Heatmap cluster figures are often used to represent data sets in the?omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data ma...Heatmap cluster figures are often used to represent data sets in the?omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data mainly to their numerical value and not to its relative behaviour. The disadvantage of using the default clustering?dendrograms of R is demonstrated. Instead, a script is provided that uses correlation as distance function, which better reveals biologically meaningful information. This optimized script was used to detect heterotic groups in Vitamaize hybrids (purple maize with high nutraceutical value). A field trial with different genetic combinations was performed through an agricultural phenomics approach (holistic evaluation of the phenotype). The grain yield data and other phenotypic variables were represented through heatmap figures. In the data set of Mexican tropical maize germplasm, at least three heterotic groups were detected, in contrast to only two heterotic groups reported earlier in temperate yellow maize from USA and Europe. This optimized script for heatmap correlation bicluster can also be used to better represent metabolomic fingerprints and transcriptomic data sets.展开更多
AMMI analysis was performed to assess yield stability of twenty-five bread wheat （Triticum aestivum L.） genotypes grown in a 5 ×5 Lattice Square Design at seven sites under irrigation during 2009 season. AMMI A...AMMI analysis was performed to assess yield stability of twenty-five bread wheat （Triticum aestivum L.） genotypes grown in a 5 ×5 Lattice Square Design at seven sites under irrigation during 2009 season. AMMI ANOVA for grain yield indicated that genotypes, environments and G×E interaction were significantly different （P 〈 0.001）. Environments, genotypes and GxE accounted for 78.9, 4.6 and 16.4% of the treatment sum of squares, respectively. The proportion of environmental and G×E interaction variation for grain yield was larger than genotypes main effects. Genotype dendogram showed nine clusters with a yield range of 6,373.546 kg·ha-1 to 7,687.243 kg.hal. W1494/6/1, SC Sky and W2045/6/13 had high yields and exhibited negligible interactions with the environment. These were widely adapted and stable across high yielding sites. RARS （Normal）, ART （Normal） and ART （Deficit） were best yielding sites among eight environmental groups. ART （Deficit） had the best mean （9,764.479 kg·ha-1） followed by RARS （Normal） at 9,522.119 kg·ha-1 Chiredzi （Group 6） had the lowest mean yield （4,393.400 kg.hal）. Results show that high yields （〉 9,000 kg·ha-1） are achievable in high altitude areas of Zimbabwe （≥1,200 masl）. Dendograms were used to characterize both genotypes and environments and the AMMI model was used to select genotypes with specific or broad adaptation.展开更多
文摘Heatmap cluster figures are often used to represent data sets in the?omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data mainly to their numerical value and not to its relative behaviour. The disadvantage of using the default clustering?dendrograms of R is demonstrated. Instead, a script is provided that uses correlation as distance function, which better reveals biologically meaningful information. This optimized script was used to detect heterotic groups in Vitamaize hybrids (purple maize with high nutraceutical value). A field trial with different genetic combinations was performed through an agricultural phenomics approach (holistic evaluation of the phenotype). The grain yield data and other phenotypic variables were represented through heatmap figures. In the data set of Mexican tropical maize germplasm, at least three heterotic groups were detected, in contrast to only two heterotic groups reported earlier in temperate yellow maize from USA and Europe. This optimized script for heatmap correlation bicluster can also be used to better represent metabolomic fingerprints and transcriptomic data sets.
文摘AMMI analysis was performed to assess yield stability of twenty-five bread wheat （Triticum aestivum L.） genotypes grown in a 5 ×5 Lattice Square Design at seven sites under irrigation during 2009 season. AMMI ANOVA for grain yield indicated that genotypes, environments and G×E interaction were significantly different （P 〈 0.001）. Environments, genotypes and GxE accounted for 78.9, 4.6 and 16.4% of the treatment sum of squares, respectively. The proportion of environmental and G×E interaction variation for grain yield was larger than genotypes main effects. Genotype dendogram showed nine clusters with a yield range of 6,373.546 kg·ha-1 to 7,687.243 kg.hal. W1494/6/1, SC Sky and W2045/6/13 had high yields and exhibited negligible interactions with the environment. These were widely adapted and stable across high yielding sites. RARS （Normal）, ART （Normal） and ART （Deficit） were best yielding sites among eight environmental groups. ART （Deficit） had the best mean （9,764.479 kg·ha-1） followed by RARS （Normal） at 9,522.119 kg·ha-1 Chiredzi （Group 6） had the lowest mean yield （4,393.400 kg.hal）. Results show that high yields （〉 9,000 kg·ha-1） are achievable in high altitude areas of Zimbabwe （≥1,200 masl）. Dendograms were used to characterize both genotypes and environments and the AMMI model was used to select genotypes with specific or broad adaptation.
