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.
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 are required for Hi-C analysis from Juicer .hic files.
There are two vignettes available on Bioconductor, focusing on idr2d and ChIA-PET data and idr2d and ChIP-seq data.
The reference manual might also be helpful if you know what you are looking for.
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")
| Platform | Status |
|---|---|
| Travis CI |  |
| Bioconductor 3.14 (release) |  |
| Bioconductor 3.15 (devel) |  |
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
The development of this method was supported by National Institutes of Health (NIH) grants 1R01HG008363 and 1R01NS078097, and the MIT Presidential Fellowship.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent