Using clusterProfiler to characterize multiomics data.

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

Predicting high-risk pre-capillary pulmonary hypertension: an echocardiographic multiparameter scoring index.

BACKGROUND: The risk stratification of pulmonary arterial hypertension proposed by the European Society of Cardiology /European Respiratory Society guidelines in 2015 and 2022 included two to three echocardiographic indicators. However, the specific value of echocardiography in risk stratification of pre-capillary pulmonary hypertension (pcPH) has not been efficiently demonstrated. Given the complex geometry of the right ventricular (RV) and influencing factors of echocardiographic parameter, there is no single echocardiographic parameter that reliably informs about PH status. We hypothesize that a multi-parameter comprehensive index can more accurately evaluate the severity of the pcPH. The purpose of this study was to develop and validate an echocardiographic risk score model to better assist clinical identifying high risk of pcPH during initial diagnosis and follow-up. METHODS: We studied 197 consecutive patients with pcPH. A multivariable echocardiographic model was constructed to predict the high risk of pcPH in the training set. Points were assigned to significant risk factors in the final model based on ß-coefficients. We validated the model internally and externally. RESULTS: The echocardiographic score was constructed by multivariable logistic regression, which showed that pericardial effusion, right atrial (RA) area, RV outflow tract proximal diameter (RVOT-Prox), the velocity time integral of the right ventricular outflow tract (TVIRVOT) and S' were predictors of high risk of pcPH. The area under curve (AUC) of the training set of the scoring model was 0.882 (95%CI: 0.809-0.956, p < 0.0001). External validation was tested in a test dataset of 77 patients. The AUC of the external validation set was 0.852. A 10-point score risk score was generated, with scores ranging from 0 to 10 in the training cohort. The estimate risk of high risk of pcPH ranged from 25.1 to 94.6%. CONCLUSIONS: The echocardiographic risk score using five echocardiographic parameters could be comprehensive and useful to predict the high risk of pcPH for initial assessment and follow-up.

Encoding biological metaverse: Advancements and challenges in neural fields from macroscopic to microscopic.

Neural fields can efficiently encode three-dimensional (3D) scenes, providing a bridge between two-dimensional (2D) images and virtual reality. This method becomes a trendsetter in bringing the metaverse into vivo life. It has initially captured the attention of macroscopic biology, as demonstrated by computed tomography and magnetic resonance imaging, which provide a 3D field of view for diagnostic biological images. Meanwhile, it has also opened up new research opportunities in microscopic imaging, such as achieving clearer de novo protein structure reconstructions. Introducing this method to the field of biology is particularly significant, as it is refining the approach to studying biological images. However, many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D perspectives. This article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine, highlighting the broad prospects of neural fields in the future biological metaverse. We stand at the threshold of an exciting new era, where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.

A better method to evaluate the reliability of echocardiography for assessment of pulmonary hypertension: comparison of tricuspid regurgitant spectrum quality grading and tricuspid valve regurgitation degree.

Background: Transthoracic echocardiography (TTE) is recommended as the most important noninvasive screening tool for the diagnosis of pulmonary hypertension (PH), sonographers usually measure the volume of regurgitant flow rather than evaluating the spectral quality, so physicians will determine whether the ultrasound measurements of pulmonary arterial systolic pressure (US-PASP) are reliable based on the volume of tricuspid regurgitation (TR). Therefore, for the first time, we grade the quality of TR spectrum (TRS) based on its integrity and clarity, aiming to assess clinical application value of different tricuspid regurgitant spectrum quality grades (TR-SQG), and investigate whether the accuracy of US-PASP is more trustworthy than TR. Methods: We retrospectively analyzed 108 patients with chronic thromboembolic PH (CTEPH) to compare the correlation and agreement between US-PASP and right heart catheterization measurements of PASP (RHC-PASP). TR area (TRA) and TRS were measured in each patient, and TR-SQG was performed. Results: The correlation coefficients between US-PASP and RHC-PASP were r=0.622 (P<0.001), r=0.754 (P<0.001), r=0.595 (P<0.001) in mild, moderate, severe TR, and r=0.301 (P=0.135), r=0.747 (P<0.001), r=0.739 (P<0.001), r=0.828 (P<0.001) in TR-SQG I-IV, respectively. Bland-Altman analysis revealed the mean biases of 5.05, 3.06, 7.62 mmHg in mild, moderate, severe TR, and -16.47, -8.07, 1.82, 6.09 mmHg in TR-SQG I-IV, respectively. In mild TR with the TR-SQG III and IV, the correlation coefficients between US-PASP and RHC-PASP were r=0.779 (P<0.001), intraclass correlation coefficient (ICC) =0.774, paired t-test P=0.160, respectively; and the consistency was significantly higher than that of mild TR without considering TR-SQG. In moderate TR with the TR-SQG III and IV, the r=0.749, ICC =0.746, paired t-test P=0.298 between US-PASP and RHC-PASP. Conclusions: The US-PASP with TR-SQG III or IV is trustworthy, and its accuracy and consistency are better than those predicted by the traditional severity of TR. The establishment of the ultrasound evaluation system of TR-SQG helps clinicians to judge whether the US-PASP is accurate, credible, and reliable.

shinyTempSignal: an R shiny application for exploring temporal and other phylogenetic signals.

The molecular clock model is fundamental for inferring species divergence times from molecular sequences. However, its direct application may introduce significant biases due to sequencing errors, recombination events, and inaccurately labeled sampling times. Improving accuracy necessitates rigorous quality control measures to identify and remove potentially erroneous sequences. Furthermore, while not all branches of a phylogenetic tree may exhibit a clear temporal signal, specific branches may still adhere to the assumptions, with varying evolutionary rates. Supporting a relaxed molecular clock model better aligns with the complexities of evolution. The root-to-tip regression method has been widely used to analyze the temporal signal in phylogenetic studies and can be generalized for detecting other phylogenetic signals. Despite its utility, there remains a lack of corresponding software implementations for broader applications. To address this gap, we present shinyTempSignal, an interactive web application implemented with the shiny framework, available as an R package and publicly accessible at https://github.com/YuLab-SMU/shinyTempSignal. This tool facilitates the analysis of temporal and other phylogenetic signals under both strict and relaxed models. By extending the root-to-tip regression method to diverse signals, shinyTempSignal helps in the detection of evolving features or traits, thereby laying the foundation for deeper insights and subsequent analyses.

Deqformer: high-definition and scalable deep learning probe design method.

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson-Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks.