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diffHic (version 1.4.2)

Differential Analyis of Hi-C Data

Description

Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

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Version

Version

1.4.2

License

GPL-3

Maintainer

Aaron Lun

Last Published

February 15th, 2017

Functions in diffHic (1.4.2)

filterPeaks

Filter bin pairs for likely peaks
clusterPairs

Cluster bin pairs
connectCounts

Count connecting read pairs
getArea

Get interaction area
getDistance

Get the linear distance for each interaction
marginCounts

Collect marginal counts for each bin
Filtering methods

Filtering strategies for bin pairs
mergePairs

Merge read pairs
normalizeCNV

Normalize CNV biases
neighborCounts

Load Hi-C interaction counts
totalCounts

Get the total counts
squareCounts

Load Hi-C interaction counts
consolidatePairs

Consolidate results for interactions
correctedContact

Iterative correction of Hi-C counts
loadData

Load data from an index file
getPairData

Get read pair data
pairParam

pairParam class and methods
plotDI

Construct a plaid plot of differential interactions
annotatePairs

Annotate bin pairs
boxPairs

Put bin pairs into boxes
plotPlaid

Construct a plaid plot of interactions
preparePairs

Prepare Hi-C pairs
DIList-wrappers

Statistical wrappers for DIList objects
diffHicUsersGuide

View diffHic user's guide
DIList-class

DIList class and methods
domainDirections

Calculate domain directionality
DNaseHiC

Methods for processing DNase Hi-C data
cutGenome

Cut up the genome
diClusters

Cluster significant bin pairs to DIs
enrichedPairs

Compute local enrichment for bin pairs
prunePairs

Prune read pairs
Filtering diagonals

Filtering of diagonal bin pairs
savePairs

Save Hi-C interactions