Friday, September 16, 2011

normalization, normalization, normalization!

Basic Microarray Analysis:
  1. Experiment (Raw Data)
  2. Image Analysis (Pick Spots)
  3. Pre-normalization analysis
    1. Background subtraction
    2. Spatial intensity
    3. Chip to Chip variance
  4. Normalization and Filtering
    1. Scale normalization
    2. Intensity dependent location normalization
  5. Post-normalization analysis
    1. Clustering
    2. Hypothesis testing
    3. Classification
    4. More complicated statistical analysis

I found this list when I was searching the interquartile range (IQR) normalization. Actually Microarray folks might have been done the whole bunch of analysis decades ago. But the question for me is still there: how to compare two samples? 

Let's say, I have two vectors: X=[x1, x2, x3, ..., xn] and Y=[y1, y2, y3, ... yn]. Before I could say X is correlated with Y simply because cor(X, Y) is large, I should make sure that each Xi and Yi are sampled from the same distribution. Right? That's the point. If they are not, I should do normalization ahead to assure that. 

Maybe I should go thru the above list (esp. the normalization part) in some weekend. 

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