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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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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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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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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).
日本語の概要は準備中です。原文の説明を表示しています。
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。