NCode™ Profiler

Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract NCode Profiler Data Analysis Software for microRNA Profiling is an advanced experimental design and analysis solution designed for two-dye expression profiling microarray experiments.

The software eliminates inherent challenges with miRNA array design and data analysis.

Experimental Design --

NCode Profiler software enables simple design of array templates using Loop Design and Dye Swap normalization methods (see below) applied to your experimental criteria for tissue samples, species, and controls.

The array templates support sample-to-sample comparison within the experiment.

They are designed with statistical considerations to reduce or eliminate any general miRNA effect, array effect, dye effect, overall tissue effect, array-miRNA interaction and dye-miRNA interaction from the data.

Data Analysis --

Once an experiment has been conducted, the raw data can be imported and analyzed using the statistical methods design into the array.

For each tissue on the array, pair wise differential expression is determined and the test statistics, fold change and p-value are generated.

Additionally, the ranking of each miRNA marker, with respect to the other markers, within the tissue are given.

Using NCode Profiler software, you can expect --

1) Simplified experimental design steps.

2) Confidence when interpreting results using proven statistical analysis using dye swap or loop design normalization models.

3) Enhanced rankings of miRNA markers for selected tissues.

4) Export of normalized data to visualization software for clustering (tree) and heat map analysis.

Note: Cluster and TreeView for analyzing and visualizing the results of complex microarray experiments is available for download. Please note the licensing agreement particularly the differences between academic/non-profit users and commercial users.

Data Analysis Methods --

Three (3) methods are typically used for normalization of 2-dye expression profiling microarray experiments:

1) Latin Squares/Loop Design/Dye Swap (used in Invitrogen’s NCode profiling services) - This model is a global linear model that is fit to the data.

This model attempts to minimize the number of arrays used in the experiment, while still controlling for the typical sources of variation. This method was initially described by Kerr, et al.

2) M vs. A, Lowess Normalization - This method is typically used for single dye array systems, but can be adapted for 2 dye systems. It seeks to normalize the data by assuming that typically there should be No differential expression on the chip.

It is also worth noting that this is a within the chip normalization. Specifically this method calculates the log ratio of the signals for each miRNA as well as the log product of each signal for each miRNA.

Then a plot is made of the log product (x-axis) versus log ratio (y-axis), this is typically called an M versus A plot. This method was initially described by Dudoit,S, et al.

3) Quantile Normalization - This is a global normalization method where the main goal is to force the histogram of any particular chip and channel to look the same, but the actual value for any particular miRNA maybe different, depending on the order of the signal within the chip/dye combination.

This method was initially described by Irizarry RA et al. as part of a larger analysis method.

System Requirements


Manufacturer Web Site NCode Profiler

Price Contact manufacturer.

G6G Abstract Number 20289

G6G Manufacturer Number 101570