Discovering significant and interpretable patterns from multifactorial DNA microarray data with poor replication.
BACKGROUND
Multivariate analyses are advantageous for the simultaneous testing of the separate and combined effects of many variables and of their interactions. In factorial designs with many factors and/or levels, however, sufficient replication is often prohibitively costly. Furthermore, complicated statements are often required for the biological interpretation of the higher-order interactions determined by standard statistical techniques like analysis of variance.


RESULTS
Because we are usually interested in finding factor-specific effects or their interactions, we assumed that the observed expression profile of a gene is a manifestation of an underlying factor-specific generative pattern (FSGP) combined with noise. Thus, a genetic algorithm was created to find the nearest FSGP for each expression profile. We then measured the distance between each profile and the corresponding nearest FSGP. Permutation testing for the distance measures successfully identified those genes with statistically significant profiles, thus yielding straightforward biological interpretations. Association networks of genes, drugs, and cell lines were created as tripartite graphs, representing significant and interpretable relations, by using a microarray experiment of gastric-cancer cell lines with a factorial design and no replication. The proposed method may benefit the combined analysis of heterogeneous expression data from the growing public repositories.
Kim
Ju Han
JH
Seoul National University Biomedical Informatics, Seoul 110-799, Republic of Korea.
Jeoung
Dooil
D
Lee
Seongeun
S
Kim
Hyeouneui
H
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005820:Genetic Testing / Q000379:methods; D014644:Genetic Variation / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods; D013274:Stomach Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics*