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Function 03.ReadingData [limma v3.28.14]
keywords
documentation
title
Topic: Reading Microarray Data from Files
description
This help page gives an overview of LIMMA functions used to read data from files.
Function 04.Background [limma v3.28.14]
keywords
documentation
title
Topic: Background Correction
description
This page deals with background correction methods provided by the backgroundCorrect, kooperberg or neqc functions. Microarray data is typically background corrected by one of these functions before normalization and other downstream analysis. backgroundCorrect works on matrices, EListRaw or RGList objects, and calls backgroundCorrect.matrix. The movingmin method of backgroundCorrect uses utility functions ma3x3.matrix and ma3x3.spottedarray. The normexp method of backgroundCorrect uses utility functions normexp.fit and normexp.signal. kooperberg is a Bayesian background correction tool designed specifically for two-color GenePix data. It is computationally intensive and requires several additional columns from the GenePix data files. These can be read in using read.maimages and specifying the other.columns argument. neqc is for single-color data. It performs normexp background correction and quantile normalization using control probes. It uses utility functions normexp.fit.control and normexp.signal. If robust=TRUE, then normexp.fit.control uses the function huber in the MASS package.
Function limmaUsersGuide [limma v3.28.14]
keywords
documentation
title
View Limma User's Guide
description
Finds the location of the Limma User's Guide and optionally opens it.
Function 11.RNAseq [limma v3.28.14]
keywords
documentation
title
Topic: Analysis of RNA-seq Data
description
This page gives an overview of LIMMA functions to analyze RNA-seq data. voom Transform RNA-seq or ChIP-seq counts to log counts per million (log-cpm) with associated precision weights. After this tranformation, RNA-seq or ChIP-seq data can be analyzed using the same functions as would be used for microarray data. voomWithQualityWeights Combines the functionality of voom and arrayWeights. diffSplice Test for differential exon usage between experimental conditions. topSplice Show a data.frame of top results from diffSplice. plotSplice Plot results from diffSplice. plotExons Plot logFC for individual exons for a given gene.
Function 09.Diagnostics [limma v3.28.14]
keywords
documentation
title
Topic: Diagnostics and Quality Assessment
description
This page gives an overview of the LIMMA functions available for microarray quality assessment and diagnostic plots. This package provides an anova method which is designed for assessing the quality of an array series or of a normalization method. It is not designed to assess differential expression of individual genes. anova uses utility functions bwss and bwss.matrix. The function arrayWeights estimates the empirical reliability of each array following a linear model fit. Diagnostic plots can be produced by imageplot Produces a spatial picture of any spot-specific measure from an array image. If the log-ratios are plotted, then this produces an in-silico representation of the well known false-color TIFF image of an array. imageplot3by2 will write imageplots to files, six plots to a page. plotFB Plots foreground versus background log-intensies. plotMD Mean-difference plots. Very versatile plot. For two color arrays, this plots the M-values vs A-values. For single channel technologies, this plots one column of log-expression values vs the average of the other columns. For fitted model objects, this plots a log-fold-change versus average log-expression. mdplot can also be useful for comparing two one-channel microarrays. plotMA MA-plots, essentially the same as mean-difference plots. plotMA3by2 will write MA-plots to files, six plots to a page. plotWithHighlights Scatterplots with highlights. This is the underlying engine for plotMD and plotMA. plotPrintTipLoess Produces a grid of MA-plots, one for each print-tip group on an array, together with the corresponding lowess curve. Intended to help visualize print-tip loess normalization. plotPrintorder For an array, produces a scatter plot of log-ratios or log-intensities by print order. plotDensities Individual channel densities for one or more arrays. An essential plot to accompany between array normalization, especially quantile normalization. plotMDS Multidimensional scaling plot for a set of arrays. Useful for visualizing the relationship between the set of samples. plotSA Sigma vs A plot. After a linear model is fitted, this checks constancy of the variance with respect to intensity level. plotPrintTipLoess uses utility functions gridr and gridc. plotDensities uses utility function RG.MA.
