Journal: Nature Communications

Article Title: Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data

doi: 10.1038/s41467-018-04696-6

Figure Lengend Snippet: An illustration of pseudotemporal analysis. a High-dimensional molecular data from a cross-sectional cohort is mapped on to a one-dimensional pseudotemporal progression scale allowing pseudotemporal behaviour of individual features to be analysed. b If the cohort contains sub-populations we may want each sub-population to be associated to distinct trajectories. c PhenoPath models observed expression as a combination of standard differential expression and pseudotime/pathway effects, including covariate-pathway interactions. d PCA representation of a simulated data set coloured by pseudotime shows a clear splitting of trajectories between covariate status x = (−1, 1). e Median Kendall- τ correlation to true pseudotime across varying fractions of genes exhibiting covariate-trajectory interactions on simulated data. PhenoPath is the only algorithm for which the accuracy of inference is independent of the (unknown) fraction. f Median AUCs measuring the accuracy of different approaches to detecting covariate-trajectory interactions using Limma Voom for differential expression analysis. As before, PhenoPath is the only algorithm for which the accuracy is independent of the underlying fraction of genes exhibiting covariate-trajectory interactions

Article Snippet: PhenoPath provides a novel contribution to the existing arsenal of pseudotemporal analysis algorithms developed across a range of application areas including single cell ‘omics and cancer.

Techniques: Expressing, Quantitative Proteomics

Journal: Nature Communications

Article Title: Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data

doi: 10.1038/s41467-018-04696-6

Figure Lengend Snippet: Vascular growth trajectories uncovered by PhenoPath in breast cancer. a Principal components visualisation of ER− dependent pseudotemporal trajectories. b Expression of a number of vascular growth markers shows upregulation over the inferred pseudotime trajectory. c A GO enrichment analysis of upregulated genes confirms the latent trajectory encodes angiogenesis pathway activation in each tumour

Article Snippet: PhenoPath provides a novel contribution to the existing arsenal of pseudotemporal analysis algorithms developed across a range of application areas including single cell ‘omics and cancer.

Techniques: Expressing, Activation Assay