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

Probe Level Analysis The Purpose Calculate an expression value for each probe set (gene) from the 11-25 PM and MM intensities Critical for later analysis. Avoiding GIGO

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

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

MAS 4 A- probe pairs selected

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)

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.

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.

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

RMA- BG Corrected Value

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

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

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