Q11. How does GeneSpring handle dye swap arrays for two color data?

While creating an experiment for two color data, GeneSpring allows you to load the data files and select the dye swapped arrays.
Ratio computation for two color data is done as follows:

Samples without dye swap:

Cy5(test) / Cy3(control)

Samples with dye swap:

Cy3(test) / Cy5(control)

Q12 When I have multiple controls in my RTPCR, how does GeneSpring calculate the control signal value for Normalization?

If you have multiple endogenous controls, their 'Ct values' are averaged (arithmetic). That value is then subtracted from target Ct values for normalization.

Q13. What does raw and normalized data in GeneSpring mean for Agilent two color technology?

The term raw signal values used in the context of Agilent Two Color data refers to the linear data after thresholding and summarization for the individual channels (cy3 and cy5).
Summarization is performed by computing the geometric mean.
The term Normalized signal value refers to the data after ratio computation, log transformation and Baseline Transformation.

Q14. Does GeneSpring outputs GC-RMA expression data in log2?

In GeneSpring, the intensity values post the pre-processing steps are displayed in the log base 2 values. These values are further used for the analysis.

Q15. Why does GeneSpring add the variance stabilization value 16 to the expression values for exon arrays, why not for expression arrays?

For Exon arrays, the background subtraction is done on a pool of probes having similar GC content (which is not the case with expression arrays). This typically results in probe sets having small expression values leading to an unreliable estimate of the variance. To offset this, an adhoc value of 16 is added to the expression values of all the probe sets.

Q16. Why only variance stabilization value 16 is added to the expression values for exon arrays, why not some other value?

The reason for adding 16 is that it is generally considered a low enough number that it will provide the required stabilization effect without changing suppressing true signal values. 8 and 32 are other options that are commonly used. usually values smaller than 16 are due to noise and you could have values 8 and 16 causing a fold change of 2, purely due to noise.

Q17. I found that even after using baseline to median of all samples, the result of statistical analysis is same, although the profile plot is different with different baseline transformation. I want to know the reason for this situation?

Statistical analysis results would not change based on the Baseline transformation to median of all samples as the actual deviation between the conditions for the particular entity would not change and therefore there would not be any change in the P-values across all the experiments based on the baseline transformation. Baseline transformation provides the user better visualization when comparing the relation between two groups without affecting the downstream analysis.

Q18. We would like to skip 'Quantile Normalization' for Affymetrix Exon Expression data and normalize the data by using some other normalization method. Is it possible to do in current GeneSpring version?

Please follow the steps to disable and to select the other normalization methods.
1. Disable the "Perform Quantile Normalization" option under ToolsOptionsAffymetrix Exon Summarization AlgorithmsExon PLIER/Iter PLIERUn-Check 'Perform Quantile Normalization'.
2. Create the Exon Expression experiment in GeneSpring.
3. After getting the data in, export 'All Entities' from the right clickExport entity list option.
4. Import it back in as a Generic Experiment. (i.e. create a custom technology using the exported data) Please

Q19. When I threshold my raw signals to 0 in the import data I get missing values, whereas when I threshold them to 1 I do not. Why is this and what is the difference between thresholding the data to 1 or 0? How do I decide what to threshold the raw signals to?

Thresholding the data to 1 is convert the values which are less than 1 to 1. This is done because a values less than 1 would give large negative value after log transformation.
 Now, any entity with the value 1 after log transformation would give a value of 0 in GeneSpring. However, when we threshold the value to 0, it would not give any value after log tranformation and hence empty boxes would be observed.
So, if would like to filter entities with missing value , you could threshold to 0 and then, go to Utilities and select “Remove entities with missing value”.

Q20. I would like to import pre-normalized data into GeneSpring. How should I do this?

Please follow the steps below to import the pre-normalized data:
Create a custom technology with the data file from:
GeneSpring Menu Bar → Annotations → Create Technology → Custom From File
Once the technology is created, please create a new experiment with those files to import into GeneSPring.
While creating an experiment, please choose the Experiment type as 'Generic Single color'.

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