5082292
Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information
MOTIVATION
It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging.


RESULTS
We describe an integrated clinicogenomic modeling approach that combines gene expression profiles and the clinically based International Prognostic Index (IPI) for personalized prediction in disease outcome. Dimension reduction methods are proposed to produce linear combinations of gene expressions, while taking into account clinical IPI information. The extracted summary measures capture all the regression information of the censored survival phenotype given both genomic and clinical data, and are employed as covariates in the subsequent survival model formulation. A case study of diffuse large-B-cell lymphoma data, as well as Monte Carlo simulations, both demonstrate that the proposed integrative modeling improves the prediction accuracy, delivering predictions more accurate than those achieved by using either clinical data or molecular predictors alone.
13836655
Dimension reduction methods for microarrays with application to censored survival data
MOTIVATION
Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles.


RESULTS
We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis.


AVAILABILITY
R programs are available upon request from the authors.


SUPPLEMENTARY INFORMATION
http://dna.ucdavis.edu/~hli/bioinfo-surv-supp.pdf.
37524397
Cluster-based estimation for sufficient dimension reduction
Sufficient dimension reduction (SDR) approaches such as ordinary least squares (OLS), sliced inverse regression, sliced average variance estimation and principal Hessian directions all require a linearity condition on the marginal distribution of the predictors X. This can be a significant limitation in practice. In this article we propose the use of clustering on the predictor space with the goal of breaking the nonlinearity among the predictors. We then estimate the dimension reduction subspace by combining estimates from individual clusters. OLS is used as a demonstration of the cluster-based estimation approach. Simulations have shown that the proposed method effectively mitigates the effects of nonlinearity; it thus has the potential to widen the applicative scope of existing SDR techniques.
3042580
A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-alpha
MOTIVATION
To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-alpha (ERalpha), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs.


RESULTS
Biologically, our proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERalpha target promoters (P-value < 0.001). The up-regulated targets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP, USF2) and (DBP, MYOGENIN); and down-regulated ERalpha target genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (DBP, MYOGENIN). Statistically, our proposed mixture model-based discriminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; such integrative power cannot be achieved by current methods.


AVAILABILITY
The software is available on request from the authors.


CONTACT
lali@iupui.edu


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
14467396
Image-based streamline generation and rendering
Seeding streamlines in 3D flow fields without considering their projections in screen space can produce visually cluttered rendering results. Streamlines will overlap or intersect with each other in the output image, which makes it difficult for the user to perceive the underlying flow structure. This paper presents a method to control the seeding and generation of streamlines in image space to avoid visual cluttering and allow a more flexible exploration of flow fields. In our algorithm, 2D images with depth maps generated by a variety of visualization techniques can be used as input from which seeds are placed and streamlines are generated. The density and rendering styles of streamlines can be flexibly controlled based on various criteria to improve visual clarity. With our image space approach, it is straightforward to implement the level of detail rendering, depth peeling, and stylized rendering of streamlines to allow for more effective visualization of 3D flow fields.
28610395
A Path-Based Approach for Efficient Structural Join with Not-Predicates
There has been much research on XML query processing. However, there has been little work on the evaluation of XML queries involving not-predicates. Such queries are useful and common in many real-life applications. In this paper, we present a model called XQuery tree to model queries involving not-predicates and describe a path-based method to evaluate such queries efficiently. A comprehensive set of experiments is carried out to demonstrate the effectiveness and efficiency of the proposed solution.
15608177
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
MOTIVATION
We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm.


METHODS
Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples.


RESULTS
The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification.


AVAILABILITY
The method is available at http://dir.niehs.nih.gov/microarray/datamining


CONTACT
LI3@niehs.nih.gov
1290713
NMPP: a user-customized NimbleGen microarray data processing pipeline
UNLABELLED
NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module.


AVAILABILITY
http://plantgenomics.biology.yale.edu/nmpp