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Copy number normalization distinguishes differential signals driven by copy number differences in ATAC-seq and ChIP-seq

ABSTRACT: A common objective across ATAC-seq and ChIP-seq analyses is to identify differential signals across contrasted conditions. However, in differential analyses, the impact of copy number variation is often overlooked. Here, we demonstrated copy number differences among samples could drive, if not dominate, differential signals. To address this, we propose a pipeline featuring copy number normalization. By comparing the averaged signal per gene copy, it effectively segregates differential signals driven by copy number differences from other factors. Further applying it to Down syndrome, we unveiled distinct dosage-dependent and -independent changes on chromosome 21. Thus, we recommend normalization as a general approach.

ORGANISM(S): Homo sapiens

PROVIDER: GSE259257 | GEO | 2025/02/17

REPOSITORIES: GEO

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Project description:Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples. We demonstrate an asymmetry in the detection of the two alleles for each SNP, which deleteriously influences both allelic proportions and copy number estimates. The asymmetry is caused by a remaining bias between the two dyes used in the Infinium II assay after using the normalization method in Illumina’s proprietary software (BeadStudio). We propose a quantile normalization strategy for correction of this dye bias. We tested the normalization strategy using 535 individual hybridizations from 10 data sets from the analysis of cancer genomes and normal blood samples generated on Illumina Infinium II 300k version 1 and 2, 370k and 550k BeadChips. We show that the proposed normalization strategy successfully removes asymmetry in estimates of both allelic proportions and copy numbers. Additionally, the normalization strategy reduces the technical variation for copy number estimates while retaining the response to copy number alterations. The proposed normalization strategy represents a valuable low-level analysis tool that improves the quality of data obtained from Illumina Infinium arrays, in particular when used for LOH and copy number variation studies. To investigate the effects of a quantile normalization of Illumina Infinium data, compared to conventional normalization using BeadStudio (www.illumina.com), we renormalized 535 individual hybridizations conducted on Illumina 300K, 370K and 550K BeadChips. Sample types included breast cancer, colon cancer, urothelial carcinoma, leukemia as well as normal blood and HapMap samples. This series includes the 6 breast cancers hybridized on Illumina HumanHap 550K BeadChips.

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Copy number normalization distinguishes differential signals driven by copy number differences in ATAC-seq and ChIP-seq

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