Multiple Dimensionality Reduction Research Articles

Predicting susceptibility to COVID-19 infection in patients on maintenance hemodialysis by cross-coupling soluble ACE2 concentration with lymphocyte count: an algorithmic approach.

Patients on maintenance hemodialysis (MHD) were more vulnerable to and had a higher mortality during the COVID-19 pandemic. As angiotensin converting enzyme 2 (ACE2) and transmembrane protease serine S1 member 2 (TMPRSS2) played crucial roles in viral entry into the human host cells, we therefore investigated in the MHD patients whether their plasma levels were associated with susceptibility to the COVID-19. Blood samples were collected from the patients in our then COVID-19 free center immediately upon lifting of the stringent quarantine measures in early December of 2022 and infection situation was observed within the following 2 weeks. Plasma levels of the soluble ACE2 (sACE2), ACE (sACE) and TMPRSS2 (sTMPRSS2) were measured with ELISA method. Data were stepwisely tested for independent effect, relevant role and synergistic action on the susceptibility by multiple logistic regression, receiver operating characteristic curve and multiple dimensionality reduction (MDR) method, respectively. Among the 174 eligible patients, 95 (54.6%) turned COVID-19 positive with a male to female ratio of 1.57 during the observation period. Comparing with the uninfected, the infected had significantly higher sACE2 and lower sTMPRSS2 levels upon comparable sACE concentration. Besides the sACE2, factors associated with susceptibility were vintage and individual session time of the hemodialysis, smoking and comorbidity of hepatitis, whereas lymphocyte counts showed a tendency (p = 0.052). Patients simultaneously manifesting higher sACE2 level and lower lymphocyte counts had an increased infection risk as confirmed by the MDR method. By sorting out the susceptible ones expeditiously, this algorithmic approach may help the otherwise vulnerable MHD patients weather over future wave of COVID-19 variants or outbreak of other viral diseases.

COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

Dimension reduction aims to project a high‐dimensional dataset into a low‐dimensional space. It tries to preserve the topological relationships among the original data points and/or induce clusters. NetDRm, an online dimensionality reduction method based on neural ensemble learning that integrates different dimension reduction methods in a synergistic way, is introduced. NetDRm is designed for datasets of multidimensional points that can be either structured (e.g., images) or unstructured (e.g., point clouds, tabular data). It starts by training a collection of deep residual encoders that learn the embeddings induced by multiple dimension reduction methods applied to the input dataset. Subsequently, a dense neural network integrates the generated encoders by emphasizing topological preservation or cluster induction. Experiments conducted on widely used multidimensional datasets (point‐cloud manifolds, image datasets, tabular record datasets) show that the proposed method yields better results in terms of topological preservation ( curves), cluster induction (V measure), and classification accuracy than the most relevant dimension reduction methods.

Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network

In response to limitations in traditional intelligent fault diagnosis methods, such as accuracy, robustness, generality, and susceptibility to noise, this article proposes Multiple Wavelet Coefficient Dimensionality Reduction and Improved Residual Network (MWCDR-IResNet). This novel approach represents the first combination of multi-wavelet transform for extracting wavelet coefficients and Principal Component Analysis (PCA) for dimensionality reduction. It incorporates a Residual Network enhanced by the Squeeze and Excitation (SE) module to focus on relevant channels and capture critical fault-related features, followed by fault classification. Validation is performed using rolling bearing datasets from Paderborn University(PBU) and Southeast University (SEU). Experimental results consistently demonstrate MWCDR-IResNet superior performance in terms of robustness, generality, and average accuracy when compared to existing methods.

Domain Decomposition Strategy for Combining Nonlinear and Linear Reduced-Order Models

Increasingly ubiquitous reliance on expensive, high-fidelity numerical simulations has led to the emergence of reduced-order modeling as an effective method to predict high-dimensional, spatially discretized field outputs in an efficient manner. This study presents a method to construct parametric, nonintrusive reduced-order models (ROMs) by leveraging domain decomposition (DD) to enable using multiple dimension reduction (DR) techniques within different spatial subdomains of the field to construct a single predictive ROM. To enforce smoothness of solutions across subdomains, points at domain interfaces are reconstructed using the data-driven gappy proper orthogonal decomposition method. The method is assessed on three high-dimensional test cases with shocks, including one with two-dimensional turbulent flow around the RAE2822. Results show that fully nonlinear ROMs predict fields more accurately in the vicinity of discontinuous features compared to their linear counterparts, which are more accurate in regions that are far from these discontinuities. Furthermore, irrespective of the DR method, DD-based ROMs outperformed their global counterparts in all test cases. Finally, models that used a combination of DR methods simultaneously showed superior performance near shockwaves and a significant improvement in total error compared to existing approaches that employ a single DR method in all subdomains.

