A review and performance evaluation of clustering frameworks for single-cell Hi-C data

Zhen, Caiwei; Wang, Yuxian; Geng, Jiaquan; Han, Lu; Li, Jingyi; Peng, Jinghao; Wang, Tao; Hao, Jianye; Shang, Xuequn; Wei, Zhongyu; Zhu, Peican; Peng, Jiajie

doi:10.1093/bib/bbac385

Cited by 7 publications

(2 citation statements)

References 60 publications

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“…Utilizing the soybean root data, which had been annotated with cell type information, we applied SpaGCN software 19 to cluster the raw SRT data consisting of 27,878 genes into six clusters. The clustering results achieved an Adjusted Rand Index (ARI) 20,21 value of 0.49, Accuracy (ACC) value of 0.66, Normalized Mutual Information (NMI) 21,22 value of 0.54, and Fowlkes-Mallows Index (FMI) 21 value of 0.66 in Fig. 2e.…”

Section: Srt Data Contains Noise That Impedes Downstream Analysesmentioning

confidence: 99%

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Kang

Sun

et al. 2023

Preprint

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The recent advancements in spatially resolved transcriptomics (SRT) technology have enabled the acquisition of gene expression data at near- or sub-single-cell resolution, along with simultaneous imaging of physical locations. Nevertheless, necessary experimental procedures such as tissue fixation, permeabilization, and tissue removal inevitably induce the diffusion of transcribed molecules. Consequently, this leads to the partial capture of ex-situ transcripts in SRT data, thereby introducing a considerable amount of noise into the dataset. To address this issue, in this study, we focused on evaluating the diffusion pattern of individual genes within tissue regions and quantitatively calculating their signal-to-noise ratio (SNR). Through this analysis, we successfully identified "invalid genes" exhibiting widespread expression across tissue regions. Then by filtering out these genes, we effectively reduced the high noise level present in SRT data. To achieve this, we developed the gene filter denoising (GF) algorithm, which utilizes the optimal transport method to compute the gene diffusion coefficient and generate denoised SRT data. One notable advantage of our GF algorithm is its ability to fully "respect" the raw sequencing data, thereby avoiding the introduction of false positives often associated with traditional interpolation and modification denoising methods. Furthermore, we conducted comprehensive validation of GF, and the GFdenoised SRT data demonstrated substantial improvements in clustering, identification of differentially expressed genes (DEGs), and cell type annotation. Taken together, we believe that the GF denoising technique will serve as an essential and crucial step in exploring SRT data and investigating the underlying biological processes.

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Section: Srt Data Contains Noise That Impedes Downstream Analysesmentioning

confidence: 99%

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Kang

Sun

et al. 2023

Preprint

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“…In addition, single-cell Hi-C techniques ( 13 , 14 ) measure 3D structures of individual genomes directly and provide data for the analysis of chromatin folding in rare cell types like stem ( 15 ) and totipotent ( 16 ) cells, thereby contributing to a better understanding of basic mechanisms in development, differentiation and human disease. For example, single-cell Hi-C has been applied to examine changes in genome organization during the cell cycle ( 17 , 18 ), cell-type-specific chromosomal architecture ( 19 ), and structure-expression interactions ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics

Li,

Schlick

2023

Nucleic Acids Research

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The structure and dynamics of the eukaryotic genome are intimately linked to gene regulation and transcriptional activity. Many chromosome conformation capture experiments like Hi-C have been developed to detect genome-wide contact frequencies and quantify loop/compartment structures for different cellular contexts and time-dependent processes. However, a full understanding of these events requires explicit descriptions of representative chromatin and chromosome configurations. With the exponentially growing amount of data from Hi-C experiments, many methods for deriving 3D structures from contact frequency data have been developed. Yet, most reconstruction methods use polymer models with low resolution to predict overall genome structure. Here we present a Brownian Dynamics (BD) approach termed Hi-BDiSCO for producing 3D genome structures from Hi-C and Micro-C data using our mesoscale-resolution chromatin model based on the Discrete Surface Charge Optimization (DiSCO) model. Our approach integrates reconstruction with chromatin simulations at nucleosome resolution with appropriate biophysical parameters. Following a description of our protocol, we present applications to the NXN, HOXC, HOXA and Fbn2 mouse genes ranging in size from 50 to 100 kb. Such nucleosome-resolution genome structures pave the way for pursuing many biomedical applications related to the epigenomic regulation of chromatin and control of human disease.

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Exploration and Visualization Methods for Chromatin Interaction Data

Sizovs,

Silina,

Melkus

et al. 2024

Lecture Notes in Computer Science

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A review and performance evaluation of clustering frameworks for single-cell Hi-C data

Cited by 7 publications

References 60 publications

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics

Exploration and Visualization Methods for Chromatin Interaction Data

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