HiCDOC: Compartments prediction and differential analysis with multiple replicates

HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions.

It provides a collection of functions assembled into a pipeline:

1. Filter:
   1. Remove chromosomes which are too small to be useful.
   2. Filter sparse replicates to remove uninformative replicates with few interactions.
   3. Filter positions (bins) which have too few interactions.
2. Normalize:
   1. Normalize technical biases using cyclic loess normalization, so that matrices are comparable.
   2. Normalize biological biases using Knight-Ruiz matrix balancing, so that all the bins are comparable.
   3. Normalize the distance effect, which results from higher interaction proportions between closer regions, with a MD loess.
3. Predict:
   1. Predict compartments using constrained K-means.
   2. Detect significant differences between experiment conditions.
4. Visualize:
   1. Plot the interaction matrices of each replicate.
   2. Plot the overall distance effect on the proportion of interactions.
   3. Plot the compartments in each chromosome, along with their concordance (confidence measure) in each replicate, and significant changes between experiment conditions.
   4. Plot the overall distribution of concordance differences.
   5. Plot the result of the PCA on the compartments' centroids.
   6. Plot the boxplots of self interaction ratios (differences between self interactions and the medians of other interactions) of each compartment, which is used for the A/B classification.

Table of contents

• Installation
• Quick Start
• Usage
  • Importing Hi-C data
    • Tabular files
    • Cooler files
    • Juicer files
    • HiC-Pro files
  • Running the HiCDOC pipeline
    • Filtering data
    • Normalizing biases
    • Predicting compartments and differences
  • Visualizing data and results
• References

Installation

To install, execute the following commands in your console:

bash Rscript -e 'install.packages("devtools")' Rscript -e 'devtools::install_github("mzytnicki/HiCDOC")'

After installation, the package can be loaded in R >= 4.0:

r library("HiCDOC")

Quick Start

To try out HiCDOC, load the simulated toy data set:

r data(exampleHiCDOCDataSet) hic.experiment <- exampleHiCDOCDataSet

Then run the default pipeline on the created object:

r hic.experiment <- HiCDOC(hic.experiment)

And plot some results:

r plotCompartmentChanges(hic.experiment, chromosome = 'Y')

Usage

Importing Hi-C data

HiCDOC can import Hi-C data sets in various different formats: - Tabular .tsv files. - Cooler .cool or .mcool files. - Juicer .hic files. - HiC-Pro .matrix and .bed files.

Tabular files

A tabular file is a tab-separated multi-replicate sparse matrix with a header:

chromosome    position 1    position 2    C1.R1    C1.R2    C2.R1    ...
3             1500000       7500000       145      184      72       ...
...

The interaction proportions between position 1 and position 2 of chromosome are reported in each condition.replicate column. There is no limit to the number of conditions and replicates.

To load Hi-C data in this format:

r hic.experiment <- HiCDOCDataSetFromTabular('path/to/data.tsv')

Cooler files

To load .cool or .mcool files generated by Cooler:

```r

Path to each file

paths = c( 'path/to/condition-1.replicate-1.cool', 'path/to/condition-1.replicate-2.cool', 'path/to/condition-2.replicate-1.cool', 'path/to/condition-2.replicate-2.cool', 'path/to/condition-3.replicate-1.cool' )

Replicate and condition of each file. Can be names instead of numbers.

replicates <- c(1, 2, 1, 2, 1) conditions <- c(1, 1, 2, 2, 3)

Resolution to select in .mcool files

binSize = 500000

Instantiation of data set

hic.experiment <- HiCDOCDataSetFromCool( paths, replicates = replicates, conditions = conditions, binSize = binSize # Specified for .mcool files. ) ```

Juicer files

To load .hic files generated by Juicer:

```r

Path to each file

paths = c( 'path/to/condition-1.replicate-1.hic', 'path/to/condition-1.replicate-2.hic', 'path/to/condition-2.replicate-1.hic', 'path/to/condition-2.replicate-2.hic', 'path/to/condition-3.replicate-1.hic' )

Replicate and condition of each file. Can be names instead of numbers.

replicates <- c(1, 2, 1, 2, 1) conditions <- c(1, 1, 2, 2, 3)

