Empirical evaluation of data transformations and ranking statistics for microarray analysis

Li Xuan Qin, Kathleen F. Kerr, Abee Boyles, Holly K. Dressman, Jonathan H. Freedman, Y. Ju Li, Renae L. Malek, David A. Schwartz, Susan Slifer, Mary C. Speer, Ivana Yang, Helmut Zarbl, Jung Lim Shin, Lichen Jing, Robert C. Sullivan, Rebecca Fry, Leona Samson, C. J. Tucker, R. D. Fannin, S. O. Sieber & 27 others J. Li, P. R. Bushel, R. S. Paules, G. A. Boorman, M. L. Cunningham, B. K. Weis, Dongseok Choi, Jodi Lapidus, Michael Lasarev, Xinfang Lu, Jean O'Malley, Patrick Pattee, Srinivasa Nagalla, Signe Todd, Matthew Rodland, Peter Spencer, William Kaufmann, Charles Perou, Ivan Rusyn, James Swenberg, Blair Bradford, Shibing Deng, Terrance J. Kavanagh, Federico M. Farin, Richard P. Beyer, Sean Quigley, Theo K. Bammler

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics out-perform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.

Original languageEnglish (US)
Pages (from-to)5471-5479
Number of pages9
JournalNucleic Acids Research
Volume32
Issue number18
DOIs
StatePublished - 2004

Fingerprint

Microarray Analysis
Genes
Software
Color

ASJC Scopus subject areas

  • Genetics

Cite this

Qin, L. X., Kerr, K. F., Boyles, A., Dressman, H. K., Freedman, J. H., Li, Y. J., ... Bammler, T. K. (2004). Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Research, 32(18), 5471-5479. https://doi.org/10.1093/nar/gkh866

Empirical evaluation of data transformations and ranking statistics for microarray analysis. / Qin, Li Xuan; Kerr, Kathleen F.; Boyles, Abee; Dressman, Holly K.; Freedman, Jonathan H.; Li, Y. Ju; Malek, Renae L.; Schwartz, David A.; Slifer, Susan; Speer, Mary C.; Yang, Ivana; Zarbl, Helmut; Shin, Jung Lim; Jing, Lichen; Sullivan, Robert C.; Fry, Rebecca; Samson, Leona; Tucker, C. J.; Fannin, R. D.; Sieber, S. O.; Li, J.; Bushel, P. R.; Paules, R. S.; Boorman, G. A.; Cunningham, M. L.; Weis, B. K.; Choi, Dongseok; Lapidus, Jodi; Lasarev, Michael; Lu, Xinfang; O'Malley, Jean; Pattee, Patrick; Nagalla, Srinivasa; Todd, Signe; Rodland, Matthew; Spencer, Peter; Kaufmann, William; Perou, Charles; Rusyn, Ivan; Swenberg, James; Bradford, Blair; Deng, Shibing; Kavanagh, Terrance J.; Farin, Federico M.; Beyer, Richard P.; Quigley, Sean; Bammler, Theo K.

In: Nucleic Acids Research, Vol. 32, No. 18, 2004, p. 5471-5479.

