Q1. I would like to know what type of normalization method is used when the guided workflow is used?

Agilent single Color: Percentile Shift
Agilent Two Color: No Normalization in Genespring
Affymetrix Expression: RMA (Summarization method)
Affymetrix Exon Expression: RMA-16 (summarization method)
Illumina Single Color: Percentile Shift
Agilent miRNA: Percentile Shift

Q2. Why is RMA chosen as default summarization algorithm in guided work flow?

RMA is chosen as the default summarization in the guided work flow because it is more popularly used as the default option, by the micro array community.

RMA considers only Perfect Matches and hence uses positive signal intensities for probe level normalization.
By not considering mismatches it reduces the noise.

Q3. In the Percentile Normalization, why is the default set as the 75th percentile?

The 75th percentile is a more robust intensity value to normalize the data. With any tissue that you are analyzing, there are a certain percentage of genes that are not expressed. Even if a gene is not expressed, a probe matching to that gene will still report an intensity value. These intensity values will most likely fall into the lower percentile ranking on the array. These values are considered noise and are less reliable.

Q4. How is Percentile shift normalization performed in GeneSpring?

The following are the order of steps in GeneSpring for percentile shift normalization:
1.Transforms the signal values to the log base
2.Arranges the log transformed signal values in increasing order.
3.Computes the rank of the required percentile (Pth percentile).
4.Now if the rank is an integer, then the Pth percentile would be the number with rank R.

Q5. Can I change the normalization type or settings of an experiment once it is created?

Normalization could be performed only while creating an experiment. If you would like to change the normalization settings, you would have to create a new experiment.

Q6. What is the difference between PLIER and IterPLIER?

The IterPLIER differs from PLIER in the aspect that it does not use all the probes for summarization. It selects only the good probes and iteratively discards the bad probes.

Q7. Why is the normalization algorithm for Affymetrix arrays called "RMA16" instead of RMA? What does the number 16 represents?

RMA16 summarization algorithm is referred to as the addition of value16 to the expression values. This is done to attain variance stabilization.

Q8. What are Core, Extended and Full Meta Probe sets?

In order to establish a hierarchy of gene confidence levels, the sources of input transcript annotations are partitioned into three types. From the highest to the lowest confidence, the types are labeled as Core, Extended, and Full.
Core: Core list comprises 17,800 transcript clusters from RefSeq and full-length GenBank mRNAs.,
Extended: The Extended list comprises 129k transcript clusters including cDNA transcripts, syntenic rat and mouse mRNA, and Ensembl, microRNA, Mitomap, Vegagene and VegaPseudogene annotations.

Q9. I know that GeneSpring GX 7.3 has the “Data transformation; Set measurement less 0.01 to 0.01” option for normalization. Is there a similar option in the latest GeneSpring version?

As part of the pre-processing step of experiment creation, thresholding is performed, due to which the raw values less than ‘1’ are threshold to 1. This is the default setup in GeneSpring, but the user has the choice to threshold the raw values to any value.
The option to transform the values to ‘0.01’ is unavailable in current GeneSpring version.

Q10. I am working with Agilent Single color technology. I am unable to specify the threshold value of less than one. I get the ?Threshold should be more than 1' error. Would this be possible in current GeneSpring version?

The threshold values cannot be specified as the value less than 1 in the standard experiment but, can be changed in a Generic experiment. The user could create the custom technology followed by Generic experiment creation to be able to change the threshold value.

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