RMA

RMA (Robust Multi-array Analysis)
RMA는 어피 진칩을 프로브수준에서 신호강도를 표준화하고, 요약하는 방법이다. 프로브 수준의 데이터로부터 시작하여 PM값들이 배경신호 정정되고, 표준화되어, 마지막으로 발현양이 요약된다. 다음의 세단계로 이루어 진다.

배경신호 정정
배경신호정정은 프로브수준 프로세싱에 있어 가장 중요한 단계이다. RMA에 사용되는 배경색정정은 비선형정정 (non-linear correction)이며, 칩당으로 이루어진다. 어피칩상의 프로브간의 PM값의 분포에 기초한다. PM값은 배경신호의 혼합이며, 광학적 잡음과 비특이결합, 등등에 의해 발생된다. The background is estimated as expectation of the signal (S) conditioned on observed PM values (O), using a kernel density estimation in both GeneSpring GX 7.3.1 and GeneSpring GX 9.0. However,, however GeneSpring GX 7.3.1 uses direct convolution while GeneSpring GX 9.0 uses Fast Fourier Transformation.

Normalization
Normalization is necessary so that multiple chips can be compared to each other, and analyzed together. The normalization procedure is aimed at making the distributions identical across arrays. The normalization used in RMA is quantile normalization. This usually gives very sharp normalizations.Both GeneSpring GX 7.3.1 and GeneSpring GX 9.0 use quantile normalization. Note that, in this procedure, all the arrays are used and no chip is discarded based on extreme value considerations.

Summarization
Once the probe-level PM values have been background-corrected and normalized, they need to be summarized into expression measures, so that the result is a single expression measure per probe-set, per chip. The summarization used is motivated by the assumption that observed log-transformed PM values follow a linear additive model containing a probe affinity effect, a gene specific effect (the expression level) and an error term. For RMA, the probe affinity effects are assumed to sum to zero, and the gene effect (expression level) is estimated using median polishing. Median polishing is a robust model fitting technique, that protects against outlier probes. Both GeneSpring GX 7.3.1 and GeneSpring GX 9.0 use same methodology for summarization.