HB-PLS:A statistical method for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression

Gene expression data features high dimensionality,multicollinearity,and non-Gaussian distribution noise,posing hurdles for identification of true regulatory genes controlling a biological process or pathway.In this study,we integrated the Huber loss function and the Berhu penalty(HB)into partial least squares(PLS)framework to deal with the high dimension and multicollinearity property of gene expression data,and developed a new method called HB-PLS regression to model the relationships between regulatory genes and pathway genes.To solve the Huber-Berhu optimization problem,an accelerated proximal gradient descent algorithm with at least 10 times faster than the general convex optimization solver(CVX),was developed.Application of HB-PLS to recognize pathway regulators of lignin biosynthesis and photosynthesis in Arabidopsis thaliana led to the identification of many known positive pathway regulators that had previously been experimentally validated.As compared to sparse partial least squares(SPLS)regression,an efficient method for variable selection and dimension reduction in handling multicollinearity,HB-PLS has higher efficacy in identifying more positive known regulators,a much higher but slightly less sensitivity/(1-specificity)in ranking the true positive known regulators to the top of the output regulatory gene lists for the two aforementioned pathways.In addition,each method could identify some unique regulators that cannot be identified by the other methods.Our results showed that the overall performance of HB-PLS slightly exceeds that of SPLS but both methods are instrumental for identifying real pathway regulators from high-throughput gene expression data,suggesting that integration of statistics,machine leaning and convex optimization can result in a method with high efficacy and is worth further exploration.

Journal Article Moso bamboo and Japanese cedar seedlings differently affected soil N_(2)O emissions

Moso bamboo expansions into Japanese cedar forests are common.The expansion effects on soil nitrous oxide(N_(2)O)emissions have not been thoroughly understood,and the underlying microbial mechanisms remain unclear.We studied bacterial and fungal contribution to soil N_(2)O emissions under moso bamboo or Japanese cedar by applying bacterial or fungal inhibitors using streptomycin and iprodione,respectively.Soil N_(2)O emissions were measured and the relative contribution of bacteria and fungi to soil N_(2)O emissions was calculated.N_(2)O emission from soil with moso bamboo was significantly higher than under Japanese cedar.Compared with control,bacterial or fungal inhibitor or their combination decreased N_(2)O emissions,indicating substantial contribution of microbial activities to N_(2)O emissions.However,the relative contribution of bacteria and fungi to N_(2)O emissions was not affected by plants.Soil organic carbon,total and ammonium nitrogen were lower in soil under moso bamboo than Japanese cedar,suggesting faster microbial decomposition under moso bamboo.Fungal inhibitor and plants interactively affected soil pH,total phosphorus and ammonium nitrogen,while bacterial inhibitor and plants interactively affected total nitrogen,indicating substantial dependence of effects by microbial communities on plant species.Moso bamboo and Japanese cedar differed in their effects on soil N_(2)O emissions with higher emissions under moso bamboo.Stimulation of N_(2)O emission under moso bamboo might occur due to higher nitrogen mineralization and subsequent denitrification induced by high root exudation.These results highlight the need to consider the effect of species shifts on N_(2)O emissions in forests.