Quality assessment of affymetrix GeneChip data

被引:106
作者
Heber, Steffen [1 ]
Sick, Beate [1 ]
机构
[1] Zurich Univ Appl Sci Winterthur, Inst Data Analysis & Proc Design, CH-8401 Winterthur, Switzerland
关键词
D O I
10.1089/omi.2006.10.358
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Affymetrix GeneChips are one of the best established microarray platforms. This powerful technique allows users to measure the expression of thousands of genes simultaneously. However, a microarray experiment is a sophisticated and time consuming endeavor with many potential sources of unwanted variation that could compromise the results if left uncontrolled. Increasing data volume and data complexity have triggered growing concern and awareness of the importance of assessing the quality of generated microarray data. In this review, we give an overview of current methods and software tools for quality assessment of Affymetrix GeneChip data. We focus on quality metrics, diagnostic plots, probe-level methods, pseudo-images, and classification methods to identify corrupted chips. We also describe RNA quality assessment methods which play an important role in challenging RNA sources like formalin embedded biopsies, laser-micro dissected samples, or single cells. No wet-lab methods are discussed in this paper.
引用
收藏
页码:358 / 368
页数:11
相关论文
共 45 条
  • [31] Correlation test to assess low-level processing of high-density oligonucleotide microarray data
    Ploner, A
    Miller, LD
    Hall, P
    Bergh, J
    Pawitan, Y
    [J]. BMC BIOINFORMATICS, 2005, 6 (1)
  • [32] RACE:: Remote Analysis Computation for gene Expression data
    Psarros, M
    Heber, S
    Sick, M
    Thoppae, G
    Harshman, K
    Sick, B
    [J]. NUCLEIC ACIDS RESEARCH, 2005, 33 : W638 - W643
  • [33] Evaluation of methods for oligonucleotide array data via quantitative real-time PCR
    Qin, LX
    Beyer, RP
    Hudson, FN
    Linford, NJ
    Morris, DE
    Kerr, KF
    [J]. BMC BIOINFORMATICS, 2006, 7 (1)
  • [34] Gene discovery in neuropharmacological and behavioral studies using Affymetrix microarray data
    Reimers, M
    Heilig, M
    Sommer, WH
    [J]. METHODS, 2005, 37 (03) : 219 - 228
  • [35] Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data
    Schadt, EE
    Li, C
    Ellis, B
    Wong, WH
    [J]. JOURNAL OF CELLULAR BIOCHEMISTRY, 2001, 84 : 120 - 125
  • [36] Comparison of seven methods for producing Affymetrix expression scores based on false discovery rates in disease profiling data
    Shedden, K
    Chen, W
    Kuick, R
    Ghosh, D
    Macdonald, J
    Cho, KR
    Giordano, TJ
    Gruber, SB
    Fearon, ER
    Taylor, JMG
    Hanash, S
    [J]. BMC BIOINFORMATICS, 2005, 6 (1)
  • [37] The positive false discovery rate:: A Bayesian interpretation and the q-value
    Storey, JD
    [J]. ANNALS OF STATISTICS, 2003, 31 (06) : 2013 - 2035
  • [38] A direct approach to false discovery rates
    Storey, JD
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2002, 64 : 479 - 498
  • [39] Unusual intron conservation near tissue-regulated exons found by splicing microarrays
    Sugnet, Charles W.
    Srinivasan, Karpagam
    Clark, Tyson A.
    O'Brien, Georgeann
    Cline, Melissa S.
    Wang, Hui
    Williams, Alan
    Kulp, David
    Blume, John E.
    Haussler, David
    Ares, Manuel, Jr.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2006, 2 (01) : 22 - 35
  • [40] Universality and flexibility in gene expression from bacteria to human
    Ueda, HR
    Hayashi, S
    Matsuyama, S
    Yomo, T
    Hashimoto, S
    Kay, SA
    Hogenesch, JB
    Iino, M
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (11) : 3765 - 3769