A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

• Main Authors: Bao, J. (Author), Duan, M. (Author), Hu, M. (Author), Hu, Z. (Author), Lu, H. (Author), Wang, J. (Author), Xu, C. (Author), Zhang, R. (Author), Zhou, F. (Author), Zhou, Y. (Author), Zhu, W. (Author)
• Format: Article
• Language: English
• Published: Cell Press 2022
• Subjects: biomarker; feature engineering; feature selection; immune thrombocytopenia; ITP; MT: Bioinformatics; stacking classification
• Online Access: View Fulltext in Publisher

 Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php © 2022 The Author(s)