A Simple Affymetrix Ratio-transformation Method Yields Comparable Expression Level Quantifications with cDNA Data

Authors: Chintanu K. Sarmah, Sandhya Samarasinghe, Don Kulasiri, Daniel Catchpoole

Abstract:

Gene expression profiling is rapidly evolving into a powerful technique for investigating tumor malignancies. The researchers are overwhelmed with the microarray-based platforms and methods that confer them the freedom to conduct large-scale gene expression profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data.

Keywords: Gene expression profiling, microarray, cDNA, Affymetrix, childhood leukaemia.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333887

References:

[1] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270(5235):467-70.
[2] Smith V, Botstein D, Brown PO. Genetic footprinting: a genomic strategy for determining a gene's function given its sequence. Proc Natl Acad Sci USA 1995;92(14):6479-83.
[3] Tan PK, Downey TJ, Spitznagel EL, Jr., Xu P, Fu D, Dimitrov DS, Lempicki RA, Raaka BM, Cam MC. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 2003;31(19):5676-84.
[4] Severgnini M, Bicciato S, Mangano E, Scarlatti F, Mezzelani A, Mattioli M, Ghidoni R, Peano C, Bonnal R, Viti F, Milanesi L, De Bellis G, Battaglia C. Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment. Anal Biochem 2006;353(1):43-56.
[5] Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W. Multiple-laboratory comparison of microarray platforms. Nat Methods 2005;2(5):345-50.
[6] Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J. Independence and reproducibility across microarray platforms. Nat Methods 2005;2(5):337-44.
[7] Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, Boysen C, Hunkapiller K, Jensen RV, Knight CR, Lee KY, Ma Y, Maqsodi B, Papallo A, Peters EH, Poulter K, Ruppel PL, Samaha RR, Shi L, Yang W, Zhang L, Goodsaid FM. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 2006;24(9):1115-22.
[8] Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Longueville Fd, Fischer GM, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Scherf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, Zhang L, Tong W, William Slikker J. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnology 2006;24(9):1151-61.
[9] Ihaka R, Gentleman R. R: a language for data analysis and graphics. J. Comput. Graph. Statist. 1996;5:299-314.
[10] Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004;5(10):R80.
[11] Grewal A, Lambert P, Stockton J. Analysis of expression data: an overview. Curr Protoc Bioinformatics 2007;Chapter 7:Unit 7.1.
[12] Mar JC, Kimura Y, Schroder K, Irvine KM, Hayashizaki Y, Suzuki H, Hume D, Quackenbush J. Data-driven normalization strategies for highthroughput quantitative RT-PCR. Bmc Bioinformatics 2009;10:110.
[13] Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003;19(2):185-93.
[14] Bomstad BM, Irizarry RA, Gautier L, Wu Z. Preprocessing high-density oligonucleotide arrays. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S Bioinformatics and computational biology solutions using R and Bioconductor. NY, USA: Springer, 2005. p. 13-32.
[15] Irizarry RA, Hobbs B, Collin F, Beazer-Barclay Y, Antonellis K, Scherf U, Speed T. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4(2):249-64.
[16] Emerson J, Hoaglin D. Analysis of two way tables by medians. In: Hoaglin DC, Mosteller F, Tukey JW Understanding robust and exploratory data analysis. NY, USA: Wiley-Interscience, 2000. p. 166- 207.
[17] Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK. A comparison of background correction methods for twocolour microarrays. Bioinformatics 2007;23(20):2700-07.
[18] McGee M, Chen Z. Parameter estimation for the exponential-normal convolution model for background correction of affymetrix GeneChip data. Stat Appl Genet Mol Biol 2006;5(1):Article24.
[19] Smyth GK, Speed T. Normalization of cDNA microarray data. Methods 2003;31(4):265-73.
[20] Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002;30(4):e15.
[21] Cleveland WS. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 1979;74(368):829-36.
[22] Park T, Yi SG, Kang SH, Lee S, Lee YS, Simon R. Evaluation of normalization methods for microarray data. BMC Bioinformatics 2003;4:33.
[23] Tseng GC, Oh MK, Rohlin L, Liao JC, Wong WH. Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res 2001;29(12):2549-57.
[24] Yang YH, Dudoit S, Luu P, Speed TP. Normalization for cDNA microarray data. In: Bittner ML, Chen Y, Dorsel AN, Dougherty ER Microarrays: optical technologies and informatics (proceedings of SPIE). San Jose, California: SPIE-International Society for Optical Engineering, 2001. p. 141-52.
[25] Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S Bioinformatics and Computational Biology Solutions using R and Bioconductor. New York: Springer, 2005. p. 397-420.
[26] Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:Article3.
[27] Miller RGJ. Simultaneous statistical inference, 2 ed. Springer, 1981.
[28] Hommel G. A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 1988;75(2):383-86.
[29] Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. 1995;57:289-300.
[30] Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 1979;6(2):65-70.
[31] Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 1988;75(4):800-02.
[32] Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 2001;29(4):1165-88.
[33] Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA, Rapp BA. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2000;28(1):10-4.