Dataset: TGF beta Dosage Experiment

Genome-wide expression data can provide important insights into normal and pathological cellular processes. However, the ability to use gene expression data to quantitatively assess the activation state of a given signaling pathway or transcriptional network in a sensitive and specific manner remains an important unmet goal. We now describe a computational algorithm, energy-paired scoring (EPS), that satisfies these criteria by predicting pathway activity using gene-gene interactions within a pathway signature in a manner analogous to the estimation of energy generated by two charged particles, as described by Coulomb’s law. We demonstrate the ability of EPS to: quantitatively assess pathway activation levels in vivo and in vitro; accurately estimate the extent of pathway inhibition achieved by gene knockdown; sensitively detect crosstalk between endogenous signaling pathways in vivo; and accurately identify compounds capable of inhibiting selected signaling pathways. Our findings indicate that EPS can accurately predict pathway activity over a wide dynamic range based upon gene expression data sets derived from multiple profiling platforms, as well as different species, tissues and cell types in both in vitro and in vivo contexts NMuMg cells were treated with 0ng, 0.15ng, 0.5ng, 1.5ng and 15ng TGF-beta1 with three replicates per dosage for 6 hrs

Species:

mouse

Samples:

15

Source:

E-GEOD-50968

Updated:

Dec.12, 2014

Registered:

Nov.12, 2014