Published online 4 October 2005
Methods Online |
Genome-wide estimation of transcript concentrations from spotted cDNA microarray data
1Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo Norway 2Department of Mathematics, University of Oslo Norway 3Norwegian Computing Center Oslo, Norway 4Department of Mathematics and Computer Science, Technische Universiteit Eindhoven The Netherlands 5Department of Radiation Biology, Health Enterprise Rikshospitalet-Radiumhospitalet Oslo, Norway
*To whom correspondence should be addressed at Department of Biostatistics, University of Oslo, PO BOX 1122 Blindern, N-0317 Oslo, Norway. Tel: +4722851004; Fax: +4722851313; Email: frigessi{at}medisin.uio.no
Received May 31, 2005. Revised August 28, 2005. Accepted August 28, 2005.
A method providing absolute transcript concentrations from spotted microarray intensity data is presented. Number of transcripts per µg total RNA, mRNA or per cell, are obtained for each gene, enabling comparisons of transcript levels within and between tissues. The method is based on Bayesian statistical modelling incorporating available information about the experiment from target preparation to image analysis, leading to realistically large confidence intervals for estimated concentrations. The method was validated in experiments using transcripts at known concentrations, showing accuracy and reproducibility of estimated concentrations, which were also in excellent agreement with results from quantitative real-time PCR. We determined the concentration for 10 157 genes in cervix cancers and a pool of cancer cell lines and found values in the range of 105–1010 transcripts per µg total RNA. The precision of our estimates was sufficiently high to detect significant concentration differences between two tumours and between different genes within the same tumour, comparisons that are not possible with standard intensity ratios. Our method can be used to explore the regulation of pathways and to develop individualized therapies, based on absolute transcript concentrations. It can be applied broadly, facilitating the construction of the transcriptome, continuously updating it by integrating future data.
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