multidimensional scaling analysis (mds Search Results


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Federation of European Neuroscience Societies clustering and multidimensional scaling (mds) analysis
Clustering And Multidimensional Scaling (Mds) Analysis, supplied by Federation of European Neuroscience Societies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Qlucore Inc dimensional scaling using principal components analysis (pca) and iso-map multidimensional scaling (mds)
Differential age-associated gene expression. A . Heat map of age-associated changes in gene expression in 87 individuals (0.2 to 29.3 years of age - average 7.7+/−7.0 yrs). 927 gene expression probes were significantly associated with age (ANOVA, false discovery rate (FDR) corrected p-value, q < 0.1, with both gender and study used as co-variates. Supervised hierarchical clustering using Kendell’s dissimilarity and Ward’s method identified three main clusters of gene expression probes: ≤6 years of age [infancy, early childhood group (408 probes)]; >6 to ≤17 years of age [late childhood, puberty group (252 probes)]; and >17 to <30 years of age [adulthood (267 probes)]. Gene expression is shown between +2.5 fold and −2.5 fold, red = increase in gene expression, green = decrease in gene expression. Human growth curve data from normal controls [ , ] shown in relation to the heat map, age groups coloured by upper limit of group bins (years). B. Multi-dimensional scaling <t>(MDS)</t> of the 927 development-related gene expression probes shows distinct development-related clustering; ellipsoids represent 2 standard deviations of normalised gene expression, colour coded by age. Axes represent proportion of variation as defined by MDS (%).
Dimensional Scaling Using Principal Components Analysis (Pca) And Iso Map Multidimensional Scaling (Mds), supplied by Qlucore Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Concept Systems Inc multidimensional scaling analysis (mds) cs global max
Differential age-associated gene expression. A . Heat map of age-associated changes in gene expression in 87 individuals (0.2 to 29.3 years of age - average 7.7+/−7.0 yrs). 927 gene expression probes were significantly associated with age (ANOVA, false discovery rate (FDR) corrected p-value, q < 0.1, with both gender and study used as co-variates. Supervised hierarchical clustering using Kendell’s dissimilarity and Ward’s method identified three main clusters of gene expression probes: ≤6 years of age [infancy, early childhood group (408 probes)]; >6 to ≤17 years of age [late childhood, puberty group (252 probes)]; and >17 to <30 years of age [adulthood (267 probes)]. Gene expression is shown between +2.5 fold and −2.5 fold, red = increase in gene expression, green = decrease in gene expression. Human growth curve data from normal controls [ , ] shown in relation to the heat map, age groups coloured by upper limit of group bins (years). B. Multi-dimensional scaling <t>(MDS)</t> of the 927 development-related gene expression probes shows distinct development-related clustering; ellipsoids represent 2 standard deviations of normalised gene expression, colour coded by age. Axes represent proportion of variation as defined by MDS (%).
Multidimensional Scaling Analysis (Mds) Cs Global Max, supplied by Concept Systems Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Great Basin Corp anosim results from kruskal’s non-metric multidimensional scaling (mds) analysis
Differential age-associated gene expression. A . Heat map of age-associated changes in gene expression in 87 individuals (0.2 to 29.3 years of age - average 7.7+/−7.0 yrs). 927 gene expression probes were significantly associated with age (ANOVA, false discovery rate (FDR) corrected p-value, q < 0.1, with both gender and study used as co-variates. Supervised hierarchical clustering using Kendell’s dissimilarity and Ward’s method identified three main clusters of gene expression probes: ≤6 years of age [infancy, early childhood group (408 probes)]; >6 to ≤17 years of age [late childhood, puberty group (252 probes)]; and >17 to <30 years of age [adulthood (267 probes)]. Gene expression is shown between +2.5 fold and −2.5 fold, red = increase in gene expression, green = decrease in gene expression. Human growth curve data from normal controls [ , ] shown in relation to the heat map, age groups coloured by upper limit of group bins (years). B. Multi-dimensional scaling <t>(MDS)</t> of the 927 development-related gene expression probes shows distinct development-related clustering; ellipsoids represent 2 standard deviations of normalised gene expression, colour coded by age. Axes represent proportion of variation as defined by MDS (%).
Anosim Results From Kruskal’s Non Metric Multidimensional Scaling (Mds) Analysis, supplied by Great Basin Corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Differential age-associated gene expression. A . Heat map of age-associated changes in gene expression in 87 individuals (0.2 to 29.3 years of age - average 7.7+/−7.0 yrs). 927 gene expression probes were significantly associated with age (ANOVA, false discovery rate (FDR) corrected p-value, q < 0.1, with both gender and study used as co-variates. Supervised hierarchical clustering using Kendell’s dissimilarity and Ward’s method identified three main clusters of gene expression probes: ≤6 years of age [infancy, early childhood group (408 probes)]; >6 to ≤17 years of age [late childhood, puberty group (252 probes)]; and >17 to <30 years of age [adulthood (267 probes)]. Gene expression is shown between +2.5 fold and −2.5 fold, red = increase in gene expression, green = decrease in gene expression. Human growth curve data from normal controls [ , ] shown in relation to the heat map, age groups coloured by upper limit of group bins (years). B. Multi-dimensional scaling (MDS) of the 927 development-related gene expression probes shows distinct development-related clustering; ellipsoids represent 2 standard deviations of normalised gene expression, colour coded by age. Axes represent proportion of variation as defined by MDS (%).

Journal: BMC Genomics

Article Title: Human growth is associated with distinct patterns of gene expression in evolutionarily conserved networks

doi: 10.1186/1471-2164-14-547

Figure Lengend Snippet: Differential age-associated gene expression. A . Heat map of age-associated changes in gene expression in 87 individuals (0.2 to 29.3 years of age - average 7.7+/−7.0 yrs). 927 gene expression probes were significantly associated with age (ANOVA, false discovery rate (FDR) corrected p-value, q < 0.1, with both gender and study used as co-variates. Supervised hierarchical clustering using Kendell’s dissimilarity and Ward’s method identified three main clusters of gene expression probes: ≤6 years of age [infancy, early childhood group (408 probes)]; >6 to ≤17 years of age [late childhood, puberty group (252 probes)]; and >17 to <30 years of age [adulthood (267 probes)]. Gene expression is shown between +2.5 fold and −2.5 fold, red = increase in gene expression, green = decrease in gene expression. Human growth curve data from normal controls [ , ] shown in relation to the heat map, age groups coloured by upper limit of group bins (years). B. Multi-dimensional scaling (MDS) of the 927 development-related gene expression probes shows distinct development-related clustering; ellipsoids represent 2 standard deviations of normalised gene expression, colour coded by age. Axes represent proportion of variation as defined by MDS (%).

Article Snippet: Model formulation consisted of three stages; first, dimensional scaling using Principal Components Analysis (PCA) and Iso-map multidimensional scaling (MDS) [ , ] was used to demonstrate data homogeneity (Qlucore Omics Explorer 2.2) and identify outliers using cross-validation (Additional file : Figure S1 A&B); secondly, to assess the effects of different distributions of age and gender in each separate data set a sliding window multi-dimensional scaling approach was used.

Techniques: Gene Expression