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 Samples

setWeight

Set the Weights of Signal Intensities Contained in a bioCond

normBioCond

Perform MA Normalization on a Set of bioCond Objects

normBioCondBySizeFactors

Normalize bioCond Objects by Their Size Factors

cmbBioCond

Combine a Set of bioCond Objects into a Single bioCond

MAplot.bioCond

Create an MA Plot on Two bioCond Objects

summary.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 Objects

extendMeanVarCurve

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 Object

estimateVarRatio

Estimate Relative Variance Ratio Factors of bioCond Objects

varRatio

Compare Variance Ratio Factors of Two bioCond Objects

distBioCond

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 Objects

MAplot.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 Objects

plot.aovBioCond

Plot an aovBioCond Object

varTestBioCond

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.


[Package MAnorm2 version 1.2.2 Index]