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We also identified a need to export results and annotation from HCS Road to third party
applications so researchers can perform calculations and generate visualizations that are
not part of a common workflow. We use TIBCO Spotfire for many of our external
visualizations because: it can retrieve data directly from the HCS Road database; it
supports multiple user-configurable visualizations; it provides tools for filtering and
annotating data, and it can perform additional analyses using internal calculations or by
communicating with Accelerys PipelinePilot (SanDiego, CA). Figure 3 shows a Spotfire
visualization for analyzing RNAi screening results. This workflow retrieves results and
treatment information from the HCS Road database. The user is presented with
information on the distribution of normalized values for each endpoint and can select
wells that pass the desired activity threshold. Additional panels identify RNAi reagents
where multiple replicate wells pass the threshold and genes where multiple different RNAi
reagents scored as hits, an analysis that is unique to RNAi screening. Within Spotfire,
HCS assay results can be cross-referenced with other information such as mRNA
expression profiling to identify RNAi reagents whose phenotype correlates with levels of
target expression in the assay cell line (not shown).
Cell-level data
Managing and analyzing cell-level data was a high priority in the development of HCS
Road. Cell level data enables the analysis of correlations between measurements at the
cellular level, the use of alternative data reduction algorithms such as the Kolmogorov-
Smirnov distance 13; 14 , classification of subpopulations by cell cycle phase 15 , and other
approaches beyond basic well-level statistics 16 . However, the volume of cell data in an
HCS experiment can be very large. Storing cell data as one row per measurement per cell
creates a table with large numbers of records and slows down data loading and retrieval.
Because cell data is typically used on a per-plate/feature basis for automated analyses and
for manual inspection, we chose to store it in files on the HCS Road file share (Fig. 1)
rather than in the database. When cell data is needed, it is automatically imported into a
database table using Oracle’s bulk data loading tools. When the cell measurements are no
longer needed the records are deleted from the Road database (but are still retained in
files). This controls database growth and improves performance compared to retaining
large numbers of records in the database.
ROI achieved:
HCS Road currently supports target identification, lead identification and lead profiling
efforts across multiple groups within BMS Applied Biotechnology. Scientists can analyze
their experiments more rapidly and the time needed to load, annotate and review
experiments has been reduced from days to hours. Integration with existing databases