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Motivation A patient's disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. Results Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies. Availability and implementation The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT. [ABSTRACT FROM AUTHOR]

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Motivation: Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging. Results: To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. Results We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a nonparametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. Availability and implementation Our code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP. [ABSTRACT FROM AUTHOR]

In many categorical response regression applications, the response categories admit a multiresolution structure. That is, subsets of the response categories may naturally be combined into coarser response categories. In such applications, practitioners are often interested in estimating the resolution at which a predictor affects the response category probabilities. In this paper, we propose a method for fitting the multinomial logistic regression model in high dimensions that addresses this problem in a unified and data‐driven way. Our method allows practitioners to identify which predictors distinguish between coarse categories but not fine categories, which predictors distinguish between fine categories, and which predictors are irrelevant. For model fitting, we propose a scalable algorithm that can be applied when the coarse categories are defined by either overlapping or nonoverlapping sets of fine categories. Statistical properties of our method reveal that it can take advantage of this multiresolution structure in a way existing estimators cannot. We use our method to model cell‐type probabilities as a function of a cell's gene expression profile (i.e., cell‐type annotation). Our fitted model provides novel biological insights which may be useful for future automated and manual cell‐type annotation methodology. [ABSTRACT FROM AUTHOR]

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016–2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33–43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES—especially the composite measure and income—is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood. [ABSTRACT FROM AUTHOR]

Motivation Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound–protein interactions, but it has not been applied to the screening of drug combinations. Results In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug–Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation. Availability and implementation Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master [ABSTRACT FROM AUTHOR]

Candida albicans, a normal member of the human microbiome and an opportunistic fungal pathogen, undergoes several morphological transitions. One of these transitions is white–opaque switching, where C. albicans alternates between 2 stable cell types with distinct cellular and colony morphologies, metabolic preferences, mating abilities, and interactions with the innate immune system. White-toopaque switching is regulated by mating type; it is repressed by the a1/α2 heterodimer in a/α cells, but this repression is lifted in a/a and α/α mating type cells (each of which are missing half of the repressor). The widely used C. albicans reference strain, SC5314, is unusual in that white–opaque switching is completely blocked when the cells are a/α; in contrast, most other C. albicans a/α strains can undergo white–opaque switching at an observable level. In this paper, we uncover the reason for this difference. We show that, in addition to repression by the a1/α2 heterodimer, SC5314 contains a second block to white–opaque switching: 4 transcription regulators of filamentous growth are upregulated in this strain and collectively suppress white–opaque switching. This second block is missing in the majority of clinical strains, and, although they still contain the a1/α2 heterodimer repressor, they exhibit a/α white–opaque switching at an observable level. When both blocks are absent, white–opaque switching occurs at very high levels. This work shows that white–opaque switching remains intact across a broad group of clinical strains, but the precise way it is regulated and therefore the frequency at which it occurs varies from strain to strain. [ABSTRACT FROM AUTHOR]

Different subtypes of the same cancer often show distinct genomic signatures and require targeted treatments. The differences at the cellular and molecular levels of tumor microenvironment in different cancer subtypes have significant effects on tumor pathogenesis and prognostic outcomes. Although there have been significant researches on the prognostic association of tumor infiltrating lymphocytes in selected histological subtypes, few investigations have systemically reported the prognostic impacts of immune cells in molecular subtypes, as quantified by machine learning approaches on multi-omics datasets. This paper describes a new computational framework, ProTICS, to quantify the differences in the proportion of immune cells in tumor microenvironment and estimate their prognostic effects in different subtypes. First, we stratified patients into molecular subtypes based on gene expression and methylation profiles by applying nonnegative tensor factorization technique. Then we quantified the proportion of cell types in each specimen using an mRNA-based deconvolution method. For tumors in each subtype, we estimated the prognostic effects of immune cell types by applying Cox proportional hazard regression. At the molecular level, we also predicted the prognosis of signature genes for each subtype. Finally, we benchmarked the performance of ProTICS on three TCGA datasets and another independent METABRIC dataset. ProTICS successfully stratified tumors into different molecular subtypes manifested by distinct overall survival. Furthermore, the different immune cell types showed distinct prognostic patterns with respect to molecular subtypes. This study provides new insights into the prognostic association between immune cells and molecular subtypes, showing the utility of immune cells as potential prognostic markers. Availability: R code is available at https://github.com/liu-shuhui/ProTICS [ABSTRACT FROM AUTHOR]

