Spatial pattern and temporal mode analysis of microarray time-series data by independent component analysis

독립성분분석에 의한 유전자 발현 시계열 데이터의 공간적 패턴과 시간적 모드 분석

In this paper we apply several variations of independent component analysis( ICA) methods, such as spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to yeast cell cycle datasets, and compare their performance in finding components that result in gene clusters coherent with annotations and in extract ins meaningful temporal modes. It turns out that the results of tICA are superior to those of PCA, sICA, and stICA in terms of gene clustering and the temporal modes extracted by stICA highlights particular cellular processes.