A systematic statistical linear modeling approach to oligonucleotide array experiments

DOI10.1016/S0025-5564(01)00107-9zbMath0997.62087OpenAlexW2049849487WikidataQ44877520 ScholiaQ44877520MaRDI QIDQ1602579

Russ Wolfinger, Tzu-Ming Chu, Bruce S. Weir

Publication date: 23 June 2002

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0025-5564(01)00107-9

zbMATH Keywords

linear mixed modelsmicroarrayprobeAffymetrix GeneChip datasplit plot

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Genetics and epigenetics (92D10)

Related Items (5)

Comparison of statistical performance of univariate and bivariate mixed models for Affymetrix^®probe level data ⋮ Treatment of Microarray Experiments as Split-Plot Designs ⋮ Quality control of platinum spike dataset by probe-level mixed models ⋮ A statistical framework for the analysis of microarray probe-level data ⋮ Analyzing Multiple‐Probe Microarray: Estimation and Application of Gene Expression Indexes

Uses Software

• SAS
• SAS/STAT
• SASmixed
• sma

Cites Work

• A simple method to improve probe set estimates from oligonucleotide arrays
• Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
• Significance analysis of microarrays applied to the ionizing radiation response
• Linear mixed models for longitudinal data
• Unnamed Item
• Unnamed Item

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