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks. [ABSTRACT FROM AUTHOR]

Summary Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE , an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE 's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path. Availability and implementation The data and code are publicly available at https://github.com/rsinghlab/scNODE. [ABSTRACT FROM AUTHOR]

Summary In this paper, we introduce multiple-matching Evidence-based Translator (mEBT) to discover genomic responses from murine expression data for human immune studies, which are significant in the given condition of mice and likely have similar responses in the corresponding condition of human. mEBT is evaluated over multiple datasets and shows improved inter-species agreement. mEBT is expected to be useful for research groups who use murine models to study human immunity. Availability and implementation http://cdal.korea.ac.kr/mebt/ Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Obesity, characterized by the accumulation of excess fat, is a complex condition resulting from the combination of genetic and epigenetic factors. Recent studies have found correspondence between DNA methylation and cell differentiation, suggesting a role of the former in cell fate determination. There is a lack of comprehensive understanding concerning the underpinnings of preadipocyte differentiation, specifically when cells are undergoing terminal differentiation (TD). To gain insight into dynamic genome-wide methylation, 3T3 L1 preadipocyte cells were differentiated by a hormone cocktail. The genomic DNA was isolated from undifferentiated cells and 4 hours, 2 days postdifferentiated cells, and 15 days TD cells. We employed whole-genome bisulfite sequencing (WGBS) to ascertain global genomic DNA methylation alterations at single base resolution as preadipocyte cells differentiate. The genome-wide distribution of DNA methylation showed similar overall patterns in pre-, post-, and terminally differentiated adipocytes, according to WGBS analysis. DNA methylation decreases at 4 hours after differentiation initiation, followed by methylation gain as cells approach TD. Studies revealed novel differentially methylated regions (DMRs) associated with adipogenesis. DMR analysis suggested that though DNA methylation is global, noticeable changes are observed at specific sites known as "hotspots." Hotspots are genomic regions rich in transcription factor (TF) binding sites and exhibit methylation-dependent TF binding. Subsequent analysis indicated hotspots as part of DMRs. The gene expression profile of key adipogenic genes in differentiating adipocytes is context-dependent, as we found a direct and inverse relationship between promoter DNA methylation and gene expression. [ABSTRACT FROM AUTHOR]

Motivations Gene regulatory networks (GRNs) are traditionally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. Results We address this issue for two regression-based GRN inference models, a weighted random forest (weigthedRF) and a generalized linear model estimated under a weighted LASSO penalty with stability selection (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis in order to optimize data integration strength in a hypothesis-driven, gene-specific manner. This integration scheme reveals a strong diversity of optimal integration intensities between genes, and offers good performance in minimizing prediction error as well as retrieving experimental interactions. Experimental results show that DIOgene compares favorably against state-of-the-art approaches and allows to recover master regulators of nitrate induction. Availability and implementation The R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction [ABSTRACT FROM AUTHOR]

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell–cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell–cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell–cell communication network is built using a cell–cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree. [ABSTRACT FROM AUTHOR]

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments. [ABSTRACT FROM AUTHOR]

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses. [ABSTRACT FROM AUTHOR]

The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor–gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision–recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN. [ABSTRACT FROM AUTHOR]

Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods. [ABSTRACT FROM AUTHOR]

Motivation Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets. Results To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications—cancer early diagnosis, cancer prognosis, and drug sensitivity prediction—using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine. Availability and implementation Deep Centroid is available at both github (github.com/xiexiexiekuan/DeepCentroid) and Figshare (https://figshare.com/articles/software/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification/24993516). [ABSTRACT FROM AUTHOR]

The authors present a cohort of 661 young adult glioblastomas diagnosed using 2016 WHO World Health Organization Classification of Tumors of the Central Nervous System , utilizing comprehensive genomic profiling (CGP) to explore their genomic landscape and assess their relationship to currently defined disease entities. This analysis explored variants with evidence of pathogenic function, common copy number variants (CNVs), and several novel fusion events not described in literature. Tumor mutational burden (TMB) mutational signatures, anatomic location, and tumor recurrence are further explored. Using data collected from CGP, unsupervised machine-learning techniques were leveraged to identify 10 genomic classes in previously assigned young adult glioblastomas. The authors relate these molecular classes to current World Health Organization guidelines and reference current literature to give therapeutic and prognostic descriptions where possible. [ABSTRACT FROM AUTHOR]

Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains. SGCAST adopts a symmetric graph convolutional auto-encoder to learn aggregated latent embeddings via integrating the gene expression similarity and the proximity of the spatial spots. This framework in SGCAST enables a mini-batch training strategy, which makes SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with a large number of spots. SGCAST improves the overall accuracy of spatial domain identification on benchmarking data. We also validated the performance of SGCAST on ST datasets at various scales across multiple platforms. Our study illustrates the superior capacity of SGCAST on analyzing spatial transcriptomic data. [ABSTRACT FROM AUTHOR]

Metabolic plasticity enables cancer cells to meet divergent demands for tumorigenesis, metastasis and drug resistance. Landscape analysis of tumor metabolic plasticity spanning different cancer types, in particular, metabolic crosstalk within cell subpopulations, remains scarce. Therefore, we proposed a new in-silico framework, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict tumor metabolic plasticity based on transcriptome data. Next, we performed an extensive metabo-plastic analysis of over 6000 tumors comprising 13 cancer types. The metabolic plasticity within distinct cell subpopulations, particularly interplay with tumor microenvironment, were explored at single-cell resolution. Ultimately, the metabo-plastic events were screened out for multiple clinical applications via machine learning methods. The pilot research indicated that 6 out of 13 cancer types exhibited signs of the Warburg effect, implying its high reliability and robustness. Across 13 cancer types, high metabolic organized heterogeneity was found, and four metabo-plastic subtypes were determined, which link to distinct immune and metabolism patterns impacting prognosis. Moreover, MMP3C analysis of approximately 60 000 single cells of eight breast cancer patients unveiled several metabo-plastic events correlated to tumorigenesis, metastasis and immunosuppression. Notably, the metabolic features screened out by MMP3C are potential biomarkers for diagnosis, tumor classification and prognosis. MMP3C is a practical cross-platform tool to capture tumor metabolic plasticity, and our study unveiled a core set of metabo-plastic pairs among diverse cancer types, which provides bases toward improving response and overcoming resistance in cancer therapy. [ABSTRACT FROM AUTHOR]

Background: In recent years, breast cancer has become the most common cancer in the world, increasing women's health risks. Approximately 60% of breast cancers are categorized as human epidermal growth factor receptor 2 (HER2)-low tumors. Recently, antibody-drug conjugates have been found to have positive anticancer efficacy in patients with HER2-low breast cancer, but more studies are required to comprehend their clinical and molecular characteristics. Methods: In this study, we retrospectively analyzed the data of 165 early breast cancer patients with pT1-2N1M0 who had undergone the RecurIndex testing. To better understand HER2-low tumors, we investigated the RecurIndex genomic profiles, clinicopathologic features, and survival outcomes of breast cancers according to HER2 status. Results: First, there were significantly more hormone receptor (HR)-positive tumors, luminal-type tumors, and low Ki67 levels in the HER2-low than in the HER2-zero. Second, RI-LR (P =.0294) and RI-DR (P =.001) scores for HER2-low and HER2-zero were statistically significant. Third, within HER2-negative disease, HR-positive/HER2-low tumors showed highest ESR1, NFATC2IP, PTI1, ERBB2, and OBSL1 expressions. Fourth, results of the survival analysis showed that lower expression of HER2 was associated with improved relapse-free survival for HR-positive tumors, but not for HR-negative tumors. Conclusions: The present study highlights the unique features of HER2-low tumors in terms of their clinical characteristics as well as their gene expression profiles. HR status may influence the prognosis of patients with HER2-low expression, and patients with HR-positive/HER2-low expression may have a favorable outcome. In this study, the data of patients with early stage breast cancer who had undergone RecurIndex testing was analyzed. To better understand HER2-low tumors, genomic profiles, clinicopathologic features, and survival outcomes of breast cancers according to HER2 status were investigated. [ABSTRACT FROM AUTHOR]

RNA and protein expression profiling technologies have revolutionized how toxicologists can study the molecular basis of adverse effects of chemicals and drugs. It is expected that these new technologies will afford efficient and high-throughput means to delineate mechanisms of action and predict toxicity of unknown agents. To reach these goals, a more thorough understanding of the constraints of the methodology is needed to design genome-scale studies and to interpret the vast amount of data collected. This paper addresses some of the limitations and uncertainties of gene expression profiling in mechanistic and predictive toxicology with respect to the expectations of toxicogenomics. The challenges associated with interpreting information from large-scale gene expression experiments in vivo is also discussed. [ABSTRACT FROM PUBLISHER]

