Comparing phylogenetic relationships and microarray-based gene expression clusters it was observed that the following pairs of closely related genes (OsWRKY18 and OsWRKY4 in cluster A, OsWRKY71 and OsWRKY79 in cluster E, OsWRKY100 and OsWRKY53 in cluster I) were co-expressed, reflecting recent duplications and potentially functional redundancy (see Figure 1).
Based on gene expression clustering of these genes, we could distinguish tumors arising in the two different backgrounds, indicating the presence of genes that are differentially expressed during the tumorigenic course.
Based on hierarchical clustering (HCL), 11 gene expression clusters were obtained.
Apart from analyzing enrichment for GO annotations, we also performed a functional analysis based on overlap of our data with gene expression clusters in C. elegans, which originate from other published microarray data sets.
Based on gene expression similarity clustering different subgroups can be identified, corresponding to the manually compiled ones in Figure 7.
Based on gene expression correlation clustering and cell-type analyses, 15 meta-genes and 3 normalization genes were defined as inputs for model variable selection (Table 2, Figure 6).
If a tumor can be divided into two distinct clusters based on gene expression profiles, is each cluster a new subclass of the tumor?
Based on gene expression, the stroma and epithelium clustered according to tissue type.
Based on gene expression, the IN5 sample largely clustered with IN9, rather than with the IN4 sample that is adjacent in the stem.
We performed statistical analyses as described in Methods for all parasitism gene probesets, which identified two clusters based on gene expression patterns.
Differentiation markers (list generated by an automatic analysis of the bibliography and displayed as supplementary material File S1) were selected for analysis and clustered based on gene expression (Figure 5B).
