Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.

A highly efficient circle positioning algorithm, called the two-step optimization Hough transform (TSHT), based on multi-resolution segmentation is proposed to solve the problems of the offset Hough transform, namely, its large memory overhead, long time consumption, and low recognition accuracy. First, using the image feature of the printed circuit board (PCB) circular identifier, the target circle is obtained using adaptive image preprocessing, and then, images of an acceptable quality are separated by shape quality inspection to improve their robustness. Second, using effective interval sampling strategies and gradually controlling the accumulative interval of parameters, the TSHT algorithm reduces the memory overhead and quickly locates the center at the pixel level. Finally, the center at the sub-pixel level is found by the least-squares method for circle fitting. The experiments prove that TSHT, as a result of its high robustness, strong anti-noise capability, fast recognition speed, and accuracy, can be successfully applied to a vision positioning system of a solder paste printing machine.

Single-cell RNA-sequencing (RNA-seq) has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Though many methods have been proposed to analyze bulk data using single-cell profile as a reference, they are limited on the interpretability, processing speed, and data size requirement. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq, to achieve precise prediction in short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with existing methods on several benchmarking datasets, TAPE is more accurate (up to 40% improvement on the real bulk data) and faster. Thus, it is sensitive enough to provide biologically meaningful predictions. For example, only TAPE can predict the tendency of increasing monocytes-to-lymphocytes ratio in COVID-19 patients from mild to serious symptoms, of which estimated indices are consistent with laboratory data. More importantly, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. Combining with single-sample gene set enrichment analysis (ssGSEA), TAPE also provides valuable clues to investigate the immune response in different virus-infected patients. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.