scholarly journals TPK: a single-cell clustering algorithm based on novel feature selection genes

2021 ◽  
Vol 1738 ◽  
pp. 012078
Author(s):  
Yaxuan Cui ◽  
Kunjie Luo ◽  
Zheyu Zhang ◽  
Saijia Liu
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Cell Clustering

scholarly journals Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Chunxiang Wang ◽  
Xin Gao ◽  
Juntao Liu
Keyword(s):  
Single Cell ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Preprocessing ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Preprocessing Method ◽  
Cell Clustering ◽  
Cell Gene Expression

Abstract Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.

scholarly journals HiCluster: A Robust Single-Cell Hi-C Clustering Method Based on Convolution and Random Walk

2018 ◽  
Author(s):  
Jingtian Zhou ◽  
Jianzhu Ma ◽  
Yusi Chen ◽  
Chuankai Cheng ◽  
Bokan Bao ◽  
...  
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Genome Structure ◽  
Real Data ◽  
Cell Types ◽  
3D Genome ◽  
Cell Clustering

3D genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe HiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real data as benchmarks, HiCluster significantly improves clustering accuracy when applied to low coverage Hi-C datasets compared to existing methods. After imputation by HiCluster, structures similar to topologically associating domains (TADs) could be identified within single cells, and their consensus boundaries among cells were enriched at the TAD boundaries observed in bulk samples. In summary, HiCluster facilitates visualization and comparison of single-cell 3D genomes.

scholarly journals A monotonicity-based gene clustering algorithm for enhancing clarity in single-cell RNA sequencing data

2020 ◽  
Author(s):  
Victor Wang ◽  
Pietro Antonio Cicalese ◽  
Chandra Mohan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Gene Clustering ◽  
Sequencing Data ◽  
Technical Noise ◽  
Cell Clustering ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies and analysis tools have allowed for meaningful insight into the roles and relationships of cells. However, high dimensionality, frequent dropout values, and technical noise remain prevalent challenges for scRNA-seq data, obscuring the already complex expression patterns. To address several shortcomings in commonly used distance metrics, we present a monotonicity-based distance metric designed to enhance the clarity of scRNA-seq data. We apply our metric in a gene clustering algorithm, which we run on several biological datasets. We compare our results to those generated by popular clustering algorithms to demonstrate that our algorithm has substantial ability to improve the accuracy of subsequent cell clustering.

scholarly journals Robust single-cell Hi-C clustering by convolution- and random-walk–based imputation

2019 ◽  
Vol 116 (28) ◽  
pp. 14011-14018 ◽  
Author(s):  
Jingtian Zhou ◽  
Jianzhu Ma ◽  
Yusi Chen ◽  
Chuankai Cheng ◽  
Bokan Bao ◽  
...  
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Genome Structure ◽  
Three Dimensional ◽  
Cell Types ◽  
Cell Clustering ◽  
Low Coverage

Three-dimensional genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe scHiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real single-cell Hi-C data as benchmarks, scHiCluster significantly improves clustering accuracy when applied to low coverage datasets compared with existing methods. After imputation by scHiCluster, topologically associating domain (TAD)-like structures (TLSs) can be identified within single cells, and their consensus boundaries were enriched at the TAD boundaries observed in bulk cell Hi-C samples. In summary, scHiCluster facilitates visualization and comparison of single-cell 3D genomes.

scholarly journals Accurate Single-Cell Clustering through Ensemble Similarity Learning

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1670
Author(s):  
Hyundoo Jeong ◽  
Sungtae Shin ◽  
Hong-Gi Yeom
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Accurate Estimation ◽  
Artificial Noise ◽  
Similarity Learning ◽  
Cell Clusters ◽  
Single Cell Sequencing ◽  
Cell Clustering ◽  
Depth Analysis

Single-cell sequencing provides novel means to interpret the transcriptomic profiles of individual cells. To obtain in-depth analysis of single-cell sequencing, it requires effective computational methods to accurately predict single-cell clusters because single-cell sequencing techniques only provide the transcriptomic profiles of each cell. Although an accurate estimation of the cell-to-cell similarity is an essential first step to derive reliable single-cell clustering results, it is challenging to obtain the accurate similarity measurement because it highly depends on a selection of genes for similarity evaluations and the optimal set of genes for the accurate similarity estimation is typically unknown. Moreover, due to technical limitations, single-cell sequencing includes a larger number of artificial zeros, and the technical noise makes it difficult to develop effective single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm that can accurately predict single-cell clusters in large-scale single-cell sequencing by effectively reducing the zero-inflated noise and accurately estimating the cell-to-cell similarities. First, we construct an ensemble similarity network based on different similarity estimates, and reduce the artificial noise using a random walk with restart framework. Finally, starting from a larger number small size but highly consistent clusters, we iteratively merge a pair of clusters with the maximum similarities until it reaches the predicted number of clusters. Extensive performance evaluation shows that the proposed single-cell clustering algorithm can yield the accurate single-cell clustering results and it can help deciphering the key messages underlying complex biological mechanisms.

scholarly journals Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data

2020 ◽  
Author(s):  
Jia Song ◽  
Yao Liu ◽  
Xuebing Zhang ◽  
Qiuyue Wu ◽  
Juan Gao ◽  
...  
Keyword(s):  
High Resolution ◽  
Noise Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Clustering Algorithm ◽  
Cellular Heterogeneity ◽  
Cell Clustering ◽  
Single Cell Rna Sequencing ◽  
Subspace Separation ◽  
Marker Identification

Abstract Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as ‘entropy subspace’ separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the ‘entropy subspace’ separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.

scholarly journals SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Wenliang Gao ◽  
Yuanyuan Li ◽  
Chujie Fang ◽  
Wei Fan ◽  
Haonan Peng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Cell ◽  
Prior Information ◽  
Clustering Algorithm ◽  
Cell Types ◽  
Clustering Method ◽  
Cell Clustering ◽  
Semisupervised Clustering ◽  
Cell Data

Clustering analysis is one of the most important technologies for single-cell data mining. It is widely used in the division of different gene sequences, the identification of functional genes, and the detection of new cell types. Although the traditional unsupervised clustering method does not require label data, the distribution of the original data, the setting of hyperparameters, and other factors all affect the effectiveness of the clustering algorithm. While in some cases the type of some cells is known, it is hoped to achieve high accuracy if the prior information about those cells is utilized sufficiently. In this study, we propose SCMAG (a semisupervised single-cell clustering method based on a matrix aggregation graph convolutional neural network) that takes into full consideration the prior information for single-cell data. To evaluate the performance of the proposed semisupervised clustering method, we test on different single-cell datasets and compare with the current semisupervised clustering algorithm in recognizing cell types on various real scRNA-seq data; the results show that it is a more accurate and significant model.

scholarly journals Accurate feature selection improves single-cell RNA-seq cell clustering

2021 ◽  
Author(s):  
Kenong Su ◽  
Tianwei Yu ◽  
Hao Wu
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Clustering ◽  
Selection Step ◽  
Good Set ◽  
Different Cell Types ◽  
The Impact

Abstract Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as ‘features’), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.

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