Unsupervised embedding of single-cell Hi-C data

Liu, Jie; Lin, Dejun; Yardimci, Gurkan

doi:10.1101/257048

Cited by 26 publications

(50 citation statements)

References 34 publications

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“…InnerProduct produced satisfactory projection of the single cells (Fig. 1a), achieving an average area under the ROC curve (AUC) of 0.943, which was as good as original HiCRep reported in the recent work (Liu et al, 2018). The AUCs from fastHiCRep and Selfish were relatively lower (Fig.…”

Section: Resultssupporting

confidence: 60%

“…We benchmarked the projection performance and run time of these methods on a recent scHi-C dataset (Nagano et al, 2017), exactly following the evaluation procedure in a recent work (Liu et al, 2018) (Supplementary Note 2). We had following observations.…”

Section: Resultsmentioning

confidence: 99%

“…These methods evaluate how likely two bulk Hi-C experiments are generated from the same biological sample. In a recent work (Liu et al, 2018), these reproducibility methods have been applied on single cell Hi-C data to evaluate similarity among n single cells, and coupled with multidimensional scaling (MDS) to project these n single cells into a lower dimensional Euclidean space. Among these methods, HiCRep (Yang et al, 2017) yielded the best performance, but its O(n 2 ) computational complexity makes it impractical when the number of cells is large.…”

Section: Introductionmentioning

confidence: 99%

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scHiCTools: a computational toolbox for analyzing single-cell Hi-C data

Feng

Leung

et al. 2019

Preprint

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Single-cell Hi-C (scHi-C) sequencing technologies allow us to investigate three-dimensional chromatin organization at the single-cell level. However, we still need computational tools to deal with the sparsity of the contact maps from single cells and embed single cells in a lower-dimensional Euclidean space. This embedding helps us understand relationships between the cells in different dimensions such as cell-cycle dynamics and cell differentiation. Here, we present an open-source computational toolbox, scHiCTools, for analyzing single cell Hi-C data. The toolbox takes singlecell Hi-C data files as input, and projects single cells in a lower-dimensional Euclidean space. The toolbox includes three commonly used methods for smoothing scHi-C data (linear convolution, random walk, and network enhancing), three projection methods for embedding single cells (fastHiCRep, Selfish, and InnerProduct), three clustering methods for clustering cells (k-means, spectral clustering, and HiCluster) and a build-in function to visualize the cells embedding in a two-dimensional or three-dimensional plot. We benchmark the embedding performance and run time of these methods on a number of scHi-C datasets, and provide some suggestions for practice use. scHiCTools, based on Python3, can run on different platforms, including Linux, macOS, and Windows. Our software package is available at https://github.com/liu-bioinfo-lab/scHiCTools.

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Section: Resultssupporting

confidence: 60%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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scHiCTools: a computational toolbox for analyzing single-cell Hi-C data

Feng

Leung

et al. 2019

Preprint

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“…Currently, a limited number of methods have been developed for the analysis of scHi-C data. We previously developed a similarity-based embedding method called HiCRep/MDS to project scHi-C data into low dimensional space and arrange cells according to their cell-cycle phases [4]. This approach leverages a similarity measure, stratum adjusted correlation coefficient, that was developed for comparing bulk Hi-C matrices [5], combined with multidimensional scaling (MDS) to preserve distances between individual scHi-C contact maps.…”

Section: Introductionmentioning

confidence: 99%

Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data

Kim

Yardimici

Bonora

et al. 2019

Preprint

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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 twelve different single-cell combinatorial indexed Hi-C (sciHi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 25,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sciHi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.

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“…We have demonstrated that sci-Hi-C is able to separate different cell types on the basis of single cell Hi-C maps and identified previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, enabling "in silico sorting" of mixed cell populations by cell-cycle stage [11][12][13]. We have since improved the initial version of our sci-Hi-C protocol, which is now greatly simplified, easier-to-adapt, and more cost-effective and have successfully applied sci-Hi-C to a diversity of mouse and human cell lines [11][12][13].…”

mentioning

confidence: 99%

Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

Ramani

Deng

Qiu

et al. 2019

Preprint

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The highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells.We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

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Unsupervised embedding of single-cell Hi-C data

Cited by 26 publications

References 34 publications

scHiCTools: a computational toolbox for analyzing single-cell Hi-C data

scHiCTools: a computational toolbox for analyzing single-cell Hi-C data

Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data

Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

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