more utilities please check example at help(Flow)
To create a workflow, we provide simple interface to pipe your tool into a single workflow, it works under situation like
Note: for complicated workflow construction, I highly recommend just use our graphical interface to do it, there is no better way.
Let’s create tools from scratch to perform a simple task
library("sevenbridges") # A tool that generates 100 random numbers t1 <- Tool( id = "runif new test 3", label = "random number", hints = requirements(docker(pull = "rocker/r-base")), baseCommand = "Rscript -e 'x = runif(100); write.csv(x, file = 'random.txt', row.names = FALSE)'", outputs = output( id = "random", type = "file", glob = "random.txt" ) ) # A tool that takes log fd <- fileDef( name = "log.R", content = "args = commandArgs(TRUE) x = read.table(args[1], header = TRUE)[,'x'] x = log(x) write.csv(x, file = 'random_log.txt', row.names = FALSE) " ) t2 <- Tool( id = "log new test 3", label = "get log", hints = requirements(docker(pull = "rocker/r-base")), requirements = requirements(fd), baseCommand = "Rscript log.R", inputs = input( id = "number", type = "file" ), outputs = output( id = "log", type = "file", glob = "*.txt" ) ) # A tool that do a mean fd <- fileDef( name = "mean.R", content = "args = commandArgs(TRUE) x = read.table(args[1], header = TRUE)[,'x'] x = mean(x) write.csv(x, file = 'random_mean.txt', row.names = FALSE)" ) t3 <- Tool( id = "mean new test 3", label = "get mean", hints = requirements(docker(pull = "rocker/r-base")), requirements = requirements(fd), baseCommand = "Rscript mean.R", inputs = input( id = "number", type = "file" ), outputs = output( id = "mean", type = "file", glob = "*.txt" ) ) f <- t1 %>>% t2
flow_output: #get_log.logf <- link(t1, t2, "#random", "#number")
flow_output: #get_log.log# # you cannot directly copy-paste it # # please push it using API, we will register each tool for you # clipr::write_clip(jsonlite::toJSON(f, pretty = TRUE)) t2 <- Tool( id = "log new test 3", label = "get log", hints = requirements(docker(pull = "rocker/r-base")), requirements = requirements(fd), baseCommand = "Rscript log.R", inputs = input( id = "number", type = "file", secondaryFiles = sevenbridges:::set_box(".bai") ), outputs = output( id = "log", type = "file", glob = "*.txt" ) ) # clipr::write_clip(jsonlite::toJSON(t2, pretty = TRUE))
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)
Now let’s connect two tools
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.
t1 <- system.file("extdata/app", "tool_unpack_fastq.json", package = "sevenbridges" ) t2 <- system.file("extdata/app", "tool_star.json", package = "sevenbridges" ) t1 <- convert_app(t1) t2 <- convert_app(t2) # check possible link link_what(t1, t2)
$File...
$File...$from
id label type fileTypes full.name
1 #output_fastq_files Output FASTQ files File... FASTQ #SBG_Unpack_FASTQs
$File...$to
id label type required prefix
1 #reads Read sequence File... TRUE <NA>
95 #sjdbGTFfile Splice junction file File... FALSE <NA>
fileTypes full.name
1 FASTA, FASTQ, FA, FQ, FASTQ.GZ, FQ.GZ, FASTQ.BZ2, FQ.BZ2 #STAR
95 GTF, GFF, TXT #STAR# link f1 <- link(t1, t2, "output_fastq_files", "reads")
flow_input: #SBG_Unpack_FASTQs.input_archive_file / #STAR.sjdbGTFfile / #STAR.genomeflow_output: #STAR.aligned_reads / #STAR.transcriptome_aligned_reads / #STAR.reads_per_gene / #STAR.log_files / #STAR.splice_junctions / #STAR.chimeric_junctions / #STAR.unmapped_reads / #STAR.intermediate_genome / #STAR.chimeric_alignments# link t1$output_id(TRUE)
File...
