limma/
lib.rs

1//! `limma` — a pure-Rust port of the Bioconductor **limma** package
2//! (Linear Models for Microarray and RNA-seq Data), validated function by
3//! function against limma 3.68.3 running on R 4.6.0.
4//!
5//! The crate has no BLAS/LAPACK, Python or R dependency at runtime: all linear
6//! algebra and special functions are implemented in pure Rust (see [`linalg`]
7//! and [`special`]), with distribution functions from `statrs`.
8//!
9//! The current scope is the statistical core of limma — `lmFit` (least
10//! squares) -> `contrasts.fit` -> `eBayes` -> `topTable`/`topTableF` — with
11//! further functions ported incrementally. See `README.md` for the per-function
12//! status table. Microarray IO readers, plotting and annotation helpers are out
13//! of scope for a pure-Rust port.
14
15pub mod arrayweights;
16pub mod auroc;
17pub mod avereps;
18pub mod batch;
19pub mod beadcountweights;
20pub mod bwss;
21pub mod classifytestsf;
22pub mod combine;
23pub mod contrasts;
24pub mod cumoverlap;
25pub mod decidetests;
26pub mod detectionpvalues;
27pub mod diffsplice;
28pub mod dups;
29pub mod ebayes;
30pub mod fit;
31pub mod fitgamma;
32pub mod fitmixture;
33pub mod genas;
34pub mod geneset;
35pub mod glsseries;
36pub mod io;
37pub mod linalg;
38pub mod logsumexp;
39pub mod lowess;
40pub mod ma3x3;
41pub mod modelmatrix;
42pub mod mrlm;
43pub mod neqc;
44pub mod norm;
45pub mod normexp;
46pub mod normwithin;
47pub mod optim;
48pub mod poolvar;
49pub mod predfcm;
50pub mod printtipweights;
51pub mod propexpr;
52pub mod proptruenull;
53pub mod qqt;
54pub mod rng;
55pub mod selectmodel;
56pub mod sepchannel;
57pub mod special;
58pub mod splines;
59pub mod toptable;
60pub mod treat;
61pub mod tricube;
62pub mod voom;
63pub mod weightedmedian;
64pub mod weights;
65pub mod wsva;
66pub mod zscore;
67pub mod zscorehyper;
68
69pub use arrayweights::{
70    array_weights, array_weights_gene_by_gene, array_weights_prwts_reml, array_weights_quick,
71};
72pub use auroc::auroc;
73pub use avereps::{avearrays, avereps};
74pub use batch::remove_batch_effect;
75pub use beadcountweights::{bead_count_weights, BeadCountWeights, BeadDispersion};
76pub use bwss::{bwss, bwss_matrix, Bwss};
77pub use classifytestsf::{classify_tests_f, classify_tests_fstat};
78pub use combine::make_unique;
79pub use contrasts::{contrasts_fit, make_contrasts};
80pub use cumoverlap::{cum_overlap, CumOverlap};
81pub use decidetests::{
82    classify_tests_p, decide_tests, decide_tests_pvalues, p_adjust, Adjust, DecideMethod,
83    TestResults, TestResultsSummary,
84};
85pub use detectionpvalues::detection_p_values;
86pub use diffsplice::{diff_splice, top_splice, DiffSplice, SpliceSort, SpliceTest, TopSpliceRow};
87pub use dups::{avedups, duplicate_correlation, uniquegenelist, unwrapdups, DupCorOutput};
88pub use ebayes::ebayes;
89pub use fit::{is_fullrank, lmfit, lmfit_weighted, non_estimable, MArrayLM};
90pub use fitgamma::fit_gamma_intercept;
91pub use fitmixture::{fitmixture, FitMixture};
92pub use genas::{genas, Genas, GenasSubset};
93pub use geneset::{
94    camera, camera_pr, contrast_as_coef, fry, gene_set_test, ids2indices, inter_gene_correlation,
95    mroast, rank_sum_test_with_correlation, roast, romer, top_romer, wilcox_gst, Alternative,
96    CameraResult, ContrastAsCoef, Direction, FryResult, FrySort, MroastRow, Roast,
97    RomerAlternative, RomerRow, RomerStatistic,
98};
99pub use glsseries::gls_series;
100pub use linalg::block_diag;
101pub use logsumexp::{logcosh, logsumexp};
102pub use lowess::{loess_fit, weighted_lowess, LoessFit, WeightedLowess};
103pub use ma3x3::{ma3x3_matrix, ma3x3_spottedarray, Ma3x3Fun};
104pub use modelmatrix::{model_matrix, unique_targets, ModelMatrix, ModelParam};
105pub use mrlm::mrlm;
106pub use neqc::{nec, neqc, normexp_fit_control, normexp_fit_detection_p};
107pub use norm::{
108    normalize_between_arrays, normalize_cyclic_loess, normalize_median_abs_values,
109    normalize_median_values, normalize_quantiles, CyclicMethod, NormalizeMethod,
110};
111pub use normexp::{
112    background_correct_matrix, normexp_fit_saddle, normexp_signal, BackgroundMethod, NormexpFit,
113};
114pub use normwithin::{
115    ma_from_rg, normalize_within_arrays, rg_from_ma, PrinterLayout, WithinArrayMethod,
116};
117pub use optim::{nelder_mead, nelder_mead_with, NelderMead};
118pub use poolvar::{pool_var, PoolVar};
119pub use predfcm::pred_fcm;
120pub use printtipweights::printtip_weights;
121pub use propexpr::propexpr;
122pub use proptruenull::{convest, prop_true_null, PropTrueNullMethod};
123pub use qqt::{qqf, qqt};
124pub use rng::{qnorm, RRng};
125pub use selectmodel::{select_model, SelectCriterion, SelectModelResult};
126pub use sepchannel::{design_i2a, design_i2m, exprs_ma, lmsc_fit};
127pub use toptable::{p_adjust_bh, top_table, top_table_f, top_treat, SortBy, TopRow, TopRowF};
128pub use treat::treat;
129pub use tricube::tricube_moving_average;
130pub use voom::{
131    choose_lowess_span, voom, voom_with_quality_weights, vooma, vooma_by_group, vooma_lm_fit,
132    VoomOutput, VoomQualityWeights, VoomaByGroupOutput, VoomaLmFit, VoomaOutput,
133};
134pub use weightedmedian::weighted_median;
135pub use weights::{as_matrix_weights, modify_weights};
136pub use wsva::wsva;
137pub use zscore::{t_zscore, zscore_from_log_tails, zscore_gamma, zscore_t, ZscoreTMethod};
138pub use zscorehyper::zscore_hyper;
limma/lib.rs

limma/
lib.rs
