Topic: Reading Microarray Data from Files
Topic: Individual Channel Analysis of Two-Color Microarrays
Topic: Hypothesis Testing for Linear Models
Topic: Diagnostics and Quality Assessment
Convert Gene Aliases to Official Gene Symbols
Turn a Microarray Linear Model Object into a Dataframe
Convert marrayNorm Object to an MAList Object
Turn a Microarray Data Object into a Matrix
Bead Count Weights for Illumina BeadChips
Between and within sums of squares for matrix
Competitive Gene Set Test Accounting for Inter-gene Correlation
Combine RGList, MAList, EList or EListRaw Objects
Convert Individual Channel Design Matrix to M-A Format
Multiple Testing Genewise Across Contrasts
Reform a Design Matrix to that Contrasts Become Coefficients
Set Status of each Spot from List of Spot Types
Multiple Testing Across Genes and Contrasts
Retrieve the Dimensions of an RGList, MAList or MArrayLM Object
Retrieve the Dimension Names of an RGList, MAList, EList, EListRaw or MArrayLM Object
Empirical Bayes Statistics for Differential Expression
Extract Log-Expression Matrix from MAList
Fit Intercept to Vector of Gamma Distributed Variates
Moment Estimation of Scaled F-Distribution
Genuine Association of Gene Expression Profiles
Extract Basic Data from Expression Data Objects
Extract the Print Layout of an Array from the GAL File
Fit Linear Model to Microarray Data by Generalized Least Squares
Convert Gene Identifiers to Indices for Gene Sets
Intra-Spot Correlation for Two Color Data
Kooperberg Model-Based Background Correction for GenePix data
Fit Linear Model to Microrray Data by Ordinary Least Squares
Fit Linear Model to Individual Channels of Two-Color Data
Two dimensional Moving Averages with 3x3 Window
M-value, A-value Expression List - class
Modify Matrix of Weights By Control Status of Rows
Fit Linear Model to Microrray Data by Robust Regression
NormExp Background Correction and Normalization Using Control Probes
Normalize Columns of a Matrix by Cyclic Loess
Normalize Columns of a Matrix to have the Median Absolute Value
Normalize Single Microarray Using Shrunk Robust Splines
Normalize Columns of a Matrix to have the same Quantiles
Fit Normal+Exp Convolution Model to Observed Intensities
Normexp Model Parameter Estimation Aided by Negative Controls
Estimate Normexp Model Parameter Using Negative Controls Inferred from Regular Probes
Expected Signal Given Observed Foreground Under Normal+Exp Model
Plot exons of differentially expressed gene
Multidimensional scaling plot of distances between gene expression profiles
Plot of regularized linear discriminant functions for microarray data
Sigma vs A plot for microarray linear model
Scatterplot With Highlighting of Special Points
Pool Sample Variances with Unequal Variances
Predictive log fold change for microarrays
Identify Order in which Spots were Printed
Estimate Proportion of True Null Hypotheses
Two Sample Wilcoxon-Mann-Whitney Rank Sum Test Allowing For Correlation
Read Illumina expression data directly from IDAT files
Read Illumina Data from a Target Dataframe
Read RGList or EListRaw from Image Analysis Output Files
Read Header Information from Microarray Raw Data File
Subset RGList, MAList, EListRaw, EList or MArrayLM Objects
Convert Two-Color Targets Dataframe from One-Row-Per-Array to One-Row-Per-Channel
Estimate Scale Factor in Mixture of t-Distributions
Table of Top GO Terms or Top KEGG Pathways
Top table of differentially spliced genes or exons
Table of Top Genes from Linear Model Fit
Moving Average Smoother With Tricube Weights
Eliminate Duplicate Names from the Gene List
Unwrap Duplicate Spot Values from Rows into Columns
Transform RNA-Seq Data Ready for Linear Modelling
Combining observational-level with sample-specific quality weights for RNA-seq analysis
Convert Mean-Variance Trend to Observation-specific Precision Weights for Microarray Data