Retrieve the Dimensions of a DGEList, DGEExact, DGEGLM, DGELRT or TopTags Object
Maximize a function given a table of values by quadratic interpolation.
Formulates the experimental models
Gene expression dataset from Calvano et al. (2005) Nature
Gene expression dataset from Idaghdour et al. (2008)
Conditional Log-Likelihoods in Terms of Delta
DGEList Constructor
Estimate Common Negative Binomial Dispersion by Conditional Maximum Likelihood
Create a Plot of Exon Usage from Exon-Level Count Data
Negative Binomial Deviance
Rotation Gene Set Tests for Digital Gene Expression Data
Quantile to Quantile Mapping between Negative-Binomial Distributions
Create a deSet object from an ExpressionSet
differential expression of Digital Gene Expression data - class
Digital Gene Expression Likelihood Ratio Test data and results - class
Empirical Robust Bayes Tagwise Dispersions for Negative Binomial GLMs using Observation Weights
Digital Gene Expression data - class
Estimate Empirical Bayes Trended Dispersion Values
Locally Weighted Mean By Column
Maximize a function given a table of values by spline interpolation.
Predictive log-fold changes
Summary of deFit and deSet
Fitted data from the full model
Calculate Normalization Factors to Align Columns of a Count Matrix
dispCoxReidInterpolateTagwise
Estimate Genewise Dispersion for Negative Binomial GLMs by Cox-Reid Adjusted Profile Likelihood
Drop Levels of a Factor that Never Occur
Genewise Negative Binomial Generalized Linear Models
Test for Differential Expression Relative to a Threshold
Plots Log-Fold Change versus Log-Concentration (or, M versus A) for Count Data
Extraction of Differential Gene Expression
Regression coefficients from full model fit
Calculate Weighted Likelihood Empirical Bayes Estimates
Estimate Common, Trended and Tagwise Negative Binomial dispersions by weighted likelihood empirical Bayes
Estimate Common Dispersion for Negative Binomial GLMs
Generate a deSet object with full and null models
Get a Recommended Value for Prior N from DGEList Object
Linear regression of the null and full models
Gene expression dataset from Rodwell et al. (2004)
Residuals of null model fit
Gini dispersion index
Competitive Gene Set Test for Digital Gene Expression Data Accounting for Inter-gene Correlation
Exact Tests for Differences between Two Groups of Negative-Binomial Counts
Estimate Trended Dispersion for Negative Binomial GLMs
expandAsMatrix
Genewise Negative Binomial Generalized Linear Models with Quasi-likelihood Tests
Plot the quasi-likelihood dispersion
Fit Negative Binomial Generalized Linear Model to Multiple Response Vectors: Low Level Functions
Check for Valid DGEList object
Estimate the q-values for a given set of p-values
Full model equation
Top table of differentially spliced genes or exons
Fitted data from the null model
Modular optimal discovery procedure (mODP)
Statistic type used in analysis
Average Log Counts Per Million
Estimate Common Dispersion for Negative Binomial GLMs
Empirical analysis of digital gene expression data in R
Goodness of Fit Tests for Multiple GLM Fits
Binomial or Multinomial Thinning of Counts
Split the Counts or Pseudocounts from a DGEList Object According To Group
The differential expression class for the model fits
Performs F-test (likelihood ratio test using Normal likelihood)
Show function for deFit and deSet
Null model equation from deSet object
Estimate Dispersion Trend by Binning for NB GLMs
Equalize Library Sizes by Quantile-to-Quantile Normalization
Empirical Bayes Tagwise Dispersions for Negative Binomial GLMs
Turn a DGEList Object into a Matrix
Estimate Genewise Dispersions from Exon-Level Count Data
Gene Ontology or KEGG Analysis of Differentially Expressed Genes
Multidimensional scaling plot of distances between digital gene expression profiles
Subset DGEList, DGEGLM, DGEExact and DGELRT Objects
Take a systematic subset of indices.
Process raw data from pooled genetic sequencing screens
Visualize the mean-variance relationship in DGE data using standardized residuals
Non-Parametric Jackstraw for Principal Component Analysis (PCA)
Residuals of full model fit
The optimal discovery procedure
The differential expression class (deSet)
View edgeR User's Guide
Extract Specified Component of a DGEList Object
Retrieve the Dimension Names of a DGE Object
Differential splicing plot
Rotation Gene Set Tests for Digital Gene Expression Data
Normalize ChIP-Seq Read Counts to Input and Test for Enrichment
Sum Over Replicate Samples
Estimate surrogate variables
Estimate Dispersion Trend for Negative Binomial GLMs
Counts per Million or Reads per Kilobase per Million
Estimate Empirical Bayes Tagwise Dispersion Values
Plots log-Fold Change versus log-Concentration (or, M versus A) for Count Data
Mean-Difference Plot of Count Data
weightedCondLogLikDerDelta
Weighted Conditional Log-Likelihood in Terms of Delta
Moving Average Smoother of Matrix Columns
Identify Genes with Splice Variants
Z-score Equivalents of Negative Binomial Deviate
Supervised normalization of data in edge
Exact Binomial Tests for Comparing Two Digital Libraries
Conditional Log-Likelihood of the Dispersion for a Single Group of Replicate Libraries
Cut numeric vector into non-empty intervals
Adjusted Profile Likelihood for the Negative Binomial Dispersion Parameter
Explore the mean-variance relationship for DGE data
Plot Biological Coefficient of Variation
Test for Differential Exon Usage
Read and Merge a Set of Files Containing Count Data
Matrix representation of null model
Good-Turing Frequency Estimation
Turn a TopTags Object into a Dataframe
Access/set qvalue slot
Table of the Top Differentially Expressed Tags
Individuals sampled in experiment
Multiple Testing Across Genes and Contrasts
Matrix representation of full model
Digital Gene Expression Generalized Linear Model results - class