The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-pheno...The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals.However,the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels(i.e.,sparse,low sparse,non-sparse).Actually,the real association patterns related to different host phenotypes are not unique.Here,we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels,designated as MiATDS.In particular,we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information.We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method.We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power.By applying to real data analysis,MiATDS displays reliable practicability too.The R package is available at https://github.com/XiaoyunHuang33/MiATDS.展开更多
Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity an...Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.展开更多
基金supported by the National Natural Science Foundation of China(61872157,61932008,61532008)the Key Research and Development Program of Hubei Province(2020BAB017)。
文摘The dysbiosis of microbiome may have negative effects on a host phenotype.The microbes related to the host phenotype are regarded as microbial association signals.Recently,statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals.However,the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels(i.e.,sparse,low sparse,non-sparse).Actually,the real association patterns related to different host phenotypes are not unique.Here,we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels,designated as MiATDS.In particular,we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information.We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method.We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power.By applying to real data analysis,MiATDS displays reliable practicability too.The R package is available at https://github.com/XiaoyunHuang33/MiATDS.
基金Supported by the National Natural Science Foundation of China (No. 61102066)China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No.Y201119890)
文摘Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.
