Quantitative values obtained by RT-qPCR can be used to examine intergroup differences and expression patterns when combined with statistical evaluation and visualization methods．This article presents an example of a basic workflow for analyzing post-RT-qPCR quantitative data．
1．Acquisition and normalization of quantitative values
First，RT-qPCR is performed using primers designed for the target genes，and Ct values are obtained for each sample．When necessary，normalization is carried out using an internal control，and relative expression levels are calculated based on ΔCt or ΔΔCt．The resulting quantitative values are then organized into a tabular format，with genes as rows and samples as columns．
2．Statistical evaluation of intergroup differences
Statistical testing may be performed to evaluate differences between groups．For example，comparisons between control and treatment groups，or among multiple conditions，can be assessed using a t-test or MB test to examine the significance of expression changes for each gene．
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3．Visualization of expression patterns by heatmap
A heatmap can be generated to represent expression levels of each gene as color intensities，allowing visual inspection of condition-dependent differences and sample-level trends．In addition，hierarchical clustering analysis（HCA）can be used to classify genes showing similar expression behavior and samples sharing related profiles．
4．Summary
By combining statistical testing，heatmap visualization，and HCA for RT-qPCR data，it is possible to evaluate intergroup differences together with expression patterns．This workflow can be positioned as one example of an analytical procedure that supports interpretation of RT-qPCR results．BCC also provides registration-free and freely available biochemical analysis tools that can be used for the workflow described here．