Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays

Yoseph Barash, Elinor Dehan, Meir Krupsky, Wilbur Franklin, Marc Geraci, Nir Friedman, Naftali Kaminski

Research output: Contribution to journalArticle

72 Citations (Scopus)

Abstract

Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. Results: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.

Original languageEnglish (US)
Pages (from-to)839-846
Number of pages8
JournalBioinformatics
Volume20
Issue number6
DOIs
StatePublished - Apr 12 2004
Externally publishedYes

Fingerprint

Analysis of Algorithms
Oligonucleotides
Microarrays
Oligonucleotide Array Sequence Analysis
Comparative Analysis
Microarray
Genes
Gene
Noise
Software
Research Personnel
Experiment
Statistical tests
Experiments
Significance Test
DNA Microarray
Signal-To-Noise Ratio
Statistical Significance
Gene expression
Statistical test

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Barash, Y., Dehan, E., Krupsky, M., Franklin, W., Geraci, M., Friedman, N., & Kaminski, N. (2004). Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinformatics, 20(6), 839-846. https://doi.org/10.1093/bioinformatics/btg487

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. / Barash, Yoseph; Dehan, Elinor; Krupsky, Meir; Franklin, Wilbur; Geraci, Marc; Friedman, Nir; Kaminski, Naftali.

In: Bioinformatics, Vol. 20, No. 6, 12.04.2004, p. 839-846.

Research output: Contribution to journalArticle

Barash, Y, Dehan, E, Krupsky, M, Franklin, W, Geraci, M, Friedman, N & Kaminski, N 2004, 'Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays', Bioinformatics, vol. 20, no. 6, pp. 839-846. https://doi.org/10.1093/bioinformatics/btg487
Barash Y, Dehan E, Krupsky M, Franklin W, Geraci M, Friedman N et al. Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinformatics. 2004 Apr 12;20(6):839-846. https://doi.org/10.1093/bioinformatics/btg487
Barash, Yoseph ; Dehan, Elinor ; Krupsky, Meir ; Franklin, Wilbur ; Geraci, Marc ; Friedman, Nir ; Kaminski, Naftali. / Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. In: Bioinformatics. 2004 ; Vol. 20, No. 6. pp. 839-846.
@article{66487d59b2964f00934fcc7c0f207794,
title = "Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays",
abstract = "Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. Results: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60{\%} of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95{\%} of the genes.",
author = "Yoseph Barash and Elinor Dehan and Meir Krupsky and Wilbur Franklin and Marc Geraci and Nir Friedman and Naftali Kaminski",
year = "2004",
month = "4",
day = "12",
doi = "10.1093/bioinformatics/btg487",
language = "English (US)",
volume = "20",
pages = "839--846",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "6",

}

TY - JOUR

T1 - Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays

AU - Barash, Yoseph

AU - Dehan, Elinor

AU - Krupsky, Meir

AU - Franklin, Wilbur

AU - Geraci, Marc

AU - Friedman, Nir

AU - Kaminski, Naftali

PY - 2004/4/12

Y1 - 2004/4/12

N2 - Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. Results: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.

AB - Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. Results: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.

UR - http://www.scopus.com/inward/record.url?scp=2342439256&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2342439256&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btg487

DO - 10.1093/bioinformatics/btg487

M3 - Article

VL - 20

SP - 839

EP - 846

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 6

ER -