Function 10.GeneSetTests [limma v3.28.14]
keywords
documentation
title
Topic: Gene Set Tests
description
This page gives an overview of the LIMMA functions for gene set testing and pathway analysis. roast Self-contained gene set testing for one set. mroast Self-contained gene set testing for many sets. fry Fast approximation to mroast, especially useful when heteroscedasticity of genes can be ignored. camera Competitive gene set testing. romer and topRomer Gene set enrichment analysis. ids2indices Convert gene sets consisting of vectors of gene identifiers into a list of indices suitable for use in the above functions. alias2Symbol and alias2SymbolTable Convert gene symbols or aliases to current official symbols. geneSetTest or wilcoxGST Simple gene set testing based on gene or probe permutation. barcodeplot Enrichment plot of a gene set. goana and topGO Gene ontology over-representation analysis of gene lists using Entrez Gene IDs. goana can work directly on a fitted model object or on one or more lists of genes. kegga and topKEGG KEGG pathway over-representation analysis of gene lists using Entrez Gene IDs. kegga can work directly on a fitted model object or on one or more lists of genes.
Function 05.Normalization [limma v3.28.14]
keywords
documentation
title
Topic: Normalization of Microarray Data
description
This page gives an overview of the LIMMA functions available to normalize data from single-channel or two-colour microarrays. Smyth and Speed (2003) give an overview of the normalization techniques implemented in the functions for two-colour arrays. Usually data from spotted microarrays will be normalized using normalizeWithinArrays. A minority of data will also be normalized using normalizeBetweenArrays if diagnostic plots suggest a difference in scale between the arrays. In rare circumstances, data might be normalized using normalizeForPrintorder before using normalizeWithinArrays. All the normalization routines take account of spot quality weights which might be set in the data objects. The weights can be temporarily modified using modifyWeights to, for example, remove ratio control spots from the normalization process. If one is planning analysis of single-channel information from the microarrays rather than analysis of differential expression based on log-ratios, then the data should be normalized using a single channel-normalization technique. Single channel normalization uses further options of the normalizeBetweenArrays function. For more details see the
Function 06.LinearModels [limma v3.28.14]
keywords
documentation
title
Topic: Linear Models for Microarrays
description
This page gives an overview of the LIMMA functions available to fit linear models and to interpret the results. This page covers models for two color arrays in terms of log-ratios or for single-channel arrays in terms of log-intensities. If you wish to fit models to the individual channel log-intensities from two colour arrays, see 07.SingleChannel. The core of this package is the fitting of gene-wise linear models to microarray data. The basic idea is to estimate log-ratios between two or more target RNA samples simultaneously. See the LIMMA User's Guide for several case studies.
Function 02.Classes [limma v3.28.14]
keywords
documentation
title
Topic: Classes Defined by this Package
description
This package defines the following data classes. RGList A class used to store raw intensities as they are read in from an image analysis output file, usually by read.maimages. MAList Intensities converted to M-values and A-values, i.e., to with-spot and whole-spot contrasts on the log-scale. Usually created from an RGList using MA.RG or normalizeWithinArrays. Objects of this class contain one row for each spot. There may be more than one spot and therefore more than one row for each probe. EListRaw A class to store raw intensities for one-channel microarray data. May or may not be background corrected. Usually created by read.maimages. EList A class to store normalized log2 expression values for one-channel microarray data. Usually created by normalizeBetweenArrays. MArrayLM Store the result of fitting gene-wise linear models to the normalized intensities or log-ratios. Usually created by lmFit. Objects of this class normally contain only one row for each unique probe. TestResults Store the results of testing a set of contrasts equal to zero for each probe. Usually created by decideTests. Objects of this class normally contain one row for each unique probe. All these data classes obey many analogies with matrices. In the case of RGList, MAList, EListRaw and EList, rows correspond to spots or probes and columns to arrays. In the case of MarrayLM, rows correspond to unique probes and the columns to parameters or contrasts. The functions summary, dim, length, ncol, nrow, dimnames, rownames, colnames have methods for these classes. Objects of any of these classes may be subsetted. Multiple data objects may be combined by rows (to add extra probes) or by columns (to add extra arrays). Furthermore all of these classes may be coerced to actually be of class matrix using as.matrix, although this entails loss of information. Fitted model objects of class MArrayLM can be coerced to class data.frame using as.data.frame. The first three classes belong to the virtual class LargeDataObject. A show method is defined for LargeDataOjects which uses the utility function printHead.
Function changeLog [limma v3.28.14]
keywords
documentation
title
Limma Change Log
description
Write as text the most recent changes from the limma package changelog.