Title:	Testing Differences Between Families of Trees
Version:	0.2.1
Date:	2024-02-11
Description:	Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.
Depends:	R (≥ 4.0.0)
Imports:	adjclust, BiocGenerics, csaw, data.table, dplyr, InteractionSet, limma, methods, stats, SummarizedExperiment, reshape2, testthat, rlang, HiCDOC, purrr
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.2.3
Suggests:	knitr, rmarkdown
URL:	https://forgemia.inra.fr/scales/treediff
BugReports:	https://forgemia.inra.fr/scales/treediff/-/issues
NeedsCompilation:	no
Packaged:	2024-02-11 21:32:52 UTC; nathalie
Author:	Nathalie Vialaneix [aut, cre], Gwendaelle Cardenas [aut], Marie Chavent [aut], Sylvain Foissac [aut], Pierre Neuvial [aut], Nathanael Randriamihamison [aut]
Maintainer:	Nathalie Vialaneix <nathalie.vialaneix@inrae.fr>
Repository:	CRAN
Date/Publication:	2024-02-17 13:20:02 UTC

Convert Hi-C to trees

Description

This function converts Hi-C data into trees, using adjClust. It takes as input a file path vector, the format of the input data, the bin size of the Hi-C array, the chromosomes to be included in the analysis, and the number of replicates. It returns a list containing all trees, metadata, index and treediff results.

Usage

HiC2Tree(files, format, binsize = NULL, index = NULL, chromosomes, replicates)

Arguments

Value

A list containing:

References

Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, and Nathalie Vialaneix (2019) Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms for Molecular Biology, 14(22), 363–389.

Examples

replicates <- 1:3
cond <- c("90", "110")
all_begins <- interaction(expand.grid(replicates, cond), sep = "-")
all_begins <- as.character(all_begins)

# single chromosome
nb_chr <- 1
chromosomes <- 1:nb_chr
all_mat_chr <- lapply(chromosomes, function(chr) {
  all_mat <- lapply(all_begins, function(ab) {
    mat_file <- paste0("Rep", ab, "-chr", chr, "_200000.bed")
  })
  all_mat <- unlist(all_mat)
})
index <- system.file("extdata", "index.200000.longest18chr.abs.bed",
                     package = "treediff")
format <- rep("HiC-Pro", length(replicates) * length(cond) * nb_chr)
binsize <- 200000
files <- system.file("extdata", unlist(all_mat_chr), package = "treediff")
replicates <- c(3, 3)
HiC2Tree(files, format, binsize, index, chromosomes, replicates)

## Not run: 
# two chromosomes
nb_chr <- 2
chromosomes <- 1:nb_chr
all_mat_chr <- lapply(chromosomes, function(chr) {
  all_mat <- lapply(all_begins, function(ab) {
    mat_file <- paste0("Rep", ab, "-chr", chr, "_200000.bed")
  })
  all_mat <- unlist(all_mat)
})
files <- system.file("extdata", unlist(all_mat_chr), package = "treediff")
format <- rep("HiC-Pro", length(replicates) * length(cond) * nb_chr)
replicates <- c(3, 3)
HiC2Tree(files, format, binsize, index, chromosomes, replicates)

## End(Not run)

Create a HiCDOCDataSet object from a set of files

Description

This function creates a count matrix from a set of files in different formats, such as tabular, cooler, juicer or HiC-Pro. It returns a list of interaction matrices.

Usage

HiCDOCDataSet(files, format, binsize = NULL, chromosomes, index = NULL)

Arguments

Value

A list containing the following objects:

HiCDOCDataSet

A list of interaction matrices of the HiCDOCDataSet class of the HiCDOC package, one for each file

indexData

A data frame of index data for each interaction in the matrices.

index_mat_chr

A data frame containing the name of the matrices and the corresponding chromosome.

Examples

## Not run: 
replicates <- 1:2
cond <- "90"
all_begins <- interaction(expand.grid(replicates, cond), sep = "-")
all_begins <- as.character(all_begins)

nb_chr <- 2
chromosomes <- 1:nb_chr
all_mat_chr <- lapply(chromosomes, function(chr) {
  all_mat <- lapply(all_begins, function(ab) {
    mat_file <- paste0("Rep", ab, "-chr", chr, "_200000.bed")
  })
  all_mat <- unlist(all_mat)
})
index <- system.file("extdata", "index.200000.longest18chr.abs.bed",
                     package = "treediff")
format <- rep("HiC-Pro", length(replicates) * length(cond) * nb_chr)
binsize <- 200000
files <- system.file("extdata", unlist(all_mat_chr), package = "treediff")
HiCDOCDataSet(files, format, binsize, chromosomes, index)

## End(Not run)

Create hierarchical clustering trees for each cluster in a given matrix

Description

This function creates a hierarchical clustering tree for each group in a given matrix. The function breaks the chromosome into clusters using the "broken stick" method and then converts the clusters into trees.

