【佳學基因檢測】微陣列基因檢測差異表達基因的鑒定和錯誤發(fā)現率

千萬不要做基因檢測合理嗎

挖掘腫瘤基因組學個性化藥物選擇時，做了一個記錄，《Curr Opin Lipidol》在. 2007 Apr;18(2):187-93.發(fā)表了一篇題目為《微陣列研究中差異表達基因的鑒定和錯誤發(fā)現率》基因檢測先進技術及各種不同技術的比較優(yōu)勢臨床研究文章。該研究由Arief Gusnanto, Stefano Calza, Yudi Pawitan等完成。促進了微陣列芯片基因檢測技術的傳播和比較，進一步強調了不同基因檢測技術的選擇及合理應用。

基因檢測技術的臨床研究內容關鍵詞：

微陣列,基因芯片,基因檢測,技術優(yōu)勢

微陣列及高密度芯片基因檢測臨床應用結果

審查基因解碼基因檢測的研究目的：突出微陣列數據分析在識別差異表達基因方面的發(fā)展，特別是通過控制錯誤發(fā)現率。賊近的發(fā)現：高通量技術（如微陣列）的出現引發(fā)了兩個基本的統(tǒng)計問題：多重性和敏感性.基因解碼基因檢測專注于識別差異表達基因的生物學問題。首先，由于測試了數以萬計的假設而產生了多重性，使標準 P 值變得毫無意義。其次，已知的賊佳單次測試程序（例如 t 測試）在高度多重測試的情況下表現不佳。處理多重性的標準基因解碼基因檢測的研究方法在微陣列環(huán)境中過于保守。錯誤發(fā)現率概念正迅速成為取代 P 值的關鍵統(tǒng)計評估工具。基因解碼基因檢測回顧了錯誤發(fā)現率基因解碼基因檢測的研究方法，并認為它對微陣列數據更明智?；蚪獯a基因檢測還討論了一些基因解碼基因檢測的研究方法來考慮來自微陣列的額外信息以提高錯誤發(fā)現率。總結：關于如何使用錯誤發(fā)現率框架代替經典 P 值來分析微陣列數據的共識越來越多。需要進一步研究原始數據的預處理，例如標準化步驟和過濾，以及尋找賊敏感的測試程序。

微陣列及高密度芯片基因檢測國際數據庫描述：

Purpose of review: To highlight the development in microarray data analysis for the identification of differentially expressed genes, particularly via control of false discovery rate.Recent findings: The emergence of high-throughput technology such as microarrays raises two fundamental statistical issues: multiplicity and sensitivity. We focus on the biological problem of identifying differentially expressed genes. First, multiplicity arises due to testing tens of thousands of hypotheses, rendering the standard P value meaningless. Second, known optimal single-test procedures such as the t-test perform poorly in the context of highly multiple tests. The standard approach of dealing with multiplicity is too conservative in the microarray context. The false discovery rate concept is fast becoming the key statistical assessment tool replacing the P value. We review the false discovery rate approach and argue that it is more sensible for microarray data. We also discuss some methods to take into account additional information from the microarrays to improve the false discovery rate.Summary: There is growing consensus on how to analyse microarray data using the false discovery rate framework in place of the classical P value. Further research is needed on the preprocessing of the raw data, such as the normalization step and filtering, and on finding the most sensitive test procedure.