Exact reconstruction of gene regulatory networks using compressive sensing.

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness.ResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.ConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies

Joint estimation of multiple related biological networks

Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical units whose underlying networks may differ but are likely to share features. Here we present a hierarchical Bayesian formulation for joint estimation of multiple networks in this nonidentically distributed setting. The approach is general: given a suitable class of graphical models, it uses an exchangeability assumption on networks to provide a corresponding joint formulation. Motivated by emerging experimental designs in molecular biology, we focus on time-course data with interventions, using dynamic Bayesian networks as the graphical models. We introduce a computationally efficient, deterministic algorithm for exact joint inference in this setting. We provide an upper bound on the gains that joint estimation offers relative to separate estimation for each network and empirical results that support and extend the theory, including an extensive simulation study and an application to proteomic data from human cancer cell lines. Finally, we describe approximations that are still more computationally efficient than the exact algorithm and that also demonstrate good empirical performance.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS761 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Integrating biological knowledge into variable selection : an empirical Bayes approach with an application in cancer biology

Background: An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results: We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions: The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge

Genomic aberrations in normal tissue adjacent to HER2-amplified breast cancers: field cancerization or contaminating tumor cells?

Field cancerization effects as well as isolated tumor cell foci extending well beyond the invasive tumor margin have been described previously to account for local recurrence rates following breast conserving surgery despite adequate surgical margins and breast radiotherapy. To look for evidence of possible tumor cell contamination or field cancerization by genetic effects, a pilot study (Study 1: 12 sample pairs) followed by a verification study (Study 2: 20 sample pairs) were performed on DNA extracted from HER2-positive breast tumors and matching normal adjacent mammary tissue samples excised 1-3 cm beyond the invasive tumor margin. High-resolution molecular inversion probe (MIP) arrays were used to compare genomic copy number variations, including increased HER2 gene copies, between the paired samples; as well, a detailed histologic and immunohistochemical (IHC) re-evaluation of all Study 2 samples was performed blinded to the genomic results to characterize the adjacent normal tissue composition bracketing the DNA-extracted samples. Overall, 14/32 (44 %) sample pairs from both studies produced genome-wide evidence of genetic aberrations including HER2 copy number gains within the adjacent normal tissue samples. The observed single-parental origin of monoallelic HER2 amplicon haplotypes shared by informative tumor-normal pairs, as well as commonly gained loci elsewhere on 17q, suggested the presence of contaminating tumor cells in the genomically aberrant normal samples. Histologic and IHC analyses identified occult 25-200 μm tumor cell clusters overexpressing HER2 scattered in more than half, but not all, of the genomically aberrant normal samples re-evaluated, but in none of the genomically normal samples. These genomic and microscopic findings support the conclusion that tumor cell contamination rather than genetic field cancerization represents the likeliest cause of local clinical recurrence rates following breast conserving surgery, and mandate caution in assuming the genomic normalcy of histologically benign appearing peritumor breast tissue

A robust prognostic signature for hormone-positive node-negative breast cancer

BACKGROUND: Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs). METHODS: We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates. RESULTS: Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients. CONCLUSIONS: RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment

Mapping and characterization of the amplicon near APOA2 in 1q23 in human sarcomas by FISH and array CGH