Usage

clusterTree(mat)

Arguments

Value

A list containing the following objects:

trees

A list of hierarchical clustering trees, one for each cluster in the matrix.

metadata

A data frame containing the following columns: names (name of each tree), chromosome, cluster, and file.

Examples

n <- 5

matrice <- matrix(runif(n*n), nrow = n, ncol = n)
matrice[lower.tri(matrice)] <- t(matrice)[lower.tri(matrice)]
matrice <- as.data.frame(matrice)
names(matrice) <- c("mat_1", "mat_2", "mat_3", "mat_4", "mat_5")

chromosome <- rep(1, n)
index1 <- sample(1:100, n, replace = TRUE)
index2 <- sample(1:100, n, replace = TRUE)

mat <- cbind(chromosome, index1, index2, matrice)

res <- clusterTree(mat)

Normalize count matrix using cyclic loess

Description

This function normalizes the count matrix using loess regression.

Usage

normalizeCount(count_matrice)

Arguments

Value

count_matrice

A data.frame of the normalized count matrix.

Examples

nb_row <- 120
chromosome <- rep(1, nb_row)
index1 <- sample(1:100, nb_row, replace = TRUE)
index2 <- sample(1:100, nb_row, replace = TRUE)

m <- data.frame("mat_1" =  sample(1:500, nb_row, replace = TRUE),
                "mat_2" =  sample(1:500, nb_row, replace = TRUE),
                "mat_3" =  sample(1:500, nb_row, replace = TRUE),
                "mat_4" =  sample(1:500, nb_row, replace = TRUE))

mat <- cbind(chromosome, index1, index2, m)

normalizeCount(mat)

Perform the treediff test

Description

Perform the treediff test to compare two sets of trees.

Usage

treediff(trees1, trees2, replicates, scale = FALSE, order_labels = FALSE)

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

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

Arguments

Details

This function compares two sets of trees using a p-value aggregation method. The p-values are obtained by the treediff method, as described in (Neuvial et al., 2023).

Value

An object of class treeTest with the following entries:

Author(s)

Gwendaëlle Cardenas
Marie Chavent marie.chavent@u-bordeaux.fr
Sylvain Foissac sylvain.foissac@inrae.fr
Pierre Neuvial pierre.neuvial@math.univ-toulouse.fr
Nathanaël Randriamihamison
Nathalie Vialaneix nathalie.vialaneix@inrae.fr

References

Neuvial Pierre, Randriamihamison Nathanaël, Chavent Marie, Foissac Sylvain and Vialaneix Nathalie (2024) A two-sample tree-based test for hierarchically organized genomic signals. Journal of the Royal Statistical Society, Series C, Forthcoming.

Examples

leaves <- c(100, 120, 50, 80)

trees <- sapply(leaves, FUN = function(leaf) {
  base_data <- matrix(rnorm(2000), nrow = leaf, ncol = 200)

## generates two sets of trees with 4 clusters with 100, 120, 50 and 80
## leaves respectively
## 4 replicates in the first condition and 6 in the second condition

  set1 <- replicate(4, sample(1:100, 50, replace = FALSE))
  set2 <- replicate(6, sample(101:200, 50, replace = FALSE))

  trees1 <- apply(set1, 2, function(asample) {
    samples <- base_data[, asample]
    out <- hclust(dist(samples), method = "ward.D2")
    return(out)
  })

  trees2 <- apply(set2, 2, function(asample) {
    samples <- base_data[, asample]
    out <- hclust(dist(samples), method = "ward.D2")
    return(out)
  })
  return(list("trees1" = trees1, "trees2" = trees2))
})

trees1 <- unlist(trees[1, ], recursive = FALSE)
trees2 <- unlist(trees[2, ], recursive = FALSE)
replicates <- c(4, 6)

tree_pvals <- treediff(trees1, trees2, replicates)
## 4 p-values, one for each cluster
tree_pvals$p.value