Weightings for each gene, depending on its length, were obtained using the probability weighting function nullp and over and underrepresented GO categories were calculated using the Wallenius approximation.
This is approximately the point at which the probability weighting function crosses the identity function, as demonstrated by various behavioral studies [5], [6], [70], [72], [73].
According to prospect tgheory, the probability weighting function, w(p), is a monotonic function that is bounded by [0,1] in both domain and range.
At the behavioral level, we obtained a significant effect of the expert's advice on the curvature of the probability weighting function, indicated by a significant effect of the presence of the advice on α.
These findings demonstrate that the presence of the expert's advice led to a significant change in the curvature of the probability weighting function in the direction of the expert's advice.
To illustrate fMRI responses within structures associated with valuation, and, importantly, differences in probability weighting as a function of the expert's advice, we analyzed the ROI activations using a previously developed method of transforming neural activations to a neural analog of the probability weighting function.
First, a probability weighting function for all genes was calculated, based on a given set of biased data for gene length with the function nullp in GoSeq.
The number of probes per gene was calculated in our final dataset to create a probability weighting function, which was then used in the GO term enrichment analysis.
A probability weighting function (PWF) was generated based on transcript length and was applied to eliminate the bias arising from this parameter.
To achieve significantly enriched KEGG pathways in GoSeq, the gene length bias is first quantified by calculating the Probability Weighting Function (PWF) which determines the probability that a gene will be differentially expressed only based on its length.
