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edgeR (version 3.14.0)

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

3.14.0

License

GPL (>=2)

Maintainer

Last Published

February 15th, 2017

Functions in edgeR (3.14.0)

dropEmptyLevels

Drop Levels of a Factor that Never Occur

Calculate Weighted Likelihood Empirical Bayes Estimates

estimateGLMTagwiseDisp

Empirical Bayes Tagwise Dispersions for Negative Binomial GLMs

Gini dispersion index

Goodness of Fit Tests for Multiple GLM Fits

dispCoxReidSplineTrend

Estimate Dispersion Trend for Negative Binomial GLMs

Negative Binomial Deviance

Digital Gene Expression Likelihood Ratio Test data and results - class

differential expression of Digital Gene Expression data - class

calcNormFactors

Calculate Normalization Factors to Align Columns of a Count Matrix

Predictive log-fold changes

Empirical analysis of digital gene expression data in R

Average Log Counts Per Million

Multiple Testing Across Genes and Contrasts

Locally Weighted Mean By Column

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

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

Differential splicing plot

processAmplicons

Process raw data from pooled genetic sequencing screens

dispCoxReidInterpolateTagwise

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

Exact Binomial Tests for Comparing Two Digital Libraries

movingAverageByCol

Moving Average Smoother of Matrix Columns

splitIntoGroups

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

estimateGLMCommonDisp

Estimate Common Dispersion for Negative Binomial GLMs

weightedCondLogLikDerDelta

Weighted Conditional Log-Likelihood in Terms of Delta

Top table of differentially spliced genes or exons

estimateTagwiseDisp

Estimate Empirical Bayes Tagwise Dispersion Values

plotMDS.DGEList

Multidimensional scaling plot of distances between digital gene expression profiles

Test for Differential Exon Usage

Explore the mean-variance relationship for DGE data

Genewise Negative Binomial Generalized Linear Models

Good-Turing Frequency Estimation

normalizeChIPtoInput

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

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

Mean-Difference Plot of Count Data

Z-score Equivalents of Negative Binomial Deviate

Table of the Top Differentially Expressed Tags

Estimate Common Dispersion for Negative Binomial GLMs

Gene Ontology or KEGG Analysis of Differentially Expressed Genes

estimateExonGenewiseDisp

Estimate Genewise Dispersions from Exon-Level Count Data

estimateGLMTrendedDisp

Estimate Trended Dispersion for Negative Binomial GLMs

estimateGLMRobustDisp

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

estimateTrendedDisp

Estimate Empirical Bayes Trended Dispersion Values

maximizeQuadratic

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

Digital Gene Expression Generalized Linear Model results - class

commonCondLogLikDerDelta

Conditional Log-Likelihoods in Terms of Delta

Sum Over Replicate Samples

Subset DGEList, DGEGLM, DGEExact and DGELRT Objects

Identify Genes with Splice Variants

Binomial or Multinomial Thinning of Counts

Genewise Negative Binomial Generalized Linear Models with Quasi-likelihood Tests

equalizeLibSizes

Equalize Library Sizes by Quantile-to-Quantile Normalization

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

Digital Gene Expression data - class

condLogLikDerSize

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

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

adjustedProfileLik

Adjusted Profile Likelihood for the Negative Binomial Dispersion Parameter

Counts per Million or Reads per Kilobase per Million

estimateCommonDisp

Estimate Common Negative Binomial Dispersion by Conditional Maximum Likelihood

Cut numeric vector into non-empty intervals

Turn a TopTags Object into a Dataframe

Turn a DGEList Object into a Matrix

DGEList Constructor

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

Extract Specified Component of a DGEList Object

Retrieve the Dimension Names of a DGE Object

Get a Recommended Value for Prior N from DGEList Object

Test for Differential Expression Relative to a Threshold

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

Check for Valid DGEList object

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

maximizeInterpolant

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

systematicSubset

Take a systematic subset of indices.

Quantile to Quantile Mapping between Negative-Binomial Distributions

Read and Merge a Set of Files Containing Count Data

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

Plot Biological Coefficient of Variation

Rotation Gene Set Tests for Digital Gene Expression Data

Rotation Gene Set Tests for Digital Gene Expression Data

Plot the quasi-likelihood dispersion

Estimate Dispersion Trend by Binning for NB GLMs

edgeRUsersGuide

View edgeR User's Guide