21世紀以來，傳統生物學已經慢慢邁向系統生物學的層次，近年來許多研究整合了不同的微陣列資料來分析細胞在某個時期的基因調控模組，並且輔以已經研究之結果，或是利用實驗方法來證實，同時，也有許多資料是為了研究微生物中的基因震盪情形所產生的。然而，利用數據分析的方法來分析從細胞中獲得充滿雜訊的資料，其分析出來的結果不能排除含有雜訊的可能性。台灣中央研究院黃鍔院士在1998年所發表的赫伯特-黃轉換法，主要的方法是利用簡單的代數方法將波型拆解成各個IMF，再利用赫伯特轉換法將IMF轉換成瞬時頻譜，如此的方法近年來被廣泛的運用在許多科學研究上，其去雜訊的效能倍受肯定。因此我們先將研究酵母菌基因震盪情形且具有48個時間點的微陣列資料利用赫伯特-黃轉換法轉換成瞬時頻譜之後，再將yeastract上的調控關係與MIT研究單位的ChIP-chip資料整合成一個可信度較高的調控關係矩陣，再對各個轉錄調控因子所調控的所有基因的瞬時頻譜，利用歐氏距離進行階層式的叢集分析，得到許多以基因為群組的基因調控模組，結果發現許多群的基因都具有某種程度的生物意義。 The turn of 21st century, traditional biology is evolutes to systems biology, much study has integrate many type of data generate from high-throughput biological techniques, or validation with experiment results, and there are many data are focus on the oscillation of genes in microbes. However, use methods of numerical analysis to analyze data from a cell may have much noise, the results it analyzed also may contain much noise. The Hilbert-Huang transform (HHT) has been published in 1998, the content of this method include empirical model decomposition (EMD) and Hilbert transform. EMD can decompose a wave into several intrinsic mode functions (IMFs), then IMF can transform into frequency domain via Hilbert transform, the performance of denoising of this method has been use in variety study. We transform time-series microarray data into frequency domain with Hilbert-Huang transform, then construct a high-confidence TF-gene regulatory matrix use ChIP-chip data and documented relation from yeastract website, based on this matrix, we can use hierarchical clustering to cluster genes regulated by each TF, then got a lot of gene cluster as regulatory module which has certain biological meaning.
