Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results

Subtypes of tic disorders in children and adolescents: based on clinical characteristics

BACKGROUND

Tic disorder (TD) is a diverse neurodevelopmental disorder with various symptoms and comorbidities. Traditional classifications based on age onset and duration fail to adequately characterize the full clinical features of TD. This study aims to redefine TD subtypes by a comprehensive analysis of clinical features and comorbidities.

METHODS

We assessed 139 children and adolescents aged 6-18 years using 14 scales covering 43 dimensions. The k-means clustering algorithm was used to identify distinct TD subtypes. Differences between these subtypes were analyzed using t-tests and network analysis, with high expected influence (EI) metric representing key symptoms within each subtype.

RESULTS

We identified two distinct subtypes of TD, with 21.6% of participants classified as subtype1 and 78.4% as subtype2. Subtype1 exhibited more severe symptoms across TD, obsessive-compulsive spectrum disorders, and attention deficit hyperactivity disorder assessments compared to subtype2, with significant differences observed in 81.4% of the scale features. Network analysis revealed differences in core symptoms between the two subtypes; subtype1 primarily involved hyperactivity and vital activities, whereas subtype2 primarily involved attention deficit, hyperactivity and conduct. Furthermore, comparisons with DSM-5 classifications revealed distinct patterns, indicating the novel nature of the identified subtypes.

CONCLUSION

Our study identified two novel TD subtypes, highlighting its heterogeneity. Subtype 1 had more severe attention deficits and impulsivity, requiring comprehensive treatment, while subtype 2 had milder symptoms, focusing on support and monitoring. These findings provide insights into TD classification and may help refine treatment strategies. However, the cross-sectional design limits causal interpretations, and reliance on parent-reported data may introduce bias.

Progress in Assessing Retinal Microglia Using Single-Cell RNA Sequencing

Retinal degeneration is the leading cause of both inherited and age-related vision loss, and often leads to neuroimmune activation and a heterogeneous population of microglia and monocyte-derived macrophages. A common method to study the innate immune response during retinal degeneration is single-cell RNA sequencing, but the best way to obtain and analyze these cells over the course of degeneration remains debated. Here, we compare two common methods of retinal cell preparation (collagenase digestion with immune cell enrichment by FACS; Col/FACS vs papain digestion of the whole retina without enrichment; Pap/Whole) and three different algorithms for database integration (CCA, RPCA, and Harmony) to examine microglia in healthy retinas. We find that the Pap/Whole dissociation produced a smaller fraction of activated microglial cells and that the Harmony integration of microglia isolated by these two methods resulted in the highest Silhouette score, indicating the greatest separation of microglia subclusters from these data sets.

A review of model evaluation metrics for machine learning in genetics and genomics

Machine learning (ML) has shown great promise in genetics and genomics where large and complex datasets have the potential to provide insight into many aspects of disease risk, pathogenesis of genetic disorders, and prediction of health and wellbeing. However, with this possibility there is a responsibility to exercise caution against biases and inflation of results that can have harmful unintended impacts. Therefore, researchers must understand the metrics used to evaluate ML models which can influence the critical interpretation of results. In this review we provide an overview of ML metrics for clustering, classification, and regression and highlight the advantages and disadvantages of each. We also detail common pitfalls that occur during model evaluation. Finally, we provide examples of how researchers can assess and utilise the results of ML models, specifically from a genomics perspective.

Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases

Biomedical research takes advantage of omic data, such as transcriptomics, to unravel the complexity of diseases. A conventional strategy identifies transcriptomic biomarkers characterized by expression patterns associated with a phenotype by relying on feature selection approaches. Hybrid ensemble feature selection (HEFS) has become increasingly popular as it ensures robustness of the selected features by performing data and functional perturbations. However, it remains difficult to make the best suited choices at each step when designing such approaches. We conducted an extensive analysis of four possible HEFS scenarios for the identification of Stage IV colorectal, Stage I kidney and lung and Stage III endometrial cancer biomarkers from transcriptomic data. These scenarios investigate the use of two types of feature reduction by filters (differentially expressed genes and variance) conjointly with two types of resampling strategies (repeated holdout by distribution-balanced stratified and random stratified) for downstream feature selection through an aggregation of thousands of wrapped machine learning models. Based on our results, we emphasize the advantages of using HEFS approaches to identify complex disease biomarkers, given their ability to produce generalizable and stable results to both data and functional perturbations. Finally, we highlight critical issues that need to be considered in the design of such strategies.

