Stabilizing Variance on Time-Course Microarray Data
碩士 === 國立成功大學 === 統計學系碩博士班 === 97 === In recent years, the development of cDNA microarray technology allows people to investigate thousands of genes simultaneously. For microarray data, it is well known that gene expression is related with its variance in some way. Researchers do try to stabilize th...
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ndltd-TW-097NCKU53370202016-05-04T04:26:10Z http://ndltd.ncl.edu.tw/handle/10637514065181449250 Stabilizing Variance on Time-Course Microarray Data 多期型微陣列資料的變異數穩定 Yu-wen Huang 黃郁雯 碩士 國立成功大學 統計學系碩博士班 97 In recent years, the development of cDNA microarray technology allows people to investigate thousands of genes simultaneously. For microarray data, it is well known that gene expression is related with its variance in some way. Researchers do try to stabilize the variance so that the detection power can be greatly improved. However, none or very few researches focus on stabilizing the variance for multiple time-course microarray data. In this thesis, we evaluate the function of variance for time course microarray data and suggest a pooled approach to stabilize the variance. We extend the existing methods, such as Started Log (sLog), Log-Linear Hybrid (Hyb), Generalized Logarithm Transformation (glog) and Spread-versus-level plot transformation (SVL) to stabilize variance for time-course data. We also consider two nonparametric methods, Data-Driven Haar-Fisz Transformation for Microarray (DDHFm) and step function approaches, to deal with this problem. Simulation study shows that the three log transformation methods are better in stabilizing variance than another methods, and DDHFm transformation may not suitable for time-course microarray data. A real time-course microarray data is illustrated for application. Shih-Huang Chan 詹世煌 2009 學位論文 ; thesis 41 en_US |
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碩士 === 國立成功大學 === 統計學系碩博士班 === 97 === In recent years, the development of cDNA microarray technology allows people to investigate thousands of genes simultaneously. For microarray data, it is well known that gene expression is related with its variance in some way.
Researchers do try to stabilize the variance
so that the detection power can be greatly improved.
However, none or very few researches focus on stabilizing the variance for multiple time-course microarray data.
In this thesis, we evaluate the function of variance for time course microarray data and suggest a pooled approach to stabilize the variance. We extend the existing methods, such as Started Log (sLog), Log-Linear Hybrid (Hyb), Generalized Logarithm Transformation (glog)
and Spread-versus-level plot transformation (SVL) to stabilize variance for time-course data. We also consider two nonparametric methods, Data-Driven Haar-Fisz Transformation for Microarray (DDHFm) and step function approaches, to deal with this problem. Simulation study shows that the three log transformation methods are better in stabilizing variance than another methods, and DDHFm transformation may not suitable for time-course microarray data.
A real time-course microarray data is illustrated for application.
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author2 |
Shih-Huang Chan |
author_facet |
Shih-Huang Chan Yu-wen Huang 黃郁雯 |
author |
Yu-wen Huang 黃郁雯 |
spellingShingle |
Yu-wen Huang 黃郁雯 Stabilizing Variance on Time-Course Microarray Data |
author_sort |
Yu-wen Huang |
title |
Stabilizing Variance on Time-Course Microarray Data |
title_short |
Stabilizing Variance on Time-Course Microarray Data |
title_full |
Stabilizing Variance on Time-Course Microarray Data |
title_fullStr |
Stabilizing Variance on Time-Course Microarray Data |
title_full_unstemmed |
Stabilizing Variance on Time-Course Microarray Data |
title_sort |
stabilizing variance on time-course microarray data |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/10637514065181449250 |
work_keys_str_mv |
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