Gong, F. | Search bioRxiv

bioRxiv2018

Diagnostic Whole Exome Sequencing in Patients with Short Stature

Short stature is among the most common reasons for children being referred to the pediatric endocrinology clinics. The cause of short stature is broad, in which genetic factors play a substantial role, especially in primary growth disorders. However, identifying the molecular causes for short stature remains as a challenge because of the high heterogeneity of the phenotypes. Here, whole exome sequencing (WES) was used to identify the genetic causes of short stature with unknown etiology for 20 patients aged from 1 to 16 years old. The genetic causes of short stature were identified in 9 of the 20 patients, corresponding to a molecular diagnostic rate of 45%. Notably, in 2 of the 9 patients identified with genetic causes, the diagnosed diseases based on WES are different from the original clinical diagnosis. Our results highlight the clinical utility of WES in the diagnosis of rare, high heterogeneity disorders.

genetics↗

bioRxiv2018

DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data

Cell fates are determined by transition-states which occur during complex biological pro-cesses such as proliferation and differentiation. The advance in single-cell RNA sequencing (scRNAseq) provides the snapshots of single cell transcriptomes, thus offering an essential opportunity to study such complex biological processes. Here, we introduce a novel algorithm, DensityPath, which visualizes and reconstructs the underlying cell developmental trajectories for large-scale scRNAseq data. DensityPath has three merits. Firstly, by adopting the nonlinear dimension reduction algorithm elastic embedding, DensityPath reveals the intrinsic structures of the data. Secondly, by applying the powerful level set clustering method, DensityPath extracts the separate high density clusters of representative cell states (RCSs) from the single cell multimodal density landscape of gene expression space, enabling it to handle the heterogeneous scRNAseq data elegantly and accurately. Thirdly, DensityPath constructs cell state-transition path by finding the geodesic minimum spanning tree of the RCSs on the surface of the density landscape, making it more computationally efficient and accurate for large-scale dataset. The cell state-transition path constructed by DensityPath has the physical interpretation as the minimum-transition-energy (least-cost) path. We demonstrate that DensityPath is capable of identifying complex cell development trajectories with bifurcating and trifurcating branches on the human preimplantation embryos. We demonstrate that DensityPath is robust and has high accuracy of pseudotime calculation and branch assignment on the real scRNAseq as well as simulated datasets.

systems biology↗