Can anyone suggest the minimum number of miRNAs to be considered for miRNA data normalization using the 'global mean normalization method'. Also, If I need to analyze about 25-30 miRNAs, should I prefer global mean normalization method or data normalization using the geometric mean of 3-4 stably expressed miRNAs identified using GeNorm and NormFinder? Thanks
Hi Amit,
Your question is linked to a process often described as data exploration and all of these methods of normal transformation, scaling and normalization is applied to your data. However, is only a part of the processing that you want to perform before you start analysing your data. If you can follow a simple workflow outlined below you should have no doubt about your findings. I have analysed more than 50 pilot/large scale studies with up to 3000 arrays.
Here comes the important bit: If you have some repeat samples in your study that would be great because they will tell you which normalization method makes your data better. E.g. repeat samples should give you the same reading (which they never do) and now you start to apply different normalization techniques and work out which one works best. Comparing the effectiveness of the normalization is done with correlation tests.
My general approach to data normalization is part of the data exploration which I apply before moving on to data analysis.
1) Data exploration (sometime called data clean-up)
2) Data transformation
3) Outlier detection and removal
4) Data normalization
5) Data analysis
6) Validation of results
The initial step in data transformation could be a simple analysis along the line of generating set of random numbers that belongs to a certain distribution family (generate the same number of data points as in your data set) and then plot it in a qq-plot. It will be very easy to spot which distribution family your dataset belongs to and then you can transform your data.
I really like to plot the normal transformed data in a histogram and it can be very helpful, because I came across a number of platform that contain systematic technical issues. A histogram of the normal transformed data is one way of spotting them. Often this technical issues show as humps or sharp peaks (zero readings) or on the left hand side of the histogram as a max reading value on the right.
Other ways for the identification of outliers could be PCA or boxplots….another of getting ride off your right hand tail is to apply a data normalization procedure.
After this you should be able to analysis your data. If not feel free to get in touch and we can discuss it offline.
Best wishes,
Jens
You can find some more tips and tricks on the webpage below.
http://www.biomarker-discovery.com/data-analysis-overview
- Badges
- Science topic
- Similar topics
- Biological Science
- Biochemistry
- Biomolecules
- Nucleic Acids
- RNA
- Small RNA
- microRNA