Random matrix analysis of localization properties of Gene co-expression network

We analyze gene co-expression network under the random matrix theory framework. The nearest neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range, and deviates after wards. Eigenvector analysis of the network using inverse participation ratio (IPR) suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets; (A) The non-degenerate part that follows RMT. (B) The non-degenerate part, at both ends and at intermediate eigenvalues, which deviate from RMT and expected to contain information about {\it important nodes} in the network. (C) The degenerate part with $zero$ eigenvalue, which fluctuates around RMT predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties

Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models

Gene-network inference by message passing

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics 2007, Kyot

Gene-network inference by message passing

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics 2007, Kyot

Gene-network inference by message passing

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics 2007, Kyot

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

Objectives:Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods:We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results:ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion:ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu)

Enabling precision medicine in neonatology, an integrated repository for preterm birth research.

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research

Percent Fat Mass Increases with Recovery, But Does Not Vary According to Dietary Therapy in Young Malian Children Treated for Moderate Acute Malnutrition.

BackgroundModerate acute malnutrition (MAM) affects 34.1 million children globally. Treatment effectiveness is generally determined by the amount and rate of weight gain. Body composition (BC) assessment provides more detailed information on nutritional stores and the type of tissue accrual than traditional weight measurements alone.ObjectiveThe aim of this study was to compare the change in percentage fat mass (%FM) and other BC parameters among young Malian children with MAM according to receipt of 1 of 4 dietary supplements, and recovery status at the end of the 12-wk intervention period.MethodsBC was assessed using the deuterium oxide dilution method in a subgroup of 286 children aged 6-35 mo who participated in a 12-wk community-based, cluster-randomized effectiveness trial of 4 dietary supplements for the treatment of MAM: 1) lipid-based, ready-to-use supplementary food (RUSF); 2) special corn-soy blend "plus plus" (CSB++); 3) locally processed, fortified flour (MI); or 4) locally milled flours plus oil, sugar, and micronutrient powder (LMF). Multivariate linear regression modeling was used to evaluate change in BC parameters by treatment group and recovery status.ResultsMean ± SD %FM at baseline was 28.6% ± 5.32%. Change in %FM did not vary between groups. Children who received RUSF vs. MI gained more (mean; 95% CI) weight (1.43; 1.13, 1.74 kg compared with 0.84; 0.66, 1.03 kg; P = 0.02), FM (0.70; 0.45, 0.96 kg compared with 0.20; 0.05, 0.36 kg; P = 0.01), and weight-for-length z score (1.23; 0.79, 1.54 compared with 0.49; 0.34, 0.71; P = 0.03). Children who recovered from MAM exhibited greater increases in all BC parameters, including %FM, than children who did not recover.ConclusionsIn this study population, children had higher than expected %FM at baseline. There were no differences in %FM change between groups. International BC reference data are needed to assess the utility of BC assessment in community-based management of acute malnutrition programs. This trial was registered at clinicaltrials.gov as NCT01015950

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.Comment: 14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 200

NKp46 Clusters at the Immune Synapse and Regulates NK Cell Polarization

Natural killer cells play an important role in first-line defense against tumor and virus-infected cells. The activity of NK cells is tightly regulated by a repertoire of cell-surface expressed inhibitory and activating receptors. NKp46 is a major NK cell activating receptor that is involved in the elimination of target cells. NK cells form different types of synapses that result in distinct functional outcomes: cytotoxic, inhibitory, and regulatory. Recent studies revealed that complex integration of NK receptor signaling controls cytoskeletal rearrangement and other immune synapse-related events. However the distinct nature by which NKp46 participates in NK immunological synapse formation and function remains unknown. In this study we determined that NKp46 forms microclusters structures at the immune synapse between NK cells and target cells. Over-expression of human NKp46 is correlated with increased accumulation of F-actin mesh at the immune synapse. Concordantly, knock-down of NKp46 in primary human NK cells decreased recruitment of F-actin to the synapse. Live cell imaging experiments showed a linear correlation between NKp46 expression and lytic granules polarization to the immune synapse. Taken together, our data suggest that NKp46 signaling directly regulates the NK lytic immune synapse from early formation to late function