generate.random.conf: Generate a Random Chain Configuration

In hbm: Hierarchical Block Matrix Analysis

Description

generate.random.conf generates a random chain configuration following a random walk/giant loop model (Sachs et al., 1995).

Usage

generate.random.conf(n, k = 3, perturb = NULL, scale = T, mean = 0, sd = 1)

Arguments

n	integer giving the chain length (number of beads in the chain).
k	integer giving the space dimension, set to 3 by default.
perturb	integer vector of nodes (indices) to perturb. This argument can be used to generate a configuration that deviates from the chain constraints of successive beads (nodes). Perturbation is achieved by sampling a new order for the beads to perturb and exchanging their coordinates in the original configuration accordingly. By default perturb is set to NULL indicating no perturbation should not be applied.
scale	boolean indicating whether or not to scale the generated configuration, set to TRUE by default.
mean	numeric giving the mean of differences distribution along each axis, set to 0 by default.
sd	numeric giving the standard deviation of differences distribution along each axis, set to 1 by default.

Details

generate.random.conf aims to generate a chromosome-like chain of n beads (nodes), in a k-D Euclidean space (k=3 by default) that follows a random walk/giant loop model (Sachs et al., 1995). This is achieved by sampling the differences between successive beads' coordinates from a normal distribution N(μ, σ) (μ = 0, σ = 1, by default), across each axis (see examples in Hu et al., 2013 and Shavit et al., 2014). The configuration is scaled by default so that the distance between the first and last beads is approximately one unit. generate.random.conf can also be used to generate configurations that deviate from the chain constraints by perturbing beads.

Value

generate.random.conf returns a n x k matrix, giving the coordinates of n beads (nodes) in a k-d space.

Author(s)

Yoli Shavit

References

Sachs, R. K., van den Engh, G., Trask, B., Yokota, H. and Hearst, J. E. A random-walk/giant-loop model for interphase chromosomes. Proceedings of the National Academy of Sciences of the United States of America, 92, 2710-4 (1995).

Hu, M. et al. Bayesian Inference of Spatial Organizations of Chromosomes. PLoS Computational Biology. 9, e1002893 (2013).

Shavit, Y., Hamey, F. K. and Lio, P. FisHiCal: an R package for iterative FISH-based calibration of Hi-C data. Bioinformatics, 30, 3120-3122 (2014).

hbm's website: http://www.cl.cam.ac.uk/~ys388/hbm/

See Also

hbm to learn how to build a hierarchical block matrix from a contact map of a random configuration.
hbm's tutorials at http://www.cl.cam.ac.uk/~ys388/hbm/

Examples

set.seed(2)
n = 100 
conf = generate.random.conf(n, k = 2)
plot(conf, xlab = "x", ylab = "y")

conf = generate.random.conf(n, k = 2, scale = FALSE)
plot(conf, xlab = "x", ylab = "y")

hbm documentation built on May 1, 2019, 6:32 p.m.