1. Pre-processing AFFY data
Lecture Topic 5
Pre-processing AFFY data

2. Probe Level Analysis The Purpose
Calculate an expression value for each probe set (gene) from the PM and MM intensities
Critical for later analysis. Avoiding GIGO

3. Difficulties Large variability Few measurements (11-25) at most
MM is very complex, it is signal plus background
Signal has to be SCALED
Probe-level effects

4. Different Methods MAS 4 Affymetrix 1996 MAS 5 Affymetrix 2002
Robust Multichip Analysis (RMA) 2002
GC-RMA 2004

5. MAS 4
A- probe pairs selected

6. Avg Diff
Calculated using differences between MM and PM of every probe pair and averaging over the probe pair
Excluded OUTLIER pairs if PM-MM > 3 SD
Was NOT a robust average
NOT log-transformed
COULD be negative (about 1/3 of the times)

7. MAS 5 Signal=TukeyBiweight{log2(PMj-IMj) Discussed this earlier.
Requires calculating IM
Adjusted PM-MM are log transformed and robust for outlying observations using Tukey Biweight.

8. Robust Multichip Analysis
ONLY uses PM and ignores MM
SACRIFICES Accuracy but major gains in PRECISION
Basic Steps:
1. Calculate chip background (*BG) and subtract from PM
2. Carry out intensity dependent normalization for PM-*BG
Lowess
Quantile Normalization (Discussed before)
Normalized PM-*BG are log transformed
Robust multichip analysis of all probes in the set and using Tukey median polishing procedure. Signal is antilog of result.

9. RMA- Step 1: Background Correction
Irrizary et al(2003)
Looks at finding the conditional expectation of the TRUE signal given the observed signal (which is assumed to be the true signal plus noise)
E(si | si+bi)
Here, si assumed to follow Exponential distribution with parameter q.
Bi assumed to follow N(me, s2e)
Estimate me and se as the mean and standard deviation of empty spots

10. RMA- BG Corrected Value

11. RMA-Normalization Lowess (for Spatial effects)
Use the background corrected intensities B(PM) to carry out normalization
Lowess (for Spatial effects)
Quantile Normalization (to allow comparability amongst replicate slides)
Normalized B(PM) are log transformed

12. RMA summarization Use MEDIAN POLISH to fit a linear model
Given a MATRIX of data:
Data= overall effects+row effects + column effects + residual
Find row and column effects by subtracting the medians of row and column successively till all the medians are less than some epsilon
Gives estimated row, column and overall effect when done

13. Median Polish of RMA
For each probe set we have a matrix (probes in rows and arrays in columns)
We assume:
Signal=probe affinity effect + logscale for expression + error
Also assume the sum of probe affinities is 0
Use MEDIAN polish to estimate the expression level in each array