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Removing unwanted variation between samples in Hi-C experiments.

ABSTRACT: Hi-C data are commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation change across the contact map. We present band-wise normalization and batch correction, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a quantitative trait loci analysis as well as differential enrichment across cell types.

SUBMITTER: Fletez-Brant K

PROVIDER: S-EPMC11074651 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Removing unwanted variation between samples in Hi-C experiments.

Fletez-Brant Kipper K Qiu Yunjiang Y Gorkin David U DU Hu Ming M Hansen Kasper D KD

Briefings in bioinformatics 20240301 3

Hi-C data are commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation change across the contact map. We present band-wise normalization and batch correction, a method for normaliza ...[more]

PMID: 38711367

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