Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-...Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-supervised and reference-guided learning approach for depth inpainting.For the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information.This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map.Then,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual information.The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values.We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes.In summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information.Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.展开更多
Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell t...Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type annotation.However,existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types,which only exist in a test set.Here,we propose RAINBOW,a reference-guided automatic annotation method based on the contrastive learning framework,which is capable of effectively identifying novel cell types in a test set.By utilizing contrastive learning and incorporating reference data,RAINBOW can effectively characterize the heterogeneity of cell types,thereby facilitating more accurate annotation.With extensive experiments on multiple scCAS datasets,we show the advantages of RAINBOW over state-of-the-art methods in known and novel cell type annotation.We also verify the effectiveness of incorporating reference data during the training process.In addition,we demonstrate the robustness of RAINBOW to data sparsity and number of cell types.Furthermore,RAINBOW provides superior performance in newly sequenced data and can reveal biological implication in downstream analyses.All the results demonstrate the superior performance of RAINBOW in cell type annotation for scCAS data.We anticipate that RAINBOW will offer essential guidance and great assistance in scCAS data analysis.The source codes are available at the GitHub website(BioX-NKU/RAINBOW).展开更多
Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing （RNA-Seq） to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in ...Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing （RNA-Seq） to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in analysis of RNA-Seq data involves mapping of short-read sequences to the reference genome. Reference-guided transcriptome assembly is an optional step, which is recommended if the aim is to identify novel transcripts. Following read mapping, the primary interest of biologists in many RNA-Seq studies is the investigation of differential expression between experimental groups. In this review, we discuss recent developments in RNA-Seq data analysis applied to model organisms, including methods and algorithms for direct mapping, reference-guided transcriptome assembly and differential expression analysis, and provide insights for the future direction of RNA-Seq.展开更多
基金partially supported by a grant from the National Natural Science Foundation of China(No.61922006).
文摘Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-supervised and reference-guided learning approach for depth inpainting.For the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information.This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map.Then,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual information.The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values.We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes.In summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information.Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.
基金National Natural Science Foundation of China,Grant/Award Number:62203236Fundamental Research Funds for the Central Universities,Nankai University,Grant/Award Number:63231137。
文摘Recent advances in single-cell chromatin accessibility sequencing(scCAS)technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type annotation.However,existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types,which only exist in a test set.Here,we propose RAINBOW,a reference-guided automatic annotation method based on the contrastive learning framework,which is capable of effectively identifying novel cell types in a test set.By utilizing contrastive learning and incorporating reference data,RAINBOW can effectively characterize the heterogeneity of cell types,thereby facilitating more accurate annotation.With extensive experiments on multiple scCAS datasets,we show the advantages of RAINBOW over state-of-the-art methods in known and novel cell type annotation.We also verify the effectiveness of incorporating reference data during the training process.In addition,we demonstrate the robustness of RAINBOW to data sparsity and number of cell types.Furthermore,RAINBOW provides superior performance in newly sequenced data and can reveal biological implication in downstream analyses.All the results demonstrate the superior performance of RAINBOW in cell type annotation for scCAS data.We anticipate that RAINBOW will offer essential guidance and great assistance in scCAS data analysis.The source codes are available at the GitHub website(BioX-NKU/RAINBOW).
文摘Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing （RNA-Seq） to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in analysis of RNA-Seq data involves mapping of short-read sequences to the reference genome. Reference-guided transcriptome assembly is an optional step, which is recommended if the aim is to identify novel transcripts. Following read mapping, the primary interest of biologists in many RNA-Seq studies is the investigation of differential expression between experimental groups. In this review, we discuss recent developments in RNA-Seq data analysis applied to model organisms, including methods and algorithms for direct mapping, reference-guided transcriptome assembly and differential expression analysis, and provide insights for the future direction of RNA-Seq.