Motivation Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. Results In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods—between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. Availability and implementation Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669). [ABSTRACT FROM AUTHOR]

In the intensive care unit, sepsis is a prevalent clinical syndrome (i.e. the final pathway to death from most infections). Peripheral blood gene expression profiling is becoming more and more accepted as a potential diagnostic or prognostic tool. This work aimed to recognize genes related to sepsis, providing potential translational therapeutic targets. RNA sequencing was performed on peripheral blood mononuclear cells from 20 healthy control subjects and 51 sepsis patients. Weighted gene coexpression network analysis was employed to pick out sepsis-related and immunocyte-related gene modules. Genes in the yellow module are primarily involved in excessive inflammation and immune suppression. STRING and Cytoscape were combined to identify ACTG1 and IQGAP1 as hub genes with highest connective degree, and prognostic predication value of ACTG1 was confirmed. Both univariate and multivariate logistic regression analyses were carried out. ACTG1 messenger RNA expression was increased in animal and in cell-related sepsis models. Small interfering RNA revealed decreasing ACTG1 can reduce the in vitro sepsis model apoptosis. We have authenticated ACTG1 as a reliable signature of a poor outcome of sepsis and promising therapeutic targets for sepsis. [ABSTRACT FROM AUTHOR]

Objectives We aimed to clarify the genetic overlaps underlying obesity-related traits, serum urate, and gout. Methods We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci, and causal relationships between obesity (the exposure variable), gout (the primary outcome) and serum urate (the secondary outcome). Summary statistics were collected from the hitherto largest genome-wide association studies conducted for BMI (N  = 806 834), waist-to-hip ratio (WHR; N  = 697 734), WHR adjusted for BMI (WHRadjBMI; N  = 694 649), serum urate (N  = 288 649), and gout (N cases = 13 179 and N controls = 750 634). Results Positive overall genetic correlations were observed for BMI (⁠ r g   = 0.27, P  = 6.62 × 10−7), WHR (⁠ r g   = 0.22, P  = 6.26 × 10−7) and WHRadjBMI (⁠ r g   = 0.07, P  = 6.08 × 10−3) with gout. Partitioning the whole genome into 1703 LD (linkage disequilibrium)-independent regions, a significant local signal at 4q22 was identified for BMI and gout. The global and local shared genetic basis was further strengthened by the multiple pleiotropic loci identified in the cross-phenotype association study, multiple shared gene–tissue pairs observed by Transcriptome-wide association studies, as well as causal relationships demonstrated by Mendelian randomization [BMI–gout: OR (odds ratio) = 1.66, 95% CI = 1.45, 1.88; WHR–gout: OR = 1.57, 95% CI = 1.37, 1.81]. Replacing the binary disease status of gout with its latent pathological measure, serum urate, a similar pattern of correlation, pleiotropy and causality was observed with even more pronounced magnitude and significance. Conclusion Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis and pleiotropic loci, as well as a causal relationship between obesity, serum urate, and gout, highlighting an intrinsic link underlying these complex traits. [ABSTRACT FROM AUTHOR]

The mechanisms underlying plant response to drought involve the expression of numerous functional and regulatory genes. Transcriptome sequencing based on the second- and/or third-generation high-throughput sequencing platforms has proven to be powerful for investigating the transcriptional landscape under drought stress. However, the full-length transcriptomes related to drought responses in the important conifer genus Pinus L. remained to be delineated using the third-generation sequencing technology. With the objectives of identifying the candidate genes responsible for drought and/or rehydration and clarifying the expression profile of key genes involved in drought regulation, we combined the third- and second-generation sequencing techniques to perform transcriptome analysis on seedling roots under drought stress and rewatering in the drought-tolerant conifer Pinus massoniana Lamb. A sum of 294,114 unique full-length transcripts were produced with a mean length of 3217 bp and N50 estimate of 5075 bp, including 279,560 and 124,438 unique full-length transcripts being functionally annotated and Gene Ontology enriched, respectively. A total of 4076, 6295 and 18,093 differentially expressed genes (DEGs) were identified in three pair-wise comparisons of drought-treatment versus control transcriptomes, including 2703, 3576 and 8273 upregulated and 1373, 2719 and 9820 downregulated DEGs, respectively. Moreover, 157, 196 and 691 DEGs were identified as transcription factors in the three transcriptome comparisons and grouped into 26, 34 and 44 transcription factor families, respectively. Gene Ontology enrichment analysis revealed that a remarkable number of DEGs were enriched in soluble sugar-related and cell wall-related processes. A subset of 75, 68 and 97 DEGs were annotated to be associated with starch, sucrose and raffinose metabolism, respectively, while 32 and 70 DEGs were associated with suberin and lignin biosynthesis, respectively. Weighted gene co-expression network analysis revealed modules and hub genes closely related to drought and rehydration. This study provides novel insights into root transcriptomic changes in response to drought dynamics in Masson pine and serves as a fundamental work for further molecular investigation on drought tolerance in conifers. [ABSTRACT FROM AUTHOR]

