Single sample networks

Methods for inference of sample and patient-specific networks

  • LIONESS is a flexible method for single-sample network integration. The machinery behind LIONESS is a leave-one-out approach. To construct a single-sample network for sample , a first network is estimated on the full dataset and a second network is estimated on the dataset with sample withheld. The single-sample network is then estimated based on the difference between these two networks. Any method that can be used to estimate a network can be used with LIONESS to estimate single-sample networks. Two common use cases are the use of LIONESS to generate single-sample GRNs based on PANDA and the use of LIONESS to generate single-sample Pearson correlation networks.

  • BONOBO is a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices.