three dimensional principal component analysis (pca) Search Results


3-dimensional principal components analysis  (Qlucore Inc)
90
Qlucore Inc 3-dimensional principal components analysis
3 Dimensional Principal Components Analysis, 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
https://www.bioz.com/result/3-dimensional principal components analysis/product/Qlucore Inc
Average 90 stars, based on 1 article reviews
3-dimensional principal components analysis - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier
two-dimensional principal component analysis (pca)  (Minitab Inc)
90
Minitab Inc two-dimensional principal component analysis (pca)
Two Dimensional Principal Component Analysis (Pca), supplied by Minitab Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/two-dimensional principal component analysis (pca)/product/Minitab Inc
Average 90 stars, based on 1 article reviews
two-dimensional principal component analysis (pca) - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier
dimensional scaling using principal components analysis (pca) and iso-map multidimensional scaling (mds)  (Qlucore Inc)
90
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
https://www.bioz.com/result/dimensional scaling using principal components analysis (pca) and iso-map multidimensional scaling (mds)/product/Qlucore Inc
Average 90 stars, based on 1 article reviews
dimensional scaling using principal components analysis (pca) and iso-map multidimensional scaling (mds) - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier
three dimensional principal component analysis (pca) visualized with  (Plotly Technologies Inc)
90
Plotly Technologies Inc three dimensional principal component analysis (pca) visualized with
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 (%).
Three Dimensional Principal Component Analysis (Pca) Visualized With, supplied by Plotly Technologies Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/three dimensional principal component analysis (pca) visualized with/product/Plotly Technologies Inc
Average 90 stars, based on 1 article reviews
three dimensional principal component analysis (pca) visualized with - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

Image Search Results


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