Heterogeneous gene expression during early arteriovenous fistula remodeling suggests that downregulation of metabolism predicts adaptive venous remodeling

Clinical outcomes of arteriovenous fistulae (AVF) for hemodialysis remain inadequate since biological mechanisms of AVF maturation and failure are still poorly understood. Aortocaval fistula creation (AVF group) or a sham operation (sham group) was performed in C57BL/6 mice. Venous limbs were collected on postoperative day 7 and total RNA was extracted for high throughput RNA sequencing and bioinformatic analysis. Genes in metabolic pathways were significantly downregulated in the AVF, whereas significant sex differences were not detected. Since gene expression patterns among the AVF group were heterogenous, the AVF group was divided into a 'normal' AVF (nAVF) group and an 'outliers' (OUT) group. The gene expression patterns of the nAVF and OUT groups were consistent with previously published data showing venous adaptive remodeling, whereas enrichment analyses showed significant upregulation of metabolism, inflammation and coagulation in the OUT group compared to the nAVF group, suggesting the heterogeneity during venous remodeling reflects early gene expression changes that may correlate with AVF maturation or failure. Early detection of these processes may be a translational strategy to predict fistula failure and reduce patient morbidity.

ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.

Enhanced clustering-based differential expression analysis method for RNA-seq data

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report. •MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

The transcriptomic expression pattern of immune checkpoints shows heterogeneity between and within cancer types

Transcriptomic expression profiles of immune checkpoint markers are of interest in order to decipher the mechanisms of immunotherapy response and resistance. Overall, 514 patients with various solid tumors were retrospectively analyzed in this study. The RNA expression levels of tumor checkpoint markers (ADORA2A, BTLA, CD276, CTLA4, IDO1, IDO2, LAG3, NOS2, PD-1, PD-L1, PD-L2, PVR, TIGIT, TIM3, VISTA, and VTCN) were ranked from 0-100 percentile based on a reference population. The expression of each checkpoint was correlated with cancer type, microsatellite instability (MSI), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) by immunohistochemistry (IHC). The cohort included 30 different tumor types, with colorectal cancer being the most common (27%). When RNA percentile rank values were categorized as "Low" (0-24), "Intermediate" (25-74), and "High" (75-100), each patient had a distinctive portfolio of the categorical expression of 16 checkpoint markers. Association between some checkpoint markers and cancer types were observed; NOS2 showed significantly higher expression in colorectal and stomach cancer (P < 0.001). Principal component analysis demonstrated no clear association between combined RNA expression patterns of 16 checkpoint markers and cancer types, TMB, MSI or PD-L1 IHC. Immune checkpoint RNA expression varies from patient to patient, both within and between tumor types, though colorectal and stomach cancer showed the highest levels of NOS2, a mediator of inflammation and immunosuppression. There were no specific combined expression patterns correlated with MSI, TMB or PD-L1 IHC. Next generation immunotherapy trials may benefit from individual analysis of patient tumors as selection criteria for specific immunomodulatory approaches.

Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders

BACKGROUND

Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD.

METHODS

Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses.

RESULTS

Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets.

CONCLUSIONS

Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.

