Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature.
Often protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edge's partial correlation between two proteins is at least moderate, then the composite's posterior probability is large.
Purpose:
To introduce an ultra‐sensitive procedure that can detect and quantify systematic MLC bank alignment error down to 0.2 mm or less. The procedure takes only 5 minutes to run and employs the familiar MapCheck2 device.
Methods:
Elekta iCOM v13.0 was used to create and deliver a modified 4‐Quadrant field on the Elekta Infinity Linac. The 2D dose distribution was collected on the MapCheck2, at 100 Source‐Detector‐Distance with no external buildup. Using SNC Patient v6.2.3 this planar dose was compared against a reference planar dose established preferably right after commissioning or the most recent Annual QA. To obtain baseline results and tolerance levels leaf bank alignment shifts ranging from −1.0 mm (in‐field) to +1 mm (out‐of‐field) in 0.1 mm increment were intentionally induced. Each shifted field is matched against a reference field with zero shift. A Y‐profile through the CAX, perpendicular to MLC traveling direction, was selected in the software. Percentage dose differences at selected points on this profile were recorded. The mean dose differences were then plotted against the corresponding shift distances.
Results:
The test is extremely sensitive in picking up MLC alignment error. Shift down to 0.2 mm can be easily detected. Furthermore, the relationship between dose deviations and MLC bank alignment errors is shown to be quite linear. For our setup, each 0.1 mm shift in MLC bank would Result in approximately 2.7% dose deviation from baseline. Based on this Result, monthly QA tolerance level at was set at +/−7%, which translates roughly to 0.25 mm in alignment error.
Conclusion:
A novel method to quantify MLC alignment error was introduced. The test is short, simple yet very effective. It's been implemented clinically as part of our institution monthly machine QA.
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