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Future directions

Generalization to other technologies than Affymetrix

Created on: 2010-05-03
Last updated: 2010-10-24

Providing support for other microarray technologies such as Illumina and Agilent becomes a more frequently requested feature in the aroma framework.  This is something we have been working on for a long time and more seriously starting in 2009.  In order to achieve this we need a technology independent way of storing data.  We have designed and implemented new simple binary file formats for this.  What is missing is a complete suite of methods for importing external data into these file formats.

We already have part of the framework in place and today we can, for instance:

What we like to add:
  • Documentation, documentation and documentation.
  • Setup specific import methods for Illumina, Agilent etc. data file(s).  This requires careful mapping of annotation data as well.

Performance improvements

Created on: 2010-11-22
Last updated: 2010-11-22

We always prioritize availability, generality, correctness, reproducibility, traceability and stability of the user as well as the developer API over speed performance.  We believe that performance can always be improved after a method has been implemented in the first place and proven correct.  Sometimes the performance improvements requires changes in the API.  Here are a some performance improvement we would like to implement:

  • The probe-level modelling (PLM)/summarization of Affymetrix arrays was designed to work out-of-the-box with virtually any chip type/CDF.  The price we pay for this is that lots of data is wrapped up and unwrapped multiple times into long nested hetereogenous list structures, which is time consuming in R.  As a first significant improvement, one can identify homogenous subsets of these list structures that have CDF elements of the same shapes and dimensions.  This would speed up the wrapping/unwrapping of data.  It would require to rewrite some of the "fit function" for PLMs.
    This idea has previously been discussed on the mailing list, cf. [ADD URLS].

 

Distributed processing

Created on: 2010-05-03
Last updated: 2010-05-04

Being able to processes the same data set on multiple machines would shorten the latency in any analysis pipeline.  In order to safely distribute the processing to multiple machines sharing data over the local file system, one needs a synchronization mechanism that prevents inconsistency due to different machines overwrite already generated results.

What we like to add:

  • Synchronization mechanism that works on all operation systems and that communicates over a local file system without having to setup a master server.