Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Abstract

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China

“…Using a time-series method may be an excellent strategy to forecast the demand for RBC transfusion with high accuracy. Time-series models have been employed in a wide variety of research fields, including public health and biomedical data aspects ( 22 ), brain studies ( 23 ), drug utilization ( 24 ), gene networks ( 25 , 26 ), and even traffic safety ( 27 ). Some studies have also examined the time-series prediction of the demand for RBCs ( 28 – 30 ).…”

Transcriptomic thermal plasticity underlying gonadal development in a turtle with ZZ/ZW sex chromosomes despite canalized genotypic sex determination

“…Gene expression profiles are highly multivariate data, composed of the expression values for all genes per pool of individuals per treatment through time (Adams & Collyer, 2009; Collyer & Adams, 2013), and can be thought of as a temporal trajectory of gene expression (phenotypic response values) over the five embryonic stages. This approach permits the analysis of hyperdimensional genome‐wide expression trajectories, whereas alternative existing approaches address gene‐by‐gene patterns in RNAseq time series (Oh & Li, 2021). First, an ANOVA was conducted to investigate how the genome‐wide transcriptomic response (means of the principal components of gene expression for all replicate RNA‐seq libraries) differed between temperatures in Chrysemys , and how they differed in Apalone by temperature, sex, and their interaction.…”

“…(phenotypic response values) over the five embryonic stages. This approach permits the analysis of hyperdimensional genome-wide expression trajectories, whereas alternative existing approaches address gene-by-gene patterns in RNAseq time series (Oh & Li, 2021).…”

Toward a hypothesis‐free understanding of how phosphorylation dynamically impacts protein turnover

“…The inference of half-life time for each peptidoform in the nonsteady state can be rather problematic due to the considerations mentioned in Section 1 (Introduction), especially for those peptidoforms carrying PTMs. Since the time-series data analysis algorithms are well-established for comparing expression of the same genes between conditions [59,[71][72][73], we reasoned that we can turn the H/L curvefitting problem into a time-series differential analysis with no prior assumption on PTM site-resolved turnover dynamics (Step 3, Figure 1). Thus, for the present PC12 dataset, after filtering out noisy signals, we performed a comparison against all H/L ratios for the paired P and NP peptidoforms.…”