Rcwl: An R interface to the Common Workflow Language (CWL)
Here we introduce the Bioconductor toolchain for usage and development of reproducible bioinformatics pipelines using packages of Rcwl and RcwlPipelines.
Rcwl provides a simple way to wrap command line tools
and build CWL data analysis pipelines programmatically within
R. It increases the ease of use, development, and maintenance
of CWL pipelines. RcwlPipelines manages a collection of
more than a hundred of pre-built and tested CWL tools and pipelines,
which are highly modularized with easy customization to meet different
bioinformatics data analysis needs.
In this vignette, we will introduce how to build and run CWL
pipelines within R/Bioconductor using Rcwlpackage.
More details about CWL can be found at https://www.commonwl.org.
Installation
- Download and install the package.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Rcwl")The development version with most up-to-date functionalities is also available from GitHub.
- Load the package into R session.
First Example
cwlProcess is the main constructor function to wrap a
command line tool into an R tool as a cwlProcess
object (S4 class). Let’s start with a simple example to wrap the
echo command and execute echo hello world in
R.
First, we need to define the input parameter for the base command
echo, here it is a string without a prefix. An
id argument is required here.
Second, we can construct a cwlProcess object by
specifying the baseCommand for the command line tool, and
InputParamList for the input parameters.
Now we have converted the command line tool echo into an
R tool: an R object of class cwlProcess
with the name of echo. We can take a look at the this
R object and use some utility functions to extract specific
information.
## class: cwlProcess
## cwlClass: CommandLineTool
## cwlVersion: v1.0
## baseCommand: echo
## inputs:
## sth (string):
## outputs:
## output:
## type: stdout## [1] "cwlProcess"
## attr(,"package")
## [1] "Rcwl"## [1] "CommandLineTool"## [1] "v1.0"## [1] "echo"## inputs:
## sth (string):## outputs:
## output:
## type: stdoutThe inputs(echo) will show the value once it is assigned
in next step. Since we didn’t define the outputs for this tool, it will
stream standard output to a temporary file by default.
The third step is to assign values (here is “Hello World!”) for the input parameters.
## inputs:
## sth (string): Hello World!Now this R version of command line tool echo is
ready to be executed.
We can install cwltool first to make sure a
cwl-runner is available.
## + /github/home/.cache/R/basilisk/1.19.0/0/bin/conda create --yes --prefix /github/home/.cache/R/basilisk/1.19.0/Rcwl/1.23.0/env_Rcwl 'python=3.11' --quiet -c conda-forge --override-channels## + /github/home/.cache/R/basilisk/1.19.0/0/bin/conda install --yes --prefix /github/home/.cache/R/basilisk/1.19.0/Rcwl/1.23.0/env_Rcwl 'python=3.11' -c conda-forge --override-channels## + /github/home/.cache/R/basilisk/1.19.0/0/bin/conda install --yes --prefix /github/home/.cache/R/basilisk/1.19.0/Rcwl/1.23.0/env_Rcwl -c conda-forge 'python=3.11' 'python=3.11' --override-channelsThe function runCWL runs the tools in R and
returns a list of: 1) actual command line that was executed, 2) filepath
to the output, and 3) running logs. The output directory by default
takes the working directory, but can be specified in outdir
argument.
## }[1;30mINFO[0m Final process status is success## List of length 3
## names(3): command output logs## [1] "\033[1;30mINFO\033[0m [job echo.cwl] /tmp/w3hc5dji$ echo \\"
## [2] " 'Hello World!' > /tmp/w3hc5dji/2e2d7b896ca603cf01fcf79b22167f18fb3a367d"## [1] "Hello World!"## [1] "\033[1;30mINFO\033[0m /github/home/.cache/R/basilisk/1.19.0/Rcwl/1.23.0/env_Rcwl/bin/cwltool 3.1.20230719185429"
## [2] "\033[1;30mINFO\033[0m Resolved '/tmp/RtmpMozipk/file153451d0a8df/echo.cwl' to 'file:///tmp/RtmpMozipk/file153451d0a8df/echo.cwl'"
## [3] "\033[1;30mINFO\033[0m [job echo.cwl] /tmp/w3hc5dji$ echo \\"
## [4] " 'Hello World!' > /tmp/w3hc5dji/2e2d7b896ca603cf01fcf79b22167f18fb3a367d"
## [5] "\033[1;30mINFO\033[0m [job echo.cwl] completed success"
## [6] "{"
## [7] " \"output\": {"
## [8] " \"location\": \"file:///tmp/RtmpMozipk/2e2d7b896ca603cf01fcf79b22167f18fb3a367d\","
## [9] " \"basename\": \"2e2d7b896ca603cf01fcf79b22167f18fb3a367d\","
## [10] " \"class\": \"File\","
## [11] " \"checksum\": \"sha1$a0b65939670bc2c010f4d5d6a0b3e4e4590fb92b\","
## [12] " \"size\": 13,"
## [13] " \"path\": \"/tmp/RtmpMozipk/2e2d7b896ca603cf01fcf79b22167f18fb3a367d\""
## [14] " }"
## [15] "}\033[1;30mINFO\033[0m Final process status is success"Users can also have the log printed out by specifying
showLog = TRUE.
