A machine-readable knowledge base of statistical and computational methods. Each entry is a Method Card: what a method can do, under what conditions, and how. Verified cards become available to the statLens Agent, and their authors are cited whenever they are used.
Tests for differential expression in sequencing count data with a negative binomial GLM per gene. Dispersions and fold changes are shrunk with empirical Bayes, which keeps estimates stable for low-count genes and small sample sizes.
Models sequencing counts per feature with negative binomial or quasi-likelihood GLMs, sharing information across features to estimate dispersions. Designed for differential expression with few replicates and complex multi-factor designs.
Fits a linear model to each feature and borrows information across features with empirical Bayes variance moderation, giving stable tests even with very few samples. Works on microarray and other continuous log-scale data, and on RNA-seq counts through voom precision weights.
Removes known batch effects from high-dimensional data. It estimates an additive and a multiplicative batch effect for every feature and shrinks them with empirical Bayes, while preserving the biological covariates you specify. ComBat-seq applies the same idea to raw counts.
Finds associations between microbial features (taxa, genes, pathways) and clinical or environmental metadata. It fits a linear or generalized linear model to each feature, with built-in normalization, transformation and filtering, and random effects for repeated sampling.