2010
DOI: 10.1186/1471-2105-11-s11-s1
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Association of tissue lineage and gene expression: conservatively and differentially expressed genes define common and special functions of tissues

Abstract: BackgroundEmbryogenesis is the process by which the embryo is formed, develops, and establishes developmental hierarchies of tissues. The recent advance in microarray technology made it possible to investigate the tissue specific patterns of gene expression and their relationship with tissue lineages. This study is focused on how tissue specific functions, tissue lineage, and cell differentiation are correlated, which is essential to understand embryonic development and organism complexity.ResultsWe performed … Show more

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Cited by 31 publications
(11 citation statements)
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“…Lastly, we investigated the relationship between state and differential gene expression (DGE) between root and seedling tissue (see Experimental Procedures). Genes were clustered into five discrete DGE bins, from 0 (no differential expression) to 1 (high differential expression), using a P ‐ranking approach (Yu et al ., ). Enrichments in these bins for the six chromatin states with genic involvement are shown in Figure (c).…”
Section: Resultsmentioning
confidence: 97%
“…Lastly, we investigated the relationship between state and differential gene expression (DGE) between root and seedling tissue (see Experimental Procedures). Genes were clustered into five discrete DGE bins, from 0 (no differential expression) to 1 (high differential expression), using a P ‐ranking approach (Yu et al ., ). Enrichments in these bins for the six chromatin states with genic involvement are shown in Figure (c).…”
Section: Resultsmentioning
confidence: 97%
“…However, such simple concatenation may not be optimal for achieving effective feature combination 13 . On the other hand, a kernel-based feature combination using multi-kernel learning offers more flexibility for feature fusion by estimating different weights on the features from different modalities 7072 , which could provide a better way to integrate the features derived from different types of FC networks.…”
Section: Methodsmentioning
confidence: 99%
“…The heatmaps of all genes and DESeq candidate features were given in Additional file 1 Figure S1 and S2. Pairs of tissues showing higher correlation were partially attributable to the tissue lineages and functional similarities [27]. For example, transcriptional age predictor trained on adipose tissue predicted transcriptional age in blood vessel with high correlation (r=0.65 in GTExAge signature) due to the similarity in anatomic and function of these two tissues [28].…”
Section: Across-tissue Age Predictionmentioning
confidence: 99%