DiffBind – DBA global constant variables {DiffBind} | R Documentation

## Constant variables used in DiffBind package

### Description

Constant variables used in DiffBind package

### Usage

    DBA_ID
    DBA_FACTOR
    DBA_TISSUE
    DBA_CONDITION
    DBA_TREATMENT
    DBA_REPLICATE
    DBA_CALLER
    DBA_CONSENSUS
    DBA_CONTROL
    DBA_ALL_ATTRIBUTES

    DBA_INTERVALS
    DBA_FRIP

    DBA_GROUP

    DBA_OLAP_PEAKS
    DBA_OLAP_ALL
    DBA_OLAP_RATE

    DBA_COR
    DBA_OLAP
    DBA_INALL

    DBA_SCORE_READS
    DBA_SCORE_READS_MINUS
    DBA_SCORE_READS_FOLD
    DBA_SCORE_RPKM
    DBA_SCORE_RPKM_FOLD
    DBA_SCORE_TMM_READS_FULL
    DBA_SCORE_TMM_READS_EFFECTIVE
    DBA_SCORE_TMM_MINUS_FULL
    DBA_SCORE_TMM_MINUS_EFFECTIVE
    DBA_SCORE_TMM_READS_FULL_CPM
    DBA_SCORE_TMM_READS_EFFECTIVE_CPM
    DBA_SCORE_TMM_MINUS_FULL_CPM
    DBA_SCORE_TMM_MINUS_EFFECTIVE_CPM
    DBA_SCORE_SUMMIT
    DBA_SCORE_SUMMIT_ADJ
    DBA_SCORE_SUMMIT_POS

    DBA_READS_DEFAULT
    DBA_READS_BAM
    DBA_READS_BED

    DBA_EDGER
    DBA_DESEQ
    DBA_DESEQ2
    DBA_EDGER_BLOCK
    DBA_DESEQ_BLOCK
    DBA_DESEQ2_BLOCK
    DBA_EDGER_CLASSIC
    DBA_DESEQ_CLASSIC
    DBA_EDGER_GLM
    DBA_DESEQ_GLM
    DBA_ALL_METHODS
    DBA_ALL_BLOCK
    DBA_ALL_METHODS_BLOCK

