Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network

Yifeng Chen; Wei Sun; Haohan Wang

doi:10.1109/BIBM.2018.8621499

Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network

Yifeng Chen, Wei Sun, Haohan Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The high-throughput chromosome conformation capture (Hi-C) technique is a useful technique to obtain the three-dimensional conformation and information of chromatin in the nucleus. However,the development of Hi-C is limited by data resolution. In this paper, we present a conditional generative adversarial network (GAN) model HiC-GAN to imporove the resolution of Hi-C data. The proposed model is capable to generate Hi-C interaction matrices with four-time-higher resolution, where the accuracy of reconstruction can be guaranteed at around 90%. The model can not only perform well within the same cell, but also be applied to other cells of different types and species. Besides, experiments show that the model can also be used to reconstruct data with low-sequence depth and different value of resolution.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2213-2220
Number of pages	8
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621499
State	Published - Jan 21 2019
Externally published	Yes
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Online availability

10.1109/BIBM.2018.8621499

Library availability

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Chen, Y., Sun, W., & Wang, H. (2019). Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2213-2220). Article 8621499 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621499

Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network. / Chen, Yifeng; Sun, Wei; Wang, Haohan.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2213-2220 8621499.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, Y, Sun, W & Wang, H 2019, Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621499, Institute of Electrical and Electronics Engineers Inc., pp. 2213-2220, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621499

Chen Y, Sun W, Wang H. Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2213-2220. 8621499 doi: 10.1109/BIBM.2018.8621499

Chen, Yifeng ; Sun, Wei ; Wang, Haohan. / Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2213-2220

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abstract = "The high-throughput chromosome conformation capture (Hi-C) technique is a useful technique to obtain the three-dimensional conformation and information of chromatin in the nucleus. However,the development of Hi-C is limited by data resolution. In this paper, we present a conditional generative adversarial network (GAN) model HiC-GAN to imporove the resolution of Hi-C data. The proposed model is capable to generate Hi-C interaction matrices with four-time-higher resolution, where the accuracy of reconstruction can be guaranteed at around 90%. The model can not only perform well within the same cell, but also be applied to other cells of different types and species. Besides, experiments show that the model can also be used to reconstruct data with low-sequence depth and different value of resolution.",

author = "Yifeng Chen and Wei Sun and Haohan Wang",

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AB - The high-throughput chromosome conformation capture (Hi-C) technique is a useful technique to obtain the three-dimensional conformation and information of chromatin in the nucleus. However,the development of Hi-C is limited by data resolution. In this paper, we present a conditional generative adversarial network (GAN) model HiC-GAN to imporove the resolution of Hi-C data. The proposed model is capable to generate Hi-C interaction matrices with four-time-higher resolution, where the accuracy of reconstruction can be guaranteed at around 90%. The model can not only perform well within the same cell, but also be applied to other cells of different types and species. Besides, experiments show that the model can also be used to reconstruct data with low-sequence depth and different value of resolution.

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Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network

Abstract

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