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Robert Tibshirani

Publications and source records attributed to Robert Tibshirani.

At least 19 recordsLinked to original sources
PubMed2006

Outlier sums for differential gene expression analysis.

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).

Computer Simulation↗
PubMed2006

Averaged gene expressions for regression.

Although averaging is a simple technique, it plays an important role in reducing variance. We use this essential property of averaging in regression of the DNA microarray data, which poses the challenge of having far more features than samples. In this paper, we introduce a two-step procedure that combines (1) hierarchical clustering and (2) Lasso. By averaging the genes within the clusters obtained from hierarchical clustering, we define supergenes and use them to fit regression models, thereby attaining concise interpretation and accuracy. Our methods are supported with theoretical justifications and demonstrated on simulated and real data sets.

Breast Neoplasms↗
PubMed2006

Are clusters found in one dataset present in another dataset?

In many microarray studies, a cluster defined on one dataset is sought in an independent dataset. If the cluster is found in the new dataset, the cluster is said to be "reproducible" and may be biologically significant. Classifying a new datum to a previously defined cluster can be seen as predicting which of the previously defined clusters is most similar to the new datum. If the new data classified to a cluster are similar, molecularly or clinically, to the data already present in the cluster, then the cluster is reproducible and the corresponding prediction accuracy is high. Here, we take advantage of the connection between reproducibility and prediction accuracy to develop a validation procedure for clusters found in datasets independent of the one in which they were characterized. We define a cluster quality measure called the "in-group proportion" (IGP) and introduce a general procedure for individually validating clusters. Using simulations and real breast cancer datasets, the IGP is compared to four other popular cluster quality measures (homogeneity score, separation score, silhouette width, and weighted average discrepant pairs score). Moreover, simulations and the real breast cancer datasets are used to compare the four versions of the validation procedure which all use the IGP, but differ in the way in which the null distributions are generated. We find that the IGP is the best measure of prediction accuracy, and one version of the validation procedure is the more widely applicable than the other three. An implementation of this algorithm is in a package called "clusterRepro" available through The Comprehensive R Archive Network (http://cran.r-project.org).

Breast Neoplasms↗
PubMed2006

Regularized linear discriminant analysis and its application in microarrays.

In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.

Computer Simulation↗
PubMed2006

A simple method for assessing sample sizes in microarray experiments.

BACKGROUND: In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments. RESULTS: Our method starts with the output from a permutation-based analysis for a set of pilot data, e.g. from the SAM package. Then for a given hypothesized mean difference and various samples sizes, we estimate the false discovery rate and false negative rate of a list of genes; these are also interpretable as per gene power and type I error. We also discuss application of our method to other kinds of response variables, for example survival outcomes. CONCLUSION: Our method seems to be useful for sample size assessment in microarray experiments.

Computer Simulation↗
PubMed2006

An evaluation of tumor oxygenation and gene expression in patients with early stage non-small cell lung cancers.

BACKGROUND: To directly assess tumor oxygenation in resectable non-small cell lung cancers (NSCLC) and to correlate tumor pO2 and the selected gene and protein expression to treatment outcomes. METHODS: Twenty patients with resectable NSCLC were enrolled. Intraoperative measurements of normal lung and tumor pO2 were done with the Eppendorf polarographic electrode. All patients had plasma osteopontin measurements by ELISA. Carbonic anhydrase-IX (CA IX) staining of tumor sections was done in the majority of patients (n = 16), as was gene expression profiling (n = 12) using cDNA microarrays. Tumor pO2 was correlated with CA IX staining, osteopontin levels, and treatment outcomes. RESULTS: The median tumor pO2 ranged from 0.7 to 46 mm Hg (median, 16.6) and was lower than normal lung pO2 in all but one patient. Because both variables were affected by the completeness of lung deflation during measurement, we used the ratio of tumor/normal lung (T/L) pO2 as a reflection of tumor oxygenation. The median T/L pO2 was 0.13. T/L pO2 correlated significantly with plasma osteopontin levels (r = 0.53, P = 0.02) and CA IX expression (P = 0.006). Gene expression profiling showed that high CD44 expression was a predictor for relapse, which was confirmed by tissue staining of CD44 variant 6 protein. Other variables associated with the risk of relapse were T stage (P = 0.02), T/L pO2 (P = 0.04), and osteopontin levels (P = 0.001). CONCLUSIONS: Tumor hypoxia exists in resectable NSCLC and is associated with elevated expression of osteopontin and CA IX. Tumor hypoxia and elevated osteopontin levels and CD44 expression correlated with poor prognosis. A larger study is needed to confirm the prognostic significance of these factors.

Adenocarcinoma↗
PubMed2005

A tail strength measure for assessing the overall univariate significance in a dataset.

