BioinformaticsBioinformaticsPeer-reviewed journal article
VSS-Hi-C: Variance-stabilized signals for chromatin contacts
Abstract
MotivationThe genome-wide chromosome conformation capture assay Hi-C is widely used to study chromatin 3D structures and their functional implications. Read counts from Hi-C indicate the strength of chromatin contact between each pair of genomic loci. These read counts are heteroskedastic: that is, a difference between the interaction frequency of 0 and 100 is much more significant than a difference between the interaction frequency of 1000 and 1100. This property impedes visualization and downstream analysis because it violates the Gaussian variable assumption of many computational tools. Thus heuristic transformations aimed at stabilizing the variance of signals like the shifted-log transformation are typically applied to data before its visualization and inputting to models with Gaussian assumption. However, such heuristic transformations cannot fully stabilize the variance because of their restrictive assumptions about the mean-variance relationship in the data. ResultsHere we present VSS-Hi-C, a data-driven variance stabilization method for Hi-C data. We show that VSS-Hi-C signals have a unit variance improving visualization of Hi-C, for example in heatmap contact maps. VSS-Hi-C signals also improve the performance of subcompartment callers relying on Gaussian observations. VSS-Hi-C is implemented as an R package and can be used for variance stabilization of different genomic and epigenomic data types with two replicates available. Availabilityhttps://github.com/nedashokraneh/vssHiC Contactmaxwl@sfu.ca
- assay: Hi-C
- research focus: dimensionality reduction
- research focus: variant calling
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 16th percentile
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- VSS: Variance-stabilized units for sequencing-based genomic signals
Faezeh Bayat, Maxwell W. Libbrecht·BioinformaticsPeer-reviewed·Jun 18, 2021
- Effective normalization for copy number variation in Hi-C data
Nicolas Servant, Nelle Varoquaux, … Jean‐Philippe Vert·BMC BioinformaticsPeer-reviewed·Sep 6, 2018
- VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm
Max R. Highsmith, Jianlin Jack Cheng·Scientific ReportsPeer-reviewed·Apr 23, 2021
- HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection
John C. Stansfield, Kellen G. Cresswell, … Mikhail G. Dozmorov·BMC BioinformaticsPeer-reviewed·Jul 31, 2018
- SHAMAN: bin-free randomization, normalization and screening of Hi-C matrices
Netta Mendelson Cohen, Pedro Olivares‐Chauvet, … Amos Tanay·bioRxiv·Sep 12, 2017
- HiNT: a computational method for detecting copy number variations and translocations from Hi-C data
Su Wang, Soohyun Lee, … Peter J. Park·Genome biologyPeer-reviewed·Mar 23, 2020