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single‐cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub‐populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic‐based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew‐normal distributions, which is capable of identifying distinct cellular sub‐populations in HST data. We further implement a novel combination of Pólya–Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network. [ABSTRACT FROM AUTHOR]

Motivation Recent advances in spatial transcriptomics technologies have enabled gene expression profiles while preserving spatial context. Accurately identifying spatial domains is crucial for downstream analysis and it requires the effective integration of gene expression profiles and spatial information. While increasingly computational methods have been developed for spatial domain detection, most of them cannot adaptively learn the complex relationship between gene expression and spatial information, leading to sub-optimal performance. Results To overcome these challenges, we propose a novel deep learning method named Spatial-MGCN for identifying spatial domains, which is a Multi-view Graph Convolutional Network (GCN) with attention mechanism. We first construct two neighbor graphs using gene expression profiles and spatial information, respectively. Then, a multi-view GCN encoder is designed to extract unique embeddings from both the feature and spatial graphs, as well as their shared embeddings by combining both graphs. Finally, a zero-inflated negative binomial decoder is used to reconstruct the original expression matrix by capturing the global probability distribution of gene expression profiles. Moreover, Spatial-MGCN incorporates a spatial regularization constraint into the features learning to preserve spatial neighbor information in an end-to-end manner. The experimental results show that Spatial-MGCN outperforms state-of-the-art methods consistently in several tasks, including spatial clustering and trajectory inference. [ABSTRACT FROM AUTHOR]

Genome profiling-based mutation assay (GPMA) is, to date, the only DNA sequence-based mutation assay that directly measures DNA alterations induced by mutagens. Here, the all-important congruence of mutagen assignment between DNA-based GPMA and the phenotype-based Ames test (the gold standard of mutagen assays) was confirmed qualitatively and semi-quantitatively by means of 94 chemical species (including previously examined 64). The high sensitivity (on the order of 10 ppb) and reproducibility of GPMA were also corroborated by the match between virtually independent experiments conducted in the distant past (10 years ago) and recently. Meanwhile, a standard experimental framework was established: the conditions of 100 parts per billion (ppb) concentration of a chemical and 15-generation culture of Escherichia coli. Moreover, a mammalian cell line (NIH 3T3) was shown to be suitable as a tester organism for the GPMA approach. Preliminary experimental results suggested that this approach can provide a qualitatively equivalent and quantitatively different mutagen assay results relative to the bacteria- based GPMA (renamed as bGPMA). This finding confirmed the effectiveness of the GPMA approach and indicates that mGPMA is a promising way to detect mammalian-cell mutagens. [ABSTRACT FROM AUTHOR]

Motivation The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells—some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them. Results We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient's classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective. Availability and implementation https://github.com/Teddy-XiongGZ/ProtoCell4P. [ABSTRACT FROM AUTHOR]

Rudimentary social systems have the potential to both advance our understanding of how complex sociality may have evolved and our understanding of how changes in social environment may influence gene expression and cooperation. Recently, studies of primitively social Hymenoptera have greatly expanded empirical evidence for the role of social environment in shaping behavior and gene expression. Here, we compare brain gene expression profiles of foragers across social contexts in the small carpenter bee, Ceratina calcarata. We conducted experimental manipulations of field colonies to examine gene expression profiles among social contexts including foraging mothers, regular daughters, and worker-like dwarf eldest daughters in the presence and absence of mother. Our analysis found significant differences in gene expression associated with female age, reproductive status, and social environment, including circadian clock gene dyw , hexamerin, and genes involved in the regulation of juvenile hormone and chemical communication. We also found that candidate genes differentially expressed in our study were also associated with division of labor, including foraging, in other primitively and advanced eusocial insects. Our results offer evidence for the role of the regulation of key developmental hormones and circadian rhythms in producing cooperative behavior in rudimentary insect societies. [ABSTRACT FROM AUTHOR]

