Genome-wide estimation of transcript concentrations from spotted cDNA microarray data.
A method providing absolute transcript concentrations from spotted microarray intensity data is presented. Number of transcripts per microg 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 10(5)-10(10) transcripts per microg 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.
Frigessi
Arnoldo
A
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway. frigessi@medisin.uio.no
van de Wiel
Mark A
MA
Holden
Marit
M
Svendsrud
Debbie H
DH
Glad
Ingrid K
IK
Lyng
Heidi
H
D001499:Bayes Theorem; D045744:Cell Line, Tumor; D005260:Female; D023281:Genomics / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000032:analysis*; D012334:RNA, Neoplasm / Q000032:analysis*; D014158:Transcription, Genetic; D002583:Uterine Cervical Neoplasms / Q000235:genetics
D012333:RNA, Messenger; D012334:RNA, Neoplasm