单细胞数据聚类在生物信息分析中具有重要作用,但受测序原理和测序平台的限制,单细胞数据集普遍存在高维稀疏性、高方差噪声和基因数据缺失的问题,导致单细胞数据在聚类分析和应用方面仍面临诸多挑战。现有的单细胞聚类方法主要针对细...单细胞数据聚类在生物信息分析中具有重要作用,但受测序原理和测序平台的限制,单细胞数据集普遍存在高维稀疏性、高方差噪声和基因数据缺失的问题,导致单细胞数据在聚类分析和应用方面仍面临诸多挑战。现有的单细胞聚类方法主要针对细胞和基因表达间的关系进行建模,忽略了对细胞间潜在特征关系的充分挖掘以及对噪声的去除,导致聚类结果不理想,从而阻碍了后期对数据的分析。针对上述问题,提出了一种联合零膨胀负二项(Zero Inflated Negative Binomial,ZINB)模型与图注意力自编码器的自优化单细胞聚类算法(Self-optimized Single Cell Clustering Using ZINB Model and Graph Attention Autoencoder,scZDGAC)。该算法首先使用ZINB模型并结合可扩展的DCA去噪算法,通过ZINB分布更好地拟合数据特征分布,提升自编码器的去噪性能,并减小噪声和数据丢失对KNN算法输出的影响;然后通过图注意力自编码器在不同权重的细胞之间传播信息,更好地捕获细胞间的潜在特征进行聚类;最后scZDGAC采用自优化的方法使原本两个独立的聚类模块和特征模块相互受益,不断迭代更新聚类中心,进一步提升聚类性能。为了对聚类结果进行评价,文中使用调整兰德指数(ARI)和标准化互信息(NMI)两个通用评价指标。在6个不同规模的单细胞数据集上与其他算法进行对比实验,结果表明,所提聚类算法在聚类性能上较其他方法有很大提高,很好地展现了该算法的鲁棒性。展开更多
The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six gene...The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six generalized linear models to examine the relationship between the occurrence of lightning-induced forest fires and meteorological factors in the Northern Daxing＇an Mountains of China. The six models included Poisson, negative binomial （NB）, zero- inflated Poisson （ZIP）, zero-inflated negative binomial （ZINB）, Poisson hurdle （PH）, and negative binomial hurdle （NBH） models. Goodness-of-fit was compared and tested among the six models using Akaike information criterion （AIC）, sum of squared errors, likelihood ratio test, and Vuong test. The predictive performance of the models was assessed and compared using independent validation data by the data-splitting method. Based on the model AIC, the ZINB model best fitted the fire occurrence data, followed by （in order of smaller AIC） NBH, ZIP, NB, PH, and Poisson models. The ZINB model was also best for pre- dicting either zero counts or positive counts （〉1）. The two Hurdle models （PH and NBH） were better than ZIP, Poisson, and NB models for predicting positive counts, but worse than these three models for predicting zero counts. Thus, the ZINB model was the first choice for modeling the occurrence of lightning-induced forest fires in this study, which implied that the excessive zero counts of lightning- induced fires came from both structure and sampling zeros.展开更多
文摘单细胞数据聚类在生物信息分析中具有重要作用,但受测序原理和测序平台的限制,单细胞数据集普遍存在高维稀疏性、高方差噪声和基因数据缺失的问题,导致单细胞数据在聚类分析和应用方面仍面临诸多挑战。现有的单细胞聚类方法主要针对细胞和基因表达间的关系进行建模,忽略了对细胞间潜在特征关系的充分挖掘以及对噪声的去除,导致聚类结果不理想,从而阻碍了后期对数据的分析。针对上述问题,提出了一种联合零膨胀负二项(Zero Inflated Negative Binomial,ZINB)模型与图注意力自编码器的自优化单细胞聚类算法(Self-optimized Single Cell Clustering Using ZINB Model and Graph Attention Autoencoder,scZDGAC)。该算法首先使用ZINB模型并结合可扩展的DCA去噪算法,通过ZINB分布更好地拟合数据特征分布,提升自编码器的去噪性能,并减小噪声和数据丢失对KNN算法输出的影响;然后通过图注意力自编码器在不同权重的细胞之间传播信息,更好地捕获细胞间的潜在特征进行聚类;最后scZDGAC采用自优化的方法使原本两个独立的聚类模块和特征模块相互受益,不断迭代更新聚类中心,进一步提升聚类性能。为了对聚类结果进行评价,文中使用调整兰德指数(ARI)和标准化互信息(NMI)两个通用评价指标。在6个不同规模的单细胞数据集上与其他算法进行对比实验,结果表明,所提聚类算法在聚类性能上较其他方法有很大提高,很好地展现了该算法的鲁棒性。
基金funded by Asia–Pacific Forests Net(APFNET/2010/FPF/001)National Natural Science Foundation of China(Grant No.31400552)
文摘The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six generalized linear models to examine the relationship between the occurrence of lightning-induced forest fires and meteorological factors in the Northern Daxing＇an Mountains of China. The six models included Poisson, negative binomial （NB）, zero- inflated Poisson （ZIP）, zero-inflated negative binomial （ZINB）, Poisson hurdle （PH）, and negative binomial hurdle （NBH） models. Goodness-of-fit was compared and tested among the six models using Akaike information criterion （AIC）, sum of squared errors, likelihood ratio test, and Vuong test. The predictive performance of the models was assessed and compared using independent validation data by the data-splitting method. Based on the model AIC, the ZINB model best fitted the fire occurrence data, followed by （in order of smaller AIC） NBH, ZIP, NB, PH, and Poisson models. The ZINB model was also best for pre- dicting either zero counts or positive counts （〉1）. The two Hurdle models （PH and NBH） were better than ZIP, Poisson, and NB models for predicting positive counts, but worse than these three models for predicting zero counts. Thus, the ZINB model was the first choice for modeling the occurrence of lightning-induced forest fires in this study, which implied that the excessive zero counts of lightning- induced fires came from both structure and sampling zeros.
