Joseph Roden

Mining gene expression data by interpreting principal components

BackgroundThere are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.ResultsWe present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation.ConclusionWe provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10s to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.

The Techsat-21 autonomous space science agent

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat-21 constellation of three spacecraft scheduled for launch in 2004. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. We also describe our working software prototype and preparations for flight.

The Diamond Eye image mining system

Diamond Eye is a new image mining system that enables users (scientists) to locate and catalog objects of interest in large image collections. This system provides a platform-independent interface to novel image mining algorithms, as well as to computational and database resources that allow scientists to browse, annotate and search through images and analyze the resulting object catalogs.

PIPE: An intelligent scientific data preparation assistant

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g., spectral densities or running averages).This paper describes the PIPE system. PIPE provides intelligent assistance developing scientific data preparation plans developed using Master Plumber, a general language for scientific data processing plans. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developers’ attention upon tho...

Towards an Objectively Defined Catalog of Galaxy Clusters from the Digitized POSS-II

The subjective nature of the Abell catalog has been widely recognized as its major limitation. We report on the preliminary results of an effort to create an unbiased catalog of clusters of galaxies from the galaxy catalogs derived from the digitized POSS-II (DPOSS).