Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Genetics (5)
- Gene expression (4)
- Cluster analysis (3)
- Comparative genomic hybridization (3)
- Cross-validation (3)
-
- Prediction (3)
- Bioinformatics (2)
- Bootstrap (2)
- Censored data (2)
- Classification (2)
- Clustering (2)
- Density estimation (2)
- Differential expression (2)
- Genomics (2)
- Loss function (2)
- Mixture models (2)
- Model selection (2)
- Multivariate outcome (2)
- Regression trees (2)
- Survival analysis (2)
- (Quasi)separation (1)
- A New Data Analysis Method (1)
- Adjust p value (1)
- Aging (1)
- Annotation metadata; Gene Ontology (GO); genomics; microarray; multiple hypothesis testing; resampling (1)
- BLUPs; Kernel function; Model/variable selection; Nonparametric regression; Penalized likelihood; REML; Score test; Smoothing parameter; Support vector machines (1)
- Beta mixture; DNA methylation; cancer; epigenetics; mixture model (1)
- Biomedical signal processing (1)
- Block (1)
- CART (1)
- Publication Year
- Publication
-
- U.C. Berkeley Division of Biostatistics Working Paper Series (8)
- UW Biostatistics Working Paper Series (6)
- Harvard University Biostatistics Working Paper Series (5)
- COBRA Preprint Series (4)
- Johns Hopkins University, Dept. of Biostatistics Working Papers (2)
-
- The University of Michigan Department of Biostatistics Working Paper Series (2)
- Bioconductor Project Working Papers (1)
- Biology Dissertations - Archive (1)
- Dissertations, Master's Theses and Master's Reports (1)
- Graduate Theses and Dissertations (1)
- STAR Program Research Presentations (1)
- Theses and Dissertations (1)
- Publication Type
Articles 31 - 33 of 33
Full-Text Articles in Multivariate Analysis
Comparative Genomic Hybridization Array Analysis, Annette M. Molinaro, Mark J. Van Der Laan, Dan H. Moore
Comparative Genomic Hybridization Array Analysis, Annette M. Molinaro, Mark J. Van Der Laan, Dan H. Moore
U.C. Berkeley Division of Biostatistics Working Paper Series
At the present time, there is increasing evidence that cancer may be regulated by the number of copies of genes in tumor cells. Through microarray technology it is now possible to measure the number of copies of thousands of genes and gene segments in samples of chromosomal DNA. Microarray comparative genomic hybridization (array CGH) provides the opportunity to both measure DNA sequence copy number gains and losses and map these aberrations to the genomic sequence. Gains can signify the over-expression of oncogenes, genes which stimulate cell growth and have become hyperactive, while losses can signify under-expression of tumor suppressor genes, …
A New Partitioning Around Medoids Algorithm, Mark J. Van Der Laan, Katherine S. Pollard, Jennifer Bryan
A New Partitioning Around Medoids Algorithm, Mark J. Van Der Laan, Katherine S. Pollard, Jennifer Bryan
U.C. Berkeley Division of Biostatistics Working Paper Series
Kaufman & Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which maps a distance matrix into a specified number of clusters. A particularly nice property is that PAM allows clustering with respect to any specified distance metric. In addition, the medoids are robust representations of the cluster centers, which is particularly important in the common context that many elements do not belong well to any cluster. Based on our experience in clustering gene expression data, we have noticed that PAM does have problems recognizing relatively small clusters in situations where good partitions around medoids clearly exist. In this …
Statistical Inference For Simultaneous Clustering Of Gene Expression Data, Katherine S. Pollard, Mark J. Van Der Laan
Statistical Inference For Simultaneous Clustering Of Gene Expression Data, Katherine S. Pollard, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function of the true data generating distribution, and an estimate is obtained by applying this function to the empirical distribution. We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as …