Quantitative real time polymerase chain reaction (qPCR) is one of the oldest techniques used for DNA quantification. Although there are many other techniques developed in recent years for DNA quantification, qPCR is still widely used in biological research. The cycle threshold (CT) value in a qPCR is a relative quantification of the actual amount of DNA and hence can be analysed to identify gene expression changes between conditions of interest. Compared to availability of analysis methods and tools for other DNA quantification methods, only few methods and software tools available to analyse CT values. Among those few methods, Delta-delta CT method, also known as Livak method, is the most commonly used method. Here, we propose a new method to analyse CT values relative to differential expression type analyses, using limma with weighted cyclic loess normalization. Limma and cyclic loess normalization are long-established statistical tools in R statistical computing environment that are developed and finetuned for microarray data analyses. We show how these classical microarray data analysis tools can be used to analyse CT values in order to yield more statistically sound results than the conventional Delta-delta CT method whenever four or more target genes are analysed at one time. The proposed method easily accommodates multiple reference genes for normalization if they are available.