Abstract
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual’s -omics profile (‘personalome’), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about ‘average’ disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review—intended for biomedical researchers, computational biologists and bioinformaticians—we survey emerging computational and translational informatics methods capable of constructing a single subject's ‘personalome’ for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive ‘personalomes’ through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.
Highlights
The arrival of precision medicine has led to a more individualbased view of diseases, with characteristics of single subjects being central to the prediction of clinical outcomes and prescription of tailored treatments
Approaches based on a single sample of an individual We identified three methods that require a single sample of an individual and a cohort reference and two approaches capable of extracting differentially expressed pathway (DEP) from within an individual’s transcriptome without external comparison
The development and analysis of personal transcriptome interpretation are essential for precision medicine, as therapeutic decision-making pertains not exclusively to genomic sequences but to Genome x Environment interactions (GxE) as well
Summary
The arrival of precision medicine has led to a more individualbased view of diseases, with characteristics of single subjects being central to the prediction of clinical outcomes and prescription of tailored treatments. Samples gathered during ostensibly healthy physiological states of the patient approaches to extract meaningful knowledge from time series transcriptome data are based on clustering algorithms [62], hidden Markov models [63], Gaussian processes [64] or Bayesian approaches [65] (for a review, see [66, 67]) These techniques can be applied for single-subject transcriptome analysis to extract DEGs or gene expression trajectory patterns from multiple experimental conditions where multiple time points are studied. A GenesetC predictive of exacerbation of pediatric asthmatic patients was confirmed in an independent cohort
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