jump to content  sitemap
The Ohio State University
Center for Biostatistics and Bioinformatics

Home > Expertise > High Dimensional Data

High Dimensional Data

  For assistance, please contact Lianbo Yu or Jianying Zhang.

Statistical Methods:

  • Clustering
  • Data reduction methods
  • Data visualization
  • Differential expression/abundance
  • Filtering
  • Normalization
  • Prognostic/diagnostic multivariate modeling
  • Variance and degrees of freedom smoothing for small sample size

Design Issues:

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

Data Type Experience:

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

Data Summarization/Interpretation:

  • Ingenuity Pathway Analysis (IPA)