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High Dimensional Data Design and Analysis

Biostatisticians with expertise in design and analysis for high-dimensional data

There are several biostatisticians in the Program who have expertise in the design and analysis for high-dimensional data.

Areas of expertise in high dimensional data include:

Statistical Methods:

  • Filtering
  • Normalization
  • Variance and degrees of freedom smoothing for small sample size gene expression studies
  • Data visualization
  • Differential expression/abundance testing
  • Clustering
  • Prognostic/diagnostic multivariate modeling
  • Biomarker discovery and validation

Design Issues:

  • Sample size to control power
  • Methods of controlling false discoveries
  • Feature selection/validation including pathway analysis

Data Type Experience:

  • Transcriptome (mRNA and miRNA by Affymetrix and Illumina)
  • Epigenetics (Methylome, MassARRAY)
  • Genomic profiling (SNP, CGH)
  • Post-translational modification (Proteomics)

Validation/Interpretation:

  • Ingenuity Pathway Analysis (IPA)

For more information on high dimensional data design and analysis, send an email to ccts-biostat@osumc.edu.

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Acknowledging CTSA grant support in publications
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