Biomedical subjects

Nan Bing

Publications and source records attributed to Nan Bing.

3 recordsLinked to original sources

PubMed2005

Finite mixture model analysis of microarray expression data on samples of uncertain biological type with application to reproductive efficiency.

Common goals of microarray experiments are the detection of genes that are differentially expressed between several biological types and the construction of classifiers that predict biological type of samples. Here we consider a situation where there is no training data. There is considerable interest in comparing expression profiles associated with successful pregnancies (SP) and unsuccessful pregnancies (UP) in model and farm animals. Successful pregnancy rate is known to be much higher in embryos generated by in vitro fertilization (IVF) than in nuclear transfer (NT) embryos, and higher under induced ovulation for large follicles (LF) than for small follicles (SF). The tasks of identifying genes differentially expressed between SP and UP, and predicting SP for future samples are not well accomplished by comparing IVF and NT, or LF and SF. A suitable method is finite mixture model analysis (FMMA), which models each observed class (IVF and NT, or LF and SF) as a mixture of two distributions, one for SP and one for UP, with different known or unknown proportions (here known to be 0.50 SP for IVF and 0.02 SP for NT). The means of the two distributions differ for the differentially expressed genes, which we identify via a likelihood ratio test. We confirm by simulation that FMMA strongly outperforms hierarchical clustering and linear discriminant analysis using the known class labels (NT, IVF). We apply FMMA to a real data set on IVF and NT embryos, and compute their posterior probabilities of SP, which confirm our prior knowledge of the SP proportions for IVF and NT.

Animals↗

PubMed2005

Genetical genomics analysis of a yeast segregant population for transcription network inference.

Genetic analysis of gene expression in a segregating population, which is expression profiled and genotyped at DNA markers throughout the genome, can reveal regulatory networks of polymorphic genes. We propose an analysis strategy with several steps: (1) genome-wide QTL analysis of all expression profiles to identify eQTL confidence regions, followed by fine mapping of identified eQTL; (2) identification of regulatory candidate genes in each eQTL region; (3) correlation analysis of the expression profiles of the candidates in any eQTL region with the gene affected by the eQTL to reduce the number of candidates; (4) drawing directional links from retained regulatory candidate genes to genes affected by the eQTL and joining links to form networks; and (5) statistical validation and refinement of the inferred network structure. Here, we apply an initial implementation of this strategy to a segregating yeast population. In 65, 7, and 28% of the identified eQTL regions, a single candidate regulatory gene, no gene, or more than one gene was retained in step 3, respectively. Overall, 768 putative regulatory links were retained, 331 of which are the strongest candidate links, as they were retained in the expression correlation analysis and were located within or near an eQTL subregion identified by a multimarker analysis separating multiple linked QTL. One or several biological processes were statistically significantly overrepresented in independent network structures or in highly interconnected subnetworks. Most of the transcription factors found in the inferred network had a putative regulatory link to only one other gene or exhibited cis-regulation.

DNA↗

PubMed2004

Discovery of meaningful associations in genomic data using partial correlation coefficients.

MOTIVATION: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION: Supplementary information can be found at: URL to be decided.

Algorithms↗