Introduction to hicream

In hicream: HI-C diffeREntial Analysis Method

Introduction

hicream is an R package designed to perform Hi-c data differential analysis. More specifically, it performs a pixel-level differential analysis using diffHic and, using a two-dimensionnal connectivity constrained clustering, renders a post hoc bound on True Discovery Proportion for each cluster. This method allows to identify differential genomic regions and quantifies signal in those regions.

library("hicream")
# checking if Python and Python modules are available to avoid errors in vignette building
modules_avail <- reticulate::py_available(initialize = TRUE) &&
  reticulate::py_module_available("sklearn") &&
  reticulate::py_module_available("kneebow") &&
  reticulate::py_module_available("pandas") &&
  reticulate::py_module_available("numpy")

How to load external data?

Using hicream requires to load data that corresponds to Hi-C matrices and their index, the latter in bed format. In the following code, the paths to Hi-C matrices and the index file path are used in the loadData function alongside the chromosome number. The option normalize = TRUE allows to perform a cyclic LOESS normalization. The output is an InteractionSet object.

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)

pighic dataset

The pighic dataset has been produced using 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. The dataset contains two elements, the first is an output of the loadData function corresponding to normalized data. The second is the vector giving, for each matrix, the condition it belongs to.

data("pighic")
head(pighic)

Perform hicream test

Once data have been loaded, the three steps of the analysis can be performed.

Use performTest function

In this first step, the output of a loadData object is used, with a vector indicating the condition of each sample. Here, we use data stored in pighic. performTest outputs the result of the diffHic pixel-level differential analysis.

resdiff <- performTest(pighic$data, pighic$conditions)
resdiff
summary(resdiff)

Several plotting options allow to look at the $p$-value map, adjusted $p$-value map or log-fold-change map.

plot(resdiff)
plot(resdiff, which_plot = "p.adj")
plot(resdiff, which_plot = "logFC")

Perform two-dimensionnal connectivity-constrained clustering

The AggloClust2D function uses the output of loadData function in order to build a connectivity graph of pixels and perform a two-dimensionnal hierarchical clustering under the connectivity constraint. The function renders a clustering corresponding to the optimal number of clusters found by the elbow heuristic. However, a clustering corresponding to any other number of clusters (chosen by the user) can be obtained by specifying a value for the input argument nbClust.

if (modules_avail) {
  res2D <- AggloClust2D(pighic$data)
  res2D 
  summary(res2D)
}

Plotting the output shows the tree that corresponds to the hierarchy of clusters.

if (modules_avail) {
  plot(res2D)
}

Perform post hoc inference

Post hoc inference is performed by the postHoc function, using the output of performTest, and a clustering (the latter can be obtained with AggloClust2D). The user sets a level of test confidence $\alpha$, typically equal to $0.05$.

if (modules_avail) {
  clusters <- res2D$clustering
  alpha <- 0.05
  resposthoc <- postHoc(resdiff, clusters, alpha)
  resposthoc
  summary(resposthoc)
}

Plotting the output of postHoc function shows the minimal proportion of True Discoveries in each cluster.

if (modules_avail) plot(resposthoc)

Session Information

sessionInfo()

hicream documentation built on Aug. 8, 2025, 7:26 p.m.