Motivation Screening new drug–target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. Results We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e. Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs' candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6766 drugs, which can be used for drug repurposing and discovery. Availability and implementation https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544. [ABSTRACT FROM AUTHOR]

Single-cell ribonucleic acid sequencing (scRNA-seq) enables the quantification of gene expression at the transcriptomic level with single-cell resolution, enhancing our understanding of cellular heterogeneity. However, the excessive missing values present in scRNA-seq data hinder downstream analysis. While numerous imputation methods have been proposed to recover scRNA-seq data, high imputation performance often comes with low or no interpretability. Here, we present IGSimpute, an accurate and interpretable imputation method for recovering missing values in scRNA-seq data with an interpretable instance-wise gene selection layer (GSL). IGSimpute outperforms 12 other state-of-the-art imputation methods on 13 out of 17 datasets from different scRNA-seq technologies with the lowest mean squared error as the chosen benchmark metric. We demonstrate that IGSimpute can give unbiased estimates of the missing values compared to other methods, regardless of whether the average gene expression values are small or large. Clustering results of imputed profiles show that IGSimpute offers statistically significant improvement over other imputation methods. By taking the heart-and-aorta and the limb muscle tissues as examples, we show that IGSimpute can also denoise gene expression profiles by removing outlier entries with unexpectedly high expression values via the instance-wise GSL. We also show that genes selected by the instance-wise GSL could indicate the age of B cells from bladder fat tissue of the Tabula Muris Senis atlas. IGSimpute can impute one million cells using 64 min, and thus applicable to large datasets. [ABSTRACT FROM AUTHOR]

Single-cell CRISPR screens have been widely used to investigate gene regulatory circuits in diverse biological systems. The recent development of single-cell CRISPR screens has enabled multimodal profiling of perturbed cells with both gene expression, chromatin accessibility and protein levels. However, current methods cannot meet the analysis requirements of different types of data and have limited functions. Here, we introduce Single-cell CRISPR screens data analysEs and perturbation modEling (SCREE) as a comprehensive and flexible pipeline to facilitate the analyses of various types of single-cell CRISPR screens data. SCREE performs read alignment, sgRNA assignment, quality control, clustering and visualization, perturbation enrichment evaluation, perturbation efficiency modeling, gene regulatory score calculation and functional analyses of perturbations for single-cell CRISPR screens with both RNA, ATAC and multimodal readout. SCREE is available at https://github.com/wanglabtongji/SCREE. [ABSTRACT FROM AUTHOR]

Non-melanoma skin cancer (NMSC) is the most common cancer in the world. Environmental exposure to carcinogens is one of the major causes of NMSC initiation and progression. In the current study, we utilized a two-stage skin carcinogenesis mouse model generated by sequential exposure to cancer-initiating agent benzo[a]pyrene (BaP) and promoting agent 12- O -tetradecanoylphorbol-13-acetate (TPA), to study epigenetic, transcriptomic and metabolic changes at different stages during the development of NMSC. BaP/TPA caused significant alterations in DNA methylation and gene expression profiles in skin carcinogenesis, as evidenced by DNA-seq and RNA-seq analysis. Correlation analysis between differentially expressed genes and differentially methylated regions found that the mRNA expression of oncogenes leucine rich repeat LGI family member 2 (Lgi2), kallikrein-related peptidase 13 (Klk13) and SRY-Box transcription factor (Sox5) are correlated with the promoter CpG methylation status, indicating BaP/TPA regulates these oncogenes through regulating their promoter methylation at different stages of NMSC. Pathway analysis identified that the modulation of macrophage-stimulating protein-recepteur d'origine nantais and high-mobility group box 1 signaling pathways, superpathway of melatonin degradation, melatonin degradation 1, sirtuin signaling and actin cytoskeleton signaling pathways are associated with the development of NMSC. The metabolomic study showed BaP/TPA regulated cancer-associated metabolisms like pyrimidine and amino acid metabolisms/metabolites and epigenetic-associated metabolites, such as S -adenosylmethionine, methionine and 5-methylcytosine, indicating a critical role in carcinogen-mediated metabolic reprogramming and its consequences on cancer development. Altogether, this study provides novel insights integrating methylomic, transcriptomic and metabolic-signaling pathways that could benefit future skin cancer treatment and interception studies. [ABSTRACT FROM AUTHOR]