"#SBG_Unpack_FASTQs.output_fastq_files" t2$input_id(TRUE)
File...
"#STAR.reads"
enum
"#STAR.readMatesLengthsIn"
int
"#STAR.readMapNumber"
int
"#STAR.limitOutSJoneRead"
int
"#STAR.limitOutSJcollapsed"
enum
"#STAR.outReadsUnmapped"
int
"#STAR.outQSconversionAdd"
enum
"#STAR.outSAMtype"
enum
"#STAR.outSortingType"
enum
"#STAR.outSAMmode"
enum
"#STAR.outSAMstrandField"
enum
"#STAR.outSAMattributes"
enum
"#STAR.outSAMunmapped"
enum
"#STAR.outSAMorder"
enum
"#STAR.outSAMprimaryFlag"
enum
"#STAR.outSAMreadID"
int
"#STAR.outSAMmapqUnique"
int
"#STAR.outSAMflagOR"
int
"#STAR.outSAMflagAND"
string
"#STAR.outSAMheaderHD"
string
"#STAR.outSAMheaderPG"
string
"#STAR.rg_seq_center"
string
"#STAR.rg_library_id"
string
"#STAR.rg_mfl"
enum
"#STAR.rg_platform"
string
"#STAR.rg_platform_unit_id"
string
"#STAR.rg_sample_id"
enum
"#STAR.outFilterType"
int
"#STAR.outFilterMultimapScoreRange"
int
"#STAR.outFilterMultimapNmax"
int
"#STAR.outFilterMismatchNmax"
float
"#STAR.outFilterMismatchNoverLmax"
float
"#STAR.outFilterMismatchNoverReadLmax"
int
"#STAR.outFilterScoreMin"
float
"#STAR.outFilterScoreMinOverLread"
int
"#STAR.outFilterMatchNmin"
float
"#STAR.outFilterMatchNminOverLread"
enum
"#STAR.outFilterIntronMotifs"
enum
"#STAR.outSJfilterReads"
int...
"#STAR.outSJfilterOverhangMin"
int...
"#STAR.outSJfilterCountUniqueMin"
int...
"#STAR.outSJfilterCountTotalMin"
int...
"#STAR.outSJfilterDistToOtherSJmin"
int...
"#STAR.outSJfilterIntronMaxVsReadN"
int
"#STAR.scoreGap"
int
"#STAR.scoreGapNoncan"
int
"#STAR.scoreGapGCAG"
int
"#STAR.scoreGapATAC"
float
"#STAR.scoreGenomicLengthLog2scale"
int
"#STAR.scoreDelOpen"
int
"#STAR.scoreDelBase"
int
"#STAR.scoreInsOpen"
int
"#STAR.scoreInsBase"
int
"#STAR.scoreStitchSJshift"
int
"#STAR.seedSearchStartLmax"
float
"#STAR.seedSearchStartLmaxOverLread"
int
"#STAR.seedSearchLmax"
int
"#STAR.seedMultimapNmax"
int
"#STAR.seedPerReadNmax"
int
"#STAR.seedPerWindowNmax"
int
"#STAR.seedNoneLociPerWindow"
int
"#STAR.alignIntronMin"
int
"#STAR.alignIntronMax"
int
"#STAR.alignMatesGapMax"
int
"#STAR.alignSJoverhangMin"
int
"#STAR.alignSJDBoverhangMin"
int
"#STAR.alignSplicedMateMapLmin"
float
"#STAR.alignSplicedMateMapLminOverLmate"
float
"#STAR.alignWindowsPerReadNmax"
int
"#STAR.alignTranscriptsPerWindowNmax"
int
"#STAR.alignTranscriptsPerReadNmax"
enum
"#STAR.alignEndsType"
enum
"#STAR.alignSoftClipAtReferenceEnds"
int
"#STAR.winAnchorMultimapNmax"
int
"#STAR.winBinNbits"
int
"#STAR.winAnchorDistNbins"
int
"#STAR.winFlankNbins"
int
"#STAR.chimSegmentMin"
int
"#STAR.chimScoreMin"
int
"#STAR.chimScoreDropMax"
int
"#STAR.chimScoreSeparation"
int
"#STAR.chimScoreJunctionNonGTAG"
int
"#STAR.chimJunctionOverhangMin"
enum
"#STAR.quantMode"
int
"#STAR.twopass1readsN"
enum
"#STAR.twopassMode"
string
"#STAR.genomeDirName"
enum
"#STAR.sjdbInsertSave"
string
"#STAR.sjdbGTFchrPrefix"
string
"#STAR.sjdbGTFfeatureExon"
string
"#STAR.sjdbGTFtagExonParentTranscript"
string
"#STAR.sjdbGTFtagExonParentGene"
int
"#STAR.sjdbOverhang"
int
"#STAR.sjdbScore"
File...
