New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

Description and analysis of variation in dairy product and concomitant nutrient purchases between Hispanic and non -Hispanic households using a panel of shoppers' purchase records

The purpose of this research was development of a method of estimating nutrient availability in populations as approximated by supermarket purchase records. Demographic information describing 12,516 panel households was obtained from a marketing and advertising program operated by H. E. Butt Grocery Company of San Antonio, Texas. A non-probability sample of 2,161 households meeting expenditure criteria was selected and all purchases of dairy products for this sample of households were organized into a database constructed to facilitate the retrieval, aggregation, and analysis of dairy product purchases and their nutrient contents. Two hypotheses were tested: (1) no difference would be found between Hispanic and non-Hispanic purchases of dairy product categories during the study period and (2) no difference would be found between Hispanic and non-Hispanic purchases of nutrients contained in those dairy products during the thirteen-week study period.^ Food purchase records were used to estimate nutrient exposure on a weekly, per capita basis for Hispanic and non-Hispanic households by linking some 40,000 dairy purchase Universal Product code (UPC) numbers with food composition values contained in USDA Handbook 8-1. Results of this study suggest Hispanic sample households consistently purchased fewer dairy products than did non-Hispanic sample households and consequently had fewer nutrients available from dairy purchases. While weekly expenditures for dairy products among the sample households remained relatively constant during the study period, shifts in the types of dairy products purchased were observed. The effect of ethnicity on dairy product and nutrient purchases was significant over the thirteen-week period. A database consisting of customer, household, and purchase information can be developed to successfully associate food item UPC numbers with a standard reference of food composition to estimate nutrient availability in a population over extended periods of time. ^