This pipeline requires paired tumor-normal data. This is because the allele-specific copy numbers are inferred from the allelic imbalance in the tumor at heterozygous SNPs. The heterozygous SNPs are identified from the allelic signals in the matched normal.
This pipeline is implemented in R and requires R packages aroma.seq, sequenza (Favero et al. 2015), and PSCBS (Bengtsson et al. 2010, Olshen et al. 2011). To install these packages and all of their dependencies to the current working directory, call the following from R:
if (!requireNamespace("pak")) install.packages("pak")
pak:::proj_create()
pak::pkg_install("[email protected]")
pak::pkg_install("HenrikBengtsson/aroma.seq")
pak::pkg_install("HenrikBengtsson/CostelloPSCNSeq@develop")In addition to the above R dependencies, the pipeline requires that samtools (Li et al. 2009) is on the PATH.
Important: The pipeline does not work with sequenza (>= 3.0.0; 2019-05-09). This is the reason why we install sequenza 2.1.2. The use of pak:::proj_create() causes all packages to be installed to ./r-packages/, which avoids clashing the R package library that is typically installed under ~/R/.
-
Run
Rscript 0.setup.Ronce. This will setup links to shared annotation data sets and lab data files on the TIPCC compute cluster. -
Make sure
./config.ymlis correct. It specify the default analysis settings. The individual entries can be overridden by individual command-line options to the belowRscriptcalls. -
Configure parallel processing following the instructions in Section 'Configure parallel processing' below.
The following scripts should be run in order:
Rscript -e CostelloPSCNSeq::pscnseq --args --what=mpileup --verbose=TRUE # ~25 min
Rscript -e CostelloPSCNSeq::pscnseq --args --what=sequenza --verbose=TRUE # ~60 min
Rscript -e CostelloPSCNSeq::pscnseq --args --what=pscbs --verbose=TRUE # ~5 min
Rscript -e CostelloPSCNSeq::pscnseq --args --what=reports --verbose=TRUE # ~2 minComment: The timings listed are typical run times for our test tumor-normal sample.
You may want to adjust inst/config.yml to process other data sets. Alternatively, you can specify another file that this default via command-line option --config, e.g. Rscript -e "CostelloPSCNSeq::pscnseq(what='mpileup', verbose=TRUE)" --config=config_set_a.yml.
To process the above four steps via the Torque/PBS scheduler, use:
$ qsub -d $(pwd) inst/scripts/1-4.submit_all.pbsThis will in turn submit the corresponding PBS scripts 1.mpileup.pbs, 2.sequenza.pbs, 3.pscbs.pbs, and 4.reports.pbs to the scheduler. Those PBS scripts are located in inst/scripts/ and "freeze" software versions to R 3.6.1 and samtools 1.3.1.
The pipeline supports both sequential and parallel processing on a large number of backends and compute resources. By default the pipeline is configured to process the data sequentially on the current machine, but this can easily be changed to run in parallel, say, on a compute cluster. In order not to clutter up the analysis scripts, these settings are preferably done in a separate .future.R (loaded automatically by the future framework) in the project root directory.
To process data via a TORQUE / PBS job scheduler using the future.batchtools package, try with the configuration that we use for our UCSF TIPCC cluster;
# Copy to project directory
$ cp inst/future-configs/batchtools/.future.R .
# Copy to project directory
$ cp inst/future-configs/batchtools/batchtools.torque.tmpl .
# Install the future.batchtools package
$ Rscript -e "install.packages('future.batchtools')"These should be generic enough to also run on other TORQUE / PBS systems. If not, see the batchtools package for how to configure the template file.
You can verify that it works by trying the following in the project directory:
> library("future")
Using future plan:
plan(list(samples = tweak(batchtools_torque, label = "sample",
resources = list(vmem = "4gb")), chromosomes = tweak(batchtools_torque,
label = "chr", resources = list(vmem = "8gb"))))This confirms that as soon as the future package is loaded, it will source the .future.R script which in turn will setup the parallel settings. It is .future.R that reports on the future plan used.
