Using Interpolation to Estimate System Uncertainty in Gene Expression Experiments
| dc.contributor | Virginia Tech | en |
| dc.contributor.author | Falin, Lee J. | en |
| dc.contributor.author | Tyler, Brett M. | en |
| dc.date.accessed | 2014-05-01 | en |
| dc.date.accessioned | 2014-06-17T20:12:05Z | en |
| dc.date.available | 2014-06-17T20:12:05Z | en |
| dc.date.issued | 2011-07-22 | en |
| dc.description.abstract | The widespread use of high-throughput experimental assays designed to measure the entire complement of a cell's genes or gene products has led to vast stores of data that are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured intervals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within unmeasured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. Although the context for developing the algorithm was gene expression measurements taken over a time series, the approach can be readily applied to any set of quantitative systems biology measurements taken following quantitative (i.e. non-categorical) treatments. In principle, the method could also be applied to combinations of treatments, in which case it could greatly simplify the task of exploring the large combinatorial space of future possible measurements. | en |
| dc.description.sponsorship | This work was supported in part by grants from the National Science Foundation #DBI-0211863, from the Agriculture and Food Research Initiative of the National Institute of Food and Agriculture of the USDA #2005-35604-15525, by the Virginia Bioinformatics Institute, and by the Virginia Tech Graduate Program in Genetics, Bioinformatics and Computational Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | en |
| dc.identifier.citation | Falin LJ, Tyler BM. Using interpolation to estimate system uncertainty in gene expression experiments. PLoS One. 2011;6:e22071. http://www.ncbi.nlm.nih.gov/pubmed/21799771 | en |
| dc.identifier.doi | https://doi.org/10.1371/journal.pone.0022071 | en |
| dc.identifier.issn | 1932-6203 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/48983 | en |
| dc.identifier.url | http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0022071 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Public Library of Science | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Algorithms | en |
| dc.subject | Biologists | en |
| dc.subject | Confidence intervals | en |
| dc.subject | Gene expression | en |
| dc.subject | Interpolation | en |
| dc.subject | Monte Carlo method | en |
| dc.subject | Probability distribution | en |
| dc.subject | Time measurement | en |
| dc.title | Using Interpolation to Estimate System Uncertainty in Gene Expression Experiments | en |
| dc.title.serial | PLoS ONE | en |
| dc.type | Article - Refereed | en |
Files
Original bundle
1 - 1 of 1