1. Select a dataset
2. Enter a gene name
3. Choose one of the available plots for the selected dataset

For each dataset it is available a list of common “pre-defined” plots and a list of dataset-specific “user-defined” box plots. An overview of all the box plots for a given dataset can be found at “Explore/Summary/Plots”.
The data used to generate the plot are available in the “Box Plots/Data” tab.

What is a box plots?
A box plot or is a convenient way of graphically depicting groups of numerical data through their quartiles. The upper and lower “hinges” correspond to the first and third quartiles (the 25th and 75th percentiles). The upper whisker extends from the hinge to the highest value that is within 1.5 * IQR of the hinge, where IQR is the inter-quartile range, or distance between the first and third quartiles. The lower whisker extends from the hinge to the lowest value within 1.5 * IQR of the hinge. Data beyond the end of the whiskers are outliers (as specified by Tukey). (References: Wikipedia, ggplot2)

Plot options:

• Remove/add a group of samples: For a specific plot the user can select which group to show. Only recomended for visualization, removing group of samples could affect the correct interpretation of the data.
  
• Scale y axis: Centering is done by subtracting the mRNA means (omitting NAs) and dividing by the standard deviations.
  
• White background: Change the plot setting to have white backround and black axis labels.
  
• Color box: Color the boxes by sample group.
  
• Change points appearance: It' is possible to change the size and the transparency of the points of the stripchart. Useful when generating small plots with a lot of points. Morst importantly, user can change the color and/or the shape of the points of the stripchart, using a different plot type category. E.g. The user can plot the Copy numbers and can color for the Subtype.
  
• Change axis labels: To set different x and y axis labels.
  
• Change font size: To change x and y axis text/labels font size.
  

Statistic:

• Summary: Table showing the sample count, mRNA median, median absolute deviation (mad), mean and standard deviation (sd).
  
• Tukey's honest significant differences (HSD): Table showing the difference between group pairs, the 95% confidence interval and the p-value of the pairwise comparisons. A graphical representation of the result of the Tukey's HSD test can also be aded to the box plot.
  
• Pairwise t-test: Pairwise comparisons between group levels. p-values are calculated with Bonferroni correction, to adjust for multiple testing.
  

How to download a plot:

• Low resolution: after changing the plot size to obtain the desired appearance, right-click on the plot to copy the image.
  
• High resolution: select the file format (.pdf, .bmp, .jpeg or .jpg), the image dimensions and press the download button.
  

Corr-Two: correlate two genes

1. Select a dataset.
   
2. Enter a gene 1.
   
3. Enter a gene 2.
   
4. Choose one of the histological group, default setting will show all the samples. For GBM datasets it's possible to specify a subtype of interest.
   

Statistic:
If method is “pearson”, the test statistic is based on Pearson's product moment correlation coefficient. If method is “kendall” or “spearman”, Kendall's tau or Spearman's rho statistic is used to estimate a rank-based measure of association. These tests may be used if the data do not necessarily come from a bivariate normal distribution.

Corr-Many: correlate a list of genes

1. Select a dataset.
   
2. Enter gene list (at least two genes have to be specified).
   
3. Choose one of the histological group, default setting will show all the samples. For GBM datasets it's possible to specify a subtype of interest.
   

Plot
Lower diagonal: scatter plot with regression line
Middle diagonal: density plot
Upper diagonal: Pearson correlation coefficient with signfiicance: *** < 0.001, ** < 0.01, * < 0.05, “.” < 0.1

Corr-All: correlate a gene with all the genes in the dataset

1. Select a dataset.
   
2. Enter a gene.
   
3. Choose one of the histological group, default setting will show all the samples. For GBM datasets it's possible to specify a subtype of interest.
   

Table
The result table shows a list of genes with a r correlation coefficient in the specified range (default is from -0.3 to +0.3).
Plot
Select a specific gene from the result table to show the correlation plot.

1. Select a dataset.
2. Enter a gene name.
3. Choose one of the histological group, default setting will show all the samples.
4. Select cutoff:
   
   • Median: split in two groups by the median
     
   • Lower quartile: split in two groups, 25% lower expressing vs the rest
     
   • Upper quartile: split in two groups, 25% higher expressing vs the rest
     
   • High vs low: split in two groups, 25% lower expressing vs 25% higher expressing
     

Differential expression analyisis is performed using the eBayes and lmFit functions of the 'limma' package.
Gene ontology and KEGG enrichment analyisis are performed with the 'clusterProfiler' package.

References:
Ritchie ME K. et al., 2015
Guangchuang Yu et al., 2012