in-silico FISH distances and matching Hi-C frequencies with 5% noise

Description

in-silico distances were computed from the random configuration conf. A power law model was then used to generate matching in-silico Hi-C frequencies. Random Noise was further added to long range frequencies to mimic a typical situation for Hi-C data. This data structure is used to illustrate the usage of prepareCalib and calibrate.

Usage

data(match)

Format

A data frame with 4950 observations on the following 2 variables.

distances

a numeric vector giving the in-silico FISH distances

.

frequencies

a numeric vector giving the in-silico Hi-C contact frequencies

.

References

Y. Shavit, F.K. Hamey, P. Lio', FisHiCal: an R package for iterative FISH-based calibration of Hi-C data, 2014 (submitted).

Examples

data(match)
plot(match$frequencies ~ match$distances, xlab = "distances", ylab = "frequencies")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(FisHiCal)
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FisHiCal/match.Rd_%03d_medium.png", width=480, height=480)
> ### Name: match
> ### Title: in-silico FISH distances and matching Hi-C frequencies with 5%
> ###   noise
> ### Aliases: match
> ### Keywords: datasets datasets
> 
> ### ** Examples
> 
> data(match)
> plot(match$frequencies ~ match$distances, xlab = "distances", ylab = "frequencies")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>