bootstrap_interactions: Hi-C interactions bootstrapping.

In rz6/CopulaHiC: Hi-C differential analysis via dependency modelling of chromatin interactions with generalized linear models

Description

Randomly samples interactions from data frame with atomic interactions into length(ratio) data frames with interactions in such a way that i-th dataframe have ratio[i] fraction of interactions of initial atomic interactions data frame.

Usage

bootstrap_interactions(
  interactions,
  ratio = c(0.5, 0.5),
  with.replacement = FALSE
)

Arguments

interactions	data frame containing row with i and j coordinate for every single interaction
ratio	numeric vector indicating on how many atomic interaction sets should initial atomic interaction set be divided; each entry of ratio vector contains fraction of interaction to be put in corresponding atomic interactions set; ratio vector must sum to 1 and all its entries must be larger than one

Value

data frame representing sparse Hi-C maps (with i, j, val columns) belonging to corresponding bootstrapped interactions subset - ratio.number column indicates index of fraction from ratio vector

Examples

# create data frame with artificial interactions, where val
# indicates total number of interactions between bins i and j
sparse.mtx <- data.frame(i = c(1,3,5,7,8,9,11), j = c(7,2,1,1,3,10,9), val = c(10,8,3,1,1,2,20))
atomic.interactions <- sparse2interactions(sparse.mtx)
print(head(atomic.interactions))
b1 <- bootstrap_interactions(atomic.interactions)
print(b1)
ratios.desired <- c(0.4,0.3,0.2,0.1)
b2 <- bootstrap_interactions(atomic.interactions, ratio = ratios.desired)
print(b2)
sum.interactions <- nrow(atomic.interactions)
ratios.sampled <- sapply(split(b2, b2$ratio.number), function(x){ sum(x$val) / sum.interactions })
print(ratios.desired)
print(ratios.sampled)

rz6/CopulaHiC documentation built on Dec. 31, 2019, 9:19 a.m.