DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate 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.

BackgroundRecent large scale deployments of health information technology have created opportunities for the integration of patient medical records with disparate public health, human service, and educational databases to provide comprehensive information related to health and development. Data integration techniques, which identify records belonging to the same individual that reside in multiple data sets, are essential to these efforts. Several algorithms have been proposed in the literatures that are adept in integrating records from two different datasets. Our algorithms are aimed at integrating multiple (in particular more than two) datasets efficiently.MethodsHierarchical clustering based solutions are used to integrate multiple (in particular more than two) datasets. Edit distance is used as the basic distance calculation, while distance calculation of common input errors is also studied. Several techniques have been applied to improve the algorithms in terms of both time and space: 1) Partial Construction of the Dendrogram (PCD) that ignores the level above the threshold; 2) Ignoring the Dendrogram Structure (IDS); 3) Faster Computation of the Edit Distance (FCED) that predicts the distance with the threshold by upper bounds on edit distance; and 4) A pre-processing blocking phase that limits dynamic computation within each block.ResultsWe have experimentally validated our algorithms on large simulated as well as real data. Accuracy and completeness are defined stringently to show the performance of our algorithms. In addition, we employ a four-category analysis. Comparison with FEBRL shows the robustness of our approach.ConclusionsIn the experiments we conducted, the accuracy we observed exceeded 90% for the simulated data in most cases. 97.7% and 98.1% accuracy were achieved for the constant and proportional threshold, respectively, in a real dataset of 1,083,878 records.

Gaining understanding of multivariate and multidimensional data through visualization

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.

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

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.

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

Comparative Analysis of Independent Ex Vivo functional Drug Screens Identifies Predictive Biomarkers of BCL-2 Inhibitor Response in AML

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.

How to combine correlated data sets—A Bayesian hyperparameter matrix method

To study the drug resistance problem caused by transporters, we leveraged multiple large-scale public data sets of drug sensitivity, cell line genetic and transcriptional profiles, and gene silencing experiments. Through systematic integration of these data sets, we built various machine learning models to predict the difference between cell viability upon drug treatment and the silencing of its target across the same cell lines. More than 50% of the models built with the same data set or with independent data sets successfully predicted the testing set with significant correlation to the ground truth data. Features selected by our models were also significantly enriched in known drug transporters annotated in DrugBank for more than 60% of the models. Novel drug-transporter interactions were discovered, such as lapatinib and gefitinib with ABCA1, olaparib and NVPADW742 with ABCC3, and gefitinib and AZ628 with SLC4A4. Furthermore, we identified ABCC3, SLC12A7, SLCO4A1, SERPINA1, and SLC22A3 as potential transporters for erlotinib, three of which are also significantly more highly expressed in patients who were resistant to therapy in a clinical trial.

Advanced medical imaging technologies have enabled biologists and other researchers in biomedicine, biochemistry and bio-informatics to gain better insight in complex, large-scale data sets. These datasets, which occupy large amounts of space, can no longer be stored on local hard drives. San Diego Supercomputer Center (SDSC) maintains a large data repository, called High Performance Storage System (HPSS), where large-scale biomedical data sets can be stored. These data sets must be transmitted over an open or closed network (Internet or Intranet) within a reasonable amount of time to make them accessible in an interactive fashion to the researchers all over the world. Our approach deals with extracting, compressing and transmitting these data sets using the Haar wavelets, over a low- to medium-bandwidth network. These compressed data sets are then transformed and reconstructed into a 3-D volume on the client side using texture mapping in Java3D. These data sets are handled using the Scalable Visualization Toolkits provided by the NPACI (National Partnership for Advanced Computational Infrastructure). Sub-volumes of the data sets are extracted to provide a detailed view of a particular region of interest (ROI). This application is being ported to C++ platform to obtain higher rendering speed and better performance but lacks platform independency.

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.

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.

Gene-expression data are now widely available and used for a wide range of clinical and diagnostic purposes. A key challenge is to select a few significant marker genes for biological studies. While it is feasible to find important genes from a single gene-expression data set, it is often more meaningful to compare the results from different but related data sets together, especially for multiple gene-expression data sets arising from different studies of a common organism or phenotype. In this paper, we present a novel framework to exploit the commonalities across different data sets by jointly learning from different data sets simultaneously through multi-task feature learning. By identifying a common subspace of genes, we can help biologists find important marker genes that span different evolutionary periods in the life cycle of cancer development. The genes thus found are more stable and more significant. Our experimental results demonstrate that more accurate models can be built using multiple data sets based on fewer labelled examples. To the best of our knowledge, we are among the first to introduce multi-task learning in the bioinformatics community to solve the lack of data problem.