Experimental design

In order to make full use of the various tools for interpreting expression data, you need to know the central concepts behind the way the data is organized in the Biomedical Genomics Workbench.

The first piece of data you are faced with is the sample. In the Workbench, a sample contains the expression values from either one array or from sequencing data of one sample.

Note that the calculation of expression levels based on the raw sequence data is described in RNA-Seq analysis and Small RNA analysis.

See more below on how to get your expression data into the Workbench as samples in Microarray data formats.

In a sample, there is a number of features, usually genes, and their associated expression levels.

To analyze differential expression, you need to tell the workbench how the samples are related. This is done by setting up an experiment. An experiment is essentially a set of samples which are grouped. By creating an experiment defining the relationship between the samples, it becomes possible to do statistical analysis to investigate differential expression between the groups. The Experiment is also used to accumulate calculations like t-tests and clustering because this information is closely related to the grouping of the samples.