Next, we can try to submit a job to the scheduler using these settings by:
> x %<-% Sys.info()[["nodename"]]In this step, future.batchtools will import the batchtools.torque.tmpl file in that we copied to project working directory. If it fails to locate that file, there will be an error. If it succeeds, a batchtools job will be submitted to the job scheduler - which can be seen when if calling qstat -u $USER in another shell.
Finally, if we try to look at the value of x;
> x
[1] "n17"
> it will block until the job is finished and then its value will be printed. Here we see that the job was running on compute node n17.
> help("pscnseq", package = "CostelloPSCNSeq")
pscnseq package:CostelloPSCNSeq R Documentation
Calling the Parent-Specific Copy-Number Pipeline Step by Step
Description:
Calling the Parent-Specific Copy-Number Pipeline Step by Step
Usage:
pscnseq(
what = c("mpileup", "sequenza", "pscbs", "reports"),
dataset = NULL,
organism = NULL,
chrs = NULL,
samples = NULL,
fasta = NULL,
gcbase = NULL,
bam_pattern = NULL,
binsize = NULL,
config = "config.yml",
session_details = !interactive(),
verbose = TRUE,
...
)
Arguments:
what: (character) The step to be performed; in order, one of
'"mpileup"', '"sequenza"', '"pscbs"', or '"reports"'.
dataset: (character) The name of the dataset as on file.
organism: (character) The name of the organism as on file.
chrs: (character vector) The name of the chromosomes to be
processed, e.g. 'c("1", "2", "X")'.
samples: (character) Pathname to a tab-delimited sample specification
file, typically named '*.tsv', e.g. 'samples.tsv'.
fasta: (character) The pathname to the FASTA reference file,
typically named '*.fa' or '*.fasta', e.g. 'hg19.fa'.
gcbase: (character) The pathname to the FASTA reference file,
typically named '*.txt.gz', e.g. 'hg19.gc50Base.txt.gz'.
bam_pattern: (character; optional) Regular expression to identify
subset of BAM files to be processed. If NULL (default), then
BAM files matching .bwa.realigned.rmDups(|.recal)(|.bam)$ are
included.
binsize: (integer or numeric) The bin size (in basepairs) used for
binning reads into bins that then are passed to the
segmentation method.
config: (character) Pathname to YAML configuration file. If NULL,
then the configuration file is skipped.
session_details: (logical) If TRUE, session details are reported before
starting the processing and after it completed.
verbose: (logical) If TRUE, then verbose output is produced, otherwise
not.
...: Not used.
Value:
Returns what the called 'pscnseq_nnn()' function returns, i.e.
'pscnseq_mpileup()', 'pscnseq_sequenza()', 'pscnseq_pscbs()', or
'pscnseq_reports()'.
Format of the samples file:
The 'samples' argument should specify the pathname to a
TAB-delimited file that provide annotation data for the samples to
be processed. This file should a row of TAB-delimited column
headers followed rows of samples with corresponding, TAB-delimited
cells. The samples file must provide columns 'Patient_ID',
'Sample_ID', and 'A0'. Any other columns are ignored. This
pipeline processes tumor-normal pairs. The pairs processed are
inferred from (Patient_ID, Sample_ID). Specifically, for each
unique 'Patient_ID', the sample entry with 'Sample_ID == "Normal"'
is used as the normal reference. There must only be such entry
per patient. Each patient may have one or more tumor samples,
which are identified as 'Sample_ID != "Normal"'.
For example, the below 'samples.tsv' file specifies two
tumor-normal pairs 'Primary-v1' vs 'Normal' and 'Primary-v2' vs
'Normal' for one patient named 'Patient123'. This file specifies
also fields 'SF', 'Kit', and 'A0', which may be used in other
pipelines but are all ignored by this pipeline.
