Rapidly scan microarray expression data for QTLs
R/qtl is an extension library for the R statistics system. It is used to analyze experimental crosses for identifying genes contributing to variation in quantitative traits (so-called quantitative trait loci, QTLs). Using a hidden Markov model, R/qtl estimates genetic maps, to identify genotyping errors, and to perform single-QTL and two-QTL, two-dimensional genome scans.
Haplotype-based QTL mapping method, often used for multiparent populations.
Genome-wide association and population-based linkage analysis tool.
Efficient mapping algorithm optimized for large datasets.
Affymetrix Microarray Suite 5 normalization method.
Probe-level normalization method that models sequence-dependent hybridization effects in microarray data.
Robust Multi-array Average normalization for microarray data.
Model-based normalization and expression summarization method for microarray data using probe-level intensity modeling.
GC-content adjusted RMA normalization method.
Method that estimates heritability by partitioning phenotypic variance into genetic and environmental components.
Normalization method that transforms values based on their rank order within a dataset.
Normalization method that applies an inverse rank transformation to enforce a uniform distribution of values.
Locally estimated scatterplot smoothing normalization.
LOESS-based normalization method that corrects systematic biases without assuming a parametric noise model.
Quantile normalization method that forces all samples to share the same empirical distribution.
Quantile normalization method applied without background correction or parametric noise assumptions.
Robust spline normalization method that adjusts microarray data to remove systematic technical variation.
Robust spline normalization method applied without background correction or parametric noise assumptions.
Statistical scoring method for identifying significant gene expression changes in microarray data.
Method calculating the log-ratio of expression values between two conditions for microarray analysis.
Variance stabilizing transformation for RNA-seq count data to reduce heteroscedasticity.
Robust Probe Normalization method for microarray data to adjust probe-level intensities.
Transcripts Per Million normalized expression values, log2 transformed.
Reads Per Kilobase Million normalization for RNA-seq data.
Normalization pipeline applied to RNA sequencing datasets.
Normalization method for targeted proteomics using Selected Reaction Monitoring.
Normalization and quantification method for SWATH-MS proteomics data.
Log2-transformed Reads Per Kilobase per Million mapped reads for RNA-seq data.
Normalization method for Illumina methylation arrays using the Sesame pipeline.
Log2-transformed Transcripts Per Million for RNA-seq expression quantification.
Regularized log transformation for RNA-seq count data to stabilize variance.
Normalization and differential expression analysis method for RNA-seq using edgeR.
Normalization pipeline for Illumina methylation arrays using the minfi Bioconductor package.
Microarray normalization method that applies a 2Z+8 transformation to expression values.
Simple log2 transformation applied to expression values to reduce skew and stabilize variance.
Regularized log transformation from the DESeq2 RNA-seq workflow.
Indicates that no normalization or averaging method applies.