Improving the accuracy of expression data analysis in time course experiments using resampling

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Title
Improving the accuracy of expression data analysis in time course experiments using resampling
Author(s)
Walter, W; Striberny, B; Gaquerel, E; Baldwin, IT; Sang-Gyu Kim; Heiland, I
Publication Date
2014-10
Journal
BMC BIOINFORMATICS, v.15, no., pp.352 -
Publisher
BIOMED CENTRAL LTD
Abstract
Background: As time series experiments in higher eukaryotes usually obtain data from different individuals collected at the different time points, a time series sample itself is not equivalent to a true biological replicate but is, rather, a combination of several biological replicates. The analysis of expression data derived from a time series sample is therefore often performed with a low number of replicates due to budget limitations or limitations in sample availability. In addition, most algorithms developed to identify specific patterns in time series dataset do not consider biological variation in samples collected at the same conditions. Results: Using artificial time course datasets, we show that resampling considerably improves the accuracy of transcripts identified as rhythmic. In particular, the number of false positives can be greatly reduced while at the same time the number of true positives can be maintained in the range of other methods currently used to determine rhythmically expressed genes. Conclusions: The resampling approach described here therefore increases the accuracy of time series expression data analysis and furthermore emphasizes the importance of biological replicates in identifying oscillating genes. Resampling can be used for any time series expression dataset as long as the samples are acquired from independent individuals at each time point
URI
https://pr.ibs.re.kr/handle/8788114/5624
ISSN
1471-2105
Appears in Collections:
Center for Genome Engineering(유전체 교정 연구단) > Journal Papers (저널논문)
Files in This Item:
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