Application of machine learning and visualization of heterogeneous datasets to uncover relationships between translation and developmental stage expression of C. elegans mRNAs.
The relationships between genes in neighboring clusters in a self-organizing map (SOM) and properties attributed to them are sometimes difficult to discern, especially when heterogeneous datasets are used. We report a novel approach to identify correlations between heterogeneous datasets. One dataset, derived from microarray analysis of polysomal distribution, contained changes in the translational efficiency of Caenorhabditis elegans mRNAs resulting from loss of specific eIF4E isoform. The other dataset contained expression patterns of mRNAs across all developmental stages. Two algorithms were applied to these datasets: a classical scatter plot and an SOM. The outputs were linked using a two-dimensional color scale. This revealed that an mRNA's eIF4E-dependent translational efficiency is strongly dependent on its expression during development. This correlation was not detectable with a traditional one-dimensional color scale.
Trutschl
Marjan
M
Department of Computer Science, Louisiana State University, Louisiana, USA.
Dinkova
Tzvetanka D
TD
Rhoads
Robert E
RE
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D017173:Caenorhabditis elegans / Q000235:genetics* / Q000378:metabolism; D039561:Eukaryotic Initiation Factor-4E / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D008040:Genetic Linkage; D008954:Models, Biological; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011132:Polyribosomes / Q000378:metabolism; D014176:Protein Biosynthesis / Q000235:genetics*; D020033:Protein Isoforms / Q000235:genetics; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism*
D039561:Eukaryotic Initiation Factor-4E; D020033:Protein Isoforms; D012333:RNA, Messenger