A sparse conditional Gaussian graphical model for analysis of genetical genomics data

Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic architecture for many gene expressions may be complex, and poorly estimated genetic architecture may compromise the inferences of the dependency structures of the genes at the transcriptional level. In this paper we introduce a sparse conditional Gaussian graphical model for studying the conditional independent relationships among a set of gene expressions adjusting for possible genetic effects where the gene expressions are modeled with seemingly unrelated regressions. We present an efficient coordinate descent algorithm to obtain the penalized estimation of both the regression coefficients and the sparse concentration matrix. The corresponding graph can be used to determine the conditional independence among a group of genes while adjusting for shared genetic effects. Simulation experiments and asymptotic convergence rates and sparsistency are used to justify our proposed methods. By sparsistency, we mean the property that all parameters that are zero are actually estimated as zero with probability tending to one. We apply our methods to the analysis of a yeast eQTL data set and demonstrate that the conditional Gaussian graphical model leads to a more interpretable gene network than a standard Gaussian graphical model based on gene expression data alone.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS494 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Collective cell migration: Implications for wound healing and cancer invasion.

During embryonic morphogenesis, wound repair and cancer invasion, cells often migrate collectively via tight cell-cell junctions, a process named collective migration. During such migration, cells move as coherent groups, large cell sheets, strands or tubes rather than individually. One unexpected finding regarding collective cell migration is that being a "multicellular structure" enables cells to better respond to chemical and physical cues, when compared with isolated cells. This is important because epithelial cells heal wounds via the migration of large sheets of cells with tight intercellular connections. Recent studies have gained some mechanistic insights that will benefit the clinical understanding of wound healing in general. In this review, we will briefly introduce the role of collective cell migration in wound healing, regeneration and cancer invasion and discuss its underlying mechanisms as well as implications for wound healing

On the Aliphatic versus Aromatic Content of the Carriers of the "Unidentified" Infrared Emission Features

Although it is generally accepted that the so-called "unidentified" infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, and 11.3 micrometer are characteristic of the stretching and bending vibrations of aromatic hydrocarbon materials, the exact nature of their carriers remains unknown: whether they are free-flying, predominantly aromatic gas-phase molecules, or amorphous solids with a mixed aromatic/aliphatic composition are being debated. Recently, the 3.3 and 3.4 micrometer features which are commonly respectively attributed to aromatic and aliphatic C-H stretches have been used to place an upper limit of ~2\% on the aliphatic fraction of the UIE carriers (i.e. the number of C atoms in aliphatic chains to that in aromatic rings). Here we further explore the aliphatic versus aromatic content of the UIE carriers by examining the ratio of the observed intensity of the 6.2 micrometer aromatic C-C feature (I6.2) to that of the 6.85 micrometer aliphatic C-H deformation feature (I6.85). To derive the intrinsic oscillator strengths of the 6.2 micrometer stretch (A6.2) and the 6.85 micrometer deformation (A6.85), we employ density functional theory to compute the vibrational spectra of seven methylated polycyclic aromatic hydrocarbon molecules and their cations. By comparing I6.85/I6.2 with A6.85/A6.2, we derive the fraction of C atoms in methyl(ene) aliphatic form to be at most ~10\%, confirming the earlier finding that the UIE emitters are predominantly aromatic. We have also computed the intrinsic strength of the 7.25 micrometer feature (A7.25), another aliphatic C-H deformation band. We find that A6.85 appreciably exceeds A7.25. This explains why the 6.85 micrometer feature is more frequently detected in space than the 7.25 micrometer feature.Comment: 18 pages, 10 figures, 3 tables; accepted for publication in MNRA