The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.
Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.
Background In traditional Chinese medicine, it is believed that the “tongue coating is produced by fumigation of stomach gas”, and that tongue coating can reflect the health status of humans, especially stomach health. Therefore, studying the relationship between the microbiome of the tongue coating and the gastric fluid is of great significance for understanding the biological basis of tongue diagnosis. Methods This paper detected the microbiomes of the tongue coating and the gastric fluid in 35 gastritis patients using metagenomic sequencing technology, systematically constructed the microbial atlas of tongue coating and gastric juice, and first described the similar characteristics between the two sites. Results There was a significant correlation between tongue coating and gastric juice in terms of microbial species composition and overall diversity. In terms of species composition, it was found that the two sites were dominated by five phyla, namely, Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria and Proteobacteria, and that most of the gastric microbial species could be detected from the patient's own tongue coating. In terms of overall diversity, a significant correlation was found between the alpha diversity of the tongue coating microbiome and the gastric juice microbiome. Furthermore, in terms of abundance, 4 classes, 2 orders, 4 families, 18 genera and 46 species were found to significantly correlate between the tongue coating and the gastric fluid. Conclusions The results provide microbiome-based scientific evidence for tongue diagnosis, and offer a new perspective for understanding the biological basis of tongue diagnosis.
Summary Although many quantitative structure–activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualization of QSAR model results remain challenging, especially for ‘black box’ models such as deep neural network (DNN). Here, we take a step forward to interpret the learned chemical features from DNN QSAR models, and present VISAR, an interactive tool for visualizing the structure–activity relationship. VISAR first provides functions to construct and train DNN models. Then VISAR builds the activity landscapes based on a series of compounds using the trained model, showing the correlation between the chemical feature space and the experimental activity space after model training, and allowing for knowledge mining from a global perspective. VISAR also maps the gradients of the chemical features to the corresponding compounds as contribution weights for each atom, and visualizes the positive and negative contributor substructures suggested by the models from a local perspective. Using the web application of VISAR, users could interactively explore the activity landscape and the color-coded atom contributions. We propose that VISAR could serve as a helpful tool for training and interactive analysis of the DNN QSAR model, providing insights for drug design, and an additional level of model validation. Availability and implementation The source code and usage instructions for VISAR are available on github https://github.com/qid12/visar. Contact shaoli@mail.tsinghua.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.
Spatially resolved transcriptomics (SRT) has greatly expanded our understanding of the spatial patterns of gene expression in histological tissue sections. However, most currently available platforms could not provide in situ single-cell spatial transcriptomics, limiting their biological applications. Here, to in silico reconstruct SRT at the single-cell resolution, we propose St2cell which combines deep learning-based frameworks with a novel convex quadratic programming (CQP)-based model. St2cell can thoroughly leverage information in high-resolution (HR) histological images, enabling the accurate segmentation of in situ single cells and identification of their transcriptomics. Applying St2cell on various SRT datasets, we demonstrated the reliability of reconstructed transcriptomics. The single-cell resolution provided by our proposed method greatly promoted the detection of elaborate spatial architectures and further facilitated the integration with single-cell RNA-sequencing data. Moreover, in a breast cancer tissue, St2cell identified general spatial structures and co-occurrence patterns of cell types in the tumor microenvironment. St2cell is also computationally efficient and easily accessible, making it a promising tool for SRT studies.
Background: Uterine Corpus Endometrial Carcinoma (UCEC) ranks fourth among female cancers in the world. Frustratingly, the 5-year survival rates for advanced patients are only 17%. KDM4B is overexpressed or dysregulated in a variety of cancers and could be associated with tumor progression and poor prognosis. Therefore, we performed bioinformatics analysis and in vitro assays to assess the role of KDM4B in UCEC. In addition, its relevance to immune cells in the tumor microenvironment was explored.Methods: The mRNA level and protein level of KDM4B in UCEC was evaluated using the TCGA, HPA and GEO database. Immunohistochemistry and western blotting were used to verify the protein expression level of KDM4B in two batches of clinical samples. Kaplan-Meier curves, Univariate and multivariate analysis were used to assess the correlation between KDM4B expression and prognosis. GO and KEGG were used to predict the function and mechanism of KDM4B, and four immunity related database were used to explore their relevance to the tumor immune microenvironment.Results: Firstly, the present study showed that KDM4B was significantly overexpressed in UCEC from several databases at the mRNA and protein levels, respectively. Immunohistochemistry and western blotting confirmed the abnormally overexpression of KDM4B. In addition, upregulation of KDM4B was associated with different clinicopathological prognostic factors. Secondly, overexpression of KDM4B was also associated with shorter OS and PFS. Univariate and multivariate analyses confirmed that KDM4B was an independent prognostic factor for poor prognosis. Then, GO and KEGG analysis revealed that KDM4B is enriched in biological processes and cellular signaling pathways closely related to immunity. Finally, KDM4B expression was closely associated with immune cell infiltration and immune checkpoints and it may be a new immune-related potential oncogene in UCEC.Conclusions: KDM4B may be a new potential oncogene for UCEC patients. It is of clinical significance that KDM4B may not only be used to assess the clinical prognosis of UCEC patients but may also be a target for immunotherapy or gene targeted therapy.
Compared with tongue diagnosis using tongue image analyzers, tongue diagnosis by smartphone has great advantages in convenience and cost for universal health monitoring, but its accuracy is affected by the shooting conditions of smartphones. Developing deep learning models with high accuracy and robustness to changes in the shooting environment for tongue diagnosis by smartphone and determining the influence of environmental changes on accuracy are necessary. In our study, a dataset of 9003 images was constructed after image pre-processing and labeling. Next, we developed an attention-based deep learning model (Deep Tongue) for 8 subtasks of tongue diagnosis, including the spotted tongue, teeth-marked tongue, and fissure tongue et al, which the average AUC of was 0.90, higher than the baseline model (ResNet50) by 0.10. Finally, we analyzed the objective reasons, the brightness of the environment and the hue of images, affecting the accuracy of tongue diagnosis by smartphone through a consistency experiment of direct subject inspection and tongue image inspection. Finally, we determined the influence of environmental changes on accuracy to quantify the robustness of the Deep Tongue model through simulation experiments. Overall, the Deep Tongue model achieved a higher and more stable classification accuracy of seven tongue diagnosis tasks in the complex shooting environment of the smartphone, and the classification of tongue coating (yellow/white) was found to be sensitive to the hue of the images and therefore unreliable without stricter shooting conditions and color correction.
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