Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data.
Hyeon-Jin KimGalip Gürkan YardimciGiancarlo BonoraVijay RamaniJie LiuRuolan QiuCholi LeeJennifer HessonCarol B WareJay ShendureZhijun DuanWilliam Stafford NoblePublished in: PLoS computational biology (2020)
Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.
Keyphrases
- genome wide
- single cell
- electronic health record
- big data
- gene expression
- rna seq
- induced apoptosis
- spinal cord injury
- dna methylation
- dna damage
- transcription factor
- cell cycle arrest
- high throughput
- data analysis
- oxidative stress
- healthcare
- cell proliferation
- endoplasmic reticulum stress
- machine learning
- high resolution
- copy number
- deep learning
- neural network