Effective evaluation of clustering algorithms on single-cell CNA data

Clustering methods are increasingly applied to single-cell DNA sequencing (scDNAseq) data to infer the subclonal structure of cancer. However, the complexity of these data exacerbates some data-science issues and affects clustering results. Additionally, determining whether such inferences are accurate and clusters recapitulate the real cell phylogeny is not trivial, mainly because ground truth information is not available for most experimental settings. Here, by exploiting simulated sequencing data representing known phylogenies of cancer cells, we propose a formal and systematic assessment of well-known clustering methods to study their performance and identify the approach providing the most accurate reconstruction of phylogenetic relationships

Single-cell DNA Sequencing Data: a Pipeline for Multi-Sample Analysis

In order to help cancer researchers in understanding tumor heterogeneity and its evolutionary dynamics, we propose a software pipeline to explore intra-tumor heterogeneity by means of scDNA sequencing data

Temporal and spatial modulation of Rho GTPases during in vitro formation of capillary vascular network. Adherens junctions and myosin light chain as targets of Rac1 and RhoA

Endothelial cells (ECs) self-organize into capillary networks when plated on extracellular matrix. In this process, Rho GTPases-mediated cytoskeletal dynamics control cell movement and organization of cell-to-matrix and cell-to-cell contacts. Time course analysis of RhoA and Rac1 activation matches specific morphological aspects of nascent pattern. RhoA-GTP increases early during EC adhesion and accumulates at sites of membrane ruffling. Rac1 is activated later and localizes in lamellipodia and at cell-to-cell contacts of organized cell chains. When ECs stretch and remodel to form capillary structures, RhoA-GTP increases again and associates with stress fibers running along the major cell axis. N17Rac1 and N19RhoA mutants impair pattern formation. Cell-to-cell contacts and myosin light chains (MLC) are targets of Rac1 and RhoA, respectively. N17Rac1 reduces the shift of beta-catenin and vascular endothelial cadherin to Triton X-100-insoluble fraction and impairs beta-catenin distribution at adherens junctions, suggesting that Rac1 controls the dynamics of cadherin-catenin complex with F-actin. During the remodeling phase of network formation, ECs show an intense staining for phosphorylated MLC along the plasma membrane; in contrast, MLC is less phosphorylated and widely diffused in N19RhoA ECs. Both N17Rac1 and N19RhoA have been used to investigate the role of wild type molecules in the main steps characterizing in vitro angiogenesis: (i) cell adhesion to the substrate, (ii) cell movement, and (iii) mechanical remodeling of matrix. N17Rac1 has a striking inhibitory effect on haptotaxis, whereas N19RhoA slightly inhibits EC adhesion and motility but more markedly Matrigel contraction. We conclude that different Rho GTPases control distinct morphogenetic aspects of vascular morphogenesis

Effective evaluation of clustering algorithms on single-cell CNA data

Clustering methods are increasingly applied to single-cell DNA sequencing (scDNAseq) data to infer the subclonal structure of cancer. However, the complexity of these data exacerbates some data-science issues and affects clustering results. Additionally, determining whether such inferences are accurate and clusters recapitulate the real cell phylogeny is not trivial, mainly because ground truth information is not available for most experimental settings. Here, by exploiting simulated sequencing data representing known phylogenies of cancer cells, we propose a formal and systematic assessment of well-known clustering methods to study their performance and identify the approach providing the most accurate reconstruction of phylogenetic relationships