Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract usef...Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.展开更多
We characterized variations in bacterioplankton community composition(BCC) in mesocosms subject to three different treatments. Two groups contained fish(group one: Cyprinus carpio; group two: Hypophthalmichthys molitr...We characterized variations in bacterioplankton community composition(BCC) in mesocosms subject to three different treatments. Two groups contained fish(group one: Cyprinus carpio; group two: Hypophthalmichthys molitrix); and group three, the untreated mesocosm, was the control. Samples were taken seven times over a 49-d period, and BCC was analyzed by PCR-denaturing gradient gel electrophoresis(DGGE) and real-time quantitative PCR(q PCR). Results revealed that introduction of C. carpio and H. molitrix had a remarkable impact on the composition of bacterioplankton communities, and the BCC was significantly diff erent between each treatment. Sequencing of DGGE bands revealed that the bacterioplankton community in the different treatment groups was consistent at a taxonomic level, but differed in its abundance. H. molitrix promoted the richness of Alphaproteobacteria and Actinobacteria, while more bands affiliated to Cyanobacteria were detected in C. carpio mesocosms. The redundancy analysis(RDA) result demonstrated that the BCC was closely related to the bottom-up(total phosphorus, chlorophyll a, phytoplankton biomass) and top-down forces(biomass of copepods and cladocera) in C. carpio and control mesocosms, respectively. We found no evidence for top-down regulation of BCC by zooplankton in H. molitrix mesocosms, while grazing by protozoa(heterotrophic nanoflagellates, ciliates) became the major way to regulate BCC. Total bacterioplankton abundances were significantly higher in C. carpio mesocosms because of high nutrient concentration and suspended solids. Our study provided insights into the relationship between fish and bacterioplankton at species level, leading to a deep understanding of the function of the microbial loop and the aquatic ecosystem.展开更多
文摘Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.
基金Supported by the National Key Technology R&D Program of China(No.2014BAC09B02)the National Water Pollution Control and Management Technology Major Projects(No.2012ZX07101-002)the High-Level Scientific Research Foundation for the Introduction of Talent(No.E07016043)
文摘We characterized variations in bacterioplankton community composition(BCC) in mesocosms subject to three different treatments. Two groups contained fish(group one: Cyprinus carpio; group two: Hypophthalmichthys molitrix); and group three, the untreated mesocosm, was the control. Samples were taken seven times over a 49-d period, and BCC was analyzed by PCR-denaturing gradient gel electrophoresis(DGGE) and real-time quantitative PCR(q PCR). Results revealed that introduction of C. carpio and H. molitrix had a remarkable impact on the composition of bacterioplankton communities, and the BCC was significantly diff erent between each treatment. Sequencing of DGGE bands revealed that the bacterioplankton community in the different treatment groups was consistent at a taxonomic level, but differed in its abundance. H. molitrix promoted the richness of Alphaproteobacteria and Actinobacteria, while more bands affiliated to Cyanobacteria were detected in C. carpio mesocosms. The redundancy analysis(RDA) result demonstrated that the BCC was closely related to the bottom-up(total phosphorus, chlorophyll a, phytoplankton biomass) and top-down forces(biomass of copepods and cladocera) in C. carpio and control mesocosms, respectively. We found no evidence for top-down regulation of BCC by zooplankton in H. molitrix mesocosms, while grazing by protozoa(heterotrophic nanoflagellates, ciliates) became the major way to regulate BCC. Total bacterioplankton abundances were significantly higher in C. carpio mesocosms because of high nutrient concentration and suspended solids. Our study provided insights into the relationship between fish and bacterioplankton at species level, leading to a deep understanding of the function of the microbial loop and the aquatic ecosystem.
