Why needing normalization technique?

In summary, normalization techniques are necessary in experiments such as microarrays and transfection to account for systematic errors that can affect the signal. Commonly used normalization procedures include Total Intensity, LOWESS, Mean centering, Ratio Statistics, and Standard deviation regularization. Microarrays are used to measure gene expression by hybridizing RNA to probes on a chip, while transfection is the method of introducing DNA into cells.
  • #1
mountain
53
0
Why needing normalization technique??

Why do we have to normalize an experiment for instance in microarray or transfection?


Thanks.
 
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  • #2
To account for differences in signal caused by systematic errors, like dye labelling efficiencies, dye scanning properties, power of the two lasers, print tip effects, spatial effects (for the microarray) and transfection efficiencies, cell survival, etc (for transfection).
 
  • #3
I have searched for a general discussion about normalization, but could not find any good ones. Do you have any useful links?
 
  • #4
Here is a link for microarrays http://www.dnamicroarrays.info/Data_Norm.html

The commonly used normalization procedures are Total Intensity normalization, LOWESS Normalization, Mean centering, Ratio Statistics, Standard deviation regularization.
 
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  • #5
Stupid Question ( I seem to be good at asking them): what are microarrays and transfection?
 
  • #6
Those are not stupid questions ;)

With a microarray you can measure gene expression. It is based on hybridizing the expressed gene products (RNA that has been converted into cDNA) to probes on a chip. When hybridization occurs you get a signal. In this way you can analyze all the genes in a genome (~30,000 in the case of humans).

Transfection is the method of introducing DNA into eukaryotic cells. It can also be used to measure gene expression, where a gene promoter is cloned in front of a reporter gene.
 

Related to Why needing normalization technique?

1. Why is normalization important in data analysis?

Normalization is important in data analysis because it helps to eliminate redundancies and inconsistencies in data. This allows for more accurate and reliable analysis, as well as easier data manipulation and comparison between different datasets.

2. What is the purpose of normalization?

The purpose of normalization is to reduce data redundancy and ensure data consistency. This allows for more efficient and accurate data analysis, as well as better data organization and management.

3. How does normalization aid in data processing?

Normalization aids in data processing by reducing the need for complex data manipulation and cleaning. It also helps to improve the accuracy and reliability of data analysis by eliminating redundant or inconsistent data.

4. What are the potential drawbacks of normalization?

One potential drawback of normalization is the loss of some data information. Normalization can also be a time-consuming process and may require advanced knowledge and skills in data analysis.

5. Are there different types of normalization techniques?

Yes, there are different types of normalization techniques, such as Min-Max normalization, Z-score normalization, and Decimal scaling normalization. Each technique has its own advantages and is suitable for different types of data and analysis.

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