"#STAR.sjdbGTFfile"
int...
"#STAR.clip3pNbases"
int...
"#STAR.clip5pNbases"
string...
"#STAR.clip3pAdapterSeq"
float...
"#STAR.clip3pAdapterMMp"
int...
"#STAR.clip3pAfterAdapterNbases"
enum
"#STAR.chimOutType"
File
"#STAR.genome"
int
"#STAR.limitSjdbInsertNsj"
enum
"#STAR.quantTranscriptomeBan"
int
"#STAR.limitBAMsortRAM" f2 <- link(t1, t2, "output_fastq_files", "reads", flow_input = "#SBG_Unpack_FASTQs.input_archive_file", flow_output = "#STAR.log_files" )
flow_input: #SBG_Unpack_FASTQs.input_archive_file / #STAR.genomeflow_output: #STAR.log_files
# clipr::write_clip(jsonlite::toJSON(f2))tool.in <- system.file("extdata/app", "tool_unpack_fastq.json", package = "sevenbridges") flow.in <- system.file("extdata/app", "flow_star.json", package = "sevenbridges") t1 <- convert_app(tool.in) f2 <- convert_app(flow.in) # consulting link_what first f2$link_map()
id
1 #STAR_Genome_Generate.sjdbGTFtagExonParentTranscript
2 #STAR_Genome_Generate.sjdbGTFtagExonParentGene
3 #STAR_Genome_Generate.sjdbGTFfile
4 #STAR_Genome_Generate.genomeFastaFiles
5 #SBG_FASTQ_Quality_Detector.fastq
6 #Picard_SortSam.input_bam
7 #STAR.winAnchorMultimapNmax
8 #STAR.winAnchorDistNbins
9 #STAR.sjdbGTFfile
10 #STAR.reads
11 #STAR.genome
12 #unmapped_reads
13 #transcriptome_aligned_reads
14 #splice_junctions
15 #reads_per_gene
16 #log_files
17 #chimeric_junctions
18 #intermediate_genome
19 #chimeric_alignments
20 #sorted_bam
21 #result
source type
1 #sjdbGTFtagExonParentTranscript input
2 #sjdbGTFtagExonParentGene input
3 #sjdbGTFfile input
4 #genomeFastaFiles input
5 #fastq input
6 #STAR.aligned_reads input
7 #winAnchorMultimapNmax input
8 #winAnchorDistNbins input
9 #sjdbGTFfile input
10 #SBG_FASTQ_Quality_Detector.result input
11 #STAR_Genome_Generate.genome input
12 #STAR.unmapped_reads output
13 #STAR.transcriptome_aligned_reads output
14 #STAR.splice_junctions output
15 #STAR.reads_per_gene output
16 #STAR.log_files output
17 #STAR.chimeric_junctions output
18 #STAR.intermediate_genome output
19 #STAR.chimeric_alignments output
20 #Picard_SortSam.sorted_bam output
21 #SBG_FASTQ_Quality_Detector.result output# then link f3 <- link(t1, f2, c("output_fastq_files"), c("#SBG_FASTQ_Quality_Detector.fastq")) link_what(f2, t1)
$File
$File$from
id label type required
2 #transcriptome_aligned_reads transcriptome_aligned_reads File FALSE
3 #splice_junctions splice_junctions File FALSE
4 #reads_per_gene reads_per_gene File FALSE
6 #chimeric_junctions chimeric_junctions File FALSE
7 #intermediate_genome intermediate_genome File FALSE
8 #chimeric_alignments chimeric_alignments File FALSE
9 #sorted_bam sorted_bam File FALSE
10 #result result File FALSE
fileTypes link_to
2 null #STAR.transcriptome_aligned_reads
3 null #STAR.splice_junctions
4 null #STAR.reads_per_gene
6 null #STAR.chimeric_junctions