## }A utility function writeCWL converts the
cwlProcess object into 2 files: a .cwl file
for the command and .yml file for the inputs, which are the
internal cwl files to be executed when runCWL is invoked.
The internal execution requires a cwl-runner (e.g.,
cwltool), which will be installed automatically with
runCWL.
## cwlout
## "/tmp/RtmpMozipk/file153439c92eca/echo.cwl"
## ymlout
## "/tmp/RtmpMozipk/file153439c92eca/echo.yml"Wrap command line tools
The package provides functions to define a CWL syntax for Command Line Tools in an intuitive way. The functions were developed based on the CWL Command Line Tool Description (v1.0). More details can be found in the official document: https://www.commonwl.org/v1.0/CommandLineTool.html.
Input Parameters
Essential Input parameters
For the input parameters, three options need to be defined usually, id, type, and prefix. The type can be string, int, long, float, double, and so on. More detail can be found at: https://www.commonwl.org/v1.0/CommandLineTool.html#CWLType.
Here is an example from CWL user guide.
Here we defined an echo with different type of input
parameters by InputParam. The stdout option
can be used to capture the standard output stream to a file.
e1 <- InputParam(id = "flag", type = "boolean", prefix = "-f")
e2 <- InputParam(id = "string", type = "string", prefix = "-s")
e3 <- InputParam(id = "int", type = "int", prefix = "-i")
e4 <- InputParam(id = "file", type = "File", prefix = "--file=", separate = FALSE)
echoA <- cwlProcess(baseCommand = "echo",
inputs = InputParamList(e1, e2, e3, e4),
stdout = "output.txt")Then we give it a try by setting values for the inputs.
echoA$flag <- TRUE
echoA$string <- "Hello"
echoA$int <- 1
tmpfile <- tempfile()
write("World", tmpfile)
echoA$file <- tmpfile
r2 <- runCWL(echoA, outdir = tempdir())## }[1;30mINFO[0m Final process status is success## [1] "\033[1;30mINFO\033[0m [job echoA.cwl] /tmp/zwjqvgx0$ echo \\"
## [2] " --file=/tmp/usxlbppm/stgbb7451f3-60b8-4de8-b85e-a30e3699a7ef/file15347bd006a7 \\"
## [3] " -f \\"
## [4] " -i \\"
## [5] " 1 \\"
## [6] " -s \\"
## [7] " Hello > /tmp/zwjqvgx0/output.txt"The command shows the parameters work as we defined. The parameter order is in alphabetical by default, but the option of “position” can be used to fix the orders.
Array Inputs
A similar example to CWL user guide. We can define three different type of array as inputs.
a1 <- InputParam(id = "A", type = "string[]", prefix = "-A")
a2 <- InputParam(id = "B",
type = InputArrayParam(items = "string",
prefix="-B=", separate = FALSE))
a3 <- InputParam(id = "C", type = "string[]", prefix = "-C=",
itemSeparator = ",", separate = FALSE)
echoB <- cwlProcess(baseCommand = "echo",
inputs = InputParamList(a1, a2, a3))Then set values for the three inputs.
