Back

HiC-SCA: A Spectral Clustering Method for Reliable A/B Compartment Assignment From Hi-C Data

Chan, J.; Kono, H.

2025-11-10 genomics
10.1101/2025.09.22.677711 bioRxiv
Show abstract

A/B compartment analysis identifies regions of active and inactive chromatin organization from Hi-C data. We present HiC Spectral Compartment Assignment (HiC-SCA), a graph-based method that models chromatin as a weighted network and uses spectral clustering to partition chromosomes into A and B compartments. HiC-SCA includes four key improvements: a noise filter that removes low-quality data to prevent erroneous results, an orientation assignment method that correctly identifies A versus B compartments, a metric for selecting and assessing confidence in compartment assignments, and a resolution selection approach that identifies the highest resolution at which analysis can be performed for a dataset. Evaluation across 21 Hi-C datasets from diverse cell types using cross-dataset correlation demonstrates that HiC-SCA achieves superior consistency compared to an established method, producing more reproducible compartment assignments between datasets of the same cell type. HiC-SCA addresses key challenges in current A/B compartment analysis and provides researchers with robust tools for analyzing Hi-C datasets with varying quality.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Genome Research
● 468 papers in training set
Top 0.3%
12.3%
Similar papers in this journal
2
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
10.7%
Similar papers in this journal
3
Genome Biology
○ 637 papers in training set
Top 0.9%
10.7%
Similar papers in this journal
4
Bioinformatics
◐ 1204 papers in training set
Top 3%
9.5%
Similar papers in this journal
5
PLOS Computational Biology
● 1863 papers in training set
Top 5%
7.1%
Similar papers in this journal
50% of probability mass above
6
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.8%
5.0%
7
GENETICS
◐ 483 papers in training set
Top 1%
5.0%
8
Nature Communications
○ 5641 papers in training set
Top 30%
4.7%
9
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 1%
4.2%
10
BMC Bioinformatics
○ 457 papers in training set
Top 2%
3.3%
11
BMC Genomics
○ 406 papers in training set
Top 3%
2.7%
12
Methods
○ 34 papers in training set
Top 0.2%
2.3%
13
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
1.9%
14
Scientific Reports
○ 3612 papers in training set
Top 50%
1.9%
15
The American Journal of Human Genetics
○ 234 papers in training set
Top 2%
1.7%
16
Nature Methods
○ 385 papers in training set
Top 6%
1.1%
17
Cell Genomics
○ 172 papers in training set
Top 4%
1.1%
18
Cell Reports Methods
○ 165 papers in training set
Top 3%
1.1%
19
PLOS Genetics
● 862 papers in training set
Top 12%
0.8%
20
eLife
● 5828 papers in training set
Top 68%
0.8%
21
Communications Biology
○ 993 papers in training set
Top 32%
0.8%
22
iScience
○ 1154 papers in training set
Top 43%
0.6%
23
PLOS ONE
● 5266 papers in training set
Top 63%
0.6%
24
Bioinformatics Advances
◐ 203 papers in training set
Top 5%
0.6%
25
Nature Computational Science
○ 55 papers in training set
Top 2%
0.6%
26
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%
27
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 46%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.