Grace S. Shieh

A weighted Kendall's tau statistic

A weighted Kendalls tau statistic ([tau]w) is proposed to measure weighted correlation. It can place more emphasis on items having low rankings than those have high rankings, or vice versa. The null limiting distribution is derived by the theory of U-statistics. An application, power comparison, and some critical values of [tau]w are presented.

Identification of the common regulators for hepatocellular carcinoma induced by hepatitis B virus X antigen in a mouse model

Hepatitis B virus X antigen plays an important role in the development of human hepatocellular carcinoma (HCC). The key regulators controlling the temporal downstream gene expression for HCC progression remains unknown. In this study, we took advantage of systems biology approach and analyzed the microarray data of the HBx transgenic mouse as a screening process to identify the differentially expressed genes and applied the software Pathway Studio to identify potential pathways and regulators involved in HCC. Using subnetwork enrichment analysis, we identified five common regulator genes: EDN1, BMP7, BMP4, SPIB and SRC. Upregulation of the common regulators was validated in the other independent HBx transgenic mouse lines. Furthermore, we verified the correlation of their RNA expression levels by using the human HCC samples, and their protein levels by using the human liver disease tissue arrays. EDN1, bone morphogenetic protein (BMP) 4 and BMP7 were upregulated in cirrhosis, BMP4, BMP7 and SRC were further upregulated in hepatocellular or cholangiocellular carcinoma samples. The trend of increasing expression of the common regulators correlates well with the progression of human liver cancer. Overexpression of the common regulators increases the cell viability, promotes migration and invasiveness and enhances the colony formation ability in Hep3B cells. Our approach allows us to identify the critical genes in hepatocarcinogenesis in an HBx-induced mouse model. The validation of the gene expressions in the liver cancer of human patients and their cellular function assays suggests that the identified common regulators may serve as useful molecular targets for the early-stage diagnosis or therapy for HCC.

A pattern recognition approach to infer time-lagged genetic interactions

MOTIVATION For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. RESULTS PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. AVAILABILITY Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm.

Inferring genetic interactions via a nonlinear model and an optimization algorithm

BackgroundBiochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.ResultsAn S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.ConclusionsGASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

BackgroundWith the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.ResultsMotivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.ConclusionSSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.

Inferences based on a bivariate distribution with von Mises marginals

There is very little literature concerning modeling the correlation between paired angular observations. We propose a bivariate model with von Mises marginal distributions. An algorithm for generating bivariate angles from this von Mises distribution is given. Maximum likelihood estimation is then addressed. We also develop a likelihood ratio test for independence in paired circular data. Application of the procedures to paired wind directions is illustrated. Employing simulation, using the proposed model, we compare the power of the likelihood ratio test with six existing tests of independence.

Comparison of Support Vector Machines to Other Classifiers Using Gene Expression Data

ABSTRACT Support vector machines (SVMs) was shown to outperform Fishers linear discriminant analysis and two classification trees (C4.5 and MOC1) in binary classification of microarray gene expression data (MGED) (Brown et al., 2000; Furey et al. 2000). However, multiclass classification is more commonly encountered in identifying tumor subtypes using MGED. Using MGED, Dudoit et al. (2002) showed that diagonal linear discriminant analysis (DLDA) outperformed other linear and quadratic discriminants, nearest neighbor, and classification trees with univariate splits. It is of interest, therefore, to compare performance of SVMs to DLDA and the latest two classification trees with linear splits, which performered better than trees with univariate splits, in multiclass classification of MGED. Furthermore, the performance of SVMs with different types of kernels were studied by three types of multiclass MGED. Finally, we investigate how irrelevant and correlated variables (features) influence the performance of the three classifiers. Some suggestions are made for multiclass classification of MGED.

