Add fgseaBatch: batch preranked GSEA over multiple stats vectors in o…#1
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…ne C++ call
- fgsea_batch_cpp() (src/rcpp_glue.cpp): Rcpp export that accepts H sorted
stats vectors and precomputed pathway positions, builds
std::vector<FgseaInput> at the R->C++ boundary, then dispatches:
GPU path: sequential for-loop (each HOG fully saturates the device)
CPU path: std::for_each(par_unseq) over HOG indices via TBB
Returns flat List with hog_idx (1-based), pathway, pval, padj, ES, NES,
nMoreExtreme, size, leadingEdge (1-based gene indices), pi0.
- fgseaBatch() (R/fgsea.R): R wrapper; precomputes sorted stats and pathway
positions on the R side (reuses .prepareStats / .pathwayPositions),
calls fgsea_batch_cpp, converts leading-edge indices back to gene names,
returns data.table with extra hog_idx column plus standard fgsea columns.
- NAMESPACE: export(fgseaBatch)
- README.md: document fgseaBatch in usage example and entry-points table
Motivation: eliminates per-query R dispatch overhead; on CPU enables TBB
parallel processing of queries rather than a sequential R for-loop; on GPU
ensures the device is kept busy across many independent queries.
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…ne C++ call
fgsea_batch_cpp() (src/rcpp_glue.cpp): Rcpp export that accepts H sorted stats vectors and precomputed pathway positions, builds std::vector at the R->C++ boundary, then dispatches: GPU path: sequential for-loop (each HOG fully saturates the device) CPU path: std::for_each(par_unseq) over HOG indices via TBB Returns flat List with hog_idx (1-based), pathway, pval, padj, ES, NES, nMoreExtreme, size, leadingEdge (1-based gene indices), pi0.
fgseaBatch() (R/fgsea.R): R wrapper; precomputes sorted stats and pathway positions on the R side (reuses .prepareStats / .pathwayPositions), calls fgsea_batch_cpp, converts leading-edge indices back to gene names, returns data.table with extra hog_idx column plus standard fgsea columns.
NAMESPACE: export(fgseaBatch)
README.md: document fgseaBatch in usage example and entry-points table
Motivation: eliminates per-query R dispatch overhead; on CPU enables TBB parallel processing of queries rather than a sequential R for-loop; on GPU ensures the device is kept busy across many independent queries.