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
normalize
Perform MA Normalization on a Set of ChIP-seq Samples
normalizeBySizeFactors
Normalize ChIP-seq Samples by Their Size Factors
estimateSizeFactors
Estimate Size Factors of ChIP-seq Samples
MAplot.default
Create an MA Plot on Two Individual ChIP-seq Samples
Creating bioCond
Objects to Represent Biological Conditions
bioCond
Create a
bioCond
Object to Group ChIP-seq SamplessetWeight
Set the Weights of Signal Intensities Contained in a
bioCond
normBioCond
Perform MA Normalization on a Set of
bioCond
ObjectsnormBioCondBySizeFactors
Normalize
bioCond
Objects by Their Size FactorscmbBioCond
Combine a Set of
bioCond
Objects into a SinglebioCond
MAplot.bioCond
Create an MA Plot on Two
bioCond
Objectssummary.bioCond
Summarize a
bioCond
Object
Modeling Mean-Variance Trend
fitMeanVarCurve
Fit a Mean-Variance Curve
setMeanVarCurve
Set the Mean-Variance Curve of a Set of
bioCond
ObjectsextendMeanVarCurve
Extend the Application Scope of a Mean-Variance Curve
plotMeanVarCurve
Plot a Mean-Variance Curve
plotMVC
Plot a Mean-Variance Curve on a Single
bioCond
ObjectestimateVarRatio
Estimate Relative Variance Ratio Factors of
bioCond
ObjectsvarRatio
Compare Variance Ratio Factors of Two
bioCond
ObjectsdistBioCond
Quantify the Distance between Each Pair of Samples in a
bioCond
vstBioCond
Apply a Variance-Stabilizing Transformation to a
bioCond
Assessing the Goodness of Fit of Mean-Variance Curves
estimatePriorDf
Assess the Goodness of Fit of Mean-Variance Curves
estimatePriorDfRobust
Assess the Goodness of Fit of Mean-Variance Curves in a Robust Manner
setPriorDf
Set the Number of Prior Degrees of Freedom of Mean-Variance Curves
setPriorDfRobust
The Robust Counterpart of
setPriorDf
setPriorDfVarRatio
Set the Number of Prior Degrees of Freedom and Variance Ratio Factors
estParamHyperChIP
The Parameter Estimation Framework of HyperChIP
Calling Differential ChIP-seq Signals between Two Conditions
diffTest.bioCond
Compare Two
bioCond
ObjectsMAplot.diffBioCond
Create an MA Plot on Results of Comparing Two
bioCond
Objects
Comparing ChIP-seq Signals across Multiple Conditions
aovBioCond
Perform a Moderated Analysis of Variance on
bioCond
Objectsplot.aovBioCond
Plot an
aovBioCond
ObjectvarTestBioCond
Call Hypervariable and Invariant Intervals for a
bioCond
plot.varTestBioCond
Plot 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.