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    <title>BioViz Graph Gallery</title>
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    <description>Recent content on BioViz Graph Gallery</description>
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    <lastBuildDate>Wed, 12 Jul 2017 00:00:00 +0000</lastBuildDate>
    
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      <title>Lineplots</title>
      <link>/post/lineplots/</link>
      <pubDate>Wed, 12 Jul 2017 00:00:00 +0000</pubDate>
      
      <guid>/post/lineplots/</guid>
      <description>Generate Data library(BioViz) library(reshape2) library(ggplot2) n=7 df &amp;lt;- data.frame(Dose1=exp((1/2)*1:n), Dose2=exp((1/3)*1:n), Dose3=exp((1/4)*1:n), Dose4=exp((1/5)*1:n), Dose5=exp((1/6)*1:n), Dose6=exp((1/7)*1:n), Visit=1:n) df &amp;lt;- melt(df, id=c(&amp;quot;Visit&amp;quot;)) colnames(df) &amp;lt;- c(&amp;quot;Visit&amp;quot;, &amp;quot;Dose&amp;quot;, &amp;quot;Measure&amp;quot;)   Lineplot with Errorbars general.lineplot(df, upper.err=1, lower.err=1, var.type=&amp;quot;errorbar&amp;quot;) ## Warning: Ignoring unknown aesthetics: fill  Lineplot with Errorbars and log transformed y-axis general.lineplot(df, upper.err=1, lower.err=1, var.type=&amp;quot;errorbar&amp;quot;, y.log10trans = T) ## Warning: Ignoring unknown aesthetics: fill  Facetted Lineplot general.lineplot(df, by=&amp;quot;Dose&amp;quot;, facet.row=3, upper.err=1, lower.err=1, var.type=&amp;quot;errorbar&amp;quot;, y.log10trans = T) ## Warning: Ignoring unknown aesthetics: fill  Lineplot with Error Ribbons general.</description>
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    <item>
      <title>Principle Components</title>
      <link>/post/principle-components/</link>
      <pubDate>Wed, 12 Jul 2017 00:00:00 +0000</pubDate>
      
      <guid>/post/principle-components/</guid>
      <description>Component Plot The components of the PCA dimensionality reduction will be displayed in a pairs plot. Each of the components are plottet against each other.
 The lower diagonal shows the data points projected to the respective two dimensional component space. The upper diagonal shows a contour plot of the data projected the respective two dimensional component space. The diagonal shows a density plot of the projection of variables to a principal component axis.</description>
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    <item>
      <title>Scatterplots</title>
      <link>/post/scatterplot/</link>
      <pubDate>Tue, 11 Jul 2017 00:00:00 +0000</pubDate>
      
      <guid>/post/scatterplot/</guid>
      <description>Definition A scatter plot (also called a scatter graph, scatter chart, scattergram, or scatter diagram) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are color-coded, one additional variable can be displayed. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.</description>
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    <item>
      <title>About</title>
      <link>/post/about/</link>
      <pubDate>Fri, 07 Jul 2017 00:00:00 +0000</pubDate>
      
      <guid>/post/about/</guid>
      <description>Description BioViz is an R package, which includes a number of visualization functions for the day-to-day work in biomarker identification. These include general visualizations (i.e. Scatter-, Line-, Densityplots and Histograms), but also more specific ones for modeling or time series.
Be aware however that this package is still under heavy development and the code as well as the interfaces will change frequently. Neither has this package been published on CRAN and therefore has not yet undergone any quality control other than my own common sense.</description>
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    <item>
      <title>Volcano Plot</title>
      <link>/post/volcano_plot/</link>
      <pubDate>Fri, 07 Jul 2017 00:00:00 +0000</pubDate>
      
      <guid>/post/volcano_plot/</guid>
      <description>Volcano Plot A volcano plot is a type of scatter-plot that is used to quickly identify changes in large data sets composed of replicate data. It plots significance versus fold-change on the y and x axes, respectively. These plots are increasingly common in omic experiments such as genomics, proteomics, and metabolomics where one often has a list of many thousands of replicate data points between two conditions and one wishes to quickly identify the most meaningful changes.</description>
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