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edgeR (version 3.10.5)

Empirical analysis of digital gene expression data in R

Description

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.

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Version

Version

3.10.5

License

GPL (>=2)

Maintainer

Yunshun Chen

Last Published

February 15th, 2017

Functions in edgeR (3.10.5)

adjustedProfileLik

Adjusted Profile Likelihood for the Negative Binomial Dispersion Parameter

Turn a TopTags Object into a Dataframe

Estimate Common Dispersion for Negative Binomial GLMs

dispCoxReidInterpolateTagwise

Estimate Genewise Dispersion for Negative Binomial GLMs by Cox-Reid Adjusted Profile Likelihood

Counts per Million or Reads per Kilobase per Million

condLogLikDerSize

Conditional Log-Likelihood of the Dispersion for a Single Group of Replicate Libraries

Cut numeric vector into non-empty intervals

dispCoxReidSplineTrend

Estimate Dispersion Trend for Negative Binomial GLMs

Multiple Testing Across Genes and Contrasts

dropEmptyLevels

Drop Levels of a Factor that Never Occur

Extract Specified Component of a DGEList Object

Estimate Common, Trended and Tagwise Negative Binomial dispersions by weighted likelihood empirical Bayes

estimateExonGenewiseDisp

Estimate Genewise Dispersions from Exon-Level Count Data

Get a Recommended Value for Prior N from DGEList Object

Goodness of Fit Tests for Multiple GLM Fits

Good-Turing Frequency Estimation

processAmplicons

Process raw data from pooled genetic sequencing screens

Predictive log-fold changes

Check for Valid DGEList object

weightedCondLogLikDerDelta

Weighted Conditional Log-Likelihood in Terms of Delta

Calculate Weighted Likelihood Empirical Bayes Estimates

Z-score Equivalents of Negative Binomial Deviate

Mean-Difference Plot of Count Data

Create a Plot of Exon Usage from Exon-Level Count Data

estimateCommonDisp

Estimate Common Negative Binomial Dispersion by Conditional Maximum Likelihood

equalizeLibSizes

Equalize Library Sizes by Quantile-to-Quantile Normalization

Test for Differential Exon Usage

Retrieve the Dimensions of a DGEList, DGEExact, DGEGLM, DGELRT or TopTags Object

estimateTagwiseDisp

Estimate Empirical Bayes Tagwise Dispersion Values

estimateTrendedDisp

Estimate Empirical Bayes Trended Dispersion Values

Explore the mean-variance relationship for DGE data

Locally Weighted Mean By Column

Fit Negative Binomial Generalized Linear Model to Multiple Response Vectors: Low Level Functions

Plots Log-Fold Change versus Log-Concentration (or, M versus A) for Count Data

Quantile to Quantile Mapping between Negative-Binomial Distributions

Read and Merge a Set of Files Containing Count Data

Sum Over Replicate Samples

Subset DGEList, DGEGLM, DGEExact and DGELRT Objects

Competitive Gene Set Test for Digital Gene Expression Data Accounting for Inter-gene Correlation

commonCondLogLikDerDelta

Conditional Log-Likelihoods in Terms of Delta

Digital Gene Expression data - class

DGEList Constructor

Empirical analysis of digital gene expression data in R

edgeRUsersGuide

View edgeR User's Guide

Exact Tests for Differences between Two Groups of Negative-Binomial Counts

Test for Differential Expression Relative to a Threshold

Gene Ontology or KEGG Analysis of Differentially Expressed Genes

movingAverageByCol

Moving Average Smoother of Matrix Columns

Negative Binomial Deviance

Rotation Gene Set Tests for Digital Gene Expression Data

Rotation Gene Set Tests for Digital Gene Expression Data

Identify Genes with Splice Variants

splitIntoGroups

Split the Counts or Pseudocounts from a DGEList Object According To Group

Exact Binomial Tests for Comparing Two Digital Libraries

Visualize the mean-variance relationship in DGE data using standardized residuals

Digital Gene Expression Likelihood Ratio Test data and results - class

estimateGLMTagwiseDisp

Empirical Bayes Tagwise Dispersions for Negative Binomial GLMs

maximizeInterpolant

Maximize a function given a table of values by spline interpolation.

estimateGLMTrendedDisp

Estimate Trended Dispersion for Negative Binomial GLMs

maximizeQuadratic

Maximize a function given a table of values by quadratic interpolation.

Plots log-Fold Change versus log-Concentration (or, M versus A) for Count Data

Top table of differentially spliced genes or exons

calcNormFactors

Calculate Normalization Factors to Align Columns of a Count Matrix

Differential splicing plot

Table of the Top Differentially Expressed Tags

Average Log Counts Per Million

Turn a DGEList Object into a Matrix

differential expression of Digital Gene Expression data - class

Estimate Dispersion Trend by Binning for NB GLMs

Digital Gene Expression Generalized Linear Model results - class

Retrieve the Dimension Names of a DGE Object

estimateGLMCommonDisp

Estimate Common Dispersion for Negative Binomial GLMs

estimateGLMRobustDisp

Empirical Robust Bayes Tagwise Dispersions for Negative Binomial GLMs using Observation Weights

Genewise Negative Binomial Generalized Linear Models

Genewise Negative Binomial Generalized Linear Models with Quasi-likelihood Tests

normalizeChIPtoInput

Normalize ChIP-Seq Read Counts to Input and Test for Enrichment

Plot Biological Coefficient of Variation

plotMDS.DGEList

Multidimensional scaling plot of distances between digital gene expression profiles

Plot the quasi-likelihood dispersion

systematicSubset

Take a systematic subset of indices.

Binomial or Multinomial Thinning of Counts