A Simple Affymetrix Ratio-transformation Method Yields Comparable Expression Level Quantifications with cDNA Data
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.
[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.
[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.
@article{"International Journal of Biological, Life and Agricultural Sciences:56567", author = "Chintanu K. Sarmah and Sandhya Samarasinghe and Don Kulasiri and Daniel Catchpoole", title = "A Simple Affymetrix Ratio-transformation Method Yields Comparable Expression Level Quantifications with cDNA Data", 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.", volume = "4", number = "1", pages = "76-6", }
