HiCSR: a Hi-C super-resolution framework for producing highly realistic contact maps

Dimmick, Michael

doi:10.1101/2020.02.24.961714

Cited by 16 publications

(30 citation statements)

References 30 publications

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“…The use of autoencoders for the task of Hi-C data super resolution was originally proposed in our preprint 10 for the task of denoising Hi-C data. They were then suggested by Dimmick et al 9 as tools for training super resolution networks by using the features extracted by passing Hi-C data through a trained autoencoder as a loss function. In this manuscript we expand upon this strategy, but replace their network with a different flavor of network called the variational autoencoder 11 .…”

Section: (Eq 1)mentioning

confidence: 99%

“…Most of the previously proposed loss functions for developing Hi-C enhancement networks draw upon loss functions prolific in the fields of computer vision [5][6][7][8][9] . While there are certainly advantages to these strategies, they derive from assumed similarities between the tasks of image superresolution and Hi-C superresolution.…”

Section: Insulation Score Lossmentioning

confidence: 99%

“…While previous work often split chromosomes into training, validation and testing sets in a sequential manner 8 9 we were concerned that differences in the 3D conformation of large vs small chromosomes 16 may contain implicit bias in contact map features that could confound training. Consequently we assembled training, validation and test sets in a non sequential manner using chromosomes 1,3,5,6,7,9,11,12,13,15,17,18,19,21 as our training set, chromosome 2,8,10,22 as our validation set and chromosomes 4,14,16,20 as our test set.…”

Section: Dataset Assemblymentioning

confidence: 99%

“…Previous work on Hi-C super resolution consistently used network input window sizes of 0.4Mb x 0.4Mb at 10kb resolution, requiring networks to split chromosome contact maps into 40x40bin matrices [5][6][7][8][9] . While this strategy has seen relative success, a major disadvantage is that certain important features of Hi-C such as TADs can span ranges larger than 0.4Mb, meaning that it is impossible for previous networks to explicitly encode important information about TAD organization.…”

Section: Dataset Assemblymentioning

confidence: 99%

“…The network DeepHiC 8 maintained the GAN loss function while extending it to also include a perceptual loss function derived from VGG-16 trained on image data. The model HiCSR 9 continued the advancement by introducing the use of a deep autoencoder as a feature extraction mechanism.…”

Section: Introductionmentioning

confidence: 99%

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VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm

Highsmith

Cheng

2020

Preprint

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Chromatin conformation plays an important role in a variety of genomic processes. Hi-C is one of the most popular assays for inspecting chromatin conformation. However, the utility of Hi-C contact maps is bottlenecked by resolution. Here we present VEHiCLE, a deep learning algorithm for resolution enhancement of Hi-C contact data. VEHiCLE utilises a variational autoencoder and adversarial training strategy to enhance contact maps, making them more viable for downstream analysis. VEHiCLE expands previous efforts at Hi-C super resolution by providing novel insight into the biologically meaningful and human interpretable feature extraction. Using a variational autoencoder VEHiCLE provides a user tunable, full generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data across multiple metrics.

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Section: (Eq 1)mentioning

confidence: 99%

Section: Insulation Score Lossmentioning

confidence: 99%

Section: Dataset Assemblymentioning

confidence: 99%

Section: Dataset Assemblymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm

Highsmith

Cheng

2020

Preprint

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VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data

Highsmith

Cheng

2021

Sci Rep

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Chromatin conformation plays an important role in a variety of genomic processes. Hi-C is one of the most popular assays for inspecting chromatin conformation. However, the utility of Hi-C contact maps is bottlenecked by resolution. Here we present VEHiCLE, a deep learning algorithm for resolution enhancement of Hi-C contact data. VEHiCLE utilises a variational autoencoder and adversarial training strategy equipped with four loss functions (adversarial loss, variational loss, chromosome topology-inspired insulation loss, and mean square error loss) to enhance contact maps, making them more viable for downstream analysis. VEHiCLE expands previous efforts at Hi-C super resolution by providing novel insight into the biologically meaningful and human interpretable feature extraction. Using a deep variational autoencoder, VEHiCLE provides a user tunable, full generative model for generating synthetic Hi-C data while also providing state-of-the-art results in enhancement of Hi-C data across multiple metrics.

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EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework

2021

Bioinformatics

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Motivation The high-throughput chromosome conformation capture (Hi-C) technique has enabled genome-wide mapping of chromatin interactions. However, high-resolution Hi-C data requires costly, deep sequencing; therefore, it has only been achieved for a limited number of cell types. Machine learning models based on neural networks have been developed as a remedy to this problem. Results In this work, we propose a novel method, EnHiC, for predicting high-resolution Hi-C matrices from low-resolution input data based on a generative adversarial network (GAN) framework. Inspired by non-negative matrix factorization, our model fully exploits the unique properties of Hi-C matrices and extracts rank-1 features from multi-scale low-resolution matrices to enhance the resolution. Using three human Hi-C datasets, we demonstrated that EnHiC accurately and reliably enhanced the resolution of Hi-C matrices and outperformed other GAN-based models. Moreover, EnHiC-predicted high-resolution matrices facilitated the accurate detection of topologically associated domains and fine-scale chromatin interactions. Availability and implementation EnHiC is publicly available at https://github.com/wmalab/EnHiC. Supplementary information Supplementary data are available at Bioinformatics online.

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HiCSR: a Hi-C super-resolution framework for producing highly realistic contact maps

Cited by 16 publications

References 30 publications

VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm

VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm

VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data

EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework

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