Asymptotic Conditional Singular Value Decomposition for High-Dimensional Genomic Data

Biometrics

Volumn 67, Issue 2, 2011, Pages 344-352

  Leek, Jeffrey T ^a  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

False discovery rate; Gene expression; Genomics; High dimensional; Singular value decomposition; Surrogate variables

Indexed keywords

ALKYLATION; CLUSTERING ALGORITHMS; GENE EXPRESSION; SINGULAR VALUE DECOMPOSITION;

FACTOR MODEL; FALSE DISCOVERY RATE; GENES EXPRESSION; GENOMIC DATA; GENOMICS; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; NUMBER OF FACTORS; SAMPLE SIZES; SURROGATE VARIABLE;

GENOME;

ARRAY; CLUSTER ANALYSIS; DATA SET; ENVIRONMENTAL FACTOR; ESTIMATION METHOD; EXPERIMENTAL STUDY; GENE EXPRESSION; GENOMICS; IDENTIFICATION METHOD; NUMERICAL MODEL; POLYMORPHISM; SURROGATE METHOD;

ALLIUM PORRUM;

EID: 79959362223     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01455.x     Document Type: Article

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