Project description:This dataset consists of single-cell RNA-seq (Drop-seq) data from thymi of day 14.5 mouse embryos. The sample includes the whole thymus, including mesenchyme, endothelium, epithelium, thymocytes, and other lymphocytes. The mouse is a Rag2-/- knockout.
Project description:Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.
Project description:The circadian clock drives the oscillatory expression of thousands of genes across all tissues and bears significant implications for human health. RNA-seq time-series experiments interrogate the mechanistic links between transcriptional rhythms and phenotypic outcomes. Analysis methods must overcome the challenges of sparse temporal sampling, noisy data, and non-strictly periodic dynamics. Moreover, there remains a need for differential cycling analysis methods that can identify complex changes in rhythmicity for 2-sample comparisons across experimental conditions. We present TimeChange -- a non-parametric and model-free method for the quantification of differential dynamics across experimental conditions. The method leverages a data transformation technique known as time-delay embedding to reconstruct the underlying state space for each gene-of-interest. Takens’ embedding theorem implies that rhythmic dynamics will exhibit circular patterns in the embedded space. TimeChange non-parametrically compares the distributions of points in the embedded space via the Fasano-Franceschini test to assess whether the topological structures differ significantly between phenotypes, thereby quantifying differences in transcriptional dynamics without requiring knowledge of the underlying model. Application of TimeChange to synthetic data shows that it accurately identifies changes in transcriptomic dynamics, including differences in amplitude/peaked-ness; changes in sawtooth asymmetries; and trending oscillatory drifts (e.g. linear, damped, and contractile). We further show that the method has potential utility beyond circadian dynamics. For instance, initial tests of TimeChange using erg rowing-machine data shows accurate classification of differential stroke dynamics in real time, suggesting potential uses of TimeChange in the field of sports science and medicine.
Project description:With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here, we present Flexible Inference from Time-Series (FITS)-a computational tool that allows inference of one of three parameters: the fitness of a specific mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments or rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on simulated data and highlight its ability to infer the fitness/mutation rate/population size. We further show that FITS can infer meaningful information even when the input parameters are inexact. In particular, FITS is able to successfully categorize a mutation as advantageous or deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate high accuracy in inference.
Project description:A high-resolution time series study of transcriptome dynamics following antimiR--mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line revealed both general and miR-9 specific aspects of the miRNA--mediated post--transcriptional dynamic response.MiR-9 inhibition induced a multiphasic gene response, with an initial direct response at approximately 4 hours and multiple later responses which showed transcription factor enrichments indicative of indirect causally downstream responses, and an overall shift of gene product function from predominantly mRNA processing at early time points to translation at later time points.