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1 at can be calculated for any two-dimensional scatter plot.
2 and ordinary least-squares regression (OLS) scatter plot.
3 tifying the lateral edges of the convex data scatter plot.
4 fitted line beyond the observed data in any scatter plot.
5 ut groups of genes can be highlighted on the scatter plots.
7 relevant regions of density in the eta-theta scatter plot and then nucleotides in colocalized cluster
8 genes, (iii) correlation coefficients in the scatter plots and (iv) distributions of gene function.
10 tware also offers graphical features such as scatter plots and bar charts to depict signal repartitio
11 here in a more complete statistical form, as scatter plots and distributions, to reveal the full rang
13 rameters and pachymetry were evaluated using scatter plots and the Spearman correlation coefficient w
14 inement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulat
15 atasets using principal coordinate analyses, scatter plots, bar plots, hierarchal clustering and heat
16 04, the root mean square error of 0.02 and a scattered plot between experimental and predicted values
18 and in a pixel-based manner using dual-color scatter plots followed by the calculation of Pearson's c
20 subtracted, re-scaled, error-barred, and/or scatter-plotted from individual spectral segment centroi
21 and directly manipulated in images, reports, scatter plots, histograms, expression profile plots and
22 limited text), and dendrogram, heat map, and scatter plot images can be saved in several different fo
24 a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms,
26 nds in the data set could be visualized by a scatter plot of the two Gompertz parameters (rate consta
29 livan and his colleagues were digitized from scatter plots of seven different indicators versus a com
30 Data were compiled from comparable studies, scatter plots of the data were created, and third-order
31 with and without changes in expression, (ii) scatter plots of the genes, (iii) correlation coefficien
32 f proteins is displayed on a two-dimensional scatter-plot, organized according to the order of genes
33 data into two-dimensional row space-derived scatter plots (row space plots) eliminates the need to m
35 ata, the software produces three-dimensional scatter plots similar in concept to Sewall Wright and Jo
36 on data sets, we use cyclic-locally weighted scatter plot smoothing (LOWESS) and linear regression no
40 ogy in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about th
41 best-fitting line through a two-dimensional scatter plot typically requires the user to assume that
44 genes against two user-selected queries on a scatter plot which can be useful for visual identificati
45 ts individual cells in a visual similar to a scatter plot, while using all pairwise distances in high
46 es based on ontology classes, and displays a scatter plot with two proposed new bibliometric statisti
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