The laser scanning and CCD image-transmitting measurement method and principle on acquiring 3-D curved surface shape data are discussed. Computer processing technique of 3-D curved surface shape(be called“ 3 - D surf...The laser scanning and CCD image-transmitting measurement method and principle on acquiring 3-D curved surface shape data are discussed. Computer processing technique of 3-D curved surface shape(be called“ 3 - D surface shape”for short) data is analysed. This technique in- cludes these concrete methods and principles such as data smoothing, fitting, reconstructing ,elimi- nating and so on. The example and result about computer processing of 3- D surface shape data are given .展开更多
Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is th...Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is the cell type identification.Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters.Among all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality reduction.In this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions.The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension.Motivated by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not.Finally,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data.We perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.展开更多
文摘The laser scanning and CCD image-transmitting measurement method and principle on acquiring 3-D curved surface shape data are discussed. Computer processing technique of 3-D curved surface shape(be called“ 3 - D surface shape”for short) data is analysed. This technique in- cludes these concrete methods and principles such as data smoothing, fitting, reconstructing ,elimi- nating and so on. The example and result about computer processing of 3- D surface shape data are given .
基金supported in part by the National Natural Science Foundation of China(No.U19A2064)the Hunan Provincial Science and Technology Program(No.2019CB1007)+1 种基金the Fundamental Research Funds for the Central Universities,CSU(No.2282019SYLB004)the Fundamental Research Funds for the Central Universities of Central South University(No.2020zzts593)。
文摘Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is the cell type identification.Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters.Among all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality reduction.In this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions.The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension.Motivated by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not.Finally,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data.We perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.