A comprehensive atlas of perineuronal net distribution and colocalization with parvalbumin in the adult mouse brain

Perineuronal nets (PNNs) surround specific neurons in the brain and are involved in various forms of plasticity and clinical conditions. However, our understanding of the PNN role in these phenomena is limited by the lack of highly quantitative maps of PNN distribution and association with specific cell types. Here, we present a comprehensive atlas of Wisteria floribunda agglutinin (WFA)-positive PNNs and colocalization with parvalbumin (PV) cells for over 600 regions of the adult mouse brain. Data analysis shows that PV expression is a good predictor of PNN aggregation. In the cortex, PNNs are dramatically enriched in layer 4 of all primary sensory areas in correlation with thalamocortical input density, and their distribution mirrors intracortical connectivity patterns. Gene expression analysis identifies many PNN-correlated genes. Strikingly, PNN-anticorrelated transcripts are enriched in synaptic plasticity genes, generalizing PNNs' role as circuit stability factors.

Minor intron splicing is critical for survival of lethal prostate cancer

The evolutionarily conserved minor spliceosome (MiS) is required for protein expression of ∼714 minor intron-containing genes (MIGs) crucial for cell-cycle regulation, DNA repair, and MAP-kinase signaling. We explored the role of MIGs and MiS in cancer, taking prostate cancer (PCa) as an exemplar. Both androgen receptor signaling and elevated levels of U6atac, a MiS small nuclear RNA, regulate MiS activity, which is highest in advanced metastatic PCa. siU6atac-mediated MiS inhibition in PCa in vitro model systems resulted in aberrant minor intron splicing leading to cell-cycle G1 arrest. Small interfering RNA knocking down U6atac was ∼50% more efficient in lowering tumor burden in models of advanced therapy-resistant PCa compared with standard antiandrogen therapy. In lethal PCa, siU6atac disrupted the splicing of a crucial lineage dependency factor, the RE1-silencing factor (REST). Taken together, we have nominated MiS as a vulnerability for lethal PCa and potentially other cancers.

Using Markov chains and temporal alignment to identify clinical patterns in Dementia

In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.

Rationale and performances of a data-driven method for computing the duration of pharmacological prescriptions using secondary data sources

The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the “true” exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21–4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.

Forest phenoclusters for Argentina based on vegetation phenology and climate

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km² ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.

Obesity is associated with a distinct brain-gut microbiome signature that connects Prevotella and Bacteroides to the brain's reward center

The prevalence of obesity has risen to its highest values over the last two decades. While many studies have either shown brain or microbiome connections to obesity, few have attempted to analyze the brain-gut-microbiome relationship in a large cohort adjusting for cofounders. Therefore, we aim to explore the connection of the brain-gut-microbiome axis to obesity controlling for such cofounders as sex, race, and diet. Whole brain resting state functional MRI was acquired, and connectivity and brain network properties were calculated. Fecal samples were obtained from 287 obese and non-obese participants (males n = 99, females n = 198) for 16s rRNA profiling and fecal metabolites, along with a validated dietary questionnaire. Obesity was associated with alterations in the brain's reward network (nucleus accumbens, brainstem). Microbial diversity (p = .03) and composition (p = .03) differed by obesity independent of sex, race, or diet. Obesity was associated with an increase in Prevotella/Bacteroides (P/B) ratio and a decrease in fecal tryptophan (p = .02). P/B ratio was positively correlated to nucleus accumbens centrality (p = .03) and negatively correlated to fecal tryptophan (p = .004). Being Hispanic, eating a standard American diet, having a high Prevotella/Bacteroides ratio, and a high nucleus accumbens centrality were all independent risk factors for obesity. There are obesity-related signatures in the BGM-axis independent of sex, race, and diet. Race, diet, P/B ratio and increased nucleus accumbens centrality were independent risk factors for obesity. P/B ratio was inversely related to fecal tryptophan, a metabolite related to serotonin biosynthesis, and positively related to nucleus accumbens centrality, a region central to the brain's reward center. These findings may expand the field of therapies for obesity through novel pathways directed at the BGM axis.

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Background

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.

Results

The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (P_DEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.

Conclusions

MBCdeg with DEGES normalization can be used in the identification of DEGs when the P_DEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04438-4.

Pharmacoepidemiological methods for computing the duration of pharmacological prescriptions using secondary data sources

PURPOSE

In pharmacoepidemiology, correctly defining the exposure period of pharmacological treatment is a challenging step when information on the time in treatment is missing or incomplete.

