Probabilistic estimation of microarray data reliability and underlying gene expression
(2003) In BMC Bioinformatics 4(40).- Abstract
- Background: The availability of high throughput methods for measurement of mRNA concentrations makes the reliability of conclusions drawn from the data and global quality control of samples and hybridization important issues. We address these issues by an information theoretic approach, applied to discretized expression values in replicated gene expression data. Results: Our approach yields a quantitative measure of two important parameter classes: First, the probability P(sigma|S) that a gene is in the biological state sigma in a certain variety, given its observed expression S in the samples of that variety. Second, sample specific error probabilities which serve as consistency indicators of the measured samples of each variety. The... (More)
- Background: The availability of high throughput methods for measurement of mRNA concentrations makes the reliability of conclusions drawn from the data and global quality control of samples and hybridization important issues. We address these issues by an information theoretic approach, applied to discretized expression values in replicated gene expression data. Results: Our approach yields a quantitative measure of two important parameter classes: First, the probability P(sigma|S) that a gene is in the biological state sigma in a certain variety, given its observed expression S in the samples of that variety. Second, sample specific error probabilities which serve as consistency indicators of the measured samples of each variety. The method and its limitations are tested on gene expression data for developing murine B-cells and a t-test is used as reference. On a set of known genes it performs better than the t-test despite the crude discretization into only two expression levels. The consistency indicators, i.e. the error probabilities, correlate well with variations in the biological material and thus prove efficient.Conclusions: The proposed method is effective in determining differential gene expression and sample reliability in replicated microarray data. Already at two discrete expression levels in each sample, it gives a good explanation of the data and is comparable to standard techniques. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/131404
- author
- Bilke, Sven LU ; Breslin, Thomas LU and Sigvardsson, Mikael LU
- organization
- publishing date
- 2003
- type
- Contribution to journal
- publication status
- published
- subject
- in
- BMC Bioinformatics
- volume
- 4
- issue
- 40
- publisher
- BioMed Central (BMC)
- external identifiers
-
- wos:000186341700002
- pmid:12967349
- scopus:0642275806
- ISSN
- 1471-2105
- DOI
- 10.1186/1471-2105-4-40
- language
- English
- LU publication?
- yes
- id
- ce2aae20-c7ce-4531-88eb-24cc62aa5852 (old id 131404)
- date added to LUP
- 2016-04-01 16:55:26
- date last changed
- 2024-01-11 17:23:13
@article{ce2aae20-c7ce-4531-88eb-24cc62aa5852,
abstract = {{Background: The availability of high throughput methods for measurement of mRNA concentrations makes the reliability of conclusions drawn from the data and global quality control of samples and hybridization important issues. We address these issues by an information theoretic approach, applied to discretized expression values in replicated gene expression data. Results: Our approach yields a quantitative measure of two important parameter classes: First, the probability P(sigma|S) that a gene is in the biological state sigma in a certain variety, given its observed expression S in the samples of that variety. Second, sample specific error probabilities which serve as consistency indicators of the measured samples of each variety. The method and its limitations are tested on gene expression data for developing murine B-cells and a t-test is used as reference. On a set of known genes it performs better than the t-test despite the crude discretization into only two expression levels. The consistency indicators, i.e. the error probabilities, correlate well with variations in the biological material and thus prove efficient.Conclusions: The proposed method is effective in determining differential gene expression and sample reliability in replicated microarray data. Already at two discrete expression levels in each sample, it gives a good explanation of the data and is comparable to standard techniques.}},
author = {{Bilke, Sven and Breslin, Thomas and Sigvardsson, Mikael}},
issn = {{1471-2105}},
language = {{eng}},
number = {{40}},
publisher = {{BioMed Central (BMC)}},
series = {{BMC Bioinformatics}},
title = {{Probabilistic estimation of microarray data reliability and underlying gene expression}},
url = {{https://lup.lub.lu.se/search/files/4820080/624205.pdf}},
doi = {{10.1186/1471-2105-4-40}},
volume = {{4}},
year = {{2003}},
}