Details
| Original language | English |
|---|---|
| Pages (from-to) | 841-846 |
| Number of pages | 6 |
| Journal | Engineering in life sciences |
| Volume | 17 |
| Issue number | 8 |
| Publication status | Published - 20 May 2017 |
Abstract
Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.
Keywords
- ANOVA, Artificial Neural Networks, DNA microarrays, Photobleaching, Transcriptomics
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Environmental Science(all)
- Environmental Engineering
- Chemical Engineering(all)
- Bioengineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Engineering in life sciences, Vol. 17, No. 8, 20.05.2017, p. 841-846.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Array Analysis Manager - An automated DNAmicroarray analysis tool simplifying microarraydata filtering, bias recognition, normalization,and expression analysis
AU - von der Haar, Marcel
AU - Lindner, Patrick
AU - Scheper, Thomas
AU - Stahl, Frank
N1 - © 2017 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.
PY - 2017/5/20
Y1 - 2017/5/20
N2 - Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.
AB - Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.
KW - ANOVA
KW - Artificial Neural Networks
KW - DNA microarrays
KW - Photobleaching
KW - Transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85020420982&partnerID=8YFLogxK
U2 - 10.1002/elsc.201700046
DO - 10.1002/elsc.201700046
M3 - Article
C2 - 32624831
AN - SCOPUS:85020420982
VL - 17
SP - 841
EP - 846
JO - Engineering in life sciences
JF - Engineering in life sciences
SN - 1618-0240
IS - 8
ER -