MAnorm2: MAnorm2: a Package for Normalizing and Comparing ChIP-seq...
In MAnorm2: Tools for Normalizing and Comparing ChIP-seq Samples
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 bioCond Object to
Group ChIP-seq Samples
setWeightSet the Weights of Signal Intensities
Contained in a bioCond
normBioCondPerform MA Normalization on a Set
of bioCond Objects
normBioCondBySizeFactorsNormalize
bioCond Objects by Their Size Factors
cmbBioCondCombine a Set of bioCond
Objects into a Single bioCond
MAplot.bioCondCreate an MA Plot on Two
bioCond Objects
summary.bioCondSummarize a bioCond
Object
Modeling Mean-Variance Trend
fitMeanVarCurveFit a Mean-Variance Curve
setMeanVarCurveSet the Mean-Variance Curve
of a Set of bioCond Objects
extendMeanVarCurveExtend the Application
Scope of a Mean-Variance Curve
plotMeanVarCurvePlot a Mean-Variance Curve
plotMVCPlot a Mean-Variance Curve on a
Single bioCond Object
estimateVarRatioEstimate Relative Variance
Ratio Factors of bioCond Objects
varRatioCompare Variance Ratio Factors of
Two bioCond Objects
distBioCondQuantify the Distance between
Each Pair of Samples in a bioCond
vstBioCondApply 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
setPriorDf
setPriorDfVarRatioSet 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
bioCond Objects
MAplot.diffBioCondCreate an MA Plot on
Results of Comparing Two bioCond Objects
Comparing ChIP-seq Signals across Multiple Conditions
aovBioCondPerform a Moderated Analysis of
Variance on bioCond Objects
plot.aovBioCondPlot an aovBioCond
Object
varTestBioCondCall Hypervariable and
Invariant Intervals for a bioCond
plot.varTestBioCondPlot a
varTestBioCond Object
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.
MAnorm2 documentation built on Oct. 29, 2022, 1:12 a.m.
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| 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.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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