Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.

Increasing value add in white collar work using lean methods

The efficient indirect office work brings competitive advantage for companies in a rapidly changing business environment. The direct work methods in factory floors have been developed already for decades, but the office work is an area where the potential to improve the value add has not been studied and utilized systematically so far. The first objective of the thesis work is to find useful method for identifying and managing value add using literature. The usefulness of the method is validated in the case company`s environment. The second objective of the work is to understand what kind of effort is required to create more efficient target setting for the white collar employees. The operative level targets should be linked more tightly to the company strategy. Lean methods are selected as a tool for the improvement, since they are widely used in all kinds of industries and they are already familiar in other functions in the case company. Based on the literature review, suitable improvement methods are selected. The core of the lean is to identify the value add of a customer and eliminate the waste. Also visual control, cross functional work team, flow office and continuous improvement are used. The methods are tested in one production line and the results and feedback indicate that methods are useful in the studied environment.