<p>A/upon SCF treatment or B/with imatinib treatment. PCA of mRNA selected genes in NIH3T3-infected cell lines as a tool to determine their capabilities to distinguish the different cell lines (WT, wild-type <i>KIT</i>; D6, D6 <i>KIT</i>; D54, D54 <i>KIT</i>; WT/D6, WT <i>KIT</i> and D6 <i>KIT</i>; WT/D54, WT <i>KIT</i> and D54 <i>KIT</i>).</p
<p>A) The array data were normalized and a hierarchical clustering was run. On top of the heatmap, t...
The parallel monitoring of the expression profiles of thousands of genes seems particularly promisin...
<p>Gene selection based on principal component analysis. A) variance explained by components 1–6 fro...
<p>(A) Two-dimensional PCA map of cell types, comparing PC1 and PC3. (B) Three-dimensional map compa...
<p>The 89 up and 62 down regulated genes were used for PCA analysis. PCA was computed for the 64 gen...
<p>Visualization of gene expression similarities of samples clearly distinguishes donor cell-specifi...
AbstractBy using principal components analysis (PCA) we demonstrate here that the information releva...
<p>A. PCA results for gene expression in the p53 signaling pathway gene set at 24 h [red, 5 GTX; blu...
<p>, a) Principal-component analysis (PCA) of the transcriptome of three different cells. The differ...
<p>Principal Component Analysis (PCA) scatter plot using Partek analysis is shown in the upper left ...
<p>Principal component analyses (PCA) of mRNA expression profiling data after (A) 4 h, (D) 12 h, or ...
<p>The expression of genes by mammary cell lines (MCF7, MDA-MB-231) cultured in control conditions (...
<p>PCA analysis on control and stress cells for various cell lines; Mel 42a, Mel 59c, Mel 103b and 2...
PCA on all differentially expressed genes with samples plotted in the first two components' space. C...
<p>Principal Component Analysis (PCA) of microarray data. PCA two-dimensional scatter plot represent...
<p>A) The array data were normalized and a hierarchical clustering was run. On top of the heatmap, t...
The parallel monitoring of the expression profiles of thousands of genes seems particularly promisin...
<p>Gene selection based on principal component analysis. A) variance explained by components 1–6 fro...
<p>(A) Two-dimensional PCA map of cell types, comparing PC1 and PC3. (B) Three-dimensional map compa...
<p>The 89 up and 62 down regulated genes were used for PCA analysis. PCA was computed for the 64 gen...
<p>Visualization of gene expression similarities of samples clearly distinguishes donor cell-specifi...
AbstractBy using principal components analysis (PCA) we demonstrate here that the information releva...
<p>A. PCA results for gene expression in the p53 signaling pathway gene set at 24 h [red, 5 GTX; blu...
<p>, a) Principal-component analysis (PCA) of the transcriptome of three different cells. The differ...
<p>Principal Component Analysis (PCA) scatter plot using Partek analysis is shown in the upper left ...
<p>Principal component analyses (PCA) of mRNA expression profiling data after (A) 4 h, (D) 12 h, or ...
<p>The expression of genes by mammary cell lines (MCF7, MDA-MB-231) cultured in control conditions (...
<p>PCA analysis on control and stress cells for various cell lines; Mel 42a, Mel 59c, Mel 103b and 2...
PCA on all differentially expressed genes with samples plotted in the first two components' space. C...
<p>Principal Component Analysis (PCA) of microarray data. PCA two-dimensional scatter plot represent...
<p>A) The array data were normalized and a hierarchical clustering was run. On top of the heatmap, t...
The parallel monitoring of the expression profiles of thousands of genes seems particularly promisin...
<p>Gene selection based on principal component analysis. A) variance explained by components 1–6 fro...