| name | bioconductor-extrachips |
| description | This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented. |
extraChIPs
Workflows
Standard Workflow
This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.
library(extraChIPs)
library(GenomicRanges)
sq <- defineSeqinfo("GRCh37")
gr1 <- GRanges("chr10:1000-2000", seqinfo = sq)
gr2 <- GRanges("chr10:1200-2200", seqinfo = sq)
peaks <- GRangesList(sample1 = gr1, sample2 = gr2)
consensus <- makeConsensus(peaks, p = 0.5)
plotOverlaps(peaks)
Input: A GRangesList of peak calls; Output: A consensus GRanges object and overlap plots.
When to Use
- Consensus Peak Definition: To define consensus peaks across replicates using
makeConsensus.
- Peak Importing and Filtering: To import and filter peaks with blacklists/greylists using
importPeaks.
- Differential Signal Analysis: To perform differential signal analysis on window/region counts using
fitAssayDiff.
- Gene Mapping: To map peaks/regions to genes and promoters using
mapByFeature.
- Visualization: To visualize peak overlaps with
plotOverlaps and signal profiles with plotProfileHeatmap or getProfileData.
When NOT to Use
- De Novo Motif Discovery: For de novo motif discovery, use
memes or universalmotif instead.
- De Novo Peak Calling: For peak calling itself, use
MACS2 or epigraHMM as extraChIPs is designed for downstream analysis of existing peak calls.
Data Requirements
- Peak Files: Peak files in narrowPeak format or GRanges objects.
- Read Counts: BAM files for read counting, or a pre-computed
RangedSummarizedExperiment object.
- Annotations: Annotation files (GTF/GFF) for mapping peaks to genes.
Key Parameters
- p (0.5): Minimum proportion of replicates in which a peak must be present to be included in the consensus.
- norm ("TMM"): Normalization method in
fitAssayDiff (e.g., "TMM" or library-size).
- fc (1.2): Fold-change threshold incorporated into testing in
fitAssayDiff.
- asRanges (TRUE): Logical indicating whether to return results as a GRanges object.
- upstream (2500): Upstream distance for defining promoters.
- downstream (500): Downstream distance for defining promoters.
Best Practices
- Define Seqinfo: Define a consistent
Seqinfo object at the start of the workflow using defineSeqinfo.
- Exclude Artifacts: Exclude blacklisted and grey-listed regions using
importPeaks to avoid false positives.
- Normalization Check: Test whether group-specific count distributions are similar using
quantro before applying TMM normalization.
- Classify Status: Use
addDiffStatus to classify regions as "Increased", "Decreased", or "Unchanged" for downstream visualization.
Common Pitfalls
- Inappropriate Normalization: Applying TMM normalization when group-specific distributions differ significantly; use
quantro to test this assumption first.
- Metadata Loss: Losing metadata columns during GRanges set operations; use
reduceMC or makeConsensus to retain metadata.
- Mapping Coordinates: Mapping peaks to genes without resetting the core ranges; use
colToRanges to restore the original peak boundaries before mapping.
Alternatives
- DiffBind: For standard affinity-based differential analysis.
- csaw: For sliding window-based differential binding analysis.
- ChIPseeker: For peak annotation and visualization.
Citations
- Ross-Innes et al. 2012, Nature (for DiffBind-style approaches)
- Hicks and Irizarry 2015, Genome Biol. (for quantro)
References
