To detect distinct expression differences between the growth conditions, paired Student t-tests of transcripts differentially expressed in ANOVA (FDR < 0,05) have been applied.
After recording, individual islet cells were lysed, and single-cell RT-PCR was used to test for transcripts of Kv1.7, insulin and glucagon.
Using D. pseudoobscura gene models, it was confirmed that exon-exon junctions were correctly identified in a random test sample of transcripts.
Figure 1b shows the statistical significance (by Student's t tests) of differential transcript cluster expression in a volcano plot format along with respective fold changes (FC).
Although only ten genes were tested, estimation of transcript abundance by EST frequency was apparently effective in identifying genes with root-specific expression, despite the majority of root ESTs coming from a normalized library source.
About 103000 entirely sequenced cDNA clones in the FANTOM3 database from the RIKEN mouse Gene Encyclopedia project facilitated the direct testing of whether transcripts from different APs tended to splice differently for a given gene.
Interestingly, the translation initiation repressor protein KHD1p binds to more than 65% (PUA: 2 × 10−9; Fisher's test) of all transcripts encoding aggregation-prone, Q/N-rich, unstructured proteins, but only 19% of the nonaggregation-prone, unstructured proteins.
The quality of the RNA preparations was tested by amplification of transcripts of the housekeeping genes, β-actin and protein phosphatase 2C β (pp2C β).
We used biological end points in uterine tissue and a signature pattern recognizing tool that identified coexpressed transcripts to develop and test a panel of transcripts in order to classify potentially estrogenic compounds using an in vivo system.
Relating MEs instead of genes to a clinical trait is a major advantage of WGCNA as it diminishes the multiple testing from thousands of transcripts to the number of modules.
In comparison with the analysis of correlations of individual genes with clinical traits, module eigengenes (MEs) offer a major advantage because they reduce multiple testing from thousands of transcripts to the number of modules.
