統計学輪講（第１２回）

統計学輪講（第12回）
日時      2012年07月10日(火)    15時40分～16時30分
場所      経済学部新棟３階第３教室
講演者    Mohammad Manir Hossain Mollah (農D3)
演題      Robust Bayesian inference and model diagnosis of microarray data
by β-likelihood

概要
In high-throughput analysis, it is generally difficult to scrutinize the irregular
patterns of expression that are not expected by the statistical model
gene by gene. In this talk, we propose a β-empirical Bayes (β-EB) approach
based on a β-likelihood measure. β-likelihood approach is regarded as a
weighted likelihood procedure. The weight of a gene t is described as a power
function of its likelihood, fβ(yt|θ). By putting low weights on the data
 that have small likelihoods with unexpected expression patterns, the inference
becomes robust. Because of its high-dimensionality, the distribution of
the weights can be used to detect outliers and diagnose the model statistically.
From the simulation results, we observe that the proposed method significantly
improves the performance in a comparison of the others in presence of outliers;
otherwise, it keeps almost equal performance. An application of the proposed
method to the head and neck cancer data detect several biologically important
DE genes, whose expressions are confirmed to be unstable. They were not detected
by standard EB approach. Analysis of the data from patients of two types of lung cancer
identified mis-specification of the model. The β-likelihood based eQTL analysis
implied that the classical approach may under estimate the number of target genes of
master transcription factors largely.