Abstract
Topic modelling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modelling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.
Competing Interest Statement
The authors have declared no competing interest.
Abbreviations
- The following abbreviations are used in this manuscript:
- hSBM
- hierarchical stochastic block model
- TP, FP, TN, FN
- True Positives, False Positives, True Negatives, False Negatives
- FDR
- False Discovery Rate
- FPKM
- Fragments Per Kilobase of transcript per Million mapped reads
- GSEA
- Gene Set Enrichment Analysis
