Please read before you start:

"For research only"
The main field of application of this software is in extracting knowledge from large datasets by putting them in context with other data, i.e. research. M-CHiPS therefore comes without a CE label or any other certificate for human medical purposes. Do not use M-CHiPS as a, or as part of a, in-vitro diagnostic system or as a, or as part of a, medicine product. In misusing M-CHiPS as a, or as part of an in-vitro diagnosticum or medicine product, the user takes full responsibility as a "manufacturer" by European law.
Read the manual
Microarray data analysis is a complex field. If there was a single-workflow solution, we would be happy to provide it. M-CHiPS can be operated by mouseklick and we made things as simple as possible for you by providing default options (buttons tagged with a smiley) for everyone who e.g. has no strong opinion about which normalization method to prefer. Nevertheless, you should know what you're doing in pressing a button. Microarray data analysis is too complex for operating data analysis tools in a trial-and-error fashion. Manufacturers cannot be held reliable for any damage caused by users operating M-CHiPS "blindly", i.e. without reading the manual.
Join our courses
If a particular combination of such operations makes sense or not often depends on the individual case. Inadaequate handling may cause misinterpretation of your data. Because of the complexity of the field, M-CHiPS manufacturers cannot be held reliable for any damage caused by inadaequate handling. Wrong decisions about which measures are adaequate for a certain dataset are often made by insufficiently trained persons. Our courses (you may want to have a look at the course materials) provide hands-on training to prevent misinterpretations and to make the most of your data.
Provide good data and enough time
High-quality data are a prerequisite. There is a plethora of good advice for QC of microarray experiments on the web. In our best-practice list we accumulated the experience of how to stay clear of the many pitfalls that apply to our devices and protocols collected in our lab since 1998. While the wet-lab part seems to be time-consuming enough, thank you, there's one thing that makes it even worse: In contrast to what many people believe in at the beginning, data analysis cannot possibly be conducted in one day before the next presentation. In fact, data analysis -- not wet-lab work, QC, hardware, funding, or software -- was (since 1999) and still is THE bottleneck in the microarray workflow! This is reported e.g. by the Association of Biomolecular Resource Facilities' Microarray Research Group (ABRF-MARG) surveys (see. electronic posters, e.g. Poster 2005). Commonly, there is a large gap between accumulating a lot of data and extracting valid results. We would like to help you to bridge this gap.

2007-10-30