Merging Gene Expression Data from Different Experiments

• Main Authors: Pei-YuLiou, 劉珮宇
• Other Authors: Shih-Huang Chan
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/71062655262704836536

碩士 === 國立成功大學 === 統計學系碩博士班 === 100 === We evaluate the similarity of two gene expression sequences from different experiments, and if possible, merging the two sequences. Aach and Church ( 2001 ) developed simple time warping algorithm to align gene expression time series, using distance matrix of two sequences through dynamic time warping to find the time warping path. When the scale of the two sequences are different, however, the results are doubtful. We sort the sequences in order, and use rank difference instead of Euclidean distance to find the optimal path. We use area ratio to evaluate the similarity of two gene expression sequences. Simulation is conducted to find a reasonable threshold in merging two sequences. Finally, we build one statistic to evaluate the performance of merging the two sequences once they pass through the threshold.