| MAnorm2 {MAnorm2} | R Documentation |
MAnorm2: a Package for Normalizing and Comparing ChIP-seq Samples
Description
MAnorm2 provides a robust method for normalizing ChIP-seq signals
across individual samples or groups of samples. It also designs a
self-contained system of statistical models for calling differential
ChIP-seq signals between two or more biological conditions as well as
for calling hypervariable ChIP-seq signals across samples.
Details
For a typical differential analysis between two biological conditions
starting with raw read counts, the standard workflow is to
sequentially call normalize, bioCond,
normBioCond,
fitMeanVarCurve, and diffTest
(see the following sections for a rough description of each of these
functions).
Examples given for diffTest provide
specific demonstrations.
MAnorm2 is also capable of calling differential ChIP-seq signals
across multiple
biological conditions. See the section below titled "Comparing ChIP-seq
Signals across Multiple Conditions".
For a hypervariable ChIP-seq analysis
starting with raw read counts, the standard workflow is to
sequentially call normalize, bioCond,
fitMeanVarCurve, estParamHyperChIP, and
varTestBioCond.
Examples given for estParamHyperChIP provide a
specific demonstration.
The following sections classify the majority of MAnorm2 functions
into different utilities. Basically, these sections also represent the order
in which the functions are supposed to be called for a
differential/hypervariable
analysis. For a complete list of MAnorm2 functions, use
library(help = "MAnorm2").
Normalizing ChIP-seq Signals across Individual Samples
normalizePerform MA Normalization on a Set of ChIP-seq Samples
normalizeBySizeFactorsNormalize ChIP-seq Samples by Their Size Factors
estimateSizeFactorsEstimate Size Factors of ChIP-seq Samples
MAplot.defaultCreate an MA Plot on Two Individual ChIP-seq Samples
Creating bioCond Objects to Represent Biological Conditions
bioCondCreate a
bioCondObject to Group ChIP-seq SamplessetWeightSet the Weights of Signal Intensities Contained in a
bioCondnormBioCondPerform MA Normalization on a Set of
bioCondObjectsnormBioCondBySizeFactorsNormalize
bioCondObjects by Their Size FactorscmbBioCondCombine a Set of
bioCondObjects into a SinglebioCondMAplot.bioCondCreate an MA Plot on Two
bioCondObjectssummary.bioCondSummarize a
bioCondObject
Modeling Mean-Variance Trend
fitMeanVarCurveFit a Mean-Variance Curve
setMeanVarCurveSet the Mean-Variance Curve of a Set of
bioCondObjectsextendMeanVarCurveExtend the Application Scope of a Mean-Variance Curve
plotMeanVarCurvePlot a Mean-Variance Curve
plotMVCPlot a Mean-Variance Curve on a Single
bioCondObjectestimateVarRatioEstimate Relative Variance Ratio Factors of
bioCondObjectsvarRatioCompare Variance Ratio Factors of Two
bioCondObjectsdistBioCondQuantify the Distance between Each Pair of Samples in a
bioCondvstBioCondApply a Variance-Stabilizing Transformation to a
bioCond
Assessing the Goodness of Fit of Mean-Variance Curves
estimatePriorDfAssess the Goodness of Fit of Mean-Variance Curves
estimatePriorDfRobustAssess the Goodness of Fit of Mean-Variance Curves in a Robust Manner
setPriorDfSet the Number of Prior Degrees of Freedom of Mean-Variance Curves
setPriorDfRobustThe Robust Counterpart of
setPriorDfsetPriorDfVarRatioSet the Number of Prior Degrees of Freedom and Variance Ratio Factors
estParamHyperChIPThe Parameter Estimation Framework of HyperChIP
Calling Differential ChIP-seq Signals between Two Conditions
diffTest.bioCondCompare Two
bioCondObjectsMAplot.diffBioCondCreate an MA Plot on Results of Comparing Two
bioCondObjects
Comparing ChIP-seq Signals across Multiple Conditions
aovBioCondPerform a Moderated Analysis of Variance on
bioCondObjectsplot.aovBioCondPlot an
aovBioCondObjectvarTestBioCondCall Hypervariable and Invariant Intervals for a
bioCondplot.varTestBioCondPlot a
varTestBioCondObject
Author and Maintainer
Shiqi Tu <tushiqi@picb.ac.cn>
References
Tu, S., et al., MAnorm2 for quantitatively comparing groups of ChIP-seq samples. Genome Res, 2021. 31(1): p. 131-145.
Chen, H., et al., HyperChIP for identifying hypervariable signals across ChIP/ATAC-seq samples. bioRxiv, 2021: p. 2021.07.27.453915.