Motivation: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values. Results: Here, we developed MARSY, a deep-learning multitask model that incorporates information on the gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug pairs, as well as the drug pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine-learning models. Using MARSY, we then predicted the synergy scores of 133 722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions. Availability and implementation: An implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY. [ABSTRACT FROM AUTHOR]

Motivation: High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data. Results: ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type. Availability and implementation: ChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications. [ABSTRACT FROM AUTHOR]

Motivation: Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas. To allow appropriate verification of predictive models before deployment, models must be deterministic. Solely fixing all random seeds is not sufficient for deterministic machine learning, as major machine learning libraries default to the usage of nondeterministic algorithms based on atomic operations. Results: Various machine learning libraries released deterministic counterparts to the nondeterministic algorithms. We evaluated the effect of these algorithms on determinism and runtime. Based on these results, we formulated a set of requirements for deterministic machine learning and developed a new software solution, the mlf-core ecosystem, which aids machine learning projects to meet and keep these requirements. We applied mlf-core to develop deterministic models in various biomedical fields including a single-cell autoencoder with TensorFlow, a PyTorch-based U-Net model for liver-tumor segmentation in computed tomography scans, and a liver cancer classifier based on gene expression profiles with XGBoost. Availability and implementation: The complete data together with the implementations of the mlf-core ecosystem and use case models are available at https://github.com/mlf-core. [ABSTRACT FROM AUTHOR]

Objectives: Enrichment for therapy-resistant cancer stem cells hampers the treatment of triple-negative breast cancer. Targeting these cells via suppression of Notch signalling can be a potential therapeutic strategy. This study aimed to uncover the mode of action of a new indolocarbazole alkaloid loonamycin A against this incurable disease. Methods: The anticancer effects were examined in triple-negative breast cancer cells using in vitro methods, including cell viability and proliferation assays, wound-healing assay, flow cytometry and mammosphere formation assay. RNA-seq technology was used to analyse the gene expression profiles in loonamycin A-treated cells. Real-time RT-PCR and western blot were to evaluate the inhibition of Notch signalling. Key findings: Loonamycin A has stronger cytotoxicity than its structural analog rebeccamycin. Besides inhibiting cell proliferation and migration, loonamycin A reduced CD44high/CD24low/− sub-population, mammosphere formation, as well as the expression of stemness-associated genes. Co-administration of loonamycin A enhanced antitumour effects of paclitaxel by inducing apoptosis. RNA sequencing results showed that loonamycin A treatment caused the inhibition of Notch signalling, accompanied by the decreased expression of Notch1 and its targeted genes. Conclusions: These results reveal a novel bioactivity of indolocarbazole-type alkaloids and provide a promising Notch-inhibiting small molecular candidate for triple-negative breast cancer therapy. [ABSTRACT FROM AUTHOR]

Strand-specific RNA-seq is a powerful tool for the discovery of novel transcripts, annotation of genomes, and profiling of gene expression levels. Tn5 transposase has been successfully applied in massive-scale sequencing projects; in particular, Tn5 adaptor modification is used in epigenetics, genomic structure, and chromatin visualization. We developed a novel dU-adaptor-assembled Tn5-mediated strand-specific RNA-sequencing protocol and compared this method with the leading dUTP method in terms of experimental procedure and multiple quality metrics of the generated libraries. The results showed that the dU-Tn5 method is easy to operate and generates a strand-specific RNA-seq library of comparable quality considering library complexity, strand-specificity, evenness, and continuity of annotated transcript coverage. We also evaluated the performance of the dU-Tn5 method in identifying nitrogen-responsive protein-coding genes and long non-coding RNAs in soybean roots. The results indicated that ~62–70% of differentially expressed genes detected from conventional libraries were also detected in dU-Tn5 libraries, indicating good agreement of our method with the current standard; moreover, their fold-changes were highly correlated (R >0.9). Thus, our method provides a promising 'do-it-yourself' stranded RNA-seq procedure for gene expression profiling. [ABSTRACT FROM AUTHOR]

