These results also highlight an interesting and important issue, namely that by selecting a representative subset of samples, significant metabolic information can be retrieved from a small number of samples, information by the presented method that can be predictively verified in follow up studies. This is important since it is not reasonable to believe that all future studies of value should include thousands of samples. Instead, there could be value in detecting potentially relevant

information in small, well designed studies. The benefits of this will be many, including Inhibitors,research,lifescience,medical an efficient use of biobank samples, as well as better possibilities for maintaining a high analytical data quality, which is a major problem when analyzing large sample sets over longer times with mass spectrometry. It is also worth noting Inhibitors,research,lifescience,medical that the same strategy as above, namely selecting representative sample subsets from acquired GC/MS data and utilizing the predictive feature of the H-MCR method, was used as an internal cross-validation procedure

for the H-MCR curve resolution as a means to obtain a robust and reliable metabolite pattern on which to base further modeling and interpretations. Thus, the whole chain of events, including multivariate Inhibitors,research,lifescience,medical curve resolution and sample classification, is subject to an internal cross-validation procedure, as well as Inhibitors,research,lifescience,medical providing the possibility for prediction of new independent samples, which to our knowledge makes the proposed strategy unique. Of high importance for an efficient screening of large sample

sets is the time and feasibility for producing representative data. Curve resolution methods are, in general, very time-consuming and demanding in terms of computer capacity, which limits the number of samples that can actually be processed. However, the predictive feature of the H-MCR method can resolve this issue. In the given example, H-MCR processing of the 16 samples took 6h and 29 min to resolve, while found predictive processing of Inhibitors,research,lifescience,medical the remaining 77 samples took <10sec/sample. This indicates that as long as the selected subset is representative in terms, retained variation large sample quantities can be efficiently processed, providing data of high quality for sample comparisons, biomarker detection and identification, and predictions. It should be pointed out that the number of samples used in this AZD6244 concentration example cannot be considered as a particularly large sample or data set. However, the point was proven that samples could be efficiently processed, with retained data quality, based on a selected set of representative samples, and as far as the method goes, there is no limitation to the number of samples that can be predictively processed in the same way as shown here.