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 |