How to Analyze NanoString nCounter Data with ROSALIND

FIVE STEPS TO SUCCESS WITH NANOSTRING

ROSALIND simplifies data analysis and works like a data hub interconnecting every stage of data interpretation. The ROSALIND Gene Expression discovery experience enables visual exploration and self-investigation of experiment results to give researchers the freedom to adjust cut-offs, add comparisons, apply covariate corrections, and even find patterns across multiple datasets, without the need for bioinformatic expertise. There are five easy steps to performing nCounter data analysis on ROSALIND.

1. EXPERIMENT DESIGN

Starting a NanoString data analysis begins with creating a new experiment and capturing the experiment design. ROSALIND walks through the key aspects of an experiment in a guided experience to record biological objectives, sample attributes and analysis parameters. These details become the basis of the experiment discovery dashboard. Researchers who publish papers and work with NCBI public data know the importance of natively supporting NCBI data models. ROSALIND fully supports the NCBI BioProject and BioSample models for metadata assignment and sample attribute descriptions. ROSALIND also enables scientists to create custom attributes to describe biological behaviors in terms relevant to the experiment. Setup of comparisons is simplified by describing and annotating samples using these familiar terms. This methodology minimizes the risk of differential expression errors when selecting samples for comparison.

For the NanoString nCounter Analysis System, ROSALIND provides scientists with a choice: a) Begin with raw RCC files produced on the nCounter instrument, or b) Use normalized data that has been processed and exported by nSolver. This provides flexibility for scientists to utilize the ROSALIND discovery experience to visualize and interpret data regardless of the data source. When analyzing raw RCC files, ROSALIND streamlines data analysis using a specialized pipeline that follows the NanoString guidelines for Advanced Analysis, includes intelligent quality control with automatic anomaly detection, Cell Type Profiler, identification of differentially expressed genes and deep pathway interpretation. Visit the technical specifications section to learn more about the ROSALIND NanoString data analysis pipeline and available reference materials.

For optimal NanoString results, an analysis pipeline must consider the panels used, genes detected and control levels before performing normalization or calculating fold changes. ROSALIND integrates and supports all gene expression and miRNA panels, including those with custom content, such as the COVID-19 Panel Plus spike-in kit. Each panel is automatically detected with the target species. In the case of custom panels, ROSALIND provides a broad list of available species and attempts to match those with the highest levels of corresponding genes.

2. EVALUATING NANOSTRING QUALITY CONTROL

Researchers must be confident in the quality control phase before gathering insights from an experiment, otherwise the results of the analysis should not be trusted. Biology’s mysteries are elusive and complex. Time should not be lost chasing corrective measures for outliers, swapped samples and the many other errors that can occur in the course of a well-designed experiment.

Some of the most important Quality Control metrics to verify are Imaging Quality, Binding Density, Limit of Detection, Control Linearity, Selection of Housekeeping Genes, Sample Correlation and multidimensional scaling (MDS) or principal component analysis (PCA) for all samples. When ROSALIND detects low imaging quality, binding density or limit of detection, an alert is provided and the values are highlighted in the QC. Researchers can eliminate offending samples and the deleterious effects on results by identifying the sample as an outlier and move confidently into the discovery and exploration phase of results interpretation.

ROSALIND Quality Control Intelligence identifies potential data quality issues and triages the data before presenting the results. This eliminates the needs for researchers to be experts in Sequencing quality control issues. Learn how researchers gain confidence in their results through Quality Control Intelligence.

3. UNLOCKING RESULTS

After a researcher has reviewed the quality control phase the interactive presentation of results is ready to begin. The next step is to unlock the experiment. ROSALIND calculates the quantity of Analysis Units (“AU”) required to unlock the results. This is generally 1 AU per single-sample RCC file for NanoString experiments, however this may differ based on counts files or other experiment parameters. Account balances and quick links for acquiring more AU are directly accessible from the unlock screen. To learn more about Analysis Units, check out the Q&A in the section below, or visit the ROSALIND Store.

4. ANALYSIS & DISCOVERY

To date, NanoString nCounter data has been analyzed using nSolver’s powerful desktop solution to import, normalize and calculate differentially expressed genes. ROSALIND enables a new, unique approach that is cloud-based and empowers scientists to collaborate and visually explore their NanoString nCounter data while providing the peace of mind that the settings and methods are approved by NanoString.

ROSALIND extends nSolver by providing an online, simplified experience for differential expression analysis and interpretation of NanoString nCounter data. This experience begins with a discovery dashboard that shows the list of significant differentially expressed genes determined by a calculated cut-off filter. Default settings for the filter begin with a fold change of 1.5 upregulated and 1.5 downregulated with a p-Adjust of 0.05. Further adjustments to achieve a significant set of genes are performed by ROSALIND, if desired by the scientist. Researchers may also create an unlimited set of their own customized filters using fold changes and p-Value parameters. Convenient on-screen controls are easily accessible for modifying these filters, adding covariant corrections, applying gene lists and signatures, and adjusting plot color palettes. In the ROSALIND gene expression experience, scientists discover insights from deep interpretation of pathways, gene ontology, diseases and drug interactions that are presented through rich interactive plots that fill the screen and respond to interactions from the scientist, showing customizable heatmaps, volcano and MA plots as well as box and bar plots.

New comparisons and meta-analysis may be added at any time. Comparisons are created using BioProject attributes. Meta-analyses created can be cross experiments and multi-omic. Each of these perspectives are available within minutes of setup, reducing internal bioinformatic workload and enabling scientists to react fluidly by focusing directly on the science of the experiment.

5. COLLABORATING & SHARING RESULTS

The discovery process rarely ends with a single point of view from a single researcher opinion. ROSALIND Spaces enables true scientist-to-scientist collaboration through virtual data rooms where scientists and collaborators can come together on related datasets anywhere in the world to interactively explore shared experiments much like working with Google Docs. Researchers access a consistent version of the data, without the need to transfer unwieldy files or reinterpret origin files. All changes are interactive, instantly available, and viewable everywhere in the world (as authorized by the organization) with real-time activity feeds and historical reports. Spaces participants can add experiments, explore pathways, change cut-offs, add meta-analyses and add new comparisons all within the shared collaborative environment.

Spaces are virtual meeting rooms where scientists meet with niche experts, clients and supporting teams to maximize the discovery value of every experiment and prepare for the next one.