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.
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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)
# Define chromosome information
sq <- defineSeqinfo("GRCh37")
# Create mock GRangesList for consensus peak calling
gr1 <- GRanges("chr10:1000-2000", seqinfo = sq)
gr2 <- GRanges("chr10:1200-2200", seqinfo = sq)
peaks <- GRangesList(sample1 = gr1, sample2 = gr2)
# Generate consensus peaks
consensus <- makeConsensus(peaks, p = 0.5)
# Plot overlaps
plotOverlaps(peaks)
Input: A GRangesList of peak calls; Output: A consensus GRanges object and overlap plots.
makeConsensus.importPeaks.fitAssayDiff.mapByFeature.plotOverlaps and signal profiles with plotProfileHeatmap or getProfileData.memes or universalmotif instead.MACS2 or epigraHMM as extraChIPs is designed for downstream analysis of existing peak calls.RangedSummarizedExperiment object.fitAssayDiff (e.g., "TMM" or library-size).fitAssayDiff.Seqinfo object at the start of the workflow using defineSeqinfo.importPeaks to avoid false positives.quantro before applying TMM normalization.addDiffStatus to classify regions as "Increased", "Decreased", or "Unchanged" for downstream visualization.quantro to test this assumption first.reduceMC or makeConsensus to retain metadata.colToRanges to restore the original peak boundaries before mapping.还没有评价。欢迎分享你的使用感受。
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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