Efficient algorithms for fast integration on large data sets from multiple sources

Dual-layer feature extraction based soft sensor methods and applications to industrial polyethylene processes

There has recently been an increase in the number of RDF knowledge bases published on the Internet. These rich RDF data sets can be useful in answering many queries, but much more interesting queries can be answered by integrating data from different data sets. This has given rise to research on automatically linking different RDF data sets representing different knowledge bases. This is challenging due to the scale and semantic heterogeneity of these data sets. Various approaches have been proposed, but there is room for improving the quality of the generated links. In this demonstration, we showcase ALEX, a system that aims at improving the quality of links between RDF data sets by using feedback provided by users on the answers to linked data queries. ALEX starts with multiple RDF data sets that are linked using any automatic linking algorithm. ALEX enables the user to issue queries that integrate data from different data sets, and to provide feedback on the answers to these queries. ALEX uses this feedback to eliminate incorrect links between the data sets and discover new links. In this demonstration, we show ALEX in action on multiple data sets from the Linked Open Data cloud.

The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets. We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles. We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented. The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.

In recent years, rapidly decreasing sequencing costs have lead to an exponential growth in the availability of both bulk and single-cell transcriptomic data. Compilation of datasets generated from different research groups would allow for many additional or improved analyses, both due to increased computational power and accessibility. However, the process of accurately and effectively integrating datasets in an easily interpretable manner is challenging, and there is an unmet need for additional tools that incorporate multiple datasets in an effective and intuitive manner. Pancreatic ductal adenocarcinoma (PDA) is the third most common cause of cancer mortality, and there are several web portals, including cBioportal or TCGA, that integrate and analyze multi-omics data from multiple large-scale projects. However, these tools fail to integrate datasets generated by independent laboratories and thus leave a large portion of the available PDA data untapped. An tool that incorporates these data into a single easy-to-use source is not only useful but necessary to the PDA research community. Here we present PADCAS (PAncreatic Ductal Cancer AnalysiS), an intuitive tool that allows researchers and clinicians rapid access to multiple datasets related to PDA. Using a friendly visualization and easy-to-use exploratory panels, PADCAS allows the user to perform various statistical analyses investigating the expression of one or multiple genes of interest in normal tissue, primary tumor, or metastatic samples in both bulk and single-cell RNA sequencing data. The tool integrates four public single-cell data sets where users can analyze gene(s) of interest in different context, such as across cell types (fibroblasts vs. macrophages vs. epithelial cells, for example) or tissue conditions. Additionally, regulatory network analyses have been performed and incorporated into the tool for a subset of the datasets, allowing users to investigate and visualize the activity of key proteins in both the ductal and stroma compartments. A complementary novel dataset generated from 23 PDA cell lines is also included with similar analysis options at the gene expression or protein activity level. PADCAS also incorporates bulk RNAseq data generated using laser capture microdissection (LCM-RNA-Seq) at Columbia University. LCM-RNA-Seq dataset includes human PDA, and low grade samples PanIN-1 and IPMN-adenomas), with cross-condition analyses performed at the gene expression level including survival analysis. Finally, all plots generated by PADCAS can be easily downloaded in different formats and sizes. The tool has been designed to be easily maintained and extended as long as new datasets and functionalities are required. Thus, PADCAS represents the first public, free-of-programming resource compilating multiple omics datasets, including single cell RNAseq, for exploratory analysis in PDAC Citation Format: Alvaro Curiel-Garcia, Lorenzo Tomassoni, Lukas J. Vlahos, Carlo H. Maurer, Andrea Califano, Kenneth P. Olive. PADCAS: A friendly web page for exploratory analysis of pancreatic ductal adenocarcinoma datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1902.

A Pharmacokinetic/Pharmacodynamic Comparison of SAAM II and PC/WinNonlin Modeling Software

A program suite for one-dimensional small-angle scattering analysis of polydisperse systems and multiple data sets is presented. The main program, POLYSAS, has a menu-driven graphical user interface calling computational modules from ATSAS package to perform data treatment and analysis. The graphical menu interface allows one to process multiple (time, concentration or temperature-dependent) data sets and interactively change the parameters for the data modelling using sliders. The graphical representation of the data is done via the Winteracter-based program SASPLOT. The package is designed for the analysis of polydisperse systems and mixtures, and permits one to obtain size distributions and evaluate the volume fractions of the components using linear and non-linear fitting algorithms as well as model-independent singular value decomposition. The use of the POLYSAS package is illustrated by the recent examples of its application to study concentration-dependent oligomeric states of proteins and time kinetics of polymer micelles for anticancer drug delivery.

