The Nanostring nCounter Analysis System is a technology that enables the digital quantification of multiplexed target RNA molecules using color-coded molecular barcodes and single-molecule imaging. This system is an emerging medium-throughput technology for measuring mRNA and miRNA abundances and for assessing copy number variants. Software tools are used for data pre-processing, normalization, diagnostics, visualization and detection of differential expression.
Offers a comprehensive and general framework to characterize NanoString nCounter data and to detect differential expression (DE) genes for both simple and complex experimental designs. As a method specifically designed for nCounter data, NanoStringDiff utilizes a negative binomial-based model to fit the discrete nature of the data and incorporates several normalization parameters in the model to fully adjust for platform source of variation, sample content variation and background noise. Simulation and real data analyses results show that NanoStringDiff outperforms the existing methods in DE detection.
A set of tools for normalizing, diagnostics and visualization of NanoString nCounter data. Key features include an extensible environment for method comparison and new algorithm development, integrated gene and sample diagnostics, and facilitated downstream statistical analysis. By standardizing code and automating error capture, NanoStringNorm will enable more reproducible and robust analysis of NanoString datasets.
Pre-processes NanoString DNA data and quantifies copy number variation. NanoStringNormCNV can be useful for quality-control, normalization, copy number detection and contains NanoString guidelines with additional data processing techniques. It is able to produce reproducible analysis in an environment that enables downstream statistical analysis and data visualization. This tool verifies that positive control probe counts correlate with their known concentrations.
An algorithm for the pre-processing of mRNA data from the NanoString nCounter software, working from the output RCC files saved as a tab delimited text file and imported into R. NAPPA has been optimised to generate high quality robust data for subsequent analyses by optimizing steps to reduce bias and variability and reducing arbitrary decisions. The NAPPA function also provides options for how each algorithmic step is performed.