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Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.

van Wieringen WN, Unger K, Leday GG, Krijgsman O, de Menezes RX, Ylstra B, van de Wiel MA - BMC Bioinformatics (2012)

Bottom Line: Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl

ABSTRACT

Background: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.

Results: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.

Conclusions: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Show MeSH
Distance matching: the DNA copy number feature closest to the gene is sought. The black points inside the red boxes represent the midpoints. Above, feature j-1 clearly is closest to the gene.
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Figure 2: Distance matching: the DNA copy number feature closest to the gene is sought. The black points inside the red boxes represent the midpoints. Above, feature j-1 clearly is closest to the gene.

Mentions: As an alternative to the label information, features may also be matched on the basis of their genomic location. The first procedure we describe that does this defines a distance measure between the genomic locations of the two probe sequences. A gene expression feature is matched to the DNA copy number feature with the closest (mid-)base pair position. The distance matching procedure has, among others, been proposed by Van Wieringen et al. [11]. See Table 2 for an algorithmic description, with Figure 2 an illustration of the crucial step of the algorithm.


Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.

van Wieringen WN, Unger K, Leday GG, Krijgsman O, de Menezes RX, Ylstra B, van de Wiel MA - BMC Bioinformatics (2012)

Distance matching: the DNA copy number feature closest to the gene is sought. The black points inside the red boxes represent the midpoints. Above, feature j-1 clearly is closest to the gene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475006&req=5

Figure 2: Distance matching: the DNA copy number feature closest to the gene is sought. The black points inside the red boxes represent the midpoints. Above, feature j-1 clearly is closest to the gene.
Mentions: As an alternative to the label information, features may also be matched on the basis of their genomic location. The first procedure we describe that does this defines a distance measure between the genomic locations of the two probe sequences. A gene expression feature is matched to the DNA copy number feature with the closest (mid-)base pair position. The distance matching procedure has, among others, been proposed by Van Wieringen et al. [11]. See Table 2 for an algorithmic description, with Figure 2 an illustration of the crucial step of the algorithm.

Bottom Line: Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl

ABSTRACT

Background: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.

Results: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.

Conclusions: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Show MeSH