GenEx - The Complete Solution for qPCR Data Analysis
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GenEx is the leading software for processing and analysis of qPCR data. The powerful functionalities of GenEx coupled with its user friendly interface and near universal qPCR instrument compatibility makes it the preferred choice for both novices and professionals to cover their data analysis needs.
Features
Data Pre-Processing - Merging, normalization, inter-plate calibration
GenEx contains excellent and comprehensive tools for data pre-processing that helps you performing the important steps, as reference gene selection and normalization, inter-plate calibration and handling of missing data, consitently and in the correct order.
Absolute Quantification - Standard curves, reverse regression, limit of detection
When the exact number of target molecules is needed for quantitative analysis of pathogens or for GMO testing GenEx can be used to construct a standard curve that relates Cq values to concentrations. With reverse regression absolute quantities including confidence intervals can be estimated for unknown samples from their Cq-values. As complement the sensitivity of a qPCR based analytical procedure can be determined with Limit of Detection (LOD) analysis.
Relative Quantification - Reference gene identification, 1-2 way ANOVA, students t-test
GenEx contains powerful tools including geNorm and Normfinder to identify the best reference genes and the optimum number of reference genes for normalization and relative quantification. Once normalized the expression of genes of interest in two groups can be compared with parametric or nonparametric tests for unpaired as well as paired experimental design. When several genes are compared GenEx corrects for multiple testing ambiguity. Several groups can be compared with ANOVA and multifactorial studies are approached with 2-way ANOVA.
Expression Profiling - PCA, Hierarchial Clustering, Kohonen Self Organizing Map
When many genes are analyzed GenEx offers a complete selection of profiling capabilities exemplified by Principal Component Analysis (PCA), Hierarchical Clustering and Kohonen Self Organizing Map (SOM) that exploit the correlation between genes’ expressions to classify either samples or genes based on common expression behavior.
Multimarker Diagnostics - Artificial Neural Networks, Support Vector Machines
For molecular diagnostic application state of the art multimarker classification methods that exploit information in reference data are available including powerful Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Potential Curves.
Experimental Design - Study design based on variance and budget constrictions
The key to successful qPCR analysis is arguably quality experimental design that balances statistical significance and experimental cost. Performing a fully nested pilot study GenEx estimates the variance contributions from the different experimental steps, advising you which steps may be improved to enhance data quality and were technical replicates shall be performed. It also indicates the number of subjects needed to achieve a desired resolution. Too few subjects and you may not be able to prove or disprove your hypothesis, while too many subjects may improve resolution beyond what is practically relevant and money is wasted. The GenEx experimental design tools predict optimized designs taking into account budgetary restrictions based on pilot studies or estimated variance contributions.
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