Biomedical subjects

E Manduchi

Publications and source records attributed to E Manduchi.

3 recordsLinked to original sources

PubMed2001

A relational schema for both array-based and SAGE gene expression experiments.

MOTIVATION AND RESULTS: A relational schema is described for capturing highly parallel gene expression experiments using different technologies. This schema grew out of efforts to build a database for collaborators working on different biological systems and using different types of platforms in their gene expression experiments as well as different types of image quantification software. The tables are conceptually organized into three categories of information: Platform, Experiment (which includes image scanning and quantification), and Data. The strengths of the schema are: (i) integrating information on array elements using a gene index; (ii) describing samples using ontologies; (iii) reducing an experiment to a single RNA source for precise descriptions yet not losing the relationships between experiments done at the same time or for the same project; and (iv) maintaining both raw and processed (e.g. cleansed and normalized) data and recording how the data is processed. The result is a novel schema, which can hold both array and non-array data, is extensible for detailed experimental descriptions that are precise and consistent, and allows for meaningful comparisons of genes between experiments.

Databases, Factual↗

PubMed2000

Generation of patterns from gene expression data by assigning confidence to differentially expressed genes.

MOTIVATION: A protocol is described to attach expression patterns to genes represented in a collection of hybridization array experiments. Discrete values are used to provide an easily interpretable description of differential expression. Binning cutoffs for each sample type are chosen automatically, depending on the desired false-positive rate for the predictions of differential expression. Confidence levels are derived for the statement that changes in observed levels represent true changes in expression. We have a novel method for calculating this confidence, which gives better results than the standard methods. Our method reflects the broader change of focus in the field from studying a few genes with many replicates to studying many (possibly thousands) of genes simultaneously, but with relatively few replicates. Our approach differs from standard methods in that it exploits the fact that there are many genes on the arrays. These are used to estimate for each sample type an appropriate distribution that is employed to control the false-positive rate of the predictions made. Satisfactory results can be obtained using this method with as few as two replicates. RESULTS: The method is illustrated through applications to macroarray and microarray datasets. The first is an erythroid development dataset that we have generated using nylon filter arrays. Clones for genes whose expression is known in these cells were assigned expression patterns which are in accordance with what was expected and which are not picked up by the standards methods. Moreover, genes differentially expressed between normal and leukemic cells were identified. These included genes whose expression was altered upon induction of the leukemic cells to differentiate. The second application is to the microarray data by Alizadeh et al. (2000). Our results are in accordance with their major findings and offer confidence measures for the predictions made. They also provide new insights for further analysis.

Algorithms↗

PubMed1999

Significance testing for direct identity-by-descent mapping.

Direct identity-by-descent mapping is a technique for narrowing down the location of the gene or genes responsible for a given genetic disease to small segments of the genome. The technique involves DNA comparisons between pairs of affected individuals. The data generated are in the form of matching segments of the genome, representing regions likely to be identical-by-descent (IBD). Regions in the genome over which there are significantly more segments aligned than is expected by chance are taken as candidate regions for the disease gene or genes. Due to the complex geometric nature of the data, significance testing involves certain mathematical difficulties. We present here a new method for measuring this significance. This method introduces a novel statistic and is appropriate whether or not the relationships between the paired individuals are known. We give examples that we have calculated by implementing this method, including an application to real data.

Chromosome Mapping↗