In the past two decades, new developments in computing, sensing and crowdsourced data have resulted in an explosion in the availability of quantitative information. The possibilities of analyzing this so-called “big data” to inform research and the decision-making process are virtually endless. In general analyses have to be done across multiple data sets in order to bring out the most value of big data. A first important step is to identify temporal correlations between data sets. Given the characteristics of big data in term of volume and velocity, techniques that identify correlations not only need to be scalable, but also need to help users in ordering the correlation across temporal resolutions so that they can focus on important relationships. There is a large body of work in this area, however, most of them either only deal with small data sets, using a fixed temporal resolution, or does not provide a quantifiable measure of a correlation significance. In this paper, we present a method based on mutual information to identify correlations in large data sets. Discovered correlations are suggested to users in an order based on their significance. Our method supports an adaptive streaming technique that minimizes duplicated computation and is implemented on top of Apache Spark for scalability using big data platforms. We also provide a comprehensive evaluation using real-world data sets from NYC Open Data, and compare our findings against a recent study.

For Yeast Protein Hubs, More Data Means More Connections

The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.

The Fast Maximum Distance to Average Vector (F-MDAV): An algorithm for [formula omitted]-anonymous microaggregation in big data

BackgroundGene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment.MethodsWe categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets.ResultsWe used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method.ConclusionsThis study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

recently reported on the results of a survey conducted by expert UK oncologists regarding their use by irinotecan for colorectal cancer, with the intent of using these data to explore the cost-effectiveness of routine UGT1A1 testing. Reading this article, we were struck with the wide variation in the experts' answers, and wondered, as did the authors, as to how accurate the experts might be. To further explore this observation, we put the same questions to seven Melbourne (Australia) oncologists who work at four institutions. We then compared these responses with prospective data that they had collected on 604 patients with metastatic colorectal cancer, who had presented with stage IV disease, or developed recurrence, between 2005 and 2009.

MotivationDeciphering gene interaction networks (GINs) from time-course gene expression (TCGx) data is highly valuable to understand gene behaviors (e.g., activation, inhibition, time-lagged causality) at the system level. Existing methods usually use a global or local proximity measure to infer GINs from a single dataset. As the noise contained in a single data set is hardly self-resolved, the results are sometimes not reliable. Also, these proximity measurements cannot handle the co-existence of the various in vivo positive, negative and time-lagged gene interactions.Methods and resultsWe propose to infer reliable GINs from multiple TCGx datasets using a novel conserved subsequential pattern of gene expression. A subsequential pattern is a maximal subset of genes sharing positive, negative or time-lagged correlations of one expression template on their own subsets of time points. Based on these patterns, a GIN can be built from each of the datasets. It is assumed that reliable gene interactions would be detected repeatedly. We thus use conserved gene pairs from the individual GINs of the multiple TCGx datasets to construct a reliable GIN for a species. We apply our method on six TCGx datasets related to yeast cell cycle, and validate the reliable GINs using protein interaction networks, biopathways and transcription factor-gene regulations. We also compare the reliable GINs with those GINs reconstructed by a global proximity measure Pearson correlation coefficient method from single datasets. It has been demonstrated that our reliable GINs achieve much better prediction performance especially with much higher precision. The functional enrichment analysis also suggests that gene sets in a reliable GIN are more functionally significant. Our method is especially useful to decipher GINs from multiple TCGx datasets related to less studied organisms where little knowledge is available except gene expression data.

The problem we address in the paper is how to learn a joint representation from data lying on multiple manifolds. We are given multiple data sets and there is an underlying common manifold among the different data set. We propose a framework to learn an embedding of all the points on all the manifolds in a way that preserves the local structure on each manifold and, in the same time, collapses all the different manifolds into one manifold in the embedding space, while preserving the implicit correspondences between the points across different data sets. The proposed solution works as extensions to current state of the art spectral-embedding approaches to handle multiple manifolds.

Motion capture data have been widely used in applications ranging from video games and animations to simulations and virtual environments. Moreover, all data-driven approaches for analysis and synthesis of motions are depending on motion capture data. Although multiple large motion capture data sets are freely available for research, there is no system which can provide a centralized access to all of them in an organized manner. In this paper we show that using a relational database management system (RDBMS) to store data does not only provide such a centralized access to the data, but also allows to include other sensor modalities (e.g. accelerometer data) and various semantic annotations. We present two applications for our system: A motion capture player where motions sequences can be retrieved from large datasets using SQL queries and the automatic construction of statistical models which can further be used for complex motion analysis and motions synthesis tasks.KeywordsMotion CaptureZero Moment PointMotion Capture DataRelational Database Management SystemSynchronization FrameThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.