## class: cwlProcess
## cwlClass: CommandLineTool
## cwlVersion: v1.0
## baseCommand: echo
## inputs:
## A (string[]): -A a b c
## B:
## type: array
## prefix: -B= d e f
## C (string[]): -C= g h i
## outputs:
## output:
## type: stdoutNow we can check whether the command behaves as we expected.
## }[1;30mINFO[0m Final process status is success## [1] "\033[1;30mINFO\033[0m [job echoB.cwl] /tmp/ph7k4gq9$ echo \\"
## [2] " -A \\"
## [3] " a \\"
## [4] " b \\"
## [5] " c \\"
## [6] " -B=d \\"
## [7] " -B=e \\"
## [8] " -B=f \\"
## [9] " -C=g,h,i > /tmp/ph7k4gq9/cd98777918a6da49bdd26b063ae784484d136517"Output Parameters
Capturing Output
The outputs, similar to the inputs, is a list of output parameters. Three options id, type and glob can be defined. The glob option is used to define a pattern to find files relative to the output directory.
Here is an example to unzip a compressed gz file. First,
we generate a compressed R script file.
zzfil <- file.path(tempdir(), "sample.R.gz")
zz <- gzfile(zzfil, "w")
cat("sample(1:10, 5)", file = zz, sep = "\n")
close(zz)We define a cwlProcess object to use “gzip” to
uncompress a input file.
ofile <- "sample.R"
z1 <- InputParam(id = "uncomp", type = "boolean", prefix = "-d")
z2 <- InputParam(id = "out", type = "boolean", prefix = "-c")
z3 <- InputParam(id = "zfile", type = "File")
o1 <- OutputParam(id = "rfile", type = "File", glob = ofile)
gz <- cwlProcess(baseCommand = "gzip",
inputs = InputParamList(z1, z2, z3),
outputs = OutputParamList(o1),
stdout = ofile)Now the gz object can be used to uncompress the previous
generated compressed file.
## }[1;30mINFO[0m Final process status is success## [1] "/tmp/RtmpMozipk/sample.R"Or we can use arguments to set some default
parameters.
z1 <- InputParam(id = "zfile", type = "File")
o1 <- OutputParam(id = "rfile", type = "File", glob = ofile)
Gz <- cwlProcess(baseCommand = "gzip",
arguments = list("-d", "-c"),
inputs = InputParamList(z1),
outputs = OutputParamList(o1),
stdout = ofile)
Gz## class: cwlProcess
## cwlClass: CommandLineTool
## cwlVersion: v1.0
## baseCommand: gzip
## arguments: -d -c
## inputs:
## zfile (File):
## outputs:
## rfile:
## type: File
## outputBinding:
## glob: sample.R
## stdout: sample.R## }[1;30mINFO[0m Final process status is successTo make it for general usage, we can define a pattern with javascript
to glob the output, which require node from “nodejs” to be
installed in your system PATH.
Or we can use the CWL built in file property, nameroot,
directly.
pfile <- "$(inputs.zfile.nameroot)"
o2 <- OutputParam(id = "rfile", type = "File", glob = pfile)
req1 <- requireJS()
GZ <- cwlProcess(baseCommand = "gzip",
arguments = list("-d", "-c"),
requirements = list(), ## assign list(req1) if node installed.
inputs = InputParamList(z1),
outputs = OutputParamList(o2),
stdout = pfile)
GZ$zfile <- zzfil
r4b <- runCWL(GZ, outdir = tempdir())## }[1;30mINFO[0m Final process status is successArray Outputs
We can also capture multiple output files with glob
pattern.
a <- InputParam(id = "a", type = InputArrayParam(items = "string"))
b <- OutputParam(id = "b", type = OutputArrayParam(items = "File"),
glob = "*.txt")
touch <- cwlProcess(baseCommand = "touch", inputs = InputParamList(a),
outputs = OutputParamList(b))
touch$a <- c("a.txt", "b.log", "c.txt")
r5 <- runCWL(touch, outdir = tempdir())## }[1;30mINFO[0m Final process status is success## [1] "/tmp/RtmpMozipk/a.txt" "/tmp/RtmpMozipk/c.txt"The “touch” command generates three files, but the output only
collects two files with “.txt” suffix as defined in the
OutputParam using the “glob” option.
Running Tools in Docker
The CWL can work with docker to simplify your software management and
communicate files between host and container. The docker container can
be defined by the hints or requirements
option.
d1 <- InputParam(id = "rfile", type = "File")
req1 <- requireDocker("r-base")
doc <- cwlProcess(baseCommand = "Rscript",
inputs = InputParamList(d1),
stdout = "output.txt",
hints = list(req1))
doc$rfile <- r4$outputThe tools defined with docker requirements can also be run locally by
disabling the docker option. In case your Rscript depends
some local libraries to run, an option from cwltools,
“–preserve-entire-environment”, can be used to pass all environment
variables.
