With deep WGS data of cfDNA from a carcinoma of unknown primary patient with very low ctDNA concentration quantified, they trained a machine learning model using bootstrapping. The results of RNA-sequencing on PBMC runs for the 5 different patients are recorded and the average of 3 of these individuals expression levels is used as a reference for gene expression. The genes are clustered into 10 clusters based on reference gene expression to increase the resolution at the core promoters. Then, genes used as a background value for PFE calculation are removed. Next, all the fragments in extended TSS regions, a region that has the center as TSS regions center and the length of 2000 base pairs, are pooled. The PFE and NDR scores are calculated for the fragments pooled. Further normalization of these scores is done based on their 95th percentile.
Using these two features, they bootstrapped, used in a weighted fashion, 600 expression prediction models developed for WGS data. Among those models, there are 200 univariable standalone NDR, 200 univariable standalone PFE, and 200 NDR-PFE integrated models.