7 null #STAR.intermediate_genome
8 null #STAR.chimeric_alignments
9 null #Picard_SortSam.sorted_bam
10 null #SBG_FASTQ_Quality_Detector.result
$File$to
id label type required prefix
1 #input_archive_file Input archive file File TRUE --input_archive_file
fileTypes
1 TAR, TAR.GZ, TGZ, TAR.BZ2, TBZ2, GZ, BZ2, ZIPf4 <- link(f2, t1, c("#Picard_SortSam.sorted_bam", "#SBG_FASTQ_Quality_Detector.result"), c("#input_archive_file", "#input_archive_file"))
flow_input: #SBG_Unpack_FASTQs.input_archive_fileflow_output: #SBG_Unpack_FASTQs.output_fastq_files# # TODO # # all outputs # # flow + flow # # print message when name wrong # clipr::write_clip(jsonlite::toJSON(f4))
With API function, you can directly load your Tool into the account. Run a task, for “how-to”, please check the complete guide for API client.
Here is a quick demo:
1. From CLI
While developing tools it is useful to test them locally first. For that we can use rabix – reproducible analyses for bioinformatics, https://github.com/rabix. To test your tool with latest implementation of rabix in Java (called bunny) you could use the Docker image RFranklin/testenv:
docker pull RFranklin/testenvDump your rabix tool as JSON into dir which also contains input data. write(rbx$toJSON, file="<data_dir>/<tool>.json"). Make inputs.json file to declare input parameters in the same directory (you can use relative paths from inputs.json to data). Create container:
docker run --privileged --name bunny -v </path/to/data_dir>:/bunny_data -dit RFranklin/testenvExecute tool
docker exec bunny bash -c 'cd /opt/bunny && ./rabix.sh -e /bunny_data /bunny_data/<tool>.json /bunny_data/inputs.json'You’ll see running logs from within container, and also output dir inside
RFranklin/testenv has R, Python, Java… so many tools can work without Docker requirement set. If you however set Docker requirement you need to pull image inside container first to run Docker container inside running bunny Docker.2. From R
library("sevenbridges") in.df <- data.frame( id = c("number", "min", "max", "seed"), description = c( "number of observation", "lower limits of the distribution", "upper limits of the distribution", "seed with set.seed" ), type = c("integer", "float", "float", "float"), label = c("number", "min", "max", "seed"), prefix = c("--n", "--min", "--max", "--seed"), default = c(1, 0, 10, 123), required = c(TRUE, FALSE, FALSE, FALSE) ) out.df <- data.frame( id = c("random", "report"), type = c("file", "file"), glob = c("*.txt", "*.html") ) rbx <- Tool( id = "runif", label = "Random number generator", hints = requirements(docker(pull = "RFranklin/runif"), cpu(1), mem(2000)), baseCommand = "runif.R", inputs = in.df, # or ins.df outputs = out.df ) params <- list(number = 3, max = 5) set_test_env("RFranklin/testenv", "mount_dir") test_tool(rbx, params)