Evidence map›Paper›PMID 42490438›Full record

ArticleScience advances2026

HiC2Self: Self-supervised denoising for bulk and single-cell Hi-C contact maps.

Rui Yang, Alireza Karbalayghareh, Christina S Leslie

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rui YangComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0000-0001-9358-9907
Alireza KarbalaygharehComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0000-0002-4308-2582
Christina S LeslieComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0000-0002-4571-5910

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Deciphering the Genomics of Gene Network Regulation of T Cell and Fibroblast States in Autoimmune InflammationU01HG012103 · NHGRI · SLOAN-KETTERING INST CAN RESEARCH · PI Christina S Leslie, Alexander Y Rudensky · 2021 to 2026
$7.5M
Discovery of diabetes-relevant β cell enhancers through 4D enhancer mapping, integrative analysis, and large-scale CRISPRi perturbation screensU01DK128852 · NIDDK · SLOAN-KETTERING INST CAN RESEARCH · PI APOSTOLOU, EFFIE, HUANGFU, DANWEI · 2020 to 2024
$3.5M
NCI NIH HHS P30 CA008748NHGRI NIH HHS U01 HG012103NIDDK NIH HHS U01 DK128852
6 · The paper itself

Abstract

Hi-C is a chromosome conformation capture assay used to study three-dimensional (3D) genome organization. Single-cell Hi-C technologies now enable the examination of 3D chromatin organization in individual cells, although these approaches often suffer from low-coverage libraries and data sparsity. Here, we introduce HiC2Self, a self-supervised framework for denoising Hi-C contact maps that requires only low-coverage data as input. HiC2Self reconstructs key structures such as topologically associating domains (TADs) and significant loops from bulk libraries, including cell-type-specific Hi-C structures, without the generalization challenges faced by supervised models. HiC2Self can also accurately reconstruct significant loops from Micro-C data at 1-kilobase resolution. When applied to single-nucleus methyl-3C data, HiC2Self successfully reconstructs local TAD structures around specific genes at 10-kilobase resolution with as few as 50 cells. Last, HiC2Self enables the examination of single-cell structures at 50-kilobase resolution in individual cells of the same cell type. HiC2Self thus provides a general tool for denoising bulk, pseudobulk, and single-cell 3D contact maps to enable downstream analyses.

Indexed as

ChromatinSingle-Cell AnalysisHumansChromatin

Identifiers

PMID42490438
PMCPMC13394478

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.