# Statistical methods for differential peak detection in Hi-C data

> **NIH NIH F31** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $24,758

## Abstract

Abstract:
Hi-C is currently the most popular assay used to probe 3D chromatin organization within the cell genome-wide.
Because loci far away in 1D genomic distance are often packed close together in 3D space, enhancer-promoter
interactions can occur between distal regions of the genome. Importantly, this genome structure is well-conserved
across cell types and even species, and dysregulation of this structure has been implicated as a source of aber-
rant gene expression associated with diseases such as Alzheimer's, autoimmune disorders, and cancer. Thus,
it is necessary that powerful methods be made available to pinpoint differential interactions between healthy and
diseased cells in order to accurately identify new sources of pathogenesis and potential pathways for treatment.
Analysis of Hi-C data is challenging because the unique spatial structure in the data, which implies both a 1D
genomic distance dependence and a 3D spatial dependence, requires careful attention. Statistical tools that do
not account for these dependencies suffer from reduced power to detect interactions, especially those between
distal chromosomal regions. Further, methods for differential peak detection between a pair of Hi-C datasets are
underdeveloped, and methods that scale to multiple joint comparisons are wholly missing. I propose to address
these problems by developing a statistically rigorous methodology for detecting differential peaks in Hi-C data
that both accounts for Hi-C's hallmark spatial dependence structure and scales to multiple joint comparisons
across biological conditions (i.e. cell types, cell lineages, or experimental and control types). I hypothesize that
this approach will greatly boost power to detect differential interactions in Hi-C samples. Moreover, with software
made available to the public, scientists will be able to apply these tools to identify new drivers of pathogenesis,
ultimately benefitting human health. I will conduct this work under the close guidance of a sponsor and co-
sponsor, respectively, with statistical and biological expertise with Hi-C data.

## Key facts

- **NIH application ID:** 9904123
- **Project number:** 5F31HG010574-02
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Hillary Koch
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $24,758
- **Award type:** 5
- **Project period:** 2019-04-01 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904123

## Citation

> US National Institutes of Health, RePORTER application 9904123, Statistical methods for differential peak detection in Hi-C data (5F31HG010574-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9904123. Licensed CC0.

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