RealTime StatMiner qPCR data analysis software

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March 11, 2014

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  • RealTime StatMiner 5.0 enables a fast, easy and reliable analysis of your qPCR data. RealTime StatMiner® enables a fast, easy and reliable analysis of RT-qPCR data. It is widely used by pharmaceutical corporations, biotechnology companies, research centers and core facilities around the world for high throughput analysis of gene expression data. It combines commonly used interactive visualizations with advanced statistics to offer rapid, reliable analysis, allowing researchers to organize their samples based on technical and biological replicates, as well as rapidly create publication ready reports. RealTime StatMiner is able to automatically detect outliers and filter low-expressed or undetermined detectors, alerting the user to problems within their data and logging all activities during analysis. It also offers the unique ability to compute DCt based multiple endogenous controls, automatically determining the most stable controls using algorithms such as GeNorm, NormFinder and Minimum Variance Median. Save time in the qPCR data analysis while controlling all the steps - Easy loading, easy reporting *No limits in size and complexity of qPCR datasets from any instrument *Tracking of all the steps and decisions made in the analysis *Intuitive and Interactive visualizations *Automatic generation of gene lists and reporting in various formats *Two modes of analysis: Click and Go® or step by step guided analysis Reliable analysis of qPCR dataNo matter the size and complexity of your dataset or the qPCR application, trust always your analysis results - Reliable analysis *Exhaustive QC and best endogenous control selection *Wide variety of peer-reviewed analysis tools *Interplate calibration for a reliable analysis of large datasets *Workflows compatible with all qPCR applications, including single-cell analysis Biological conclusions from your qPCR data analysisGo beyond the statistical analysis of your qPCR data and get meaningful biological insights - Biological conclusions *Interactive hierarchical clustering and PCA analysis *Identification of genes with significant differential expression *Classification of samples or genes by their expression patterns *Integration with biological pathway analysis

    DiagnosticsDrug DiscoveryMass SpectrometryPersonalized Medicine

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