Research output: Contribution to journalArticle

Qin, LX, Kerr, KF, Boyles, A, Dressman, HK, Freedman, JH, Li, YJ, Malek, RL, Schwartz, DA, Slifer, S, Speer, MC, Yang, I, Zarbl, H, Shin, JL, Jing, L, Sullivan, RC, Fry, R, Samson, L, Tucker, CJ, Fannin, RD, Sieber, SO, Li, J, Bushel, PR, Paules, RS, Boorman, GA, Cunningham, ML, Weis, BK, Choi, D, Lapidus, J, Lasarev, M, Lu, X, O'Malley, J, Pattee, P, Nagalla, S, Todd, S, Rodland, M, Spencer, P, Kaufmann, W, Perou, C, Rusyn, I, Swenberg, J, Bradford, B, Deng, S, Kavanagh, TJ, Farin, FM, Beyer, RP, Quigley, S & Bammler, TK 2004, 'Empirical evaluation of data transformations and ranking statistics for microarray analysis', Nucleic Acids Research, vol. 32, no. 18, pp. 5471-5479. https://doi.org/10.1093/nar/gkh866
Qin, Li Xuan ; Kerr, Kathleen F. ; Boyles, Abee ; Dressman, Holly K. ; Freedman, Jonathan H. ; Li, Y. Ju ; Malek, Renae L. ; Schwartz, David A. ; Slifer, Susan ; Speer, Mary C. ; Yang, Ivana ; Zarbl, Helmut ; Shin, Jung Lim ; Jing, Lichen ; Sullivan, Robert C. ; Fry, Rebecca ; Samson, Leona ; Tucker, C. J. ; Fannin, R. D. ; Sieber, S. O. ; Li, J. ; Bushel, P. R. ; Paules, R. S. ; Boorman, G. A. ; Cunningham, M. L. ; Weis, B. K. ; Choi, Dongseok ; Lapidus, Jodi ; Lasarev, Michael ; Lu, Xinfang ; O'Malley, Jean ; Pattee, Patrick ; Nagalla, Srinivasa ; Todd, Signe ; Rodland, Matthew ; Spencer, Peter ; Kaufmann, William ; Perou, Charles ; Rusyn, Ivan ; Swenberg, James ; Bradford, Blair ; Deng, Shibing ; Kavanagh, Terrance J. ; Farin, Federico M. ; Beyer, Richard P. ; Quigley, Sean ; Bammler, Theo K. / Empirical evaluation of data transformations and ranking statistics for microarray analysis. In: Nucleic Acids Research. 2004 ; Vol. 32, No. 18. pp. 5471-5479.
@article{16972d4ad46b4e0bb15d23326a691841,
title = "Empirical evaluation of data transformations and ranking statistics for microarray analysis",
abstract = "There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics out-perform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.",
author = "Qin, {Li Xuan} and Kerr, {Kathleen F.} and Abee Boyles and Dressman, {Holly K.} and Freedman, {Jonathan H.} and Li, {Y. Ju} and Malek, {Renae L.} and Schwartz, {David A.} and Susan Slifer and Speer, {Mary C.} and Ivana Yang and Helmut Zarbl and Shin, {Jung Lim} and Lichen Jing and Sullivan, {Robert C.} and Rebecca Fry and Leona Samson and Tucker, {C. J.} and Fannin, {R. D.} and Sieber, {S. O.} and J. Li and Bushel, {P. R.} and Paules, {R. S.} and Boorman, {G. A.} and Cunningham, {M. L.} and Weis, {B. K.} and Dongseok Choi and Jodi Lapidus and Michael Lasarev and Xinfang Lu and Jean O'Malley and Patrick Pattee and Srinivasa Nagalla and Signe Todd and Matthew Rodland and Peter Spencer and William Kaufmann and Charles Perou and Ivan Rusyn and James Swenberg and Blair Bradford and Shibing Deng and Kavanagh, {Terrance J.} and Farin, {Federico M.} and Beyer, {Richard P.} and Sean Quigley and Bammler, {Theo K.}",
year = "2004",
doi = "10.1093/nar/gkh866",
language = "English (US)",
volume = "32",
pages = "5471--5479",
journal = "Nucleic Acids Research",
issn = "0305-1048",
publisher = "Oxford University Press",
number = "18",

}

TY - JOUR

T1 - Empirical evaluation of data transformations and ranking statistics for microarray analysis

AU - Qin, Li Xuan

AU - Kerr, Kathleen F.

AU - Boyles, Abee

AU - Dressman, Holly K.

AU - Freedman, Jonathan H.

AU - Li, Y. Ju

AU - Malek, Renae L.

AU - Schwartz, David A.

AU - Slifer, Susan

AU - Speer, Mary C.

AU - Yang, Ivana

AU - Zarbl, Helmut

AU - Shin, Jung Lim

AU - Jing, Lichen

AU - Sullivan, Robert C.

AU - Fry, Rebecca

AU - Samson, Leona

AU - Tucker, C. J.

AU - Fannin, R. D.

AU - Sieber, S. O.

AU - Li, J.

AU - Bushel, P. R.

AU - Paules, R. S.

AU - Boorman, G. A.

AU - Cunningham, M. L.

AU - Weis, B. K.

AU - Choi, Dongseok

AU - Lapidus, Jodi

AU - Lasarev, Michael

AU - Lu, Xinfang

AU - O'Malley, Jean

AU - Pattee, Patrick

AU - Nagalla, Srinivasa

AU - Todd, Signe

AU - Rodland, Matthew

AU - Spencer, Peter

AU - Kaufmann, William

AU - Perou, Charles

AU - Rusyn, Ivan

AU - Swenberg, James

AU - Bradford, Blair

AU - Deng, Shibing

AU - Kavanagh, Terrance J.

AU - Farin, Federico M.

AU - Beyer, Richard P.

AU - Quigley, Sean

AU - Bammler, Theo K.

PY - 2004

Y1 - 2004

N2 - There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics out-perform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.

AB - There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics out-perform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.

UR - http://www.scopus.com/inward/record.url?scp=9144268360&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=9144268360&partnerID=8YFLogxK

U2 - 10.1093/nar/gkh866

DO - 10.1093/nar/gkh866

M3 - Article

VL - 32

SP - 5471

EP - 5479

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

IS - 18

ER -