For all the TCGA NGS datasets , we first have selected the genes appeared in all the datasets and then filtered out the genes whose total read counts across samples are less than 50 , resulting in roughly 14,000 genes in each dataset . We first have divided the lung cancer datasets into training and test sets , and then the differential gene expression analysis has been performed on the training set using DeSeq2 [ Love et al . , 2014 ] , by which 1,000 out of the top 5,000 genes with higher log2 fold change between LUAD and LUSC have been selected for consequent analyses . We first check the subtyping accuracy by directly applying linear SVM to the raw counts in the target domain , which gives an average accuracy of 59.28 % with a sample standard deviation ( STD ) of 5.54 % from ten independent runs . We also transform the count data to standard normal data after removing the sequencing depth effect using DESeq2 [ Love et al . , 2014 ] and then apply regularized logistic regression provided by the LIBLINEAR ( https://www.csie.ntu.edu.tw/~cjlin/liblinear/ ) package [ Fan et al . , 2008 ] . The classification accuracy becomes 74.10 % ± 4.41 % . Table 1 provides cancer subtyping performance comparison between BMDL , NB - HDP , HDP - NBFA , hGNBP , as well as the baseline hGNBP - NBFA using only the samples form the target domain . In only HDP based methods can not improve the results in the low - related setup , but also the performance will be degraded with more severe " negative transfer " adversarial effects when using more source samples . The reason for this is that HDP assumes a latent factor with higher weight in the shared DP will occur more frequently within each sample [ Williamson et al . , 2010 ] . This might be an undesirable assumption , especially when the domains are distantly related . For example , a latent factor might not be present throughout the HNSC samples but dominant within the samples of lung cancer . HDP based methods are not able to discover these latent factors given observed samples due to the limited number of lung cancer samples . In addition to this undesirable assumption , NB - HDP does not account for the sequencing - depth heterogeneity of different samples , which may lead to biased results deteriorating subtyping performance as shown in Table 1 .