Mikael Kubista1,2, Amin Forootan2,3, Björn Sjögren3,4
1TATAA Biocenter, Sweden; 2MultiD Analyses AB; 3Goteborg University; 4Lawrence Livermore National Laboratory
As qPCR is becoming mature technology with widespread use requirements on validation, quality assessment and reporting increases. This is particularly important when data are submitted for approval by regulatory bodies, reported to clients, or published in quality journals. For molecular diagnostic applications this includes determining PCR efficiency with confidence intervals, establishing the linear range of the assay, its limit of quantification, limit of detection and random error. For routine applications also estimates of repeatability and reproducibility may be relevant. For the samples analyzed estimated target concentrations shall be indicated with confidence intervals. These analyses are recommended by the Clinical and Laboratory Standards Institute. In my presentation I will show how these analyses are performed on qPCR using GenEx.
Goal of expression profiling is to explain biological phenomena. Workflow starts by planning, designing and optimizing an experiment, collecting the data, analyzing the data, and extracting biological information. Typically large amounts of data are collected that are batch imported and pre-processed to remove variation between runs, reduce intersubject variation, and minimize technical noise. Missing data are also handled. The data are then analyzed using powerful multivariate statistical methods including hierarchical clustering, principal component analysis (PCA), and self-organized maps. Dynamic PCA is used for variable selection to identify the most relevant genes explaining the observations. Finally, the data are passed from GenEx for cloud based pathway analysis with the Ingenuity iReport. In will present seamless workflow from the collection of data to the extraction of biological information using GenEx.
Download a free GenEx trial version on GenEx.gene-quantification.info
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