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One catalogue of research methods — learn how each one works, when to use it, and what it can’t do.

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Entries are compiled from published sources for reference. Verifying the accuracy and suitability of any information for your own use remains your responsibility.

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21 methods in Life Sciences · StatisticsClear
Methods at the intersection of your two filters.
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bioinformatics

Bayesian ChIP-seq peak calling

Bayesian ChIP-seq peak calling applies probabilistic models — typically Poisson, negative binomial, or hidden Markov models with Bayesian inference — to detect genomic regions enriched for a protein of interest in chromatin immunoprecipitation followed by sequencing experiments. By explicitly modelling read-count noise

2 sources2008
bioinformatics

Bayesian Copy Number Variation Analysis

Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approa

2 sources2004
bioinformatics

Bayesian epigenome-wide association study

A Bayesian EWAS is a genome-scale association analysis that links epigenetic marks — most commonly CpG-site DNA methylation — to a phenotype or trait of interest, replacing or supplementing the classical frequentist p-value framework with a Bayesian probabilistic model. It yields posterior probabilities of association

2 sources2010
bioinformatics

Bayesian epigenome-wide association study in educational research

A Bayesian epigenome-wide association study (Bayesian EWAS) scans hundreds of thousands of DNA methylation sites across the genome to identify those statistically associated with an educational outcome — such as cognitive ability, attainment, or socioeconomic exposure during schooling. Unlike classical frequentist EWAS

2 sources2010
bioinformatics

Bayesian eQTL analysis

Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled f

2 sources2000
bioinformatics

Bayesian Gene Set Enrichment Analysis

Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GS

2 sources2004
bioinformatics

Bayesian genome-wide association study in educational research

Bayesian genome-wide association study (Bayesian GWAS) applies Bayesian statistical models to millions of single-nucleotide polymorphisms (SNPs) to identify genetic variants associated with educational outcomes such as years of schooling or cognitive test scores. Unlike classical frequentist GWAS, Bayesian approaches a

2 sources2013
bioinformatics

Bayesian GWAS

Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a conti

2 sources2007
bioinformatics

Bayesian Metabolomics Analysis

Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributio

2 sources2005
bioinformatics

Bayesian Microbiome Diversity Analysis

Bayesian microbiome diversity analysis applies probabilistic models — chiefly Dirichlet-Multinomial and related hierarchical frameworks — to 16S rRNA or shotgun metagenomic count data to estimate alpha-diversity (within-sample richness and evenness) and beta-diversity (between-sample compositional differences) while pr

2 sources2010
bioinformatics

Bayesian Pathway Enrichment Analysis

Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and

2 sources2001
bioinformatics

Bayesian Phylogenetic Analysis

Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference

2 sources1996
bioinformatics

Bayesian Proteomics Analysis

Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrat

2 sources2000
bioinformatics

Bayesian RNA-seq differential expression

Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is d

2 sources2010
bioinformatics

Bayesian Sequence Alignment

Bayesian sequence alignment treats the alignment of biological sequences (DNA, RNA, or protein) as a probabilistic inference problem rather than a deterministic optimization. Instead of returning a single best alignment, it samples from a posterior distribution over all plausible alignments given a substitution model a

2 sources2001
bioinformatics

Bayesian single-cell RNA-seq analysis

Bayesian single-cell RNA-seq analysis applies probabilistic generative models to the sparse, overdispersed count matrices produced by single-cell RNA sequencing. By placing prior distributions over latent biological variables — cell state, batch effects, dropout — the framework propagates uncertainty through every down

2 sources2018
bioinformatics

Bayesian Variant Calling

Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based ca

2 sources2010
food science

Freeze-Drying (Lyophilization)

Freeze-drying, also called lyophilization, is a low-temperature dehydration process in which water is first frozen solid and then removed by sublimation under reduced pressure, bypassing the liquid phase entirely. Widely used in food science, pharmaceuticals, and biotechnology, it preserves the physical structure, nutr

2 sources1890
ecology

Life Table Response Experiment

Life Table Response Experiments (LTRE) decompose observed temporal changes in population growth rate (lambda) into contributions from changes in specific vital rates (survival, reproduction). Developed by Caswell (2000) and applied extensively by Wisdom and colleagues, LTRE reveals which demographic changes drove obser

3 sources2000
bioinformatics

Network-based gene set enrichment analysis

Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression sig

2 sources2010
ecology

SIAR Mixing Model

The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates i

3 sources2010