DIADEM

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Authors: Rafal Zaborowski, Bartek Wilczynski

Institution: Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland

License: MIT + file LICENSE

For more information please contact r.zaborowski@mimuw.edu.pl or bartek@mimuw.edu.pl

Quick summary

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DIADEM is R package for differential analysis of Hi-C data. It takes advantage of significant correlations of main diagonals between different Hi-C data sets (cell lines, experiments, etc.). The number of diagonals (maximum genomic distance between interacting regions) depends on chromosome and data quality but usually will equal to about 5% of total number of bins in given chromosome contact map. DIADEM uses GLM to model relationship between corresponding cells of a pair of Hi-C datasets at given genomic distance and then quantifies deviatons from the model in probabilistic way. The only required input are raw Hi-C contact map files in numpy npz format.

For more details, examples and quick start refer to vignette (DIADEM_vignette.html file). You can also browse documentation of individual functions or objects within the package using standard R syntax (i.e.: help(foo) or ?foo) or have a look at reference manual (DIADEM.pdf file).

The indepth description of our model together with detailed analysis and motivation will be described in manuscript (in preparation - available soon).

NOTE: DIADEM_vignette.html file can't be properly rendered from within github. Therefore it is recommended to download the file (or just clone the repo) and then open it.

Prerequisites

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The code is written in R, but data storage is done with numpy, so main requirements are (versions for which tests were performed are given in parenthesis):

• R (3.4.4)
• python (2.7.12)
• numpy (1.12.1)

Additionally following R packages are required:

• magrittr
• reticulate
• Matrix
• fields
• parallel
• igraph
• raster
• latex2exp
• intervals
• robustreg
• MASS

Following additional packages are required for examples and plotting:

• ggplot2
• reshape2
• gridExtra

NOTE: Some of the above R packages require GSL (GNU Scientific Library). Before installation make sure that libgsl-dev is installed (sudo apt-get install libgsl-dev on Ubuntu).

Installation

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Two ways of installation are possible (both require R package devtools to be installed):

1. from github repository:

   devtools::install_github("rz6/DIADEM")

2. from source: clone repository - by default to directory: diadem, cd to directory containing cloned repo, open R and run:

   devtools::install("diadem")

Usage

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Import DIADEM package and list functions inside it:

library("DIADEM")
getNamespaceExports("DIADEM")

Sample data

DIADEM package contains sample data of Hi-C contact maps and TADs. Both of them are in 2 formats:

1. Numpy npz file (Hi-C maps) and csv file (TADs) - to access it run:

   • Hi-C contact maps npz files

     # file name of MSC-HindIII-1_40kb-raw_maps
     mtx.fname.msc <- system.file("extdata", "MSC-HindIII-1_40kb-raw.npz", package = "DIADEM", mustWork = TRUE)
     # file name of IMR90-MboI-1_40kb-raw_maps
     mtx.fname.imr90 <- system.file("extdata", "IMR90-MboI-1_40kb-raw.npz", package = "DIADEM", mustWork = TRUE)
     # load data
     maps.msc <- read_npz(mtx.fname.msc)
     maps.imr90 <- read_npz(mtx.fname.msc)

   • TADs csv files

     # file name of MSC-HindIII-1_40kb-raw_tads
     tads.fname.msc <- system.file("extdata", "MSC-HindIII-1_40kb-raw.tadIS", package = "DIADEM", mustWork = TRUE)
     # load data
     tads.msc <- read.csv(tads.fname.msc)

2. list with Hi-C contact maps and TADs available in package environment:

   • Hi-C contact maps

     # list available Hi-C maps
     print(names(DIADEM::sample_hic_maps))
     # get one
     map.imr90 <- DIADEM::sample_hic_maps[["IMR90-MboI-1_40kb-raw"]]
     print(names(map.imr90))
     print(head(map.imr90[["18"]]))

   • TADs

     # list available TADs
     print(names(DIADEM::sample_tads))
     # get one
     tads.imr90 <- DIADEM::sample_tads[["IMR90-MboI-1_40kb-raw"]]
     print(head(tads.imr90))