Analysis of data from viral DNA microchips

被引:75
作者
Amaratunga, D [1 ]
Cabrera, J [1 ]
机构
[1] Rutgers State Univ, Dept Stat, Piscataway, NJ 08855 USA
关键词
gene expression; median; mixed effects model; Monte Carlo simulation; resistant location estimation; standardization;
D O I
10.1198/016214501753381814
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Viral DNA microchips, arrays of viral genes printed over a glass slide, are powerful tools for rapidly characterizing the expression pattern of these genes in an infection. The chips are exposed to a solution of fluorescently labeled cDNAs prepared from either mock or true infected human fibroblast cells and the expression levels of the various genes are recorded with the objective of detecting which viral genes are expressed to a significantly higher degree when exposed to the true infection as compared to the mock infection. The data were initially examined visually via image plots and scatterplots, These reveal that analysis of such data presents many challenges owing to, among other problems, high interchip and intrachip variability with low signal-to-noise ratio, differential intensity scales that have to be adjusted nonlinearly, nonGaussian data, data for a large number of genes with little replication, scratches and dark spots on the chips, dust, outliers, and an inability to quantitate intensities below a detection limit, or above a threshold. The first step of the analysis was to standardize the chips to a single intensity scale using a photograph analogy. Next, the average expression level of each gene was estimated using a highly resistant repeated median estimator to avoid being misled by aberrant values. Finally, a simulation-based approach was used to make a distribution-free assessment of significance.
引用
收藏
页码:1161 / 1170
页数:10
相关论文
共 8 条
  • [1] DNA microarrays of the complex human cytomegalovirus genome: Profiling kinetic class with drug sensitivity of viral gene expression
    Chambers, J
    Angulo, A
    Amaratunga, D
    Guo, HQ
    Jiang, Y
    Wan, JS
    Bittner, A
    Frueh, K
    Jackson, MR
    Peterson, PA
    Erlander, MG
    Ghazal, P
    [J]. JOURNAL OF VIROLOGY, 1999, 73 (07) : 5757 - 5766
  • [2] Chen Y, 1997, J Biomed Opt, V2, P364, DOI 10.1117/12.281504
  • [3] Performance assessment through bootstrap
    Cho, K
    Meer, P
    Cabrera, J
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (11) : 1185 - 1198
  • [4] Manly B. F. J., 1997, RANDOMIZATION BOOTST
  • [5] QUANTITATIVE MONITORING OF GENE-EXPRESSION PATTERNS WITH A COMPLEMENTARY-DNA MICROARRAY
    SCHENA, M
    SHALON, D
    DAVIS, RW
    BROWN, PO
    [J]. SCIENCE, 1995, 270 (5235) : 467 - 470
  • [6] SLONIM DK, 2000, P 4 ANN INT C COMP M, P263
  • [7] Plant functional genomics
    Somerville, C
    Somerville, S
    [J]. SCIENCE, 1999, 285 (5426) : 380 - 383
  • [8] Tukey JW., 1977, EXPLORATORY DATA ANA