Yin-Jing Tien

GAP: A graphical environment for matrix visualization and cluster analysis

GAP is a Java-designed exploratory data analysis (EDA) software for matrix visualization (MV) and clustering of high-dimensional data sets. It provides direct visual perception for exploring structures of a given data matrix and its corresponding proximity matrices, for variables and subjects. Various matrix permutation algorithms and clustering methods with validation indices are implemented for extracting embedded information. GAP has a friendly graphical user interface for easy handling of data and proximity matrices. It is more powerful and effective than conventional graphical methods when dimension reduction techniques fail or when data is of ordinal, binary, and nominal type.

Anti-diabetic properties of three common Bidens pilosa variants in Taiwan

Bidens pilosa L. var. radiata (BPR), B. pilosa L. var. pilosa (BPP), and B. pilosa L. var. minor (BPM) are common variants of a plant often used as a folk remedy for diabetes in Taiwan. However, the three variants are often misidentified and little is known about their relative anti-diabetic efficacy and chemical composition. In this paper, we have first developed a method based on GC-MS and cluster analysis with visualization to assist in rapidly determining the taxonomy of these three Bidens variants. GC-MS was used to determine the chemical compositions of supercritical extracts, and differences and similarities in the variants were determined by hierarchical cluster analysis. Next, the HPLC profiles of the methanol crude extracts in the Bidens plants and evaluated anti-diabetic effects of methanol crude extracts were compared, as well as three polyacetylenic compounds of the Bidens plants using db/db mice. Single-dose and long-term experiments showed that the BPR extract had higher glucose-lowering and insulin-releasing activities than extracts from the other two variants, and that cytopiloyne was the most effective pure compound among the three polyacetylenic compounds. BPR extract and cytopiloyne also significantly reduced the percentage of the glycosylated hemoglobin A1c in db/db mice. Besides, both animal studies and HPLC analysis demonstrated a good correlation between anti-diabetic efficacy of the Bidens extracts and the particular polyacetylenes present.

Morus alba and active compound oxyresveratrol exert anti-inflammatory activity via inhibition of leukocyte migration involving MEK/ERK signaling

BackgroundMorus alba has long been used in traditional Chinese medicine to treat inflammatory diseases; however, the scientific basis for such usage and the mechanism of action are not well understood. This study investigated the action of M. alba on leukocyte migration, one key step in inflammation.MethodsGas chromatography-mass spectrometry (GC-MS) and cluster analyses of supercritical CO2 extracts of three Morus species were performed for chemotaxonomy-aided plant authentication. Phytochemistry and CXCR4-mediated chemotaxis assays were used to characterize the chemical and biological properties of M. alba and its active compound, oxyresveratrol. fluorescence-activated cell sorting (FACS) and Western blot analyses were conducted to determine the mode of action of oxyresveratrol.ResultsChemotaxonomy was used to help authenticate M. alba. Chemotaxis-based isolation identified oxyresveratrol as an active component in M. alba. Phytochemical and chemotaxis assays showed that the crude extract, ethyl acetate fraction and oxyresveratrol from M. alba suppressed cell migration of Jurkat T cells in response to SDF-1. Mechanistic study indicated that oxyresveratrol diminished CXCR4-mediated T-cell migration via inhibition of the MEK/ERK signaling cascade.ConclusionsA combination of GC-MS and cluster analysis techniques are applicable for authentication of the Morus species. Anti-inflammatory benefits of M. alba and its active compound, oxyresveratrol, may involve the inhibition of CXCR-4-mediated chemotaxis and MEK/ERK pathway in T and other immune cells.

Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

BackgroundThe hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.ResultsThis study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen [3] as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends.ConclusionWe demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV)

BackgroundSevere acute respiratory syndrome (SARS), a recent epidemic human disease, is caused by a novel coronavirus (SARS-CoV). First reported in Asia, SARS quickly spread worldwide through international travelling. As of July 2003, the World Health Organization reported a total of 8,437 people afflicted with SARS with a 9.6% mortality rate. Although immunopathological damages may account for the severity of respiratory distress, little is known about how the genome-wide gene expression of the host changes under the attack of SARS-CoV.ResultsBased on changes in gene expression of peripheral blood, we identified 52 signature genes that accurately discriminated acute SARS patients from non-SARS controls. While a general suppression of gene expression predominated in SARS-infected blood, several genes including those involved in innate immunity, such as defensins and eosinophil-derived neurotoxin, were upregulated. Instead of employing clustering methods, we ranked the severity of recovering SARS patients by generalized associate plots (GAP) according to the expression profiles of 52 signature genes. Through this method, we discovered a smooth transition pattern of severity from normal controls to acute SARS patients. The rank of SARS severity was significantly correlated with the recovery period (in days) and with the clinical pulmonary infection score.ConclusionThe use of the GAP approach has proved useful in analyzing the complexity and continuity of biological systems. The severity rank derived from the global expression profile of significantly regulated genes in patients may be useful for further elucidating the pathophysiology of their disease.

