Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
Five amaranth (<i>Amaranthus</i> spp.) accessions from central and southern region of Malawi were characterised at Lilongwe University of Agriculture and Natural Resources using agro-morphological traits. ...Five amaranth (<i>Amaranthus</i> spp.) accessions from central and southern region of Malawi were characterised at Lilongwe University of Agriculture and Natural Resources using agro-morphological traits. A total of thirteen descriptors, defined by Plant Genetic Resources Institute (IPGRI), were used to c<span>haracterise the amaranth accessions under study. Field experiments were c</span>arried out for two seasons in August to November, 2018 and January to March, 2019. The experiments were laid out in a Randomised Complete Block Design (RCBD), which was replicated four times. The qualitative (plant growth habit, leaf colour, inflorescence colour, stem colour, inflorescence spininess, seed <span>colour) and quantitative traits (plant height, stem girth, leaf length, leaf wi</span>dth, inflorescence length, days to 80% flowering, grain yield, leaf yield, and days to 80% maturity)<i> </i>evaluated were significant in defining the uniqueness of different amaranth accessions evaluated. Significant differences (P < 0.05) obtained from analysis of variance were observed in all the parameters studied. Correlation analysis was conducted using Genstat statistical package version 18 while cluster analysis was done using R statistical software. The agro-morphological characterisation results showed a wide range of variation for most of the qualitative characters. Wide variability was present in all the qualitative characters except for plant growth habit where all the accessions exhibited erect plant growth habit. These results point to high possibility of genetic di<span>versity of amaranth accessions in Malawi, it could be exploited in future br</span>eeding purposes and deserving conservation.展开更多
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
文摘Five amaranth (<i>Amaranthus</i> spp.) accessions from central and southern region of Malawi were characterised at Lilongwe University of Agriculture and Natural Resources using agro-morphological traits. A total of thirteen descriptors, defined by Plant Genetic Resources Institute (IPGRI), were used to c<span>haracterise the amaranth accessions under study. Field experiments were c</span>arried out for two seasons in August to November, 2018 and January to March, 2019. The experiments were laid out in a Randomised Complete Block Design (RCBD), which was replicated four times. The qualitative (plant growth habit, leaf colour, inflorescence colour, stem colour, inflorescence spininess, seed <span>colour) and quantitative traits (plant height, stem girth, leaf length, leaf wi</span>dth, inflorescence length, days to 80% flowering, grain yield, leaf yield, and days to 80% maturity)<i> </i>evaluated were significant in defining the uniqueness of different amaranth accessions evaluated. Significant differences (P < 0.05) obtained from analysis of variance were observed in all the parameters studied. Correlation analysis was conducted using Genstat statistical package version 18 while cluster analysis was done using R statistical software. The agro-morphological characterisation results showed a wide range of variation for most of the qualitative characters. Wide variability was present in all the qualitative characters except for plant growth habit where all the accessions exhibited erect plant growth habit. These results point to high possibility of genetic di<span>versity of amaranth accessions in Malawi, it could be exploited in future br</span>eeding purposes and deserving conservation.