    DBA_DATA_FRAME
    DBA_DATA_GRANGES
    DBA_DATA_RANGEDDATA
    DBA_DATA_SUMMARIZED_EXPERIMENT
    DBA_DATA_DBAOBJECT

### Arguments

`DBA_ID` | DBA peakset metadata: Peakset ID
`DBA_FACTOR` | DBA peakset metadata: Factor
`DBA_TISSUE` | DBA peakset metadata: Tissue
`DBA_CONDITION` | DBA peakset metadata: Condition
`DBA_TREATMENT` | DBA peakset metadata: Treatment
`DBA_REPLICATE` | DBA peakset metadata: Replicate
`DBA_CALLER` | DBA peakset metadata: Peak Caller
`DBA_CONSENSUS` | DBA peakset metadata: Is this a consensus peakset?
`DBA_CONTROL` | DBA peakset metadata: ID of Control sample
`DBA_ALL_ATTRIBUTES` | DBA peakset metadata: all attributes that can be used in certain plot labels (cf `dba.plotVenn`), equivalent to `c(DBA_ID, DBA_TISSUE, DBA_FACTOR, DBA_CONDITION,DBA_TREATMENT, DBA_REPLICATE, DBA_CALLER)`
`DBA_INTERVALS` | DBA peakset metadata: Number of intervals in peakset
`DBA_FRIP` | DBA peakset metadata: Fraction of Reads in Peaks (number of reads in intervals divided by total number of reads in library)
`DBA_GROUP` | DBA peakset metadata: color PCA plot using contras groups
`DBA_OLAP_PEAKS` | dba.overlap mode: return overlapping/unique peaksets
`DBA_OLAP_ALL` | dba.overlap mode: return report of correlations/overlaps for each pair of samples
`DBA_OLAP_RATE` | dba.overlap mode: return overlap rates
`DBA_COR` | When plotting a heatmap from an overlap record, use the correlation value.
`DBA_OLAP` | When plotting a heatmap from an overlap record, use the percentage overlap value.
`DBA_INALL` | When plotting a heatmap from an overlap record, use the number of peaks in common to both samples.
`DBA_SCORE_READS` | dba.count score is number of reads in ChIP
`DBA_SCORE_READS_FOLD` | dba.count score is number of reads in ChIP divided by number of reads in Control
`DBA_SCORE_READS_MINUS` | dba.count score is number of reads in ChIP minus number of reads in Control
`DBA_SCORE_RPKM` | dba.count score is RPKM of ChIP
`DBA_SCORE_RPKM_FOLD` | dba.count score is RPKM of ChIP divided by RPKM of Control
`DBA_SCORE_TMM_READS_FULL` | dba.count score is TMM normalized (using edgeR), using ChIP read counts and Full Library size
`DBA_SCORE_TMM_READS_EFFECTIVE` | dba.count score is TMM normalized (using edgeR), using ChIP read counts and Effective Library size
`DBA_SCORE_TMM_MINUS_FULL` | dba.count score is TMM normalized (using edgeR), using ChIP read counts minus Control read counts and Full Library size
`DBA_SCORE_TMM_MINUS_EFFECTIVE` | dba.count score is TMM normalized (using edgeR), using ChIP read counts minus Control read counts and Effective Library size
`DBA_SCORE_TMM_READS_FULL_CPM` | dba.count score is TMM normalized (using edgeR), using ChIP read counts and Full Library size, reported in counts-per-million.
`DBA_SCORE_TMM_READS_EFFECTIVE_CPM` | dba.count score is TMM normalized (using edgeR), using ChIP read counts and Effective Library size, reported in counts-per-million.
`DBA_SCORE_TMM_MINUS_FULL_CPM` | dba.count score is TMM normalized (using edgeR), using ChIP read counts minus Control read counts and Full Library size, reported in counts-per-million.
`DBA_SCORE_TMM_MINUS_EFFECTIVE_CPM` | dba.count score is TMM normalized (using edgeR), using ChIP read counts minus Control read counts and Effective Library size, reported in counts-per-million.
`DBA_SCORE_SUMMIT` | dba.count score is summit height (highest pile-up).
`DBA_SCORE_SUMMIT_ADJ` | dba.count score is summit height (highest pile-up), adjusted for library size.
`DBA_SCORE_SUMMIT_POS` | dba.count score is summit location (position of highest pile-up).
`DBA_READS_DEFAULT` | When counting read files, use the file extension to determine the file type.
`DBA_READS_BAM` | When counting read files, assume the file type is BAM, regardless of the file extension.
`DBA_READS_BED` | When counting read files, assume the file type is BED (or zipped BED), regardless of the file extension.
`DBA_EDGER` | differential analysis method: edgeR (default: DBA_EDGER_GLM)
`DBA_DESEQ2` | differential analysis method: DESeq2 (using a single-factor GLM)
`DBA_EDGER_BLOCK` | differential analysis method: edgeR with blocking factors (GLM)
`DBA_DESEQ2_BLOCK` | differential analysis method: DESeq2 with blocking factors (GLM)
`DBA_DESEQ` | differential analysis method: DESeq (default: DBA_DESEQ_CLASSIC)
`DBA_DESEQ_BLOCK` | differential analysis method: DESeq with blocking factors (GLM)
`DBA_EDGER_CLASSIC` | differential analysis method: "classic" edgeR for two-group comparisons
`DBA_DESEQ_CLASSIC` | differential analysis method: "classic" DESeq for two-group comparisons
`DBA_EDGER_GLM` | differential analysis method: use GLM in edgeR for two-group comparisons
`DBA_DESEQ_GLM` | differential analysis method: use GLM in DESeq for two-group comparisons
`DBA_ALL_METHODS` | use both analysis methods: `c(DBA_EDGER, DBA_DESEQ2)`
`DBA_ALL_BLOCK` | report on block results for both analysis methods: `c(DBA_EDGER_BLOCK, DBA_DESEQ2_BLOCK)`
`DBA_ALL_METHODS_BLOCK` | report on block results for all analysis methods, both blocked and unblocked: `c(DBA_ALL_METHODS, DBA_ALL_BLOCK)`
`DBA_DATA_GRANGES` | Use GRanges class for peaksets and reports. This is the default (DBA$config$DataType = DBA_DATA_GRANGES).
`DBA_DATA_RANGEDDATA` | Use RangedData class for peaksets and reports. Can be set as default (DBA$config$DataType = DBA_DATA_RANGEDDATA).
`DBA_DATA_FRAME` | Use data.frame class for peaksets and reports. Can be set as default (DBA$config$DataType = DBA_DATA_FRAME).
`DBA_DATA_SUMMARIZED_EXPERIMENT` | Return report as a `SummarizedExperiment`.
`DBA_DATA_DBAOBJECT` | Return a result-based DBA object from `dba.plotVenn`.

### Note

Variables with ALL CAP names are used as constants within DiffBind.

### Author(s)

Rory Stark

[Package DiffBind version 2.12.0 Index]