Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome conformation capture assays such as Hi-C as well as newer technologies such as PLAC-seq, HiChIP and region capture Hi-C. When using Fit-Hi-C with Hi-C data, we strongly suggest using bias values from matrix balancing-based normalization methods such as ICE or KR to control for experimental and techical biases in significance estimation. While using bias values, please make sure to use RAW counts and NOT the normalized counts as normalization will be taken into account through bias values. Here we provide an R implementation of Fit-Hi-C. Compared to its original implementation in Python (https://noble.gs.washington.edu/proj/fit-hi-c), Fit-Hi-C R port has the following advantages:
To install this package, start R and enter
## try http:// if https:// URLs are not supported if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("FitHiC")
Duan_yeast_EcoRI
FRAGSFILE and INTERSFILE are located in system.file("extdata",
"fragmentLists/Duan_yeast_EcoRI.gz", package = "FitHiC")
and system.file(
"extdata", "contactCounts/Duan_yeast_EcoRI.gz", package = "FitHiC")
,
respectively. When input data is ready, run as follows:
library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Duan_yeast_EcoRI.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Duan_yeast_EcoRI.gz", package = "FitHiC") outdir <- file.path(getwd(), "Duan_yeast_EcoRI") FitHiC(fragsfile, intersfile, outdir, libname="Duan_yeast_EcoRI", distUpThres=250000, distLowThres=10000)
Internally, Fit-Hi-C will successively call generate_FragPairs
,
read_ICE_biases
, read_All_Interactions
, calculateing_Probabilities
,
fit_Spline
methods. The execution of Fit-Hi-C will be successfully completed
till the following log appears:
library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Duan_yeast_EcoRI.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Duan_yeast_EcoRI.gz", package = "FitHiC") outdir <- file.path(getwd(), "Duan_yeast_EcoRI") FitHiC(fragsfile, intersfile, outdir, libname="Duan_yeast_EcoRI", distUpThres=250000, distLowThres=10000)
library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Duan_yeast_EcoRI.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Duan_yeast_EcoRI.gz", package = "FitHiC") outdir <- file.path(getwd(), "Duan_yeast_EcoRI") FitHiC(fragsfile, intersfile, outdir, libname="Duan_yeast_EcoRI", distUpThres=250000, distLowThres=10000, visual=TRUE)
The output files come from two internal methods called by Fit-Hi-C.
output <- file.path(getwd(), "Duan_yeast_EcoRI", "Duan_yeast_EcoRI.fithic_pass1.txt") data <- read.table(output, header=TRUE) knitr::kable(head(data, n=6L), caption="Duan_yeast_EcoRI.fithic_pass1.txt") output <- file.path(getwd(), "Duan_yeast_EcoRI", "Duan_yeast_EcoRI.fithic_pass2.txt") data <- read.table(output, header=TRUE) knitr::kable(head(data, n=6L), caption="Duan_yeast_EcoRI.fithic_pass2.txt")
output <- file.path(getwd(), "Duan_yeast_EcoRI", "Duan_yeast_EcoRI.spline_pass1.significances.txt.gz") data <- read.table(gzfile(output), header=TRUE) knitr::kable(head(data, n=6L), align="crcrrrr", caption="Duan_yeast_EcoRI.spline_pass1.significances.txt.gz") output <- file.path(getwd(), "Duan_yeast_EcoRI", "Duan_yeast_EcoRI.spline_pass2.significances.txt.gz") data <- read.table(gzfile(output), header=TRUE) knitr::kable(head(data, n=6L), align="crcrrrr", caption="Duan_yeast_EcoRI.spline_pass2.significances.txt.gz")
If visual
is set to TRUE, corresponding images will be also outputed:
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Duan_yeast_HindIII
Similarly, Duan_yeast_HindIII can be run as follows:
fragsfile <- system.file("extdata", "fragmentLists/Duan_yeast_HindIII.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Duan_yeast_HindIII.gz", package = "FitHiC") outdir <- file.path(getwd(), "Duan_yeast_HindIII") FitHiC(fragsfile, intersfile, outdir, libname="Duan_yeast_HindIII", distUpThres=250000, distLowThres=10000)
library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") outdir <- file.path(getwd(), "Dixon_hESC_HindIII_hg18_w40000_chr1") FitHiC(fragsfile, intersfile, outdir, libname="Dixon_hESC_HindIII_hg18_w40000_chr1", noOfBins=50, distUpThres=5000000, distLowThres=50000, visual=TRUE)