METHODS

In this review, we describe several methods for defining exposure to pharmacological treatments using secondary data sources that lack such information.

RESULTS AND CONCLUSION

Several methods for assessing the duration of redeemed prescriptions and combining them into temporal sequences are available. We present a set of considerations to make researchers aware of the potentials and pitfalls of these methods that may aid in minimizing biases in research using these methods. Additionally, we highlight that, to date, there is no one-size-fits-all solution. Thus, the choice of method should be based on their area of applicability combined with a careful mapping to the research scenario under investigation.

Matrix adhesion and remodeling diversifies modes of cancer invasion across spatial scales

The metastasis of malignant epithelial tumors begins with the egress of transformed cells from the confines of their basement membrane (BM) to their surrounding collagen-rich stroma. Invasion can be morphologically diverse: when breast cancer cells are separately cultured within BM-like matrix, collagen I (Coll I), or a combination of both, they exhibit collective-, dispersed mesenchymal-, and a mixed collective-dispersed (multimodal)- invasion, respectively. In this paper, we asked how distinct these invasive modes are with respect to the cellular and microenvironmental cues that drive them. A rigorous computational exploration of invasion was performed within an experimentally motivated Cellular Potts-based modeling environment. The model comprised of adhesive interactions between cancer cells, BM- and Coll I-like extracellular matrix (ECM), and reaction-diffusion-based remodeling of ECM. The model outputs were parameters cognate to dispersed- and collective- invasion. A clustering analysis of the output distribution curated through a careful examination of subsumed phenotypes suggested at least four distinct invasive states: dispersed, papillary-collective, bulk-collective, and multimodal, in addition to an indolent/non-invasive state. Mapping input values to specific output clusters suggested that each of these invasive states are specified by distinct input signatures of proliferation, adhesion and ECM remodeling. In addition, specific input perturbations allowed transitions between the clusters and revealed the variation in the robustness between the invasive states. Our systems-level approach proffers quantitative insights into how the diversity in ECM microenvironments may steer invasion into diverse phenotypic modes during early dissemination of breast cancer and contributes to tumor heterogeneity.

Unsupervised subtyping and methylation landscape of pancreatic ductal adenocarcinoma

Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive form of pancreatic cancer that typically manifests itself at an advanced stage and does not respond to most treatment modalities. The survival rate of a PDAC patient is less than 5%, with a median survival of just a couple of months. A better understanding of the molecular pathology of PDAC is needed to guide research for the development of better clinical treatment modalities for PDAC patients. Gene expression studies performed to date have identified different subtypes of PDAC with prognostic and clinical relevance. Subtypes identified to date are highly heterogeneous since pancreatic cancer is heterogeneous cancer. Tumor microenvironment and stroma constitute a major chunk of PDAC and contribute to the heterogeneity. Better subtyping methods are need of the hour for better prognosis and classification of PDAC for future personalized treatment. In this work, we have performed an integrated analysis of DNA methylation and gene expression datasets to provide better mechanistic and molecular insights into Pancreatic cancers, especially PDAC. The use of varied and diverse datasets has provided valuable insights into different cancer types and can play an integral role in revealing the complex nature of underlying biological mechanisms. We performed subtyping of TCGA-PAAD gene expression and methylation datasets into different subtypes using state-of-the-art normalization methods and unsupervised clustering methods that reveal latent hidden factors, leading to additional insights for subtyping. Differential expression and differential methylation were performed for each of the subtypes obtained from clustering. Our analysis gave a consensus of five cluster solution with relevant pathways like MAPK, MET. The five subtypes corresponded to the tumor and stromal subtypes. This analysis helps in distinguishing and identifying different subtypes based on enriched putative genes. These results help propose novel experimentally-verifiable PDAC subtyping and demonstrate that using varied data sets and integrated methods can contribute to disease prognostication and precision medicine in PDAC treatment.

