Title:	HI-C diffeREntial Analysis Method
Version:	0.0.1
Date:	2025-07-23
Maintainer:	Elise Jorge <elise.jorge@inrae.fr>
Description:	Perform Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.
License:	GPL (≥ 3)
URL:	https://forgemia.inra.fr/scales/hicream
BugReports:	https://forgemia.inra.fr/scales/hicream/-/issues
Depends:	R (≥ 4.0.0), reticulate
SystemRequirements:	Python (>= 3.9)
Imports:	adjclust, auk, BiocGenerics, csaw, diffHic, dplyr, edgeR, GenomeInfoDb, GenomicRanges, InteractionSet, limma, Matrix, methods, reshape2, rlang, S4Vectors, stats, SummarizedExperiment, utils, viridis
Suggests:	knitr, rmarkdown
Encoding:	UTF-8
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-23 14:26:38 UTC; nathalie
Author:	Elise Jorge [aut, cre], Sylvain Foissac [aut], Pierre Neuvial [aut], Nathalie Vialaneix [aut], Gilles Blanchard [ctb], Guillermo Durand [ctb], Nicolas Enjalbert-Courrech [ctb], Etienne Roquain [ctb]
Repository:	CRAN
Date/Publication:	2025-07-25 15:40:02 UTC

Performs Constrained 2D Agglomerative Clustering

Description

This function performs a connectivity constrained 2D agglomerative clustering using scikit-learn function AgglomerativeClustering and outputs an object of class hclust that stores the hierarchy of merges and value of criterion at each merge. It also outputs the optimal level of the hierarchy with respect to the elbow heuristic.

Usage

AggloClust2D(counts, nbClust = NULL)

## S3 method for class 'res2D'
print(x, ...)

## S3 method for class 'res2D'
summary(object, ...)

## S3 method for class 'res2D'
plot(x, ...)

Arguments

Value

An object of class res2D containing:

Author(s)

Élise Jorge elise.jorge@inrae.fr
Sylvain Foissac sylvain.foissac@inrae.fr
Pierre Neuvial pierre.neuvial@math.univ-toulouse.fr
Nathalie Vialaneix nathalie.vialaneix@inrae.fr

Examples

data("pighic")
res2D <- AggloClust2D(pighic$data)
if (!is.null(res2D)) {# in case Python or modules are not available
  clusters <- res2D$clustering
  print(res2D)
  summary(res2D)
  plot(res2D)
}

Load and Normalize Hi-C Data

Description

This function loads data necessary for the analysis and outputs them in a suitable format for performTest and 2Dclust.

Usage

loadData(files, index, chromosome, normalize = TRUE)

Arguments

Value

An InteractionSet corresponding to all interactions present in at least one of the input matrices and corresponding counts across all matrices.

Author(s)

Élise Jorge elise.jorge@inrae.fr
Sylvain Foissac sylvain.foissac@inrae.fr
Pierre Neuvial pierre.neuvial@math.univ-toulouse.fr
Nathalie Vialaneix nathalie.vialaneix@inrae.fr

Examples

replicates <- 1:2
cond <- "90"
allBegins <- interaction(expand.grid(replicates, cond), sep = "-")
allBegins <- as.character(allBegins)
chromosome <- 1
nbChr <- 1
allMat <- sapply(allBegins, function(ab) {
  matFile <- paste0("Rep", ab, "-chr", chromosome, "_200000.bed")
  })
index <- system.file("extdata", "index.200000.longest18chr.abs.bed",
                    package = "hicream")
                    format <- rep("HiC-Pro", length(replicates) * length(cond) * nbChr)
binsize <- 200000
files <- system.file("extdata", unlist(allMat), package = "hicream")
exData <- loadData(files, index, chromosome, normalize = TRUE)

Perform diffHic Test

Description

This function performs diffHic differential analysis for every pixel of the matrix.

Usage

performTest(
  matrices,
  cond,
  outFile = NULL,
  filterLc = FALSE,
  filterFlank = FALSE,
  flank = NULL
)

## S3 method for class 'resdiff'
print(x, ...)

## S3 method for class 'resdiff'
summary(object, ...)

## S3 method for class 'resdiff'
plot(x, whichPlot = c("p.value", "p.adj", "logFC"), ...)

Arguments

Value

An object of class resdiff with the following entries:

Author(s)

Élise Jorge elise.jorge@inrae.fr
Sylvain Foissac sylvain.foissac@inrae.fr
Pierre Neuvial pierre.neuvial@math.univ-toulouse.fr
Nathalie Vialaneix nathalie.vialaneix@inrae.fr

Examples

data("pighic")
resdiff <- performTest(pighic$data, pighic$conditions)
resdiff
summary(resdiff)
plot(resdiff)
plot(resdiff, whichPlot = "p.adj")
plot(resdiff, whichPlot = "logFC")

Dataset "pighic"

Description

6 Hi-C matrices (3 in each condition) obtained from two different developmental stages of pig embryos (90 and 110 days of gestation) corresponding to chromosome 1.

Format

An object with two entries:

data

an object of class InteractionSet containing 6 Hi-C matrices from 2 conditions (3 each) for 21 interactions.

conditions

a two-level factor indicating the condition of each matrix in data.

Details

The data are identical to the dataset present in the package treediff.

References

Nathalie Vialaneix, Gwendaelle Cardenas, Marie Chavent, Sylvain Foissac, Pierre Neuvial, and Nathanael Randriamihamison (2023). treediff: Testing Differences Between Families of Trees. https://cran.r-project.org/package=treediff

Examples

data(pighic)

Perform Post Hoc Inference

Description

This function performs post-hoc inference on all provided clusters using performTest results.

Usage

postHoc(resdiff, clusters, alpha)

## S3 method for class 'resposthoc'
print(x, ...)

## S3 method for class 'resposthoc'
summary(object, ...)

## S3 method for class 'resposthoc'
plot(x, ...)

Arguments

Value

An object of class resposthoc containing a matrix with true positive proportions for each interaction and a dataframe with the following entries:

Author(s)

Élise Jorge elise.jorge@inrae.fr
Sylvain Foissac sylvain.foissac@inrae.fr
Pierre Neuvial pierre.neuvial@math.univ-toulouse.fr
Nathalie Vialaneix nathalie.vialaneix@inrae.fr

Examples

data("pighic")
resdiff <- performTest(pighic$data, pighic$conditions)
res2D <- AggloClust2D(pighic$data)
if (!is.null(res2D)) { # in case Python or modules are not available
  clusters <- res2D$clustering
  alpha <- 0.05
  resposthoc <- postHoc(resdiff, clusters, alpha)
  resposthoc
  summary(resposthoc)
  plot(resposthoc)
}