TCC v1.12.1

by Jianqiang Sun

TCC: Differential expression analysis for tag count data with robust normalization strategies

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.

Functions in TCC

Name Description
WAD Calculate WAD statistic for individual genes
clusterSample Perform hierarchical clustering for samples from expression data
estimateDE Estimate degrees of differential expression (DE) for individual genes
plot Plot a log fold-change versus log average expression (so-called M-A plot)
calcNormFactors Calculate normalization factors
filterLowCountGenes Filter genes from a TCC-class object
hypoData_mg A simulation dataset for comparing three-group tag count data, focusing on RNA-seq
nakai DNA microarray data set
ROKU detect tissue-specific (or tissue-selective) patterns from microarray data with many kinds of samples
getNormalizedData Obtain normalized count data
getResult Obtain the summaries of results after the differential expression analysis
arab Arabidopsis RNA-Seq data set
calcAUCValue Calculate AUC value from a TCC-class object
hypoData A simulation dataset for comparing two-group tag count data, focusing on RNA-seq
plotFCPseudocolor Create a pseudo-color image of simulation data
TCC A package for differential expression analysis from tag count data with robust normalization strategies
TCC-class A container for storing information used in TCC
hypoData_ts A sample microarray data for detecting tissue-specific patterns.
simulateReadCounts Generate simulation data from negative binomial (NB) distribution
makeFCMatrix Generate the fold change matrix for simulating count data
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Type Package
License GPL-2
Copyright Authors listed above
biocViews Sequencing, DifferentialExpression, RNASeq

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