【論】Liu,2006,Probe-level measurement error improve～

Xuejun Liu, Marta Milo, Neil D Lawrence and Magnus Rattray
Probe-level measurement error improves accuracy in detecting differential gene expression
Bioinformatics 2006 22(17):2107-2113
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・遺伝子抽出の精度には、（通常無視される） Probe-level measurement が影響しているので、これを考慮に入れたオリジナルの Baysian hierarchical model を提案する。この方法はPPLR (probability of positive log-ratio)に基づく。
・データ
1.Golden spike-in dataset（人工データ）[Choe,2005]
2.A real mouse time-course dataset（実データ）[Lin,2004]
・比較法（お互いに計算上関連している）
1.MAP (maximun a posteriori) approximation
2.オリジナル法 Variational method
3.MCMC (Markov chain Monte Carlo)

・問題点「(1) Microarray experiments are associated with low precision probe-level measurements, especially for weakly expressed genes (probe-level measurement error).
(2) The small number of replicates makes it difficult to obtain an accurate variance estimate for each gene across replicates (between-replicate variance).」
・概要「We have presented an approach using probe-level measurement error in order to improve the detection of differential gene expression and compared three different computation methods, MAP approximation, a variational method and MCMC, to solve the intractability in the model owing to the incorporation of probe-level measurement error.」