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets. [ABSTRACT FROM AUTHOR]

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug–disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net. [ABSTRACT FROM AUTHOR]

Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we present DIST, a deep learning-based method that imputes the gene expression profiles on unmeasured locations and enhances the gene expression for both original measured spots and imputed spots by self-supervised learning and transfer learning. We evaluate the performance of DIST for imputation, clustering, differential expression analysis and functional enrichment analysis. The results show that DIST can impute the gene expression accurately, enhance the gene expression for low-quality data, help detect more biological meaningful differentially expressed genes and pathways, therefore allow for deeper insights into the biological processes. [ABSTRACT FROM AUTHOR]

The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM's exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan-Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co-infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD-1 and CTLA-4. We also identified Pan-Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-β signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed 'GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population-based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan-immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti-PD-1) in advance GC (AUC = 0.833). Intriguingly, PAD-II (PIAMhighPIDGlow) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD-I, PIAMhighPIDGhigh, 0%; PAD-III, PIAMlowPIDGhigh, 0%; PAD-IV, PIAMlowPIDGlow, 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti-PD-L1) (P = 0.018). In all, we provided 'GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy. [ABSTRACT FROM AUTHOR]

Background and Aims Allopolyploidization is a widespread phenomenon known to generate novel phenotypes by merging evolutionarily distinct parental genomes and regulatory networks in a single nucleus. The objective of this study was to investigate the transcriptional regulation associated with phenotypic novelty in coffee beans of the allotetraploid Coffea arabica. Methods A genome-wide comparative transcriptomic analysis was performed in C. arabica and its two diploid progenitors, C. canephora and C. eugenioides. Gene expression patterns and homeologue expression were studied on seeds at five different maturation stages. The involvement of homeologue expression bias (HEB) in specific traits was addressed both by functional enrichment analyses and by the study of gene expression in the caffeine and chlorogenic acid biosynthesis pathways. Key Results Expression-level dominance in C. arabica seed was observed for most of the genes differentially expressed between the species. Approximately a third of the genes analysed showed HEB. This proportion increased during seed maturation but the biases remained equally distributed between the sub-genomes. The relative expression levels of homeologues remained relatively constant during maturation and were correlated with those estimated in leaves of C. arabica and interspecific hybrids between C. canephora and C. eugenioides. Functional enrichment analyses performed on genes exhibiting HEB enabled the identification of processes potentially associated with physiological traits. The expression profiles of the genes involved in caffeine biosynthesis mirror the differences observed in the caffeine content of mature seeds of C. arabica and its parental species. Conclusions Neither of the two sub-genomes is globally preferentially expressed in C. arabica seeds, and homeologues appear to be co-regulated by shared trans -regulatory mechanisms. The observed HEBs are thought to be a legacy of gene expression differences inherited from diploid progenitor species. Pre-existing functional divergences between parental species appear to play an important role in controlling the phenotype of C. arabica seeds. [ABSTRACT FROM AUTHOR]

Background No reliable biomarkers to predict response to TNF inhibitors (TNFi) in RA patients currently exist. The aims of this study were to replicate changes in gene co-expression modules that were previously reported in response to TNFi therapy in RA; to test if changes in module expression are specific to TNFi therapy; and to determine whether module expression transitions towards a disease-free state in responding patients. Method Published transcriptomic data from the whole blood of disease-free controls (n  = 10) and RA patients, treated with the TNFi adalimumab (n  = 70) or methotrexate (n  = 85), were studied. Treatment response was assessed using the EULAR response criteria following 3 or 6 months of treatment. Change in transcript expression between pre- and post-treatment was recorded for previously defined modules. Linear mixed models tested whether modular expression after treatment transitioned towards a disease-free state. Results For 25 of the 27 modules, change in expression between pre- and post-treatment in the adalimumab cohort replicated published findings. Of these 25 modules, six transitioned towards a disease-free state by 3 months (P   <  0.05), irrespective of clinical response. One module (M3.2), related to inflammation and TNF biology, significantly correlated with response to adalimumab. Similar patterns of modular expression, with reduced magnitude, were observed in the methotrexate cohort. Conclusion This study provides independent validation of changes in module expression in response to therapy in RA. However, these effects are not specific to TNFi. Further studies are required to determine whether specific modules could assist molecular classification of therapeutic response. [ABSTRACT FROM AUTHOR]