近幾年，由於高通量單細胞核糖核酸定序（single-cell RNA sequencing, scRNA-seq ）技術的進步，揭露了細胞間的異質性。然而，scRNA-seq 資料具有大量的零計數、變異大且基因表達的分佈複雜等特性，這對於基因表達差異分析的統計方法與計算發展帶來重大影響。本研究基於零膨脹負二項模型（zero-inflated negative binomial model ），提出一統計方法來偵測表達差異的基因。此外，本研究透過統計模擬與實例分析，比較本研究所提出之方法與現有表達差異方法的表現。比較結果顯示本研究所提出之方法在 ROC 曲線（receiver operating characteristic）底下的面積 AUC （area under curve）與 F1 分數（F1 score）的表現優於其他方法。

The recent advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology reveal cellular heterogeneity. However, the distinct challenges in scRNA-seq including stochastic dropout, increased variability and complex expression distribution, have implications for both statistical methodologies and computational developments for differential gene expression analysis. In this thesis, we proposed a new method, scDEseq, which employed zero-inflated negative binomial model to identify differentially expressed genes between biological conditions in scRNA-seq data. In addition, we used simulated data and real data to evaluate the performance of proposed method and compare its performance with the existing methods in terms of sensitivity, specificity, accuracy, precision, F1 score and AUC (area under curve). Results indicate that the proposed method has an overall powerful performance in terms of F1 score and AUC.

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