Matrix Visualization and Information Mining

Many statistical techniques, particularly multivariate methodologies, focus on extracting information from data and proximity matrices. Rather than rely solely on numerical characteristics, matrix visualization allows one to graphically reveal structure in a matrix.This article reviews the history of matrix visualization, then gives a more detailed description of its general framework, along with some extensions. Possible research directions in matrix visualization and information mining are sketched. Color versions of figures presented in this article, together with software packages, can be obtained from http://gap.stat.sinica.edu.tw/.

Development of a brief self-report questionnaire for screening putative pre-psychotic states

BACKGROUND Several self-report instruments were developed to capture psychotic prodrome, and were claimed to have good predictive validity. The feasibility of screening is questionable considering the heterogeneity of the targeted populations and the negative ramifications of false positive identification. This study developed a questionnaire using data covering a wide range of clinical characteristics. METHODS One hundred and eleven putative pre-psychotic participants, 129 normal comparison subjects, and 95 non-psychotic psychiatric outpatients completed a 231-item questionnaire comprising a 110-item Wisconsin psychotic prone scale, 74-item schizotypal personality questionnaire, 33-item basic symptoms, and 14-item cognitive symptoms. Items showing the best discriminating power, estimated using chi-square statistics with Bonferroni correction, were extracted to create a brief version. A two-stage cut-off approach emphasizing specific items was applied to maximize sensitivity and specificity. The concurrent validity of the proposed approach was estimated using a ten-fold cross-validation procedure. RESULTS A 15-item self-report questionnaire was developed. Respondents checking at least eight items, or those checking three to seven items including any of the three referring to feeling stress in crowds, aloofness, and perceptual disturbance, would be considered putatively pre-psychotic with the largest sensitivity+specificity (0.784+0.705=1.489). This cut-off selection was the best estimate by calculating 1000 permutations in the cross-validation procedure. CONCLUSIONS This investigation proposes a different orientation for applying questionnaires to screen putative pre-psychotic states, with less emphasis on attenuated psychotic symptoms and predictive values. Besides providing a handy tool for increasing awareness and referral, the instructions of such a screening questionnaire should be carefully worded.

Effect of Bidens pilosa on infection and drug resistance of Eimeria in chickens

Extensive use of current anti-coccidial drugs together with drug resistance and residue has raised concerns about public health and poultry development. Here, we studied the anti-coccidial properties of Bidens pilosa. A phytochemical approach was developed for analysis of B. pilosa utilized as a feed additive. The protective effects of B. pilosa supplemented chicken diet were evaluated chickens infected with Eimeria tenella. B. pilosa, at doses of 0.5%, 1% and 5% of the chicken diet, significantly protected against E.tenella as measured by reduction in mortality, weight loss, fecal oocyst excretion and gut pathology in chickens. Finally, drug resistance of E. tenella to B. pilosa was assessed in chickens using the anti-coccidial index. This index showed that B. pilosa induced little, if any, drug resistance to Eimeria in chickens. Collectively, this work suggests that B. pilosa may serve as a novel, natural remedy for coccidiosis with low drug resistance in chickens.

Exploratory data analysis of interval-valued symbolic data with matrix visualization

Symbolic data analysis (SDA) has gained popularity over the past few years because of its potential for handling data having a dependent and hierarchical nature. Amongst many methods for analyzing symbolic data, exploratory data analysis (EDA: Tukey, 1977) with graphical presentation is an important one. Recent developments of graphical and visualization tools for SDA include zoom star, closed shapes, and parallel-coordinate-plots. Other studies project high dimensional symbolic data into lower dimensional spaces using symbolic data versions of principal component analysis, multidimensional scaling, and self-organizing maps. Most graphical and visualization approaches for exploring symbolic data structure inherit the advantages of their counterparts for conventional (non-symbolic) data, but also their disadvantages. Here we introduce matrix visualization (MV) for visualizing and clustering symbolic data using interval-valued symbolic data as an example; it is by far the most popular symbolic data type in the literature and the most commonly encountered one in practice. Many MV techniques for visualizing and clustering conventional data are converted to symbolic data, and several techniques are newly developed for symbolic data. Various examples of data with simple to complex structures are brought in to illustrate the proposed methods.

Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition

Motivation: Heatmap is a popular visualization technique in biology and related fields. In this study, we extend heatmaps within the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. MV can explore the embedded information structure of high‐dimensional large‐scale datasets effectively without dimension reduction. The benefit of the proposed covariate‐adjusted heatmap is in the exploration of conditional association structures among the subjects or variables that cannot be done with conventional MV. Results: For adjustment of a discrete covariate, the conditional correlation is estimated by the within and between analysis. This procedure decomposes a correlation matrix into the within‐ and between‐component matrices. The contribution of the covariate effects can then be assessed through the relative structure of the between‐component to the original correlation matrix while the within‐component acts as a residual. When a covariate is of continuous nature, the conditional correlation is equivalent to the partial correlation under the assumption of a joint normal distribution. A test is then employed to identify the variable pairs which possess the most significant differences at varying levels of correlation before and after a covariate adjustment. In addition, a z‐score significance map is constructed to visualize these results. A simulation and three biological datasets are employed to illustrate the power and versatility of our proposed method. Availability and implementation: GAP is available to readers and is free to non‐commercial applications. The installation instructions, the users manual, and the detailed tutorials can be found at http://gap.stat.sinica.edu.tw/Software/GAP. Supplementary information: Supplementary Data are available at Bioinformatics online.