Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: Challenges and solutions

Yoonha Choi, Tiffany Ting Liu, Daniel G. Pankratz, Thomas V. Colby, Neil M. Barth, David A. Lynch, P. Sean Walsh, Ganesh Raghu, Giulia C. Kennedy, Jing Huang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Background: We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. Results: We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. Conclusions: We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNAsequencing for the classification of UIP.

Original languageEnglish (US)
Article number101
JournalBMC genomics
StatePublished - May 9 2018

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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