Sequenza workflow, Given a pair of cellularity and ploidy parameters, the function returns the most likely allele-specific copy numbers with the corresponding log-posterior probability of the fit, for given values of B-allele frequency and depth ratio. Sequenza workflow, Given a pair of cellularity and ploidy parameters, the function returns the most likely allele-specific copy numbers with the corresponding log-posterior probability of the fit, for given values of B-allele frequency and depth ratio.
java -jar cromwell.jar run sequenza.wdl --inputs inputs.json
| Parameter | Value | Description |
|---|---|---|
snpFile |
File | File (data file with CNV calls from Varscan). |
cnvFile |
File | File (data file with SNV calls from Varscan). |
outputFileNamePrefix |
String | Output prefix to prefix output file names with. |
reference |
String | Version of genome reference |
| Parameter | Value | Default | Description |
|---|---|---|---|
gammaRange |
Array[String] | ["50", "100", "200", "300", "400", "500", "600", "700", "800", "900", "1000", "1250", "1500", "2000"] | List of gamma parameters for tuning Sequenza seqmentation step, used by copynumber package. |
| Parameter | Value | Default | Description |
|---|---|---|---|
preprocessInputs.rScript |
String | "$RSTATS_CAIRO_ROOT/bin/Rscript" | path to Rscript |
preprocessInputs.preprocessScript |
String | "$SEQUENZA_SCRIPTS_ROOT/bin/SequenzaPreProcess_v2.2.R" | Path to the preprocessing .R script |
preprocessInputs.modules |
String | "sequenza/2.1.2m sequenza-scripts/2.1.5m" | modules needed to run preprocessing step |
preprocessInputs.timeout |
Int | 20 | timeout for this step in Hr, default is 20 |
preprocessInputs.jobMemory |
Int | 38 | Memory allocated for this job |
runSequenza.rScript |
String | "$RSTATS_CAIRO_ROOT/bin/Rscript" | Path to Rscript |
runSequenza.sequenzaScript |
String | "$SEQUENZA_SCRIPTS_ROOT/bin/SequenzaProcess_v2.2.R" | Sequenza wrapper script, instructions for running the pipeline |
runSequenza.modules |
String | "sequenza/2.1.2m sequenza-scripts/2.1.5m sequenza-res/2.1.2" | Names and versions of modules |
runSequenza.female |
String? | None | logical, TRUE or FALSE. default is TRUE |
runSequenza.cancerType |
String? | None | acronym for cancer type (from ploidy table) |
runSequenza.minReadsNormal |
Float? | None | threshold of minimum number of observation of depth ratio in a segment |
runSequenza.minReadsBaf |
Int? | None | threshold of minimum number of observation of B-allele frequency in a segment |
runSequenza.windowSize |
Int | 100000 | parameter to define window size for segmentation |
runSequenza.timeout |
Int | 20 | Timeout in hours, needed to override imposed limits |
runSequenza.jobMemory |
Int | 24 | Memory allocated for this job |
formatJson.jobMemory |
Int | 8 | Memory allocated for this job |
formatJson.width |
Int | 1200 | width of the summary plot, default is 1200 |
formatJson.height |
Int | 400 | height of the summary plot, default is 400 |
formatJson.modules |
String | "sequenza-scripts/2.1.5m rmarkdown/0.1m" | Names and versions of modules |
formatJson.summaryPlotScript |
String | "$SEQUENZA_SCRIPTS_ROOT/bin/plot_gamma_solutions.R" | service script for plotting data from gamma solutions file, summary plot |
formatJson.sequenzaRmd |
String | "$SEQUENZA_SCRIPTS_ROOT/bin/SequenzaSummary.Rmd" | Path to rmarkdown file for producing a .pdf report |
formatJson.rScript |
String | "$RSTATS_CAIRO_ROOT/bin/Rscript" | Path to Rscript |
| Output | Type | Description |
|---|---|---|
resultZip |
File | All results from sequenza runs using gamma sweep. |
resultJson |
File? | Combined json file with ploidy and contamination data. |
gammaSummaryPlot |
File | png for summary plot showing the effect of different gamma values |
gammaMarkdownPdf |
File | rmarkdown pdf with all gamma-specific panels along with gamma effect summary plot |
This section lists command(s) run by sequenza workflow
- Running sequenza
Sequenza produces the most likely allele-specific copy numbers for given values of B-allele frequency and depth ratio
Preprocessing:
Rscript PREPROCESS_SCRIPT -s VARSCAN_SNP_FILE -c VARSCAN_CNV_FILE -y TRUE -p PREFIX
Prepearing data file using Varscan results:
set -euo pipefail
Rscript SEQUENZA_SCRIPT -s SEQZ_FILE -r REFERENCE -z GENOME_SIZE
-w WINDOW_SIZE
-g GAMMA
-p PREFIX
-l PLOIDY_FILE (Optional)
-f FEMALE_FLAG (Optional)
-t CANCER_TYPE (Optional)
-n MIN_READS_NORMAL (Optional)
-a MIN_READS_BAF (OPtional)
zip -qr PREFIX_results.zip sol* PREFIX*
Running analysis:
...
In this section Sequenza runs for a range of gamma values (fragment shown):
cellularity = []
ploidy = []
no_segments = []
for g in gammas:
print(g)
solutions = pd.read_table(os.path.join("gammas", g, "~{prefix}" + '_alternative_solutions.txt'))
row = solutions.loc[solutions['SLPP'].idxmax()]
cellularity.append(float(row['cellularity']))
ploidy.append(float(row['ploidy']))
path_seg = os.path.join("gammas", g, "~{prefix}" + '_Total_CN.seg')
no_segments.append(len(open(path_seg).readlines()) - 1)
gamma_solutions = pd.DataFrame({"gamma": gammas,
"cellularity": cellularity,
"ploidy": ploidy,
"no_segments": no_segments})
gamma_solutions.to_csv('gamma_solutions.csv', index=False)
...
For support, please file an issue on the Github project or send an email to gsi@oicr.on.ca .
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