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Fig. 1 | BMC Bioinformatics

Fig. 1

From: NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis

Fig. 1

Flowchart of NIMBus. For a given disease \(d (1\le d\le D)\), \({s}_{d}\) represents the total number of samples for that disease. In addition, there are a total of \(m\) features which are denoted as \({f}_{1}\dots {f}_{m}\). The mutations from the samples and the features are binned on the bins \({b}_{1}\dots {b}_{n}\) for a total of \(n\). Two resulting matrices are produced, \({\varvec{Y}}\) and \({\varvec{X}}\). The matrix \({\varvec{Y}}\) is a \(D\times n\) matrix consisting of mutation counts while \({\varvec{X}}\) is an \(n\times m\) matrix consisting of feature values. Training the negative binomial model gives, for each disease, \(\mu\) and \(\sigma\) values for each bin, \(n\). The trained model can be applied to a set of user defined regions, \(1\dots K\), to evaluate relative mutation burden. This testing is associated with a set of P-values, \(p\), for each of the K regions. The P-values from multiple regions may be combined using Fisher’s method

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