Contrast normalization of oligonucleotide arrays

被引:36
|
作者
Åstrand, M [1 ]
机构
[1] AstraZeneca R&D, Stat & Math Sci, S-43183 Molndal, Sweden
关键词
oligonucleotide array; normalize; curve-fitting; orthogonal; loess;
D O I
10.1089/106652703763255697
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Affymetrix high-density oligonucleotide array is a tool that has the capacity to simultaneously measure the abundance of thousands of mRNA sequences in biological samples. In order to allow direct array-to-array comparisons, normalization is a necessity. When deciding on an appropriate normalization procedure there are a couple questions that need to be addressed, e.g., on which level should the normalization be performed: On the level of feature intensities or on the level of expression indexes? Should all features/expression indexes be used or can we choose a subset of features likely to be unregulated? Another question is how to actually perform the normalization: normalize using the overall mean intensity or use a smooth normalization curve? Most of the currently used normalization methods are linear; e.g., the normalization method implemented in the Affymetrix software GeneChip is based on the overall mean intensity. However, along with alternative methods of summarizing feature intensities into an expression index, nonlinear methods have recently started to appear. For many of these alternative methods, the natural choice is to normalize on the level of feature intensities, either using all feature intensities or only perfect match intensities. In this report, a nonlinear normalization procedure aimed for normalizing feature intensities is proposed.
引用
收藏
页码:95 / 102
页数:8
相关论文
共 50 条
  • [1] Normalization of oligonucleotide arrays based on the least-variant set of genes
    Calza, Stefano
    Valentini, Davide
    Pawitan, Yudi
    [J]. BMC BIOINFORMATICS, 2008, 9 (1)
  • [2] Normalization of oligonucleotide arrays based on the least-variant set of genes
    Stefano Calza
    Davide Valentini
    Yudi Pawitan
    [J]. BMC Bioinformatics, 9
  • [3] Inter-gene correlation on oligonucleotide arrays - How much does normalization matter?
    Gold, DL
    Wang, J
    Coombes, KR
    [J]. AMERICAN JOURNAL OF PHARMACOGENOMICS, 2005, 5 (04) : 271 - 279
  • [4] Transformation and normalization of oligonucleotide microarray data
    Geller, SC
    Gregg, JP
    Hagerman, P
    Rocke, DM
    [J]. BIOINFORMATICS, 2003, 19 (14) : 1817 - 1823
  • [5] Oligonucleotide microarray data distribution and normalization
    Sidorov, IA
    Hosack, DA
    Gee, D
    Yang, J
    Cam, MC
    Lempicki, RA
    Dimitrov, DS
    [J]. INFORMATION SCIENCES, 2002, 146 (1-4) : 67 - 73
  • [6] Oligonucleotide microarray data distribution and normalization
    Sidorov, IA
    Hosack, DA
    Gee, D
    Yang, J
    Cam, MC
    Lempicki, RA
    Dimitrov, DS
    [J]. PROCEEDINGS OF THE 6TH JOINT CONFERENCE ON INFORMATION SCIENCES, 2002, : 1219 - 1222
  • [7] Design of oligonucleotide arrays at interfaces
    Boncheva, M
    Scheibler, L
    Lincoln, P
    Vogel, H
    Åkerman, B
    [J]. LANGMUIR, 1999, 15 (13) : 4317 - 4320
  • [8] Assembly fabrication of oligonucleotide arrays
    Xiao, PF
    Bu, Y
    Zhang, XD
    Wu, HP
    Zhou, GH
    Lu, ZH
    [J]. JOURNAL OF NANOSCIENCE AND NANOTECHNOLOGY, 2005, 5 (08) : 1211 - 1215
  • [9] SUCCESSIVE NORMALIZATION OF RECTANGULAR ARRAYS
    Olshen, Richard A.
    Rajaratnam, Bala
    [J]. ANNALS OF STATISTICS, 2010, 38 (03): : 1638 - 1664
  • [10] Normalization of local contrast in mammograms
    Veldkamp, WJH
    Karssemeijer, N
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2000, 19 (07) : 731 - 738