Welcome to PascalX’s documentation!
PascalX is a python3 library (source) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the Pascal methodology, which is based on \(\chi^2\) statistics. The cumulative distribution function of the weighted \(\chi^2\) distribution can be calculated approximately or exactly via a multi-precision C++ implementation of Ruben’s and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described here.
Highlights:
Multi-precision weighted \(\chi^2\) cdf calculation (approximately or exactly up to 100 digits)
Parallelization over chromosomes and/or genes
GPU support for acceleration of linear algebra operations
Fast random access to reference panel genomic data via own indexed SNP database
Gene-wise coherence test between two GWAS
Support for SNP selection via external data
Tissue enrichment test (experimental)
Warning
PascalX is a research level tool. No warranty or guarantee whatsoever for its correct functionality is given. You should perform your own consistency checks on results PascalX implies.
Contents
Index
License and citation policy
The PascalX package is an open-source package under AGPLv3.
If you make use of PascalX for your research, please cite the PascalX publication:
If you make use of the X-scorer (gene-wise coherence test between GWAS), please cite the work:
Contact
For all technical issues (bug reports, etc.), please open a ticket on the GitHub page.