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multiHiCcompare

Joint normalization of multiple Hi-C matrices, visualization and detection of differential chromatin interactions, supporting covariates.

https://github.com/dozmorovlab/multihiccompare

Science Score: 23.0%

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Joint normalization of multiple Hi-C matrices, visualization and detection of differential chromatin interactions, supporting covariates.

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  • Open Issues: 10
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Created almost 8 years ago · Last pushed about 4 years ago
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README.md

multiHiCcompare

BioC status Lifecycle: stable <!-- badges: end -->

Overview

multiHiCcompare is an extension of the original HiCcompare R package. multiHiCcompare provides functions for the joint normalization and comparison of complex Hi-C experiments. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices.

multiHiCcompare accepts four-column text files storing chromatin interaction matrices in a sparse matrix format. There are many sources of public Hi-C data such as the Aiden Lab (.hic files) and the Mirnylab FTP site (.cool files). multiHiCcompare performs differential chromatin interaction analysis between two biological conditions, one or multiple Hi-C matrices per condition.

multiHiCcompare implements a cyclic loess joint normalization algorithm to remove bias between multiple Hi-C datasets and prepare them for comparison. multiHiCcompare also provides a general linear model-based difference detection method, implemented in the edgeR R package.

The main functions of multiHiCcompare are:

  • cyclic_loess() and fastlo() for performing joint normalization.
  • hic_exactTest() and hic_glm() for performing comparisons between experimental conditions.

Some example Hi-C data are included in the package. Refer to the multiHiCcompare vignette for full usage instructions, vignette("multiHiCcompare")

Installation

First, make sure you have all dependencies installed in R.

``` r install.packages(c('dplyr', 'data.table', 'devtools', 'qqman', 'metap', 'pheatmap', 'pbapply'))

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("BiocParallel", "HiCcompare", "edgeR", "GenomicRanges", "GenomeInfoDbData")) ```

To install multiHiCcompare from Bioconductor, use the following commands.

``` r

It is recommended to use the GitHub release until the next Bioconductor update

BiocManager::install("multiHiCcompare")

library(multiHiCcompare)

```

Or to install the latest version of multiHiCcompare directly from the GitHub.

``` r library(devtools)

Stable release

installgithub('dozmorovlab/multiHiCcompare', buildvignettes = TRUE)

Developmental version

installgithub('jstansfield0/multiHiCcompare', buildvignettes = TRUE)

library(multiHiCcompare) ```

Usage

To use multiHiCcompare, you will first need to obtain some Hi-C data. Data is available from the sources listed in the overview, along with many others. You will need to extract the data and read it into R as either a 3 column sparse upper triangular matrix and then combine it with an additional column for the chromosome. Hi-C data ready to be used in multiHiCcompare should look like the following:

r chr region1 region2 IF 1 22 16000000 16000000 11 2 22 16100000 16100000 1 3 22 16200000 16200000 3 4 22 16300000 16300000 15 5 22 16400000 16400000 3 6 22 16400000 16500000 1 ...

The four columns correspond to the chromosome, the start location of the first interacting region (in base pairs), the start location of the second interacting region, and the interaction frequency (IF) of the interaction.

Below is an example analysis of a Hi-C experiment.

``` r

load data

library(multiHiCcompare) data("r1", "r2", "r3", "r4")

make hicexp object

hicexp <- make_hicexp(r1, r2, r3, r4, groups = c(1, 1, 2, 2))

jointly normalize data

hicexp <- cyclic_loess(hicexp)

compare groups

hicexp <- hic_exactTest(hicexp)

view manhattan plot of results

manhattan_hicexp(hicexp) ```

Citation

Stansfield, John C, Kellen G Cresswell, and Mikhail G Dozmorov. MultiHiCcompare: Joint Normalization and Comparative Analysis of Complex Hi-C Experiments. Bioinformatics, January 22, 2019.

Learn more

Stansfield, John C., Duc Tran, Tin Nguyen, and Mikhail G. Dozmorov. R Tutorial: Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets. Current Protocols in Bioinformatics, May 2019

HiCcompareWorkshop - "Detection of Differentially Interacting Chromatin Regions From Multiple Hi-C Datasets" workshop presented on Bioconductor 2020 conference

Contributions & Support

issue for any of these or contact the author directly: @jstansfield0 (stansfieldjc@vcu.edu)

Contributors

Authors: @jstansfield0 (stansfieldjc@vcu.edu) & @mdozmorov (mikhail.dozmorov@vcuhealth.org)

Owner

  • Name: Dozmorov Lab
  • Login: dozmorovlab
  • Kind: organization

Genomics, bionformatics, computational biology, 3D genome, Hi-C

GitHub Events

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  • Issues event: 4
  • Watch event: 2
  • Issue comment event: 6
  • Fork event: 1
Last Year
  • Issues event: 4
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Last synced: over 2 years ago

All Time
  • Total Commits: 206
  • Total Committers: 7
  • Avg Commits per committer: 29.429
  • Development Distribution Score (DDS): 0.277
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
jstansfield0 s****c@v****u 149
Mikhail Dozmorov m****v@g****m 39
Nitesh Turaga n****a@g****m 12
root r****t@S****u 2
vobencha v****a@g****m 2
Armen Abnousi a****i@o****m 1
menggf m****f@g****m 1
Committer Domains (Top 20 + Academic)
sombscqn07711-l.rams.adp.vcu.edu: 1 vcu.edu: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 24
  • Total pull requests: 2
  • Average time to close issues: 3 months
  • Average time to close pull requests: 6 days
  • Total issue authors: 22
  • Total pull request authors: 2
  • Average comments per issue: 2.21
  • Average comments per pull request: 0.5
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 0
  • Average time to close issues: about 1 month
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.67
  • Average comments per pull request: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 15,556 total
  • Total dependent packages: 2
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
bioconductor.org: multiHiCcompare

Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available

  • Versions: 5
  • Dependent Packages: 2
  • Dependent Repositories: 0
  • Downloads: 15,556 Total
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Forks count: 8.8%
Average: 18.7%
Stargazers count: 21.8%
Downloads: 63.1%
Maintainers (1)
mikhail.dozmorov@gmail.com
Last synced: 11 months ago