BACKGROUND: Amplification of the q21-q23 region on chromosome 1 is frequently found in sarcomas and a variety of other solid tumours. Previous analyses of sarcomas have indicated the presence of at least two separate amplicons within this region, one located in 1q21 and one located near the apolipoprotein A-II (APOA2) gene in 1q23. In this study we have mapped and characterized the amplicon in 1q23 in more detail. RESULTS: We have used fluorescence in situ hybridisation (FISH) and microarray-based comparative genomic hybridisation (array CGH) to map and define the borders of the amplicon in 10 sarcomas. A subregion of approximately 800 kb was identified as the core of the amplicon. The amplification patterns of nine possible candidate target genes located to this subregion were determined by Southern blot analysis. The genes activating transcription factor 6 (ATF6) and dual specificity phosphatase 12 (DUSP12) showed the highest level of amplification, and they were also shown to be over-expressed by quantitative real-time reverse transcription PCR (RT-PCR). In general, the level of expression reflected the level of amplification in the different tumours. DUSP12 was expressed significantly higher than ATF6 in a subset of the tumours. In addition, two genes known to be transcriptionally activated by ATF6, glucose-regulated protein 78 kDa and -94 kDa (GRP78 and GRP94), were shown to be over-expressed in the tumours that showed over-expression of ATF6. CONCLUSION: ATF6 and DUSP12 seem to be the most likely candidate target genes for the 1q23 amplification in sarcomas. Both genes have possible roles in promoting cell growth, which makes them interesting candidate targets

Tracking counterpart signatures in Saturn's auroras and ENA imagery during large-scale plasma injection events

Saturn's morningside auroras consist mainly of rotating, transient emission patches, following periodic reconnection in the magnetotail. Simultaneous responses in global energetic neutral atom (ENA) emissions have been observed at similar local times, suggesting a link between the auroras and large‐scale injections of hot ions in the outer magnetosphere. In this study, we use Cassini's remote sensing instruments to observe multiple plasma injection signatures within coincident auroral and ENA imagery, captured during 9 April 2014. Kilometric radio emissions also indicate clear injection activity. We track the motion of rotating signatures in the auroras and ENAs to test their local time relationship. Two successive auroral signatures—separated by ~4 hr UT—form postmidnight before rotating to the dayside while moving equatorward. The first has a clear ENA counterpart, maintaining a similar local time mapping throughout ~9 hr observation. Mapping of the ionospheric equatorward motion post‐dawn indicates a factor ~5 reduction of the magnetospheric source region's radial speed at a distance of ~14‐20 RS, possibly a plasma or magnetic boundary. The second auroral signature has no clear ENA counterpart; viewing geometry was relatively unchanged, so the ENAs were likely too weak to detect by this time. A third, older injection signature, seen in both auroral and ENA imagery on the nightside, may have been sustained by field‐aligned currents linked with the southern planetary period oscillation system, or the re‐energization of ENAs around midnight local times. The ENA injection signatures form near magnetic longitudes associated with magnetotail thinning

Propuesta de virtualización de servidores con Hyper-V en el centro de datos de la Facultad de Ciencias Médicas de la UNAN-Managua

La importancia del crecimiento en la potencia de cómputo y la existencia de problemas relacionados con el uso del hardware, ha hecho de la virtualización la solución más idónea para resolver tales dificultades, dentro de sus propósitos se encuentran hacer uso eficiente de los recursos y disminuir el costo total asociado a los mismos. Este trabajo de investigación fue realizado con la finalidad de proponer una solución para la virtualización servidores. La virtualización es una tecnología que permite la creación de equipos, basados en software, que reproducen el ambiente de una máquina física en sus aspectos de CPU, memoria, almacenamiento y entrada y salida de dispositivos. Se limita a trabajar básicamente con Hyper-V con el fin de acotar y definir la solución de virtualización , debido a la numerosa cantidad de soluciones que existen actualmente, como lo son VMware, Cytrix, entre otros. El enfoque principal se encontrará relacionado principalmente a la virtualización de servidores, a la disposición de Hyper-V para trabajar en cluster y al tipo de cluster que se puede implementar. El objetivo general de este trabajo es entonces, proponer una solución para efectuar la virtualización ya manera explicativa se describe como trabaja un cluster de alta disponibilidad con Hyper-V para efectuar tareas de migración de maquinas virtuales, empleando técnicas propias que vienen incorporadas en el software, como Live Migration ó Quick Migration que facilitan de gran forma la gestión y administración del entorno virtual. También se describirá brevemente los detalles técnicos para la implementación del centro de datos, la disposición de las áreas funcionales, el diagrama de distribución y otros parámetros importantes a tenerse en cuenta para disponer de un centro de datos confiable