Statistical colocalization in biological imaging with false discovery control
In this paper, we present a novel object-based statistical colocalization method. Our colocalization relies on multiple hypothesis tests on the distances between all pairs of the (spot-shaped) objects from the two markers. We wish to test among all these pairs how many are significantly close to each other such that they cannot occur just "by chance". Two objects are decided to be co-localized if the test on their distance is significant. For this purpose, we first extract the objects by applying a wavelet-based spot detection approach which fully takes into account the mixed-Poisson-Gaussian noise process of confocal fluorescence images. Then, we build a null hypothesis model in which the distribution of the distance between two independently randomly drawn detections in the cell is estimated by a kernel method. The observed distances are tested against this null model. Our tests control the false discovery rate (FDR) of the co-localizations. Simulations show that this approach has a good specificity. Furthermore, our method has been successfully applied in a real problem of protein colocalization analysis during the endocytic process.