bootstrap_sparse: Bootstraps interactions from contact map given in sparse...

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

Description

For details of bootstrapping procedure see bootstrap_interactions.

Usage

bootstrap_sparse(sparse.mtx, ratio = c(0.5, 0.5), with.replacement = FALSE)

Arguments

sparse.mtx	data.frame Hi-C contact map in sparse format with mandatory columns i, j, val
ratio	numeric vector indicating on how many atomic interaction sets should initial atomic interaction set be divided; the values inside this vector depend on which sampling is used (with or without replacement); 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
with.replacement	logical indicating whether to perform sampling with or without (default) replacement

Value

list with data frames (Hi-C maps in sparse format) containing sampled interactions (according to specified ratio vector)

See Also

bootstrap_interactions for details of Hi-C interactions bootstrapping procedure

Examples

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))
bootstrapped <- bootstrap_sparse(sparse.mtx)
print(bootstrapped)

rz6/DIADEM documentation built on Dec. 31, 2019, 3:51 a.m.