Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

Richard Shippy, Timothy J. Sendera, Randall Lockner, Chockalingam Palaniappan, Tamma Kaysser-Kranich, George S Watts, John Alsobrook

Research output: Contribution to journalArticle

118 Citations (Scopus)

Abstract

Background: Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. Results: In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of interplatform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. Conclusions: As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

Original languageEnglish (US)
Article number61
JournalBMC Genomics
Volume5
DOIs
StatePublished - Sep 2 2004

Fingerprint

Oligonucleotide Array Sequence Analysis
Noise
Overlapping Genes
Genes
Guidelines
Microarray Analysis
Real-Time Polymerase Chain Reaction
RNA
Technology

ASJC Scopus subject areas

  • Medicine(all)
  • Biotechnology
  • Genetics

Cite this

Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations. / Shippy, Richard; Sendera, Timothy J.; Lockner, Randall; Palaniappan, Chockalingam; Kaysser-Kranich, Tamma; Watts, George S; Alsobrook, John.

In: BMC Genomics, Vol. 5, 61, 02.09.2004.

Research output: Contribution to journalArticle

Shippy, Richard ; Sendera, Timothy J. ; Lockner, Randall ; Palaniappan, Chockalingam ; Kaysser-Kranich, Tamma ; Watts, George S ; Alsobrook, John. / Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations. In: BMC Genomics. 2004 ; Vol. 5.
@article{eeed8f395c01461ca117e8c6270d42af,
title = "Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations",
abstract = "Background: Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. Results: In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips{\circledR} and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of interplatform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. Conclusions: As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.",
author = "Richard Shippy and Sendera, {Timothy J.} and Randall Lockner and Chockalingam Palaniappan and Tamma Kaysser-Kranich and Watts, {George S} and John Alsobrook",
year = "2004",
month = "9",
day = "2",
doi = "10.1186/1471-2164-5-61",
language = "English (US)",
volume = "5",
journal = "BMC Genomics",
issn = "1471-2164",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

AU - Shippy, Richard

AU - Sendera, Timothy J.

AU - Lockner, Randall

AU - Palaniappan, Chockalingam

AU - Kaysser-Kranich, Tamma

AU - Watts, George S

AU - Alsobrook, John

PY - 2004/9/2

Y1 - 2004/9/2

N2 - Background: Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. Results: In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of interplatform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. Conclusions: As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

AB - Background: Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. Results: In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of interplatform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. Conclusions: As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

UR - http://www.scopus.com/inward/record.url?scp=9144256854&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=9144256854&partnerID=8YFLogxK

U2 - 10.1186/1471-2164-5-61

DO - 10.1186/1471-2164-5-61

M3 - Article

VL - 5

JO - BMC Genomics

JF - BMC Genomics

SN - 1471-2164

M1 - 61

ER -