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
Conditional Log-Likelihood of the Dispersion for a Single Group of Replicate Libraries
Cut numeric vector into non-empty intervals
Estimate Dispersion Trend for Negative Binomial GLMs
Multiple Testing Across Genes and Contrasts
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
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
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
Estimate Common Negative Binomial Dispersion by Conditional Maximum Likelihood
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
Estimate Empirical Bayes Tagwise Dispersion Values
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
Conditional Log-Likelihoods in Terms of Delta
Digital Gene Expression data - class
DGEList Constructor
Empirical analysis of digital gene expression data in R
View edgeR User's Guide
Exact Tests for Differences between Two Groups of Negative-Binomial Counts
expandAsMatrix
Test for Differential Expression Relative to a Threshold
Gene Ontology or KEGG Analysis of Differentially Expressed Genes
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
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
Empirical Bayes Tagwise Dispersions for Negative Binomial GLMs
Maximize a function given a table of values by spline interpolation.
Estimate Trended Dispersion for Negative Binomial GLMs
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
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
Estimate Common Dispersion for Negative Binomial GLMs
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
Normalize ChIP-Seq Read Counts to Input and Test for Enrichment
Plot Biological Coefficient of Variation
Multidimensional scaling plot of distances between digital gene expression profiles
Plot the quasi-likelihood dispersion
Take a systematic subset of indices.
Binomial or Multinomial Thinning of Counts