Research on the optimization algorithm for spatially resolved single-cell transcriptomes
As both single-cell and spatial transcriptome techniques have certain shortcomings,techniques integrating single-cell transcriptome and spatial transcriptome were developed.In order to improve the similarity of mapping single-cell matrix to spatial matrix and reduce the loss function value,the RK-Tangram algorithm was developed by improving the objective function of the Tangram algorithm for deep learning and also correcting the gradient value of the optimization algorithm Adam,inspired by the Runge-Kutta method.Applying it to three sets of simulated data and real data from mouse brain cortical,motor and visual regions,compared with the original Tangram algorithm,the results showed that the RK-Tangram algorithm not only improved the similarity of the mapping and reduced the loss function value,but also extended the genome-wide mapping of the spatial transcriptome and corrected spatial measurements with low-quality.In addition,deconvoluting the spatial transcriptome's data to single cell's provided a higher resolution mapping of tissue types.
deep learninggradient descentdeconvolutiontranscriptome
万方数据
维普