## }[1;30mINFO[0m Final process status is successRunning Tools in Cluster server
The CWL can also work in high performance clusters with batch-queuing
system, such as SGE, PBS, SLURM and so on, using the Bioconductor
package BiocParallel. Here is an example to submit jobs
with “Multicore” and “SGE”.
library(BiocParallel)
sth.list <- as.list(LETTERS)
names(sth.list) <- LETTERS
## submit with multicore
result1 <- runCWLBatch(cwl = echo, outdir = tempdir(), inputList = list(sth = sth.list),
BPPARAM = MulticoreParam(26))
## submit with SGE
result2 <- runCWLBatch(cwl = echo, outdir = tempdir(), inputList = list(sth = sth.list),
BPPARAM = BatchtoolsParam(workers = 26, cluster = "sge",
resources = list(queue = "all.q")))Writing Pipeline
We can connect multiple tools together into a pipeline. Here is an
example to uncompress an R script and execute it with
Rscript.
Here we define a simple Rscript tool without using
docker.
d1 <- InputParam(id = "rfile", type = "File")
Rs <- cwlProcess(baseCommand = "Rscript",
inputs = InputParamList(d1))
Rs## class: cwlProcess
## cwlClass: CommandLineTool
## cwlVersion: v1.0
## baseCommand: Rscript
## inputs:
## rfile (File):
## outputs:
## output:
## type: stdoutTest run:
## }[1;30mINFO[0m Final process status is success## [1] "[1] 7 3 1 5 4"The pipeline includes two steps, decompressing with predefined
cwlProcess of GZ and compiling with
cwlProcess of Rs. The input file is a
compressed file for the first “Uncomp” step.
i1 <- InputParam(id = "cwl_zfile", type = "File")
s1 <- cwlStep(id = "Uncomp", run = GZ,
In = list(zfile = "cwl_zfile"))
s2 <- cwlStep(id = "Compile", run = Rs,
In = list(rfile = "Uncomp/rfile"))In step 1 (‘s1’), the pipeline runs the cwlProcess of
GZ, where the input zfile is defined in ‘i1’
with id of “cwl_zfile”. In step 2 (‘s2’), the pipeline runs the
cwlProcess of Rs, where the input
rfile is from the output of the step 1 (“Uncomp/rfile”)
using the format of <step>/<output>.
The pipeline output will be defined as the output of the step 2
(“Compile/output”) using the format of
<step>/<output> as shown below.
The cwlWorkflow function is used to initiate the
pipeline by specifying the inputs and outputs.
Then we can simply use + to connect all steps to build the
final pipeline.
cwl <- cwlWorkflow(inputs = InputParamList(i1),
outputs = OutputParamList(o1))
cwl <- cwl + s1 + s2
cwl## class: cwlWorkflow
## cwlClass: Workflow
## cwlVersion: v1.0
## inputs:
## cwl_zfile (File):
## outputs:
## cwl_cout:
## type: File
## outputSource: Compile/output
## steps:
## Uncomp:
## run: Uncomp.cwl
## in:
## zfile: cwl_zfile
## out:
## - rfile
## Compile:
## run: Compile.cwl
## in:
## rfile: Uncomp/rfile
## out:
## - outputLet’s run the pipeline.
## }[1;30mINFO[0m Final process status is success## [1] "[1] 4 5 1 10 7"Tips: Sometimes, we need to adjust some arguments of certain tools in
a pipeline besides of parameter inputs. The function
arguments can help to modify arguments for a tool, tool in
a pipeline, or even tool in a sub-workflow. For example,
## class: cwlProcess
## cwlClass: CommandLineTool
## cwlVersion: v1.0
## baseCommand: gzip
## arguments: -d -c -f
## inputs:
## zfile (File): /tmp/RtmpMozipk/sample.R.gz
## outputs:
## rfile:
## type: File
## outputBinding:
## glob: $(inputs.zfile.nameroot)
## stdout: $(inputs.zfile.nameroot)Scattering pipeline
The scattering feature can specifies the associated workflow step or
subworkflow to execute separately over a list of input elements. To use
this feature, ScatterFeatureRequirement must be specified
in the workflow requirements. Different scatter methods can
be used in the associated step to decompose the input into a discrete
set of jobs. More details can be found at: https://www.commonwl.org/v1.0/Workflow.html#WorkflowStep.
