Data analysis may be done using an Excel spreadsheet or using a variety of software programs. When setting up experiments, one should consider running replicate plates and multiple samples to obtain the most reliable data as variability can occur in the pipetting and sample preparation steps. The initial data analysis steps involve calculating the expression levels. This can be done by delta delta Ct (ΔΔCt) analysis or by the relative standard curve method. Assuming primer efficiency is consistent across the panel of genes in the PCR array, delta delta Ct analysis is the preferred method for PCR array analysis. Another consideration is deciding on the best way to normalize data. Most PCR arrays contain one or more putative housekeeping genes (ie Actb, Gapdh), so the data may be normalized to one of these genes or to the average or median value of the expression of a group of housekeeping genes. Alternatively, one can normalize to the average or median expression of all of the genes on the array.
The spreadsheets shown on the page may be downloaded and used as a template for quantitating PCR array data based on normalization to housekeeping gene expression or “all gene” expression. One can replace the specific gene lists and well locations with your own data and add/remove columns based on the specific number of arrays, replicates and samples from your own experiments. These spreadsheets compare one set of samples to another for one particular experimental variable. For comparing multiple samples, one can consider using the data analysis techniques often used for analyzing microarray data such as clustering, etc.