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README.md
# IDR2D: Irreproducible Discovery Rate for Genomic Interactions [![license: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![DOI](https://img.shields.io/badge/DOI-10.1093%2Fnar%2Fgkaa030-blue.svg)](https://doi.org/10.1093/nar/gkaa030) [![BioC](https://img.shields.io/badge/BioC-1.8.1-brightgreen.svg)](https://doi.org/doi:10.18129/B9.bioc.idr2d) [![platforms](https://www.bioconductor.org/shields/availability/release/idr2d.svg)](https://www.bioconductor.org/packages/release/bioc/html/idr2d.html#archives) [![Coverage Status](https://coveralls.io/repos/github/kkrismer/idr2d/badge.svg?branch=master)](https://coveralls.io/github/kkrismer/idr2d?branch=master) https://idr2d.mit.edu Chromatin interaction data from protocols such as ChIA-PET and HiChIP provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce a generalization of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. ## Installation The *idr2d* package is part of Bioconductor since release 3.10. To install it on your system, enter: ``` if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("idr2d") ``` Alternatively, the development version can be installed directly from this repository: ``` if (!requireNamespace("remotes", quietly = TRUE)) { install.packages("remotes") } remotes::install_github("kkrismer/idr2d") ``` R 3.6 (or higher) and Bioconductor 3.10 (or higher) is required in both cases. Additionally, the 64-bit version of Python 3.5 (or higher) and the Python package [hic-straw](https://pypi.org/project/hic-straw/) are required for Hi-C analysis from Juicer *.hic* files. ## Usage There are two vignettes available on Bioconductor, focusing on [*idr2d* and ChIA-PET data](https://www.bioconductor.org/packages/release/bioc/vignettes/idr2d/inst/doc/idr2d.html) and [*idr2d* and ChIP-seq data](https://www.bioconductor.org/packages/release/bioc/vignettes/idr2d/inst/doc/idr1d.html). The [reference manual](https://bioc.ism.ac.jp/packages/devel/bioc/manuals/idr2d/man/idr2d.pdf) might also be helpful if you know what you are looking for. ### Example code for ChiP-seq, ChIA-PET and Hi-C experiments Analyzing results from replicate **ChIP-seq** experiments (stored in tab-delimited files *chip-seq-rep1.txt* and *chip-seq-rep2.txt*): ``` library(idr2d) rep1_df <- read.table("chip-seq-rep1.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE) rep2_df <- read.table("chip-seq-rep2.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE) idr_results <- estimate_idr1d(rep1_df, rep2_df, value_transformation = "identity") summary(idr_results) rep1_idr_df <- idr_results$rep1_df draw_idr_distribution_histogram(rep1_idr_df) draw_rank_idr_scatterplot(rep1_idr_df) draw_value_idr_scatterplot(rep1_idr_df) ``` Analyzing results from replicate **ChIA-PET** experiments (stored in tab-delimited files *chia-pet-rep1.txt* and *chia-pet-rep2.txt*): ``` library(idr2d) rep1_df <- read.table("chia-pet-rep1.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE) rep2_df <- read.table("chia-pet-rep2.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE) idr_results <- estimate_idr2d(rep1_df, rep2_df, value_transformation = "identity") summary(idr_results) rep1_idr_df <- idr_results$rep1_df draw_idr_distribution_histogram(rep1_idr_df) draw_rank_idr_scatterplot(rep1_idr_df) draw_value_idr_scatterplot(rep1_idr_df) ``` Analyzing chromosome 1 results in 1 Mbp resolution from replicate **Hi-C** experiments (stored in Juicer .hic files *hic-rep1.hic* and *hic-rep2.hic*): ``` library(idr2d) rep1_df <- parse_juicer_matrix("hic-rep1.hic", resolution = 1e+06, chromosome = "chr1") rep2_df <- parse_juicer_matrix("hic-rep2.hic", resolution = 1e+06, chromosome = "chr1") idr_results_df <- estimate_idr2d_hic(rep1_df, rep2_df) summary(idr_results_df) draw_idr_distribution_histogram(idr_results_df) draw_rank_idr_scatterplot(idr_results_df) draw_value_idr_scatterplot(idr_results_df) draw_hic_contact_map(idr_results_df, idr_cutoff = 0.05, chromosome = "chr1") ``` Analyzing chromosome 1 results in 1 Mbp resolution from replicate **Hi-C** experiments (stored in ICE normalized HiC-Pro .matrix and .bed files *rep1_1000000_iced.matrix*, *rep1_1000000_abs.bed* and *rep2_1000000_iced.matrix*, *rep2_1000000_abs.bed*): ``` library(idr2d) rep1_df <- parse_hic_pro_matrix("rep1_1000000_iced.matrix", "rep1_1000000_abs.bed", chromosome = "chr1") rep2_df <- parse_hic_pro_matrix("rep2_1000000_iced.matrix", "rep2_1000000_abs.bed", chromosome = "chr1") idr_results_df <- estimate_idr2d_hic(rep1_df, rep2_df) summary(idr_results_df) draw_idr_distribution_histogram(idr_results_df) draw_rank_idr_scatterplot(idr_results_df) draw_value_idr_scatterplot(idr_results_df) draw_hic_contact_map(idr_results, idr_cutoff = 0.05, chromosome = "chr1") ``` ## Build status | Platform | Status | |------|------| | Travis CI | [![Travis build status](https://travis-ci.com/kkrismer/idr2d.svg?branch=master)](https://travis-ci.com/kkrismer/idr2d) | | Bioconductor 3.14 (release) | [![BioC release](https://bioconductor.org/shields/build/release/bioc/idr2d.svg)](http://bioconductor.org/checkResults/release/bioc-LATEST/idr2d/) | | Bioconductor 3.15 (devel) | [![BioC devel](https://bioconductor.org/shields/build/devel/bioc/idr2d.svg)](http://bioconductor.org/checkResults/devel/bioc-LATEST/idr2d/) | ## Citation If you use IDR2D in your research, please cite: **IDR2D identifies reproducible genomic interactions** Konstantin Krismer, Yuchun Guo, and David K. Gifford Nucleic Acids Research, Volume 48, Issue 6, 06 April 2020, Page e31; DOI: https://doi.org/10.1093/nar/gkaa030 ## Funding The development of this method was supported by National Institutes of Health (NIH) grants 1R01HG008363 and 1R01NS078097, and the MIT Presidential Fellowship.