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SAIGEgds - Scalable Implementation of Generalized Mixed Models Using GDS Files in Phenome-Wide Association Studies

Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package. Optional OpenCL-based GPU acceleration is supported for the GRM cross-product computation in null model fitting and for GRM construction.

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softwaregeneticsstatisticalmethodgenomewideassociationgdsgwasmixed-modelphewasopenblascpp

6.90 score 7 stars 21 scripts 437 downloads

SCArray - Large-scale single-cell omics data manipulation with GDS files

Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

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infrastructuredatarepresentationdataimportsinglecellrnaseqcpp

5.26 score 1 stars 1 dependents 15 scripts 476 downloads

SCArray.sat - Large-scale single-cell RNA-seq data analysis using GDS files and Seurat

Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets.

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datarepresentationdataimportsinglecellrnaseq

4.48 score 1 stars 5 scripts 341 downloads

GDSAnnotator - Whole-genome variant annotation data manipulation using GDS files

Fast and memory-efficient manipulation of whole-genome variant annotation data stored in Genomic Data Structure (GDS) files. The annotated GDS (aGDS) format introduced by FAVOR stores the FAVOR annotations of the variants together with the genotypes in one SeqArray GDS file. This package generalizes the format to annotation GDS files of any source, with or without genotypes. Annotation resources and annotator outputs, including FAVOR, gnomAD, Ensembl VEP and SnpEff, are converted into compact SeqArray-compatible GDS files with random access. Variants can be given as chr-pos-ref-alt strings, data frames, GRanges objects, VCF files or objects, or SeqArray GDS files. They are annotated by querying the annotation files, and the annotations can be written back into a genotype GDS file. Summary statistics of the variants and their annotations are computed in a single pass over a file and rendered as a VEP/SnpEff-like report. The package is intended for large-scale whole-genome sequencing studies.

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infrastructuredatarepresentationdataimportsequencingwholegenomegeneticsannotationvariantannotationgenomeannotationgenomicvariation

3.88 score