We propose an overall measure of significance for a set of hypothesis tests. The 'tail strength' is a simple function of the p-values computed for each of the tests. This measure is useful, for example, in assessing the overall univariate strength of a large set of features in microarray and other genomic and biomedical studies. It also has a simple relationship to the false discovery rate of the collection of tests. We derive the asymptotic distribution of the tail strength measure, and illustrate its use on a number of real datasets.

Biometry↗
PubMed2005

Gene expression profiling predicts survival in conventional renal cell carcinoma.

BACKGROUND: Conventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival. METHODS AND FINDINGS: Gene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status (p < 0.001). CONCLUSIONS: cRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

Adult↗
PubMed2005

Gene expression profiling differentiates germ cell tumors from other cancers and defines subtype-specific signatures.

Germ cell tumors (GCTs) of the testis are the predominant cancer among young men. We analyzed gene expression profiles of 50 GCTs of various subtypes, and we compared them with 443 other common malignant tumors of epithelial, mesenchymal, and lymphoid origins. Significant differences in gene expression were found among major histological subtypes of GCTs, and between them and other malignancies. We identified 511 genes, belonging to several critical functional groups such as cell cycle progression, cell proliferation, and apoptosis, to be significantly differentially expressed in GCTs compared with other tumor types. Sixty-five genes were sufficient for the construction of a GCT class predictor of high predictive accuracy (100% training set, 96% test set), which might be useful in the diagnosis of tumors of unknown primary origin. Previously described diagnostic and prognostic markers were found to be expressed by the appropriate GCT subtype (AFP, POU5F1, POV1, CCND2, and KIT). Several additional differentially expressed genes were identified in teratomas (EGR1 and MMP7), yolk sac tumors (PTPN13 and FN1), and seminomas (NR6A1, DPPA4, and IRX1). Dynamic computation of interaction networks and mapping to existing pathways knowledge databases revealed a potential role of EGR1 in p21-induced cell cycle arrest and intrinsic chemotherapy resistance of mature teratomas.

Chromosomes, Human↗
PubMed2005

Hybrid hierarchical clustering with applications to microarray data.

In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other points. Theoretical connections between mutual clusters and bottom-up clustering methods are established, aiding in their interpretation and providing an algorithm for identification of mutual clusters. We illustrate the technique on simulated and real microarray datasets.

Algorithms↗
PubMed2005

Early detection of breast cancer based on gene-expression patterns in peripheral blood cells.

INTRODUCTION: Existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In this study, we investigated whether early detection of breast cancer is possible by analyzing gene-expression patterns in peripheral blood cells. METHODS: Using macroarrays and nearest-shrunken-centroid method, we analyzed the expression pattern of 1,368 genes in peripheral blood cells of 24 women with breast cancer and 32 women with no signs of this disease. The results were validated using a standard leave-one-out cross-validation approach. RESULTS: We identified a set of 37 genes that correctly predicted the diagnostic class in at least 82% of the samples. The majority of these genes had a decreased expression in samples from breast cancer patients, and predominantly encoded proteins implicated in ribosome production and translation control. In contrast, the expression of some defense-related genes was increased in samples from breast cancer patients. CONCLUSION: The results show that a blood-based gene-expression test can be developed to detect breast cancer early in asymptomatic patients. Additional studies with a large sample size, from women both with and without the disease, are warranted to confirm or refute this finding.

Analysis of Variance↗
PubMed2005

Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease.

The propensity for developing atherosclerosis is dependent on underlying genetic risk and varies as a function of age and exposure to environmental risk factors. Employing three mouse models with different disease susceptibility, two diets, and a longitudinal experimental design, it was possible to manipulate each of these factors to focus analysis on genes most likely to have a specific disease-related function. To identify differences in longitudinal gene expression patterns of atherosclerosis, we have developed and employed a statistical algorithm that relies on generalized regression and permutation analysis. Comprehensive annotation of the array with ontology and pathway terms has allowed rigorous identification of molecular and biological processes that underlie disease pathophysiology. The repertoire of atherosclerosis-related immunomodulatory genes has been extended, and additional fundamental pathways have been identified. This highly disease-specific group of mouse genes was combined with an extensive human coronary artery data set to identify a shared group of genes differentially regulated among atherosclerotic tissues from different species and different vascular beds. A small core subset of these differentially regulated genes was sufficient to accurately classify various stages of the disease in mouse. The same gene subset was also found to accurately classify human coronary lesion severity. In addition, this classifier gene set was able to distinguish with high accuracy atherectomy specimens from native coronary artery disease vs. those collected from in-stent restenosis lesions, thus identifying molecular differences between these two processes. These studies significantly focus efforts aimed at identifying central gene regulatory pathways that mediate atherosclerotic disease, and the identification of classification gene sets offers unique insights into potential diagnostic and therapeutic strategies in atherosclerotic disease.

Animals↗
PubMed2005

Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival.