Here is an example to execute multiple R scripts. First, we need to
set the input and output types to be array of “File”, and add the
requirements. In the “Compile” step, the scattering input is required to
be set with the scatter option.
i2 <- InputParam(id = "cwl_rfiles", type = "File[]")
o2 <- OutputParam(id = "cwl_couts", type = "File[]", outputSource = "Compile/output")
req1 <- requireScatter()
cwl2 <- cwlWorkflow(requirements = list(req1),
inputs = InputParamList(i2),
outputs = OutputParamList(o2))
s1 <- cwlStep(id = "Compile", run = Rs,
In = list(rfile = "cwl_rfiles"),
scatter = "rfile")
cwl2 <- cwl2 + s1
cwl2## class: cwlWorkflow
## cwlClass: Workflow
## cwlVersion: v1.0
## requirements:
## - class: ScatterFeatureRequirement
## inputs:
## cwl_rfiles (File[]):
## outputs:
## cwl_couts:
## type: File[]
## outputSource: Compile/output
## steps:
## Compile:
## run: Compile.cwl
## in:
## rfile: cwl_rfiles
## out:
## - output
## scatter: rfileMultiple R scripts can be assigned to the workflow inputs and executed.
## }[1;30mINFO[0m Final process status is success## [1] "/tmp/RtmpMozipk/23d16e04fc450b71ca50b8be1b9588e465c07d04"
## [2] "/tmp/RtmpMozipk/23d16e04fc450b71ca50b8be1b9588e465c07d04_2"Web Application
cwlProcess example
Here we build a tool with different types of input parameters.
e1 <- InputParam(id = "flag", type = "boolean",
prefix = "-f", doc = "boolean flag")
e2 <- InputParam(id = "string", type = "string", prefix = "-s")
e3 <- InputParam(id = "option", type = "string", prefix = "-o")
e4 <- InputParam(id = "int", type = "int", prefix = "-i", default = 123)
e5 <- InputParam(id = "file", type = "File",
prefix = "--file=", separate = FALSE)
e6 <- InputParam(id = "array", type = "string[]", prefix = "-A",
doc = "separated by comma")
mulEcho <- cwlProcess(baseCommand = "echo", id = "mulEcho",
label = "Test parameter types",
inputs = InputParamList(e1, e2, e3, e4, e5, e6),
stdout = "output.txt")
mulEcho## class: cwlProcess
## cwlClass: CommandLineTool
## cwlVersion: v1.0
## baseCommand: echo
## inputs:
## flag (boolean): -f
## string (string): -s
## option (string): -o
## int (int): -i 123
## file (File): --file=
## array (string[]): -A
## outputs:
## output:
## type: stdout
## stdout: output.txtcwlProcess to Shiny App
Some input parameters can be predefined in a list, which will be
converted to select options in the webapp. An upload
parameter can be used to defined whether to generate an upload interface
for the file type option. If FALSE, the upload field will be text input
(file path) instead of file input.
inputList <- list(option = c("option1", "option2"))
app <- cwlShiny(mulEcho, inputList, upload = TRUE)
runApp(app)
Working with R functions
We can wrap an R function to cwlProcess object by simply
assigning the R function to baseCommand. This could be
useful to summarize results from other tools in a pipeline. It can also
be used to benchmark different parameters for a method written in R.
Please note that this feature is only implemented by Rcwl,
but not available in the common workflow language.
fun1 <- function(x)x*2
testFun <- function(a, b){
cat(fun1(a) + b^2, sep="\n")
}
assign("fun1", fun1, envir = .GlobalEnv)
assign("testFun", testFun, envir = .GlobalEnv)
p1 <- InputParam(id = "a", type = "int", prefix = "a=", separate = F)
p2 <- InputParam(id = "b", type = "int", prefix = "b=", separate = F)
o1 <- OutputParam(id = "o", type = "File", glob = "rout.txt")
TestFun <- cwlProcess(baseCommand = testFun,
inputs = InputParamList(p1, p2),
outputs = OutputParamList(o1),
stdout = "rout.txt")
TestFun$a <- 1
TestFun$b <- 2
r1 <- runCWL(TestFun, cwlArgs = "--preserve-entire-environment")## }[1;30mINFO[0m Final process status is success## [1] "6"The runCWL function wrote the testFun
function and its dependencies into an R script file automatically and
call Rscript to run the script with parameters. Each
parameter requires a prefix from corresponding argument in the R
function with “=” and without a separator. Here we assigned the R
function and its dependencies into the global environment because it
will start a new environment when the vignette is compiled.
