Probabilistic estimation of microarray data reliability and underlying gene expression.
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.
Bilke
Sven
S
Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. sven@thep.lu.se
Breslin
Thomas
T
Sigvardsson
Mikael
M
D000465:Algorithms; D000818:Animals; D001402:B-Lymphocytes / Q000737:chemistry / Q000378:metabolism; D002454:Cell Differentiation / Q000235:genetics; D019295:Computational Biology / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data*; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005786:Gene Expression Regulation / Q000235:genetics; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010641:Phenotype; D011336:Probability; D012333:RNA, Messenger / Q000096:biosynthesis / Q000235:genetics
D012333:RNA, Messenger