TY - JOUR
T1 - Physically grounded approach for estimating gene expression from microarray data
AU - McMullen, Patrick D.
AU - Morimoto, Richard I.
AU - Nunes Amaral, Luís A.
PY - 2010/8/3
Y1 - 2010/8/3
N2 - High-throughput technologies, including gene-expression microarrays, hold great promise for the systems-level study of biological processes. Yet, challenges remain in comparing microarray data from different sources and extracting information about low-abundance transcripts. We demonstrate that these difficulties arise from limitations in the modeling of the data. We propose a physically motivated approach for estimating gene-expression levels from microarray data, an approach neglected in the microarray literature. We separately model the noises specific to sample amplification, hybridization, and fluorescence detection, combining these into a parsimonious description of the variability sources in a microarray experiment. We find that our model produces estimates of gene expression that are reproducible and unbiased. While the details of our model are specific to gene-expression microarrays, we argue that the physically grounded modeling approach we pursue is broadly applicable to other molecular biology technologies.
AB - High-throughput technologies, including gene-expression microarrays, hold great promise for the systems-level study of biological processes. Yet, challenges remain in comparing microarray data from different sources and extracting information about low-abundance transcripts. We demonstrate that these difficulties arise from limitations in the modeling of the data. We propose a physically motivated approach for estimating gene-expression levels from microarray data, an approach neglected in the microarray literature. We separately model the noises specific to sample amplification, hybridization, and fluorescence detection, combining these into a parsimonious description of the variability sources in a microarray experiment. We find that our model produces estimates of gene expression that are reproducible and unbiased. While the details of our model are specific to gene-expression microarrays, we argue that the physically grounded modeling approach we pursue is broadly applicable to other molecular biology technologies.
KW - Process modeling
KW - Statistical power
UR - http://www.scopus.com/inward/record.url?scp=77956390442&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956390442&partnerID=8YFLogxK
U2 - 10.1073/pnas.1000938107
DO - 10.1073/pnas.1000938107
M3 - Article
C2 - 20643961
AN - SCOPUS:77956390442
VL - 107
SP - 13690
EP - 13695
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 31
ER -