Prediction of severity and subtype of fibrosing disease using model informed by inflammation and extracellular matrix gene index

Fibrosis is a chronic disease with heterogeneous clinical presentation, rate of progression, and occurrence of comorbidities. Systemic sclerosis (scleroderma, SSc) is a rare rheumatic autoimmune disease that encompasses several aspects of fibrosis, including highly variable fibrotic manifestation and rate of progression. The development of effective treatments is limited by these variabilities. The fibrotic response is characterized by both chronic inflammation and extracellular remodeling. Therefore, there is a need for improved understanding of which inflammation-related genes contribute to the ongoing turnover of extracellular matrix that accompanies disease. We have developed a multi-tiered method using Naïve Bayes modeling that is capable of predicting level of disease and clinical assessment of patients based on expression of a curated 60-gene panel that profiles inflammation and extracellular matrix production in the fibrotic disease state. Our novel modeling design, incorporating global and parametric-based methods, was highly accurate in distinguishing between severity groups, highlighting the importance of these genes in disease. We refined this gene set to a 12-gene index that can accurately identify SSc patient disease state subsets and informs knowledge of the central regulatory pathways in disease progression.

Activity-Dependent Remodeling of Synaptic Protein Organization Revealed by High Throughput Analysis of STED Nanoscopy Images

The organization of proteins in the apposed nanodomains of pre- and postsynaptic compartments is thought to play a pivotal role in synaptic strength and plasticity. As such, the alignment between pre- and postsynaptic proteins may regulate, for example, the rate of presynaptic release or the strength of postsynaptic signaling. However, the analysis of these structures has mainly been restricted to subsets of synapses, providing a limited view of the diversity of synaptic protein cluster remodeling during synaptic plasticity. To characterize changes in the organization of synaptic nanodomains during synaptic plasticity over a large population of synapses, we combined STimulated Emission Depletion (STED) nanoscopy with a Python-based statistical object distance analysis (pySODA), in dissociated cultured hippocampal circuits exposed to treatments driving different forms of synaptic plasticity. The nanoscale organization, characterized in terms of coupling properties, of presynaptic (Bassoon, RIM1/2) and postsynaptic (PSD95, Homer1c) scaffold proteins was differently altered in response to plasticity-inducing stimuli. For the Bassoon - PSD95 pair, treatments driving synaptic potentiation caused an increase in their coupling probability, whereas a stimulus driving synaptic depression had an opposite effect. To enrich the characterization of the synaptic cluster remodeling at the population level, we applied unsupervised machine learning approaches to include selected morphological features into a multidimensional analysis. This combined analysis revealed a large diversity of synaptic protein cluster subtypes exhibiting differential activity-dependent remodeling, yet with common features depending on the expected direction of plasticity. The expanded palette of synaptic features revealed by our unbiased approach should provide a basis to further explore the widely diverse molecular mechanisms of synaptic plasticity.

Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.

TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

Objective

Differential expression (DE) is a fundamental step in the analysis of RNA-Seq count data. We had previously developed an R/Bioconductor package (called TCC) for this purpose. While this package has the unique feature of an in-built robust normalization method, its use has so far been limited to R users only. There is thus, a need for an alternative to DE analysis by TCC for non-R users.

Results

Here, we present a graphical user interface for TCC (called TCC-GUI). Non-R users only need a web browser as the minimum requirement for its use (https://infinityloop.shinyapps.io/TCC-GUI/). TCC-GUI is implemented in R and encapsulated in Shiny application. It contains all the major functionalities of TCC, including DE pipelines with robust normalization and simulation data generation under various conditions. It also contains (i) tools for exploratory analysis, including a useful score termed average silhouette that measures the degree of separation of compared groups, (ii) visualization tools such as volcano plot and heatmap with hierarchical clustering, and (iii) a reporting tool using R Markdown. By virtue of the Shiny-based GUI framework, users can obtain results simply by mouse navigation. The source code for TCC-GUI is available at https://github.com/swsoyee/TCC-GUI under MIT license.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4179-2) contains supplementary material, which is available to authorized users.