2 research outputs found
Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data
Identifying groups that share common features among datasets through
clustering analysis is a typical problem in many fields of science,
particularly in post-omics and systems biology research. In respect of this,
quantifying how a measure can cluster or organize intrinsic groups is important
since currently there is no statistical evaluation of how ordered is, or how
much noise is embedded in the resulting clustered vector. Many of the
literature focuses on how well the clustering algorithm orders the data, with
several measures regarding external and internal statistical measures; but none
measure has been developed to statistically quantify the noise in an arranged
vector posterior a clustering algorithm, i.e., how much of the clustering is
due to randomness. Here, we present a quantitative methodology, based on
autocorrelation, to assess this problem.Comment: 9 pages, 6 figure
Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations
The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations. Using simulation, we showed that inferring the DFE correlation from the joint allele frequency spectrum is statistically precise and robust. Using population genomic data, we inferred DFE correlations of populations in humans, Drosophila melanogaster, and wild tomatoes. In these species, we found that the overall correlation of the joint DFE was inversely related to genetic differentiation. In humans and D. melanogaster, deleterious mutations had a lower DFE correlation than tolerated mutations, indicating a complex joint DFE. Altogether, the DFE correlation can be reliably inferred, and it offers extensive insight into the genetics of population divergence.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]