Bioinformatics

Abstract

The Gene Network Estimation Tool (GNET) is designed to build gene regulatory networks from transcriptomic gene expression data with a probabilistic graphical model. The data preprocessing, model construction, and visualization modules of the original GNET software were developed on different programming platforms, which were inconvenient for users to deploy and use. Here we present GNET2, an improved implementation of GNET as an integrated R package. GNET2 provides more flexibility for parameter initialization and regulatory module construction based on the core iterative modeling process of the original algorithm. The data exchange interface of GNET2 is handled within an R session automatically. Given the growing demand for regulatory network reconstruction from transcriptomic data, GNET2 offers a convenient option for gene regulatory network inference on large datasets
Brief：轉(zhuǎn)錄因子及其調(diào)控基因推斷，R package

Abstract

Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics, and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes, and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions, and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive, and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data.
Brief：癌癥細胞系數(shù)據(jù)庫，共三個功能“：1根據(jù)基因名提供該基因在各癌癥的研究并提供pubmed鏈接；2集成公共數(shù)據(jù)，根據(jù)提供基因集進行在線GSEA；3根據(jù)基因名檢索個癌細胞系對該基因依賴性及基因表達量

Abstract

The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples.
Brief：提出一種基于單類高斯過程（ One Class Gaussian Processes）的機器學習方法通過與已批準藥物標靶的相似性對候選蛋白進行排序，同時提出一個超參數(shù)的選擇方法，基本內(nèi)涵是為每個訓練樣本使用不同超參數(shù)，給予密集區(qū)域的樣本更多的權(quán)重（這個超參數(shù)主要解決了此類學習問題只有正例樣本因此難以根據(jù)邊際似然最大化來選擇超參數(shù)的問題）__僅正例機器學習模型訓練

Abstract

One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor, and the resolution parameters, among others.
Brief：多次對單細胞各樣本進行向下采樣，并重新聚類后計算各聚類間的 Jaccard index 以此評估單細胞聚類的穩(wěn)定性

Abstract

Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a “black box”, barely providing biological and clinical interpretability from the box.
To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative- restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small cell lung cancer (NSCLC) patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. Additionally, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (p-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine.
**Brief：基于半受限玻爾茲曼機（ semi-restricted Boltzmann machines）構(gòu)建早期癌癥判別模型，但相對于其他機器學習模型僅有高預測精度但無臨床解釋性特點，ECMarker提供基因的判別權(quán)重，因此具有可解釋性
**

Abstract

The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilisation. Here we present COSIFER, a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks.
Brief：集成10中不同的網(wǎng)絡推斷方法，提供web及python包；允許使用一個或多個方法集成推斷，集成推斷通過現(xiàn)有三種框架整合1）WOC;2) WOC (hard);3)Similarity Network Fusion (SNF)

Abstract

Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.
Brief：提供了一種edge-based的生物信息學分析方法：將基因表達矩陣轉(zhuǎn)換成Rank形式后，通過計算背景網(wǎng)絡的edge的rank以代表基因互作的擾動情況，最后將癌癥樣本的rank矩陣減去正常樣本的擾動矩陣從而歸一化；implemented in R and is available at https://github.com/Marscolono/SSPGI.git

Abstract

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of
tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting
immune cells from external tumors onto the atlas facilitated an automated cell annotation system for a harmonized interpretation. To enable in situ mapping of immune populations for digital pathology, we applied SPOTlight, combining single-cell and spatial transcriptomics data and identifying striking spatial
immune cell patterns in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immuno-therapy.
Brief：整合了50w泛癌免疫細胞數(shù)據(jù)，并可作為query數(shù)據(jù)集用于annotation

Abstract

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding
detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to per?turbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ?210 clinically relevant proteins in >12,000 cancer cell line samples in response to 170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of ‘‘protein?drug’’ connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.
Brief：大規(guī)模藥物-癌癥細胞系擾動反向蛋白微陣列數(shù)據(jù)

Genome Research

Abstract

Single-cell RNA-seq’s (scRNA-seq) unprecedented cellular resolution at a genome-wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts. However, scRNA-seq data sets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present a single-cell RNA-seq analysis and klustering evaluation (SAKE), a robust method for scRNA-seq analysis that provides quantitative statistical metrics at each step of the analysis pipeline. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range of cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors (BRAFi). Single-cell RNA-seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor-resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resis?tance. These experimentally validated markers of BRAFi resistance share overlap with previous analyses in different mela?noma cell lines, demonstrating the generality of these findings and highlighting the utility of single-cell analysis to elucidate mechanisms of BRAFi resistance.
Brief：通過非負矩陣因子分解對單細胞數(shù)據(jù)進行聚類后分析并搭建了分析流程，SAKE;將其運用到癌細胞的耐藥研究發(fā)現(xiàn)了潛在的耐藥細胞群體，提示非負因子矩陣分解在解析細胞異質(zhì)性的能力上一定程度優(yōu)于直接基于基因表達量的聚類方法

Abstract

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sen?sitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important
implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas
Brief：提供一個熵依賴的度量以衡量單細胞亞群的“純度”，不同平臺，不同測序深度均不會影響其衡量準確度

Abstract

Identifying gene expression programs underlying both cell-type identity and cellularactivities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis. DOI: https://doi.org/10.7554/eLife.43803.001
Brief：提出一種優(yōu)化的非負矩陣分解的方法，主要思想是多次進行矩陣因子分解并對結(jié)果進行平均從而提高穩(wěn)健性，運用于單細胞數(shù)據(jù)解析非細胞亞群特異性的細胞激活程序表達

Abstract

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability
Brief：基于學習的reference依賴的單細胞注釋框架，除了ref的識別準確外，在識別unsigned cell type的情況表現(xiàn)良好，并且可以基于多重標簽，進行校正后分配出細胞類型外標簽的能力

Abstract

The functional specialization of cell types arises during development and is shaped by cell–cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell
states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell–cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.
Brief：通過細胞通訊及轉(zhuǎn)錄因子等信息重構(gòu)矩陣分析細胞狀態(tài)

Abstract

We revealed the clinical characteristics of CRC patients and COVID-19 patients, including predisposing genes, survival rate and prognosis. Moreover, the results of molecular docking analysis indicated that niacin exerted effective binding capacity in COVID-19. Further, we disclosed the targets, biological functions and signaling pathways of niacin in CRC/COVID-19. The analysis indicated that niacin could help in treating CRC/COVID-19 through cytoprotection, enhancement of immunologic functions, inhibition of inflammatory reactions and regulation of cellular microenvironment. Furthermore, five core pharmacological targets of niacin in CRC/COVID-19 were also identified, including BCL2L1, PTGS2, IL1B, IFNG and SERPINE1.
Brief：結(jié)直腸癌COVID-19感染風險增加，且由于免疫能力低下及缺乏抗COVID-19藥物，需要為其篩選可選藥物：1鑒定COVID相關(guān)基因在CRC患者的差異表達；2差異基因基于多變量COX回歸定義風險分數(shù)；3數(shù)據(jù)庫搜索煙酸藥理靶點后發(fā)現(xiàn)與COVID-19_CRC基因集有14個overlap；4String確定5個核心基因；5分子對接，提示煙酸和核心基因之一IL1B親和力較高

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein–protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.
Brief：肺纖維化是COVID-19發(fā)病危險因素，因此探索肺纖維化及COVID-19患者轉(zhuǎn)錄組，鑒定了相應miRNA-TF調(diào)控網(wǎng)絡并鑒定網(wǎng)絡中hub基因，在數(shù)據(jù)庫中鑒定了可能的治療藥物（DSigDB）