Patient_ID Sample_ID SF Kit A0
Patient123 Normal SF00121N Xgen Exome Research Panel X00001
Patient123 Primary-v1 SF00121-v1 Xgen Exome Research Panel X00002
Patient123 Primary-v2 SF00121-v2 Xgen Exome Research Panel X00003
This
Configuration File:
The default arguments can be set in an YAML-formatted
configuration file as given by argument 'config'. The default is
to look for a file named 'config.yml' in the current directory.
To skip this file, specify 'config = NULL'. An example of such a
file is:
organism: Homo_sapiens
chromosomes: c(1:22, "X", "Y", "M")
fasta: annotationData/organisms/Homo_sapiens/GRCh37,hg19/UCSC/hg19.fa
gcbase: annotationData/organisms/Homo_sapiens/GRCh37,hg19/UCSC/hg19.gc50Base.txt.gz
dataset: CostelloP_2015-Exome,bwa,realigned,rmDups,recal
binsize: 100e3
samples: sampleData/samples.tsv
Specifying arguments via command-line options:
The arguments can be overridden by command-line options, e.g.
'--organism=Homo_sapiens' will take precedence of argument
'organism', which in turn will take precedent of what is specified
in the configuration file.
How to call pipeline from the command line:
Below is how you could run the pipeline step by step. The
'--args' option tells 'Rscript' that any options following should
be passed as arguments to this function.
Rscript -e CostelloPSCNSeq::pscnseq --args --help
Rscript -e CostelloPSCNSeq::pscnseq --args --what=mpileup # ~25 min
Rscript -e CostelloPSCNSeq::pscnseq --args --what=sequenza # ~60 min
Rscript -e CostelloPSCNSeq::pscnseq --args --what=pscbs # ~5 min
Rscript -e CostelloPSCNSeq::pscnseq --args --what=reports # ~2 min
-
Bengtsson H, Neuvial P, Speed TP. TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays, BMC Bioinformatics, 2010. DOI: 10.1186/1471-2105-11-245. PMID: 20462408, PMCID: PMC2894037
-
Olshen AB, Bengtsson H, Neuvial P, Spellman PT, Olshen RA, Seshan VA. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation, Bioinformatics, 2011. DOI: 10.1093/bioinformatics/btr329. PMID: 21666266. PMCID: PMC3137217
-
Favero F, Joshi T, Marquard AM, Birkbak NJ, Krzystanek M, Li Q, Szallasi Z and Eklund AC. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data, Annals of Oncology, 2015. DOI: 10.1093/annonc/mdu479, PMID: 25319062, PMCID: PMC4269342
-
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and 1000 Genome Project Data Processing Subgroup, The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 2009. DOI: 10.1093/bioinformatics/btp352, PMID: 19505943, PMCID: PMC2723002
R package CostelloPSCNSeq is only available via GitHub and can be installed in R as:
remotes::install_github("HenrikBengtsson/Costello-PSCN-Seq", ref="master")To install the pre-release version that is available in Git branch develop on GitHub, use:
remotes::install_github("HenrikBengtsson/Costello-PSCN-Seq", ref="develop")This will install the package from source.
This Git repository uses the Git Flow branching model (the git flow extension is useful for this). The develop branch contains the latest contributions and other code that will appear in the next release, and the master branch contains the code of the latest release.
Contributing to this package is easy. Just send a pull request. When you send your PR, make sure develop is the destination branch on the CostelloPSCNSeq repository. Your PR should pass R CMD check --as-cran, which will also be checked by and when the PR is submitted.
We abide to the Code of Conduct of Contributor Covenant.
| Resource | GitHub | GitHub Actions | Travis CI | AppVeyor CI |
|---|---|---|---|---|
| Platforms: | Multiple | Multiple | Linux & macOS | Windows |
| R CMD check | ||||
| Test coverage |
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