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library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Dixon_hESC_HindIII_hg18_combineFrags10_chr1.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Dixon_hESC_HindIII_hg18_combineFrags10_chr1.gz", package = "FitHiC") outdir <- file.path(getwd(), "Dixon_hESC_HindIII_hg18_combineFrags10_chr1") FitHiC(fragsfile, intersfile, outdir, libname="Dixon_hESC_HindIII_hg18_combineFrags10_chr1", noOfBins=200, distUpThres=5000000, distLowThres=50000, visual=TRUE)
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library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Dixon_mESC_HindIII_mm9_combineFrags10_chr1.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Dixon_mESC_HindIII_mm9_combineFrags10_chr1.gz", package = "FitHiC") outdir <- file.path(getwd(), "Dixon_mESC_HindIII_mm9_combineFrags10_chr1") FitHiC(fragsfile, intersfile, outdir, libname="Dixon_mESC_HindIII_mm9_combineFrags10_chr1", noOfBins=200, distUpThres=5000000, distLowThres=50000, visual=TRUE)
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library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") outdir <- file.path(getwd(), "Dixon_hESC_HindIII_hg18_w40000_chr1.afterICE") biasfile <- system.file("extdata", "biasPerLocus/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") FitHiC(fragsfile, intersfile, outdir, biasfile, libname="Dixon_hESC_HindIII_hg18_w40000_chr1", noOfBins=50, distUpThres=5000000, distLowThres=50000, visual=TRUE)
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library("FitHiC") fragsfile <- system.file("extdata", "fragmentLists/data_5000000_abs.bed.gz", package = "FitHiC") intersfile <- system.file("extdata", "contactCounts/data_5000000.matrix.gz", package = "FitHiC") biasfile <- system.file("extdata", "biasPerLocus/data_5000000_iced.matrix.biases.gz", package = "FitHiC") outdir <- file.path(getwd(), "data_5000000") FitHiC(fragsfile, intersfile, outdir, biasfile, libname="data_5000000", distUpThres=500000000, distLowThres=5000000, visual=TRUE, useHiCPro=TRUE)
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There are two different options for running FitHiC:
Use Hi-C pro pipeline;
Prepare at least two input files described below:
FRAGSFILE This file stores the information about midpoints (or start indices) of the fragments. It should consist of 5 columns: first column stands for chromosome name; third column stands for the midPoint; fourth column stands for the hitCount; second column and fifth column will be ignored so you can set them to 0. HitCount (4th column) is only used for filtering in conjuction with the "mappabilityThreshold" option. By default setting bins that need to be filtered to "0" and others to "1" and leaving the mappabilityThreshold option to its default value of 1 is enough. You do not need to compute hitCount (4th column) unless you will explicitly filter using a custom threshold on marginal counts set by the "mappabilityThreshold" option.
fragsfile <- system.file("extdata", "fragmentLists/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") data <- read.table(gzfile(fragsfile), header=FALSE, col.names=c("Chromosome Name", "Column 2", "Mid Point", "Hit Count", "Column 5")) knitr::kable(head(data, n=6L), align = "crrrr", caption="FRAGSFILE")
intersfile <- system.file("extdata", "contactCounts/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") data <- read.table(gzfile(intersfile), header=FALSE, col.names=c("Chromosome1 Name", "Mid Point 1", "Chromosome2 Name", "Mid Point 2", "Hit Count")) knitr::kable(head(data, n=6L), align = "crcrr", caption="INTERSFILE")
biasfile <- system.file("extdata", "biasPerLocus/Dixon_hESC_HindIII_hg18_w40000_chr1.gz", package = "FitHiC") data <- read.table(gzfile(biasfile), header=FALSE, col.names=c("Chromosome Name", "Mid Point", "Bias")) knitr::kable(head(data, n=6L), align = "crr", caption="BIASFILE")
Besides, OUTDIR, the path where the output files will be stored, is also required to be specified.
For questions about the use of Fit-Hi-C method, to request pre-processed Hi-C data and/or additional features and scripts, or to report bugs and provide feedback please e-mail Ferhat Ay.
Ferhat Ay \<ferhatay at lji dot org>
Fit-Hi-C R package maintainer Ruyu Tan \<rut003 at ucsd dot edu>
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