## Short answer: we use the same normalization methods as nSolver.

**miRNA RCC Normalization**

ROSALIND® follows the NanoString nSolver® protocol for data normalization of miRNA nCounter RCC Analysis.

Normalization is a two-step data transformation that balances counts between lanes, allowing you to make meaningful biological comparisons. A positive control normalization factor is calculated using the positive controls that are spiked into every sample. This normalization adjusts for variations that exist across samples, lanes, cartridges, days and includes differences in technique. A codeset normalization factor is calculated using reference probes to adjust for differences in analyte abundance and/or quality across samples. Reference probes are determined by sorting probes by average counts and selecting the top hundred.

We use the same procedure to generate normalization factors:

1. Calculate the geometric mean of the selected probes.

2. Calculate the arithmetic mean of these geometric means for all sample lanes.

3. Divide the arithmetic mean by the geometric mean of each lane to generate a lane-specific normalization factor.

4. Multiply the counts for every probe by its lane-specific normalization factor.

### How does ROSALIND calculate differential expression for my data?

For miRNA nCounter data, ROSALIND calculates fold changes and p-values for comparisons defined during experiment setup using the t-test method. P-value adjustment is performed using the Benjamini-Hochberg method of estimating false discovery rates (FDR). More information on calculating fold change ratios for miRNA can be found on page 47 of the nSolver 4.0 Analysis Software User Manual.