Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
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Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
library(HiCDCPlus)
outdir <- tempdir()
# Generate genomic features (GC, mappability, effective length)
construct_features(
output_path = paste0(outdir, "/hg19_50kb_GATC"),
gen = "Hsapiens",
gen_ver = "hg19",
sig = "GATC",
bin_type = "Bins-uniform",
binsize = 50000,
chrs = "chr21"
)
# Initialize gi_list from the bintolen file
gi_list <- generate_bintolen_gi_list(
bintolen_path = paste0(outdir, "/hg19_50kb_GATC_bintolen.txt.gz")
)
# Expand 1D features to 2D for modeling
gi_list <- expand_1D_features(gi_list)
Input: Genome version, restriction enzyme pattern, and bin size; Output: A gi_list object containing 2D genomic features ready for interaction calling.
HiCDCPlus or HiCDCPlus_parallel.hicdcdiff.construct_features.gi_list_topdom..hic files using extract_hic_eigenvectors.MACS2 or epigraHMM.DESeq2 or edgeR directly..hic, .matrix, or .allValidPairs formats.HiCDCPlus.hicdc2hic (e.g., 'pvalue', 'qvalue', 'normcounts', 'zvalue', 'raw').hicdcdiff.construct_features before initializing the gi_list to ensure GC and length biases are modeled.expand_1D_features prior to running the negative binomial regression.HiCDCPlus_parallel for efficient genome-wide interaction calling across multiple chromosomes.hic2icenorm_gi_list before running gi_list_topdom.HiCDCPlus; always call expand_1D_features first.hicdcdiff with biological replicates to ensure robust statistical power.还没有评价。欢迎分享你的使用感受。
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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
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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).
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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
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