Högskolan i Skövde

Gene expression analysis can be performed by a number of methods. One of the most common methods is using relative qPCR  to assess the relative expression of a determined set of genes compared to a reference gene. Analysis methods benefits from an as homogeneous sample set as possible, as great variety in original sample disease status, quality, type, or distribution may yield an uneven base expression between replicates. Additionally normalization of qPCR data will not work if there are missing values in the data. There are methods for handling non-detects (i.e. missing values) in the data, where most of them are only recommended to use when there is a single, or very few, value missing. By integrating microarray expression data with qPCR data, the data quality could be improved on, eradicating the need to redo an entire experiment when too much data is missing or sample data too is heterogeneous. In this project, publically available microarray data, with similar sample status of a given qPCR dataset, was downloaded and processed. The qPCR dataset included 51 genes, where a set of four DLG genes has been chosen for in-depth analysis. For handling missing values, mean imputation and inserting Cq value 40 were used, as well as a novel method initialized where microarray data was used to replace missing values. In summary replacing missing values with microarray data did not show any significant difference to the other two methods in three of the four DLG genes. From this project, it is also suggested an initialized approach towards testing the possibility of qPCR and microarray data integration.