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

BMC Genomics
Yoonha ChoiJing Huang

Abstract

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. 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. 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 RNA sequencing for the classification of UIP.

References

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Citations

Aug 4, 2020·Physiological Genomics·Ahmad AlimadadiXi Cheng
Nov 19, 2017·BMC Pulmonary Medicine·Yoonha ChoiJing Huang
Jul 30, 2020·American Journal of Respiratory and Critical Care Medicine·Luca RicheldiGanesh Raghu

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Methods Mentioned

BETA
biopsy
biopsies
RNA-seq
PCA

Software Mentioned

STAR RNA - seq aligner
HTSeq
DESeq2
BRAVE
Ensembl
LOPO
Bioconductor R package

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