In gene expression profiling studies,including single-cell RNA sequencing(scRNA-seq)analyses,the identification and characterization of co-expressed genes provides critical information on cell identity and function.Ge...In gene expression profiling studies,including single-cell RNA sequencing(scRNA-seq)analyses,the identification and characterization of co-expressed genes provides critical information on cell identity and function.Gene co-expression clustering in scRNA-seq data presents certain challenges.We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately,and produce results that substantially limit biological expectations of co-expressed genes.Herein,we present single-cell Latent-variable Model(scLM),a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context.Importantly,scLM can simultaneously cluster multiple single-cell datasets,i.e.,consensus clustering,enabling users to leverage single-cell data from multiple sources for novel comparative analysis.scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets.Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy.To illustrate the biological insights of scLM,we apply it to our in-house and public experimental scRNA-seq datasets.scLM identifies novel functional gene modules and refines cell states,which facilitates mechanism discovery and understanding of complex biosystems such as cancers.A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.展开更多
The H∞ proportional-integral-differential(PID) feedback for arbitrary-order delayed multi-agent system is investigated to improve the system performance. The closed-loop multi-input multi-output(MIMO) control framewo...The H∞ proportional-integral-differential(PID) feedback for arbitrary-order delayed multi-agent system is investigated to improve the system performance. The closed-loop multi-input multi-output(MIMO) control framework with the distributed PID controller is firstly described for the multi-agent system in a unified way. Then, by using the matrix theory, the prescribed H∞performance criterion of the multi-agent system is shown to be equivalent to several independent H∞ performance constraints of the single-input single-output(SISO) subsystem with respect to the eigenvalues of the Laplacian matrix. Subsequently, for each subsystem,the set of the PID controllers satisfying the required H∞ performance constraint is analytically characterized based on the extended Hermite-Biehler theorem. Finally, the three-dimensional set of the decentralized H∞ PID control parameters is derived by finding the intersection of the H∞ PID regions for all the decomposed subsystems. The simulation results reveal the effectiveness of the proposed method.展开更多
Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditiona...Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditional consensus algorithm for bounded space is only applicable to rectangular bouncing boundaries, not suitable for non-rectangular space. In order to extend the previous consensus algorithm to the non- rectangular space, the concept of mirrored velocity is introduced, which can convert the discontinuous real velocity to continuous mirrored velocity, and expand a bounded space into an infinite space. Using the consensus algorithm, it is found that the mirrored velocities of multi-agents asymptotically converge to the same values. Because each mirrored velocity points to a unique velocity in real space, it can be concluded that the real velocities of multi-agents also asymptotically converge. Finally, the effectiveness of the proposed consensus algorithm is examined by theoretical proof and numerical simulations. Moreover, an experiment is performed with the algorithm in a real multi-robot system successfully.展开更多
基金the Cancer Genomics,Tumor Tissue Repository,and Bioinformatics Shared Resources under the NCI Cancer Center Support Grant to the Comprehensive Cancer Center of Wake Forest University Health Sciences,USA(Grant No.P30CA012197)。
文摘In gene expression profiling studies,including single-cell RNA sequencing(scRNA-seq)analyses,the identification and characterization of co-expressed genes provides critical information on cell identity and function.Gene co-expression clustering in scRNA-seq data presents certain challenges.We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately,and produce results that substantially limit biological expectations of co-expressed genes.Herein,we present single-cell Latent-variable Model(scLM),a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context.Importantly,scLM can simultaneously cluster multiple single-cell datasets,i.e.,consensus clustering,enabling users to leverage single-cell data from multiple sources for novel comparative analysis.scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets.Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy.To illustrate the biological insights of scLM,we apply it to our in-house and public experimental scRNA-seq datasets.scLM identifies novel functional gene modules and refines cell states,which facilitates mechanism discovery and understanding of complex biosystems such as cancers.A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.
基金supported by National Natural Science Foundationof China(Nos.61273116 and 61074039)National Natural ScienceFund for Distinguished Young Scholar of China(No.61026016)Natural Science Foundation of Zhejiang Province(No.Y1111012)
文摘The H∞ proportional-integral-differential(PID) feedback for arbitrary-order delayed multi-agent system is investigated to improve the system performance. The closed-loop multi-input multi-output(MIMO) control framework with the distributed PID controller is firstly described for the multi-agent system in a unified way. Then, by using the matrix theory, the prescribed H∞performance criterion of the multi-agent system is shown to be equivalent to several independent H∞ performance constraints of the single-input single-output(SISO) subsystem with respect to the eigenvalues of the Laplacian matrix. Subsequently, for each subsystem,the set of the PID controllers satisfying the required H∞ performance constraint is analytically characterized based on the extended Hermite-Biehler theorem. Finally, the three-dimensional set of the decentralized H∞ PID control parameters is derived by finding the intersection of the H∞ PID regions for all the decomposed subsystems. The simulation results reveal the effectiveness of the proposed method.
基金The National Natural Science Foundation of China(No.61273110)the Specialized Fund for the Doctoral Program of Higher Education(No.20130092130002)
文摘Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditional consensus algorithm for bounded space is only applicable to rectangular bouncing boundaries, not suitable for non-rectangular space. In order to extend the previous consensus algorithm to the non- rectangular space, the concept of mirrored velocity is introduced, which can convert the discontinuous real velocity to continuous mirrored velocity, and expand a bounded space into an infinite space. Using the consensus algorithm, it is found that the mirrored velocities of multi-agents asymptotically converge to the same values. Because each mirrored velocity points to a unique velocity in real space, it can be concluded that the real velocities of multi-agents also asymptotically converge. Finally, the effectiveness of the proposed consensus algorithm is examined by theoretical proof and numerical simulations. Moreover, an experiment is performed with the algorithm in a real multi-robot system successfully.
