TileProbe: Modeling tiling array probe effects using publicly available data

Jennifer Toolan Judy, Hong Kai Ji

Research output: Contribution to journalArticle

Abstract

Motivation: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals. Results: We propose TileProbe, a new technique that builds upon the MATalgorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely.

Original languageEnglish (US)
Pages (from-to)2369-2375
Number of pages7
JournalBioinformatics
Volume25
Issue number18
DOIs
StatePublished - Sep 2009

Fingerprint

Tiling
Probe
Modeling
Chip
Datasets
Resolve
Continue
Model

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

Cite this

TileProbe : Modeling tiling array probe effects using publicly available data. / Judy, Jennifer Toolan; Ji, Hong Kai.

In: Bioinformatics, Vol. 25, No. 18, 09.2009, p. 2369-2375.

Research output: Contribution to journalArticle

Judy, Jennifer Toolan ; Ji, Hong Kai. / TileProbe : Modeling tiling array probe effects using publicly available data. In: Bioinformatics. 2009 ; Vol. 25, No. 18. pp. 2369-2375.
@article{c5488da0bbec4a668f83e9a6576ba3df,
title = "TileProbe: Modeling tiling array probe effects using publicly available data",
abstract = "Motivation: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals. Results: We propose TileProbe, a new technique that builds upon the MATalgorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely.",
author = "Judy, {Jennifer Toolan} and Ji, {Hong Kai}",
year = "2009",
month = "9",
doi = "10.1093/bioinformatics/btp425",
language = "English (US)",
volume = "25",
pages = "2369--2375",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "18",

}

TY - JOUR

T1 - TileProbe

T2 - Modeling tiling array probe effects using publicly available data

AU - Judy, Jennifer Toolan

AU - Ji, Hong Kai

PY - 2009/9

Y1 - 2009/9

N2 - Motivation: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals. Results: We propose TileProbe, a new technique that builds upon the MATalgorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely.

AB - Motivation: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals. Results: We propose TileProbe, a new technique that builds upon the MATalgorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely.

UR - http://www.scopus.com/inward/record.url?scp=69849089440&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=69849089440&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btp425

DO - 10.1093/bioinformatics/btp425

M3 - Article

C2 - 19592393

AN - SCOPUS:69849089440

VL - 25

SP - 2369

EP - 2375

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 18

ER -