Asymptotic conditional singular value decomposition for high-dimensional genomic data.

ABSTRACT: High-dimensional data, such as those obtained from a gene expression microarray or second generation sequencing experiment, consist of a large number of dependent features measured on a small number of samples. One of the key problems in genomics is the identification and estimation of factors that associate with many features simultaneously. Identifying the number of factors is also important for unsupervised statistical analyses such as hierarchical clustering. A conditional factor model is the most common model for many types of genomic data, ranging from gene expression, to single nucleotide polymorphisms, to methylation. Here we show that under a conditional factor model for genomic data with a fixed sample size, the right singular vectors are asymptotically consistent for the unobser

SUBMITTER: Leek JT 

PROVIDER: S-EPMC3165001 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature