heise3 / ExtremaRank

# ExtremaRank
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**Which of your omics candidates survive removing a few biological samples?**
ExtremaRank audits whether omics candidates survive shared sample deletion.
v0.6 ships a **native R package**: exact R/C++ computation, direct Bioconductor
model refits, sparse donor pseudobulk and experiment-object interfaces.
v0.5 adds explicit coverage, candidate refit witnesses, grouped deletions,
disk scores, resumable model fits, reproducible parallel workers, delayed
input, preflight storage estimates, plots and a tighter conditional Welch floor.
v0.6 adds one-pass native CPU pseudobulk and optional exact integer Metal aggregation
on macOS, with bounded staging buffers and measured provenance. CPU remains the
default. CUDA execution is not implemented. It runs entirely in R without Python,
reticulate or a subprocess bridge.
The existing Python implementation also provides **individual membership/sign certificates**, **minimum deletion
change bounds**, **safe Welch pruning**, **automatic limma/voom/edgeR/DESeq2
refits**, and **donor-level single-cell pseudobulk**.
The Python core has **no third-party dependencies** and runs on Python 3.10+.
The R package uses Rcpp, BH, Matrix and digest, with optional Bioconductor models.
Real examples cover paired bulk RNA-seq, independent microarrays, an 8-donor
single-cell study, technical protein measurements, and biological lipid data.
Native certificates and observed external-model refits have different guarantees.
Mac 优化 / Windows 复现 · v0.6 measurements · v0.5 新功能 · 原生 R 包中文指南 · R package ·
中文 v0.3 说明 · Individual certificates ·
Model refits / single cell · Study inputs ·
Proof · Prior art ·
Data attribution · Example report
## Install and run
### Native R
 [code] 
For precomputed donor-by-feature effects, call `extremarank_effects(effects)`.
For independent groups, use `design = "welch"`. To run limma, limma-voom, edgeR
or DESeq2 directly in R, call `refit_extremarank()`. For SingleCellExperiment
raw counts, call `pseudobulk_extremarank(sce, assay = "counts")` before analysis.
These models retain an **observed sensitivity** scope. Native exact audits
can return certificates for the declared deletion budget.
The helper verifies SHA-256 and selects precompiled packages for R 4.6 on
Windows x86_64 and CRAN macOS R.framework (arm64/x86_64). Other supported R
versions and Homebrew R use source installation. Source installation needs a
C++17 compiler (Rtools on Windows, Xcode command
line tools on macOS). There is no Python installation step. See the
R package README for a runnable toy example and
native R validation for actual verification results.
The small R source archive installs without downloading the full benchmark
repository. Alternatively, use `remotes::install_github("heise3/ExtremaRank",
subdir = "r/extremarank", ref = "v0.6.0", upgrade = "never")`.
### Python
 [code] 
For `study`, supply a feature-by-sample matrix and aligned sample metadata;
declare target/refer