Modeling and comparing the organization of circular genomes

Motivation: Most prokaryotic genomes are circular with a single chromosome (called circular genomes), which consist of bacteria and archaea. Orthologous genes (abbreviated as orthologs) are genes directly evolved from an ancestor gene, and can be traced through different species in evolution. Shared orthologs between bacterial genomes have been used to measure their genome evolution. Here, organization of circular genomes is analyzed via distributions of shared orthologs between genomes. However, these distributions are often asymmetric and bimodal; to date, there is no joint distribution to model such data. This motivated us to develop a family of bivariate distributions with generalized von Mises marginals (BGVM) and its statistical inference. Results: A new measure based on circular grade correlation and the fraction of shared orthologs is proposed for association between circular genomes, and a visualization tool developed to depict genome structure similarity. The proposed procedures are applied to eight pairs of prokaryotes separated from domain down to species, and 13 mycoplasma bacteria that are mammalian pathogens belonging to the same genus. We close with remarks on further applications to many features of genomic organization, e.g. shared transcription factor binding sites, between any pair of circular genomes. Thus, the proposed procedures may be applied to identifying conserved chromosome backbones, among others, for genome construction in synthetic biology. Availability: All codes of the BGVM procedures and 1000+ prokaryotic genomes are available at http://www.stat.sinica.edu.tw/∼gshieh/bgvm.htm. Contact: gshieh@stat.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.

The Overexpression of FEN1 and RAD54B May Act as Independent Prognostic Factors of Lung Adenocarcinoma.

Synthetic lethality arises when a combination of mutations in two or more genes leads to cell death. However, the prognostic role of concordant overexpression of synthetic lethality genes in protein level rather than a combination of mutations is not clear. In this study, we explore the prognostic role of combined overexpression of paired genes in lung adenocarcinoma. We used immunohistochemical staining to investigate 24 paired genes in 93 lung adenocarcinoma patients and Kaplan-Meier analysis and Cox proportional hazards models to evaluate their prognostic roles. Among 24 paired genes, only FEN1 (Flap endonuclease 1) and RAD54B (RAD54 homolog B) were overexpressed in lung adenocarcinoma patients with poor prognosis. Patients with expression of both FEN1 and RAD54B were prone to have advanced nodal involvement and significantly poor prognosis (HR = 2.35, P = 0.0230). These results suggest that intensive follow up and targeted therapy might improve clinical outcome for patients who show expression of both FEN1 and RAD54B.

CSNK1E/CTNNB1 are synthetic lethal to TP53 in colorectal cancer and are markers for prognosis.

Two genes are called synthetic lethal (SL) if their simultaneous mutations lead to cell death, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells specifically, but leave normal cells intact. We present an integrated approach to uncovering SL pairs in colorectal cancer (CRC). Screening verified SL pairs using microarray gene expression data of cancerous and normal tissues, we first identified potential functionally relevant (simultaneously differentially expressed) gene pairs. From the top-ranked pairs, ~ 20 genes were chosen for immunohistochemistry (IHC) staining in 171 CRC patients. To find novel SL pairs, all 169 combined pairs from the individual IHC were synergistically correlated to five clinicopathological features, e.g. overall survival. Of the 11 predicted SL pairs, MSH2-POLB and CSNK1E-MYC were consistent with literature, and we validated the top two pairs, CSNK1E-TP53 and CTNNB1-TP53 using RNAi knockdown and small molecule inhibitors of CSNK1E in isogenic HCT-116 and RKO cells. Furthermore, synthetic lethality of CSNK1E and TP53 was verified in mouse model. Importantly, multivariate analysis revealed that CSNK1E-P53, CTNNB1-P53, MSH2-RB1, and BRCA1-WNT5A were independent prognosis markers from stage, with CSNK1E-P53 applicable to early-stage and the remaining three throughout all stages. Our findings suggest that CSNK1E is a promising target for TP53-mutant CRC patients which constitute ~ 40% to 50% of patients, while to date safety regarding inhibition of TP53 is controversial. Thus the integrated approach is useful in finding novel SL pairs for cancer therapeutics, and it is readily accessible and applicable to other cancers.