Resources
RcwlPipelines
The Rcwl package can be utilized to develop pipelines
for best practices of reproducible research, especially for
Bioinformatics study. Multiple Bioinformatics pipelines, such as RNA-seq
alignment, quality control and quantification, DNA-seq alignment and
variant calling, have been developed based on the tool in an R package
RcwlPipelines, which contains the CWL recipes and the
scripts to create the pipelines. Examples to analyze real data are also
included.
The package is currently available in GitHub.
To install the package.
The project website https://rcwl.org/ serves as a central hub for all related resources. It provides guidance for new users and tutorials for both users and developers. Specific resources are listed below.
Tutorial book
The tutorial book provides
detailed instructions for developing Rcwl tools/pipelines,
and also includes examples of some commonly-used tools and pipelines
that covers a wide range of Bioinformatics data analysis needs.
The R recipes and cwl scripts
The R scripts to build the CWL tools and pipelines are now
residing in a dedicated GitHub repository,
which is intended to be a community effort to collect and contribute
Bioinformatics tools and pipelines using Rcwl and CWL.
Tool collections in CWL format
Plenty of Bioinformatics tools and workflows can be found from GitHub
in CWL format. They can be imported to cwlProcess object by
readCWL function, or can be used directly.
Docker for Bioinformatics tools
Most of the Bioinformatics software are available in docker containers, which can be very convenient to be adopted to build portable CWL tools and pipelines.
SessionInfo
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Rcwl_1.23.0 S4Vectors_0.45.2 BiocGenerics_0.53.3
## [4] generics_0.1.3 yaml_2.3.10 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] dir.expiry_1.15.0 xfun_0.49 bslib_0.8.0
## [4] htmlwidgets_1.6.4 visNetwork_2.1.2 lattice_0.22-6
## [7] batchtools_0.9.17 vctrs_0.6.5 tools_4.4.2
## [10] base64url_1.4 parallel_4.4.2 tibble_3.2.1
## [13] fansi_1.0.6 pkgconfig_2.0.3 R.oo_1.27.0
## [16] Matrix_1.7-1 data.table_1.16.2 checkmate_2.3.2
## [19] RColorBrewer_1.1-3 lifecycle_1.0.4 stringr_1.5.1
## [22] compiler_4.4.2 progress_1.2.3 codetools_0.2-20
## [25] httpuv_1.6.15 htmltools_0.5.8.1 sys_3.4.3
## [28] buildtools_1.0.0 sass_0.4.9 later_1.3.2
## [31] pillar_1.9.0 crayon_1.5.3 jquerylib_0.1.4
## [34] R.utils_2.12.3 BiocParallel_1.41.0 cachem_1.1.0
## [37] mime_0.12 basilisk_1.19.0 brew_1.0-10
## [40] tidyselect_1.2.1 digest_0.6.37 stringi_1.8.4
## [43] purrr_1.0.2 dplyr_1.1.4 maketools_1.3.1
## [46] fastmap_1.2.0 grid_4.4.2 cli_3.6.3
## [49] magrittr_2.0.3 DiagrammeR_1.0.11 utf8_1.2.4
## [52] withr_3.0.2 prettyunits_1.2.0 filelock_1.0.3
## [55] promises_1.3.0 backports_1.5.0 rappdirs_0.3.3
## [58] rmarkdown_2.29 igraph_2.1.1 reticulate_1.40.0
## [61] png_0.1-8 R.methodsS3_1.8.2 hms_1.1.3
## [64] shiny_1.9.1 evaluate_1.0.1 knitr_1.49
## [67] basilisk.utils_1.19.0 rlang_1.1.4 Rcpp_1.0.13-1
## [70] xtable_1.8-4 glue_1.8.0 BiocManager_1.30.25
## [73] rstudioapi_0.17.1 jsonlite_1.8.9 R6_2.5.1