Based on the hypothesis that features of the molecular program of normal wound healing might play an important role in cancer metastasis, we previously identified consistent features in the transcriptional response of normal fibroblasts to serum, and used this "wound-response signature" to reveal links between wound healing and cancer progression in a variety of common epithelial tumors. Here, in a consecutive series of 295 early breast cancer patients, we show that both overall survival and distant metastasis-free survival are markedly diminished in patients whose tumors expressed this wound-response signature compared to tumors that did not express this signature. A gene expression centroid of the wound-response signature provides a basis for prospectively assigning a prognostic score that can be scaled to suit different clinical purposes. The wound-response signature improves risk stratification independently of known clinico-pathologic risk factors and previously established prognostic signatures based on unsupervised hierarchical clustering ("molecular subtypes") or supervised predictors of metastasis ("70-gene prognosis signature").

Breast Neoplasms↗
PubMed2005

A method for calling gains and losses in array CGH data.

Array CGH is a powerful technique for genomic studies of cancer. It enables one to carry out genome-wide screening for regions of genetic alterations, such as chromosome gains and losses, or localized amplifications and deletions. In this paper, we propose a new algorithm 'Cluster along chromosomes' (CLAC) for the analysis of array CGH data. CLAC builds hierarchical clustering-style trees along each chromosome arm (or chromosome), and then selects the 'interesting' clusters by controlling the False Discovery Rate (FDR) at a certain level. In addition, it provides a consensus summary across a set of arrays, as well as an estimate of the corresponding FDR. We illustrate the method using an application of CLAC on a lung cancer microarray CGH data set as well as a BAC array CGH data set of aneuploid cell strains.

Algorithms↗
PubMed2005

The 'miss rate' for the analysis of gene expression data.

Multiple testing issues are important in gene expression studies, where typically thousands of genes are compared over two or more experimental conditions. The false discovery rate has become a popular measure in this setting. Here we discuss a complementary measure, the 'miss rate', and show how to estimate it in practice.

Computer Simulation↗
PubMed2005

Array-based comparative genomic hybridization identifies localized DNA amplifications and homozygous deletions in pancreatic cancer.

Pancreatic cancer, the fourth leading cause of cancer death in the United States, is frequently associated with the amplification and deletion of specific oncogenes and tumor-suppressor genes (TSGs), respectively. To identify such novel alterations and to discover the underlying genes, we performed comparative genomic hybridization on a set of 22 human pancreatic cancer cell lines, using cDNA microarrays measuring approximately 26,000 human genes (thereby providing an average mapping resolution of <60 kb). To define the subset of amplified and deleted genes with correspondingly altered expression, we also profiled mRNA levels in parallel using the same cDNA microarray platform. In total, we identified 14 high-level amplifications (38-4934 kb in size) and 15 homozygous deletions (46-725 kb). We discovered novel localized amplicons, suggesting previously unrecognized candidate oncogenes at 6p21, 7q21 (SMURF1, TRRAP), 11q22 (BIRC2, BIRC3), 12p12, 14q24 (TGFB3), 17q12, and 19q13. Likewise, we identified novel polymerase chain reaction-validated homozygous deletions indicating new candidate TSGs at 6q25, 8p23, 8p22 (TUSC3), 9q33 (TNC, TNFSF15), 10q22, 10q24 (CHUK), 11p15 (DKK3), 16q23, 18q23, 21q22 (PRDM15, ANKRD3), and Xp11. Our findings suggest candidate genes and pathways, which may contribute to the development or progression of pancreatic cancer.

Cell Line, Tumor↗
PubMed2004

Mouse strain-specific differences in vascular wall gene expression and their relationship to vascular disease.

OBJECTIVE: Different strains of inbred mice exhibit different susceptibility to the development of atherosclerosis. The C3H/HeJ and C57Bl/6 mice have been used in several studies aimed at understanding the genetic basis of atherosclerosis. Under controlled environmental conditions, variations in susceptibility to atherosclerosis reflect differences in genetic makeup, and these differences must be reflected in gene expression patterns that are temporally related to the development of disease. In this study, we sought to identify the genetic pathways that are differentially activated in the aortas of these mice. METHODS AND RESULTS: We performed genome-wide transcriptional profiling of aortas from C3H/HeJ and C57Bl/6 mice. Differences in gene expression were identified at baseline as well as during normal aging and longitudinal exposure to high-fat diet. The significance of these genes to the development of atherosclerosis was evaluated by observing their temporal pattern of expression in the well-studied apolipoprotein E model of atherosclerosis. CONCLUSIONS: Gene expression differences between the 2 strains suggest that aortas of C57Bl/6 mice have a higher genetic propensity to develop inflammation in response to appropriate atherogenic stimuli. This study expands the repertoire of factors in known disease-related signaling pathways and identifies novel candidate genes for future study. To gain insights into the molecular pathways that are differentially activated in strains of mice with varied susceptibility to atherosclerosis, we performed comprehensive transcriptional profiling of their vascular wall. Genes identified through these studies expand the repertoire of factors in disease-related signaling pathways and identify novel candidate genes in atherosclerosis.

Aging↗