CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron

The Charge Coupled Device (CCD) scanner determines the concentration of the microarray by capturing the intensity of the fluorescent signal on the microarray in combination with the standard curve. Due to the characteristics of semiconductors, the CCD sensor in the scanner we designed suffers from saturation, the non-linear relationship between photoelectric response and the light intensity collected by CCD, which poses a challenge for fitting the standard curve of microarray scanner. The Least Squares Algorithm (LSA) still has a large relative error even in the case of high-order fitting, especially in the region of the fluorescence image with small gray level. However, the standard curve is critical to the highly accurate measuring of the instrument. In view of the poor curve fitting performance of LSA, Weighted Least Squares (WLS), and Penalized Least Squares (PLS), as well as the small dataset, this paper proposes the Multi-Layer Perceptron (MLP) neural network algorithm with the minimization of relative error as the constraint, which is applied to the standard curve fitting of the scanner. The gray-level of the fluorescent probe in detection image was obtained as the data set acquired by the microarray scanner at different exposure time. And the relative error and the standard deviation of the relative errors were used as evaluation indicators. In our experiments we compared the MLP neural network with relative error minimization as the constraint with the LSA and the MLP neural network with sum of square errors (SSE) minimization as the constraint. The experimental results show that the MLP neural network constrained by minimizing the relative error has good fitting performance for the standard curve of CCD scanner, with the maximum relative error of only 0.89% while the standard deviation of relative error of only 0.25%. It can be seen that this method provides a new approach for standard curve fitting of microarray scanner.