In total, in this study we examined 31 separate patient cohorts consisting of 1,696 independent patient samples collected using various microarray platforms from diverse institutions in different countries to identify a robust and reproducible signature.
The datasets used in our study were generated using diverse microarray platforms and originating from different laboratories.
In summary, this study objectively evaluates several published signatures in independent cohorts from diverse microarray platforms and unifies results of previous gene expression studies in breast cancer.
One reason is because we included in our analysis 12 different datasets produced using diverse microarray platforms with different gene contents.
Moreover, without re-training, URSA can be used to classify samples from diverse microarray platforms and even from next-generation sequencing technology.
We build upon these studies and show that a highly reproducible reference sample can be created from a pool of RNAs derived from cell lines, and demonstrate that this reference samples gives good "coverage" across many diverse microarray platforms.
Owing to the underlying heterogeneity among included studies, such as differences in the tumor of origins (oral, pharynx, or larynx), multiple microarray platforms, different analytical approaches taken, and diverse endpoints, we sought to examine the validity of three staged classification of the HNSCC transcriptome.
These findings were confirmed using multiple published studies in diverse cohorts of patients across different microarray platforms, as well as independent experiments that identified Wnt induced targets which were consistently upregulated in TNBC subtypes.
Here we have used an extensive set of transcriptomics data from a diverse range of sources, covering different microarray platforms, species of Populus as well as Arabidopsis to identify genes that are actively transcribed during the process of leaf development.
Using a meta-analysis of more than 40 diverse microarray studies which included different microarray platforms (long and short oligonucleotide arrays, cDNA and bead microarrays) and different methods of normalizations (MAS5, RMA, GC-RMA, VSN, LOWESS), we demonstrate a robust interaction between gene expression in signaling and metabolic pathways.
