Reproducibility of gene expression across generations of Affymetrix microarrays

被引:54
|
作者
Nimgaonkar, A [1 ]
Sanoudou, D
Butte, AJ
Haslett, JN
Kunkel, LM
Beggs, AH
Kohane, IS
机构
[1] Harvard Med Sch, Informat Program, Childrens Hosp, Boston, MA 02115 USA
[2] Harvard Univ, Div Hlth Sci & Technol, Cambridge, MA 02138 USA
[3] MIT, Cambridge, MA 02139 USA
[4] Harvard Univ, Childrens Hosp, Div Genet, Sch Med, Boston, MA 02115 USA
关键词
D O I
10.1186/1471-2105-4-27
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: The development of large-scale gene expression profiling technologies is rapidly changing the norms of biological investigation. But the rapid pace of change itself presents challenges. Commercial microarrays are regularly modified to incorporate new genes and improved target sequences. Although the ability to compare datasets across generations is crucial for any long-term research project, to date no means to allow such comparisons have been developed. In this study the reproducibility of gene expression levels across two generations of Affymetrix GeneChips(R) (HuGeneFL and HG-U95A) was measured. Results: Correlation coefficients were computed for gene expression values across chip generations based on different measures of similarity. Comparing the absolute calls assigned to the individual probe sets across the generations found them to be largely unchanged. Conclusion: We show that experimental replicates are highly reproducible, but that reproducibility across generations depends on the degree of similarity of the probe sets and the expression level of the corresponding transcript.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Reproducibility of gene expression across generations of Affymetrix microarrays
    Ashish Nimgaonkar
    Despina Sanoudou
    Atul J Butte
    Judith N Haslett
    Louis M Kunkel
    Alan H Beggs
    Isaac S Kohane
    BMC Bioinformatics, 4
  • [2] Transformation of expression intensities across generations of affymetrix microarrays using sequence matching and regression modeling
    Bhattacharya, S
    Mariani, TJ
    NUCLEIC ACIDS RESEARCH, 2005, 33 (18) : 1 - 10
  • [3] Reproducibility and selection of probe sets across generations of microarrays.
    Nimgaonkar, A
    Sanoudou, D
    Butte, A
    Haslett, JN
    Kunkel, LM
    Beggs, AH
    Kohane, IS
    AMERICAN JOURNAL OF HUMAN GENETICS, 2001, 69 (04) : 457 - 457
  • [4] GenePicker:: replicate analysis of Affymetrix gene expression microarrays
    Finocchiaro, G
    Parise, P
    Minardi, SP
    Alcalay, M
    Müller, H
    BIOINFORMATICS, 2004, 20 (18) : 3670 - 3672
  • [5] Analysis of gene expression in the stem cell hiererchy using Affymetrix microarrays.
    Ivanova, NB
    Hackney, J
    Moore, KA
    Beavis, A
    Lemischka, IR
    BLOOD, 2001, 98 (11) : 64A - 64A
  • [6] A learned comparative expression measure for Affymetrix GeneChip DNA microarrays
    Sheffler, W
    Upfal, E
    Sedivy, J
    Noble, WS
    2005 IEEE COMPUTATIONAL SYSTEMS BIOINFORMATICS CONFERENCE, PROCEEDINGS, 2005, : 144 - 154
  • [7] G-spots cause incorrect expression measurement in Affymetrix microarrays
    Upton, Graham J. G.
    Langdon, William B.
    Harrison, Andrew P.
    BMC GENOMICS, 2008, 9 (1)
  • [8] Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity
    Koji Kadota
    Yuji Nakai
    Kentaro Shimizu
    Algorithms for Molecular Biology, 4
  • [9] Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity
    Kadota, Koji
    Nakai, Yuji
    Shimizu, Kentaro
    ALGORITHMS FOR MOLECULAR BIOLOGY, 2009, 4
  • [10] GCSscore: an R package for differential gene expression analysis in Affymetrix/Thermo-Fisher whole transcriptome microarrays
    Guy M. Harris
    Shahroze Abbas
    Michael F. Miles
    BMC Genomics, 22