Resolution to select

binSize <- 500000

Instantiation of data set

hic.experiment <- HiCDOCDataSetFromHiC( paths, replicates = replicates, conditions = conditions, binSize = binSize ) ```

HiC-Pro files

To load .matrix and .bed files generated by HiC-Pro:

```r

Path to each matrix file

matrixPaths = c( 'path/to/condition-1.replicate-1.matrix', 'path/to/condition-1.replicate-2.matrix', 'path/to/condition-2.replicate-1.matrix', 'path/to/condition-2.replicate-2.matrix', 'path/to/condition-3.replicate-1.matrix' )

Path to each bed file

bedPaths = c( 'path/to/condition-1.replicate-1.bed', 'path/to/condition-1.replicate-2.bed', 'path/to/condition-2.replicate-1.bed', 'path/to/condition-2.replicate-2.bed', 'path/to/condition-3.replicate-1.bed' )

Replicate and condition of each file. Can be names instead of numbers.

replicates <- c(1, 2, 1, 2, 1) conditions <- c(1, 1, 2, 2, 3)

Instantiation of data set

hic.experiment <- HiCDOCDataSetFromHiCPro( matrixPaths = matrixPaths, bedPaths = bedPaths, replicates = replicates, conditions = conditions ) ```

Running the HiCDOC pipeline

Once your data is loaded, you can run all the filtering, normalization, and prediction steps with:

r hic.experiment <- HiCDOC(hic.experiment)

This one-liner runs all the steps detailed below.

Filtering data

Remove small chromosomes of length smaller than 100 positions:

r hic.experiment <- filterSmallChromosomes(hic.experiment, threshold = 100)

Remove sparse replicates filled with less than 30% non-zero interactions:

r hic.experiment <- filterSparseReplicates(hic.experiment, threshold = 0.3)

Remove weak positions with less than 1 interaction in average:

r hic.experiment <- filterWeakPositions(hic.experiment, threshold = 1)

Normalizing biases

Normalize technical biases such as sequencing depth:

r hic.experiment <- normalizeTechnicalBiases(hic.experiment)

Normalize biological biases (such as GC content, number of restriction sites, etc.):

r hic.experiment <- normalizeBiologicalBiases(hic.experiment)

Normalize the distance effect resulting from higher interaction proportions between closer regions:

r hic.experiment <- normalizeDistanceEffect(hic.experiment, loessSampleSize = 20000)

Predicting compartments and differences

Predict A and B compartments and detect significant differences:

r hic.experiment <- detectCompartments( hic.experiment, kMeansDelta = 0.0001, kMeansIterations = 50, kMeansRestarts = 20 )

Visualizing data and results

Plot the interaction matrix of each replicate:

r plotInteractions(hic.experiment, chromosome = '3')

Plot the overall distance effect on the proportion of interactions:

r plotDistanceEffect(hic.experiment)

List and plot compartments with their concordance (confidence measure) in each replicate, and significant changes between experiment conditions:

```r compartments(hic.experiment)

concordances(hic.experiment)

differences(hic.experiment)

plotCompartmentChanges(hic.experiment, chromosome = '3') ```

Plot the overall distribution of concordance differences:

r plotConcordanceDifferences(hic.experiment)

Plot the result of the PCA on the compartments' centroids:

r plotCentroids(hic.experiment, chromosome = '3')

Plot the boxplots of self interaction ratios (differences between self interactions and the median of other interactions) of each compartment:

r plotSelfInteractionRatios(hic.experiment, chromosome = '3')

References

John C Stansfield, Kellen G Cresswell, Mikhail G Dozmorov, multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments, Bioinformatics, 2019, https://doi.org/10.1093/bioinformatics/btz048

Philip A. Knight, Daniel Ruiz, A fast algorithm for matrix balancing, IMA Journal of Numerical Analysis, Volume 33, Issue 3, July 2013, Pages 1029–1047, https://doi.org/10.1093/imanum/drs019

Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl, Constrained K-means Clustering with Background Knowledge, Proceedings of 18th International Conference on Machine Learning, 2001, Pages 577-584, https://pdfs.semanticscholar.org/0bac/ca0993a3f51649a6bb8dbb093fc8d8481ad4.pdf