Each of six human tumor types (prostate, breast, lung, colon, ovary, and pancreas) was used in the 〈〈profile search〉〉 function in the Oncomine database to find the available microarray datasets related to the specific cancer type.
Recently, large microarray datasets related to the plant diurnal cycle (e.g. diurnal and circadian microarray data for Arabidopsis [1], [15], [18], [19], maize [20], [21], [22], barley [23] and soybean [24]) were published and some are available in public databases.
Supplementary data 3 Microarray datasets related to pathogen resistance in Arabidopsis.
> We collected and analyzed four microarray datasets related to human diseases (Table 4).
To assess the frequency with which cytokine genes are identified as present or absent in human kidney we performed a meta-analysis of publicly available (GEO) Affymetrix microarray datasets related to human kidney tissue.
Five microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.
In the present study, over 800 publicly available microarray datasets related to the V. vinifera L. transcriptome were selected to construct global co-expression networks (GCNs), consisting of 463 datasets from the Nimblegen whole-genome arrays and 403 datasets from the 16 K Affymetrix Genechip arrays.
To reveal the function of statin induced miRNA in atherosclerotic plaque, we analyzed four microarray datasets related to atherosclerosis from GEO database, and the alteration of pathways in the atherosclerotic lesion group were selected by enrich analysis of significant upregulated gene (Additional file 5: Table S3).
We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers.
SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process.
