Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell,which can reflect the heterogeneity between cells.However,batch effects caused by non-biological factors may hinder dat...Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell,which can reflect the heterogeneity between cells.However,batch effects caused by non-biological factors may hinder data integration and downstream analysis.Although the batch effect can be evaluated by visualizing the data,which actually is subjective and inaccurate.In this work,we propose a quantitative method cKBET,which considers the batch and cell type information simultaneously.The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types.We verify the performance of our cKBET method on simulated and real biological data sets.The experimental results show that our cKBET method is superior to existing methods in most cases.In general,our cKBET method can detect batch effect with either balanced or unbalanced cell types,and thus evaluate batch correction methods.展开更多
Single-cell RNA sequencing(scRNA-seq)allows researchers to examine the transcriptome at the single-cell level and has been increasingly employed as technologies continue to advance.Due to technical and biological reas...Single-cell RNA sequencing(scRNA-seq)allows researchers to examine the transcriptome at the single-cell level and has been increasingly employed as technologies continue to advance.Due to technical and biological reasons unique to scRNA-seq data,denoising and batch effect correction are almost indispensable to ensure valid and powerful data analysis.However,various aspects of scRNA-seq data pose grand challenges for such essential tasks pertaining to data pre-processing,normalization or harmonization.In this review,we first discuss properties of scRNA-seq data that contribute to the challenges for denoising and batch effect correction from a computational perspective.We then focus on reviewing several state-of-the-art methods for dropout imputation and batch effect correction,comparing their strengths and weaknesses.Finally,we benchmarked three widely used correction tools using two hematopoietic scRNA-seq datasets to show their performance in a real data application.展开更多
The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performa...The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performance was investigated in a fixed-bed system with respect to the adsorption superficial velocity,ionic strength and pH.A mathematical model was used to simulate the mass transfer mechanism,taking film mass transfer,pore diffusion and axial dispersion into account.The model predictions were consistent with the experi-mental data and were consequently used to determine the mass transfer coefficients.展开更多
基金supported by the NSFC projects(Grant No.11631012).
文摘Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell,which can reflect the heterogeneity between cells.However,batch effects caused by non-biological factors may hinder data integration and downstream analysis.Although the batch effect can be evaluated by visualizing the data,which actually is subjective and inaccurate.In this work,we propose a quantitative method cKBET,which considers the batch and cell type information simultaneously.The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types.We verify the performance of our cKBET method on simulated and real biological data sets.The experimental results show that our cKBET method is superior to existing methods in most cases.In general,our cKBET method can detect batch effect with either balanced or unbalanced cell types,and thus evaluate batch correction methods.
文摘Single-cell RNA sequencing(scRNA-seq)allows researchers to examine the transcriptome at the single-cell level and has been increasingly employed as technologies continue to advance.Due to technical and biological reasons unique to scRNA-seq data,denoising and batch effect correction are almost indispensable to ensure valid and powerful data analysis.However,various aspects of scRNA-seq data pose grand challenges for such essential tasks pertaining to data pre-processing,normalization or harmonization.In this review,we first discuss properties of scRNA-seq data that contribute to the challenges for denoising and batch effect correction from a computational perspective.We then focus on reviewing several state-of-the-art methods for dropout imputation and batch effect correction,comparing their strengths and weaknesses.Finally,we benchmarked three widely used correction tools using two hematopoietic scRNA-seq datasets to show their performance in a real data application.
文摘The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performance was investigated in a fixed-bed system with respect to the adsorption superficial velocity,ionic strength and pH.A mathematical model was used to simulate the mass transfer mechanism,taking film mass transfer,pore diffusion and axial dispersion into account.The model predictions were consistent with the experi-mental data and were consequently used to determine the mass transfer coefficients.
