Adaptive informatics for multifactorial and high-content biological data

Nat Methods. 2011 Jun;8(6):487-93. doi: 10.1038/nmeth.1600. Epub 2011 Apr 24.


Whereas genomic data are universally machine-readable, data from imaging, multiplex biochemistry, flow cytometry and other cell- and tissue-based assays usually reside in loosely organized files of poorly documented provenance. This arises because the relational databases used in genomic research are difficult to adapt to rapidly evolving experimental designs, data formats and analytic algorithms. Here we describe an adaptive approach to managing experimental data based on semantically typed data hypercubes (SDCubes) that combine hierarchical data format 5 (HDF5) and extensible markup language (XML) file types. We demonstrate the application of SDCube-based storage using ImageRail, a software package for high-throughput microscopy. Experimental design and its day-to-day evolution, not rigid standards, determine how ImageRail data are organized in SDCubes. We applied ImageRail to collect and analyze drug dose-response landscapes in human cell lines at single-cell resolution.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Antineoplastic Agents / administration & dosage
  • Cell Line, Tumor
  • Computational Biology / methods*
  • Data Interpretation, Statistical*
  • Database Management Systems
  • Databases, Factual
  • Dose-Response Relationship, Drug
  • ErbB Receptors / antagonists & inhibitors
  • Gefitinib
  • Humans
  • Microscopy / statistics & numerical data
  • Programming Languages
  • Quinazolines / administration & dosage
  • Software*


  • Antineoplastic Agents
  • Quinazolines
  • ErbB Receptors
  • Gefitinib