Sample correlation plot is a data visualization used to compare a number of variables across two samples. A hypothesis underlying many gene expression experiments (next generation sequencing or microarray) is that most genes/transcripts are not differentially regulated between the conditions, causing most of the data points to fall on the diagonal (i.e. regression line with slope of 1). If that is not the case, a normalization method should be applied before the statistical analysis. Therefore, you may want to run sample correlation plots and your data set before and after the normalization.

Sample correlation in Partek^® Flow^® can be performed after quantification by selecting a Gene counts or Transcript counts data node, or on a Normalized counts node in case that you want to assess its effect on the data. The Sample correlation option is visible in the Visualizations section of the tool box (Figure 1). The task has no particular setup dialog (and creates no task node), but launches immediately.Similarity matrix task is only available on bulk RNA-seq count matrix data node. It is used to compute the correlation of every sample/or feature vs every other sample/or feature. The result is a matrix with the same set of samples/or features on rows and columns, the value in the matrix is correlation coefficient --r.

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