Python API Reference
This is the API documentation for the BGE Toolkit, and provides detailed information on the Python programming interface.
Use import bge_toolkit to access this functionality.
bge_toolkit.qc
The concordance functionality in the BGE Toolkit is meant to compute concordance between an exome sequencing dataset and the corresponding imputed dataset across arbitrary metrics to identify appropriate variant QC thresholds.
The Sample QC functionality in the BGE Toolkit is meant to run standard analyses for computing sample QC statistics. The pipeline is broken into three components:
Find high quality variants.
Use a random forest model from gnomAD to identify ancestry population labels per sample.
Compute summary statistics including contamination rates, principal components, sex check, estimating call rate via GQ, and running hl.sample_qc() and identifying outliers by ancestry population.
Note: Relatedness checks have not been implemented yet due to a memory leak in Hail QoB.
- Sample QC Reference
- sample_qc
- High Level Functions
- Lower Level Functions
- SampleQCResult
SampleQCResultSampleQCResult.ancestry_popSampleQCResult.chimera_readsSampleQCResult.close()SampleQCResult.contaminationSampleQCResult.export()SampleQCResult.filter_to()SampleQCResult.gq_fractionSampleQCResult.load()SampleQCResult.passing_samples()SampleQCResult.pcsSampleQCResult.plot_chimera_reads()SampleQCResult.plot_gq_fraction_density()SampleQCResult.plot_pcs()SampleQCResult.plot_qc_metric_boxplots()SampleQCResult.plot_qc_metric_density()SampleQCResult.qc_metricsSampleQCResult.sample_idSampleQCResult.sex_infoSampleQCResult.tableSampleQCResult.write()