Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis
Autor: | Susana Vinga, Cláudia S. Constantino, A. Carvalho |
---|---|
Rok vydání: | 2021 |
Předmět: |
Elastic net regularization
Multivariate statistics Computer science Computer applications to medicine. Medical informatics R858-859.7 Computational biology Disease cluster 01 natural sciences Biochemistry 010104 statistics & probability 03 medical and health sciences Pattern mining Multivariate time series clustering Genetics Imputation (statistics) 0101 mathematics Cluster analysis Molecular Biology Longitudinal gene expression data Imputation 030304 developmental biology QA299.6-433 0303 health sciences Proportional hazards model Dimensionality reduction Methodology Missing data Computer Science Applications Computational Mathematics Computational Theory and Mathematics Regularised optimisation Analysis |
Zdroj: | BioData Mining, Vol 14, Iss 1, Pp 1-18 (2021) BioData Mining |
ISSN: | 1756-0381 |
Popis: | Background Longitudinal gene expression analysis and survival modeling have been proved to add valuable biological and clinical knowledge. This study proposes a novel framework to discover gene signatures and patterns in a high-dimensional time series transcriptomics data and to assess their association with hospital length of stay. Methods We investigated a longitudinal and high-dimensional gene expression dataset from 168 blunt-force trauma patients followed during the first 28 days after injury. To model the length of stay, an initial dimensionality reduction step was performed by applying Cox regression with elastic net regularization using gene expression data from the first hospitalization days. Also, a novel methodology to impute missing values to the genes selected previously was proposed. We then applied multivariate time series (MTS) clustering to analyse gene expression over time and to stratify patients with similar trajectories. The validation of the patients’ partitions obtained by MTS clustering was performed using Kaplan-Meier curves and log-rank tests. Results We were able to unravel 22 genes strongly associated with hospital’s discharge. Their expression values in the first days after trauma showed to be good predictors of the length of stay. The proposed mixed imputation method allowed to achieve a complete dataset of short time series with a minimum loss of information for the 28 days of follow-up. MTS clustering enabled to group patients with similar genes trajectories and, notably, with similar discharge days from the hospital. Patients within each cluster have comparable genes’ trajectories and may have an analogous response to injury. Conclusion The proposed framework was able to tackle the joint analysis of time-to-event information with longitudinal multivariate high-dimensional data. The application to length of stay and transcriptomics data revealed a strong relationship between gene expression trajectory and patients’ recovery, which may improve trauma patient’s management by healthcare systems. The proposed methodology can be easily adapted to other medical data, towards more effective clinical decision support systems for health applications. |
Databáze: | OpenAIRE |
Externí odkaz: |