Statistical Bioinformatics Center

Congratulations 2008-2009 Graduates!

Professor Rebecca Doerge with Dr. Lingling An Dr. Lingling An and Professor Rebecca W. Doerge

Congratulations to our recent Ph.D. graduate, Lingling An (Aug. 2008) on her achievements.

Dr. Lingling An performed her Ph.D. research in the area of high-dimensional dynamic cluster analysis under the direction of Professor Rebecca W. Doerge. Dr. An, an active member of the Statistical Bioinformatics Center, participated in a great number of very productive side projects during her time at Purdue University. In particular, Dr. An has worked closely with the members of the National Science Foundation funded "Functional Genomics of Plant Polyploids", as well as the National Institute of Health funded "Molecular Analysis of Synaptic Transmission Mutants". The title of Dr. An’s dissertation is: "Dynamic Clustering of Time Series Gene Expression".

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The cycle of theory, experiment, and information is nowhere more important than in the life sciences, where we are learning how to piece together various levels of expertise into a global or systems-level understanding of biology. Statistical Bioinformatics is involved at each level: accumulation, organization, and analysis of biological data. Hypotheses that are initiated and tested can be refined, and new experiments formulated for the purpose of supplying more information.

Statistical Bioinformatics

Statistical Bioinformatics Center News

  • Two-Dimensional Correlation Optimized Warping Algorithm for Aligning GCxGC-MS Data

    Anal. Chem., 80, 8, 2664 - 2671, 2008, 10.1021/ac7024317

    Figure 3. Figure 3 (Click to see larger image)



    Abstract
    A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and second dimensions on the basis of applying a one-dimensional COW algorithm to characteristic vectors, nongrid points can be interpolatively warped. This 2-D algorithm was directly applied to total ion counts (TIC) chromatographic profiles of homogeneous chemical samples, i.e., samples including mostly identical compounds. For heterogeneous chemical samples, the 2-D algorithm is first applied to certain selected ion counts chromatographic profiles, and the resultant warping parameters are then used to warp the corresponding TIC chromatographic profiles. The developed 2-D COW algorithm can also be applied to align other 2-D separation images, e.g., LC×LC data, LC×GC data, GC×GC data, LC×CE data, and CE×CE data.

    Dabao Zhang, Xiaodong Huang, Fred E. Regnier, and Min Zhang