Normalization strategy of microarray gene expression data

Ya-zhou Wu; Ling Zhang; Ming-hui Huang; Meng-su Yang; Dong Yi

doi:10.1016/S1000-1948(07)60040-9

Normalization strategy of microarray gene expression data

Ya-zhou Wu
, Ling Zhang
, Ming-hui Huang
, Meng-su Yang
, Dong Yi

CityUHK Shenzhen Research Institute

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

Abstract

Objective: To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods: With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization (TIN) and locally weighted linear regression normalization (LWLRN). Results: These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion: These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer. © 2007 The Editorial Board of Journal of Medical Colleges of PLA.

Original language	English
Pages (from-to)	195-200
Journal	Journal of Medical Colleges of PLA
Volume	22
Issue number	4
DOIs	https://doi.org/10.1016/S1000-1948(07)60040-9
Publication status	Published - 2007

Research Keywords

Expression ratio
Gene chip
Normalization factor
Significant difference

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title = "Normalization strategy of microarray gene expression data",

abstract = "Objective: To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods: With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization (TIN) and locally weighted linear regression normalization (LWLRN). Results: These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion: These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer. {\textcopyright} 2007 The Editorial Board of Journal of Medical Colleges of PLA.",

keywords = "Expression ratio, Gene chip, Normalization factor, Significant difference",

author = "Ya-zhou Wu and Ling Zhang and Ming-hui Huang and Meng-su Yang and Dong Yi",

year = "2007",

doi = "10.1016/S1000-1948(07)60040-9",

language = "English",

volume = "22",

pages = "195--200",

journal = "Journal of Medical Colleges of PLA",

issn = "1000-1948",

publisher = "Elsevier Ltd.",

number = "4",

}

TY - JOUR

T1 - Normalization strategy of microarray gene expression data

AU - Wu, Ya-zhou

AU - Zhang, Ling

AU - Huang, Ming-hui

AU - Yang, Meng-su

AU - Yi, Dong

PY - 2007

Y1 - 2007

N2 - Objective: To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods: With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization (TIN) and locally weighted linear regression normalization (LWLRN). Results: These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion: These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer. © 2007 The Editorial Board of Journal of Medical Colleges of PLA.

AB - Objective: To discuss strategies and methods of normalization on how to deal with and analyze data for different chips with the combination of statistics, mathematics and bioinformatics in order to find significant difference genes. Methods: With Excel and SPSS software, high or low density chips were analyzed through total intensity normalization (TIN) and locally weighted linear regression normalization (LWLRN). Results: These methods effectively reduced systemic errors and made data more comparable and reliable. Conclusion: These methods can search the genes of significant difference, although normalization methods are being developed and need to be improved further. Great breakthrough will be obtained in microarray data normalization analysis and transformation with the development of non-linear technology, software and hardware of computer. © 2007 The Editorial Board of Journal of Medical Colleges of PLA.

KW - Expression ratio

KW - Gene chip

KW - Normalization factor

KW - Significant difference

UR - https://www.scopus.com/pages/publications/77950223128

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77950223128&origin=recordpage

U2 - 10.1016/S1000-1948(07)60040-9

DO - 10.1016/S1000-1948(07)60040-9

M3 - RGC 22 - Publication in policy or professional journal

SN - 1000-1948

VL - 22

SP - 195

EP - 200

JO - Journal of Medical Colleges of PLA

JF - Journal of Medical Colleges of PLA

IS - 4

ER -

Normalization strategy of microarray gene expression data

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Research Keywords

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