On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing

We have undertaken two-dimensional gel electrophoresis proteomic profiling on a series of cell lines with different recombinant antibody production rates. Due to the nature of gel-based experiments not all protein spots are detected across all samples in an experiment, and hence datasets are invariably incomplete. New approaches are therefore required for the analysis of such graduated datasets. We approached this problem in two ways. Firstly, we applied a missing value imputation technique to calculate missing data points. Secondly, we combined a singular value decomposition based hierarchical clustering with the expression variability test to identify protein spots whose expression correlates with increased antibody production. The results have shown that while imputation of missing data was a useful method to improve the statistical analysis of such data sets, this was of limited use in differentiating between the samples investigated, and highlighted a small number of candidate proteins for further investigation. (c) 2006 Elsevier B.V. All rights reserved.

Relative validity of food intake estimates using a food frequency questionnaire is associated with sex, age, and other personal characteristics

We invostigated the validity of food intake estimates obtained by a self-administered FFQ relative to weighed food records (WFR) and the extent to which demographic, anthropometric, and social characteristics explain differences between these methods. A community-based sample of 96 Australian adults completed a FFQ and 12 d of WFR over 12 mo. The FFQ was adapted to the Australian setting from the questionnaire used in the US Nurses' Health Study. Spearman rank correlation coefficients ranged from 0.08 for other vegetables to 0.88 for tea. Exact agreement by quartiles of intake ranged from 27% (eggs) to 63% (tea). Differences between FFQ and WFR regressed on personal characteristics were significantly associated with at least 1 characteristic for 20 of the 37 foods. Sex was significantly associated with differences for 17 food groups, including 5 specific vegetable groups and 2 total fruit and vegetable groups. Use of dietary supplements and the presence of a medical condition were associated with differences for 5 foods; age, school leaving age, and occupation were associated with differences for 1-3 foods. BMI was rot associated with differences for any foods. Regression models explained from 3% (wholemeal bread) to 37% (for all cereals and products) of variation in differences between methods. We conclude that the relative validity of intake estimates obtained by FFQ is different for men and women for a large number of foods. These results highlight the need for appropriate adjustment of diet-disease relations for factors affecting the validity of food intake estimates.