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Constrained Pseudo-Time Ordering for Clinical Transcriptomics Data
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DOI：10.1109/TCBB.2024.3442669.png)
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Abstract
En 中文
Time series RNASeq studies can enable understanding of the dynamics of disease progression and treatment response in patients. They also provide information on biomarkers, activated and repressed pathways, and more. While useful, data from multiple patients is challenging to integrate due to the heterogeneity in treatment response among patients, and the small number of timepoints that are usually profiled. Due to the heterogeneity among patients, relying on the sampled time points to integrate data across individuals is challenging and does not lead to correct reconstruction of the response patterns. To address these challenges, we developed a new constrained based pseudo-time ordering method for analyzing transcriptomics data in clinical and response studies. Our method allows the assignment of samples to their correct placement on the response curve while respecting the individual patient order. We use polynomials to represent gene expression over the duration of the study and an EM algorithm to determine parameters and locations. Application to four treatment response datasets shows that our method improves on prior methods and leads to accurate orderings that provide new biological insight on the disease and response.
Keywords:
Diseases
Gene expression
Polynomials
Psoriasis
Curve fitting
COVID-19
Transcriptomics
Clinical transcriptomic data
estimation maximization with constraints
partial ordering
pseudo-time ordering
pseudotime ordering
RNASeq clinical trial data analysis
time series analysis
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6.4K
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