Name
Abstract | ||
|---|---|---|
In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative-or compositional-data, differential expression needs careful interpretation, and correlation-a statistical workhorse for analyzing pairwise relationships-is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic. which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1371/journal.pcbi.1004075 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | DocType | Volume |
bioinformatics,biomedical research | Journal | 11 |
Issue | ISSN | Citations |
3 | 1553-734X | 5 |
PageRank | References | Authors |
0.59 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Lovell | 1 | 24 | 6.37 |
| V. Pawlowsky-Glahn | 2 | 6 | 1.22 |
| J.J. Egozcue | 3 | 8 | 1.42 |
| Samuel Marguerat | 4 | 6 | 1.29 |
| Jürg Bähler | 5 | 129 | 12.66 |