Perform differential binding affinity analysis

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Description

Performs differential binding affinity analysis

Usage

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dba.analyze(DBA, method=DBA$config$AnalysisMethod, 
            bSubControl=TRUE, bFullLibrarySize=TRUE, bTagwise=TRUE,
            filter=0, filterFun=max,
            bCorPlot=DBA$config$bCorPlot,  
            bReduceObjects=TRUE, 
            bParallel=DBA$config$RunParallel)

Arguments

DBA

DBA object. If no contrasts are specified (DBA$contrast is NULL), default contrasts will be added via a call to dba.contrast.

method

method, or vector of methods, by which to analyze differential binding affinity. Supported methods:

  • DBA_EDGER

  • DBA_DESEQ2

also, for backward compatibility:

  • DBA_DESEQ

Additionally, if this value is set to DBA_ALL_METHODS, this is equivalent to specifying c(DBA_EDGER,DBA_DESEQ2).

bSubControl

logical indicating whether Control read counts are subtracted for each site in each sample before performing analysis.

bFullLibrarySize

logical indicating if the full library size (total number of reads in BAM/SAM/BED file) for each sample is used for scaling normalization. If FALSE, the total number of reads present in the peaks for each sample is used (generally preferable if overall biding levels are expected to be similar between samples).

bTagwise

logical indicating if dispersion should be calculated on a tagwise (or per-condition) basis. If there are only a very few members of each group in a contrast (e.g. no replicates), this should be set to FALSE.

filter

value to use for filtering intervals with low read counts. Each contrast will be filtered separately. The filterFun will be applied t each interval, and any scores below the filter value will be removed prior to analysis.

filterFun

function that will be invoked for each interval with a vector of scores for each sample. Returns a score that will be evaluated against the filter value (only intervals with a score at least as high as filter will be kept). Default is max, indicating that at least one sample should have a score of at least filter; other useful values include sum (indicating that all the scores added together should be at least filter) and mean (setting a minimum mean normalized count level). Users can supply their own function as well.

bCorPlot

logical indicating whether to plot a correlation heatmap for the analyzed data (first contrast only). If no sites are significantly differentially bound using the default thresholds, no heatmap will be plotted.

bReduceObjects

logical indicating whether strip the analysis objects of unnecessary fields to save memory. If it is desired to use the DBA$contrasts[[n]]$edgeR and/or DBA$contrasts[[n]]$DESeq2 objects directly in the edgeR and/or DESeq2 packages, this should be set to FALSE.

bParallel

logical indicating that the analyses is to be done in parallel using multicore (one process for each contrast for each method, plus an additional process per method).

Details

See the DBA User Guide for more details on how the edgeR and DESeq2 analyses are carried out.

Value

DBA object with results of analysis added to DBA$contrasts.

Note

If there is a blocking factor for the contrast(s) specified using a previous call to dba.contrast, a multi-factor analysis will automatically be carried out in addition to a single factor analysis.

Author(s)

Rory Stark

See Also

dba.contrast, dba.report

Examples

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data(tamoxifen_counts)

tamoxifen <- dba.analyze(tamoxifen)
tamoxifen

data(tamoxifen_counts)
tamoxifen <- dba.contrast(tamoxifen,categories=DBA_CONDITION,block=tamoxifen$masks$MCF7)
tamoxifen <- dba.analyze(tamoxifen,method=DBA_ALL_METHODS)
tamoxifen