Mathematical scheduling model of complex industrial process combining swarm intelligence algorithm and swarm dimension reduction technology

The swarm intelligence algorithm is widely used in scheduling problems due to its superior searchability and adaptability, but it faces high computational costs and low efficiency when dealing with high-dimensional complex data. To enhance the handling of large-scale and intricate data, as well as enhance the accuracy and efficiency of scheduling, this study develops a mathematical model for industrial scheduling. The model is based on swarm intelligence algorithms such as symbiotic evolution algorithms, grey wolf algorithms, cuckoo algorithms, population dimensionality reduction techniques, and resource optimal allocation calculation methods. The final test results showed that the accuracy of the model in calculating the objective function was about 80 % in cases of high complexity and about 90 % in cases of low complexity. Compared with other algorithms, its accuracy was improved by 7 %, error rate was reduced by 50 %, time cost was reduced by about 30 %, and conversion rate was fast and more stable on average after 200 generations. The experimental results indicate that the proposed mathematical scheduling model, which integrates multiple intelligent algorithms and population dimensionality reduction technologies, has certain practices and feasibility in large-scale and multi-dimensional production for enterprises. This study combines swarm intelligence algorithms with population dimensionality reduction techniques, which is an innovative attempt at complex industrial scheduling problems.

Comparison of dimensionality reduction methods and evaluation of prediction effect based on cardiovascular disease data

Cardiovascular diseases have become the leading cause of death in the world, and the mortality rate caused by them is still on the rise, which is a major challenge facing the world. In the study of cardiovascular diseases, there are many kinds and quantities of factors leading to the disease, so how to screen more effective factors for research and accurate prediction is an important problem. The aim of this study is to conduct an in-depth comparative analysis of multiple dimensionality reduction methods for cardiovascular disease data. This paper evaluates the results produced by various dimensionality reduction methods and models, and uses Accuracy Rate, Confusion Matrix and Area Under Curve (AUC) and other indicators to evaluate their prediction effects. The study finds that the decision tree model has the best performance and is superior to LDA linear discriminant analysis in terms of accuracy and data dimension. The principal component analysis method is relatively complex, although it can effectively reduce the data dimension, its accuracy in forecasting decreases, and this method is not conducive to horizontal and vertical comparison and the accumulation of statistical data.

Associations between childhood maltreatment and physiological dysregulation in adulthood: Methodological decisions and implications

BackgroundChildhood maltreatment is linked with health problems in adulthood. Theoretical models suggest that maltreatment leads to dysregulation in several bodily systems, and this has been corroborated using measures of physiological function (i.e., biomarkers). Methodological decisions involving the measurement of maltreatment and dimension reduction with respect to biomarkers (i.e., combining information across multiple measures) may influence research findings. ObjectiveThe present study compares associations between childhood maltreatment and adult physiological dysregulation using multiple dimension reduction approaches and measures of maltreatment. Participants and settingParticipants were recruited, as children, to a prospective study of the correlates and consequences of childhood maltreatment. 253 participants were retained and provided biomarker data at midlife. Physiological dysregulation was operationalized with a conventional allostatic load approach and a novel statistical distance approach. MethodsRegression models were employed with allostatic load or statistical distance as the outcome and prospectively or retrospectively measured child maltreatment as the primary predictor. ResultsWhen using allostatic load as the outcome, prospectively measured childhood maltreatment was positively associated with physiological dysregulation (b = 0.70, SE = 0.31, p = 0.02). When using statistical distance as the outcome, retrospectively measured childhood maltreatment was positively associated with physiological dysregulation (b = 0.69, SE = 0.19 p < 0.001). ConclusionsWe report a positive association between childhood maltreatment and physiological dysregulation at midlife. However, the significance and magnitude of effects varied with different maltreatment and physiological dysregulation measures. Further review of the methods used to study adult health conditions and their relation to childhood maltreatment is needed.

Aerodynamic Optimization Framework for a Three-Dimensional Nacelle Based on Deep Manifold Learning-Assisted Geometric Multiple Dimensionality Reduction

As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles.

AI-assisted traffic matrix prediction using GA-enabled deep ensemble learning for hybrid SDN

A hybrid software-defined network (SDN), which is a network where traditional routers and SDN protocols coexist during the incremental deployment of SDNs, requires real-time link traffic information for effective deployment. This has called for the need of accurate real-time data analytics and traffic prediction methods. To date, various traffic prediction frameworks have been studied to facilitate analysis and extraction of valuable information from huge sets of incomplete and noisy data. However, due to the linear nature of network design, mainly characterized by manual control plane forwarding configurations, existing traffic prediction frameworks cannot perform consistent traffic prediction over multiple datasets in modern dynamic networks. To address this issue, ensemble-driven approaches based on deep learning (DL) have recently been suggested as a promising solution. Nevertheless, determining the most appropriate combination of baseline DL architectures to be adopted for accurate traffic prediction remains a challenge. This paper proposes a novel DL framework for improved traffic prediction in hybrid SDNs. The framework combines a deep ensemble learning model utilizing multiple dimensionality reduction algorithms and a genetic algorithm (GA). The multi-objective GA is used to perform dynamic optimization of the connection weights and thresholds of the deep ensemble learning model while overcoming the local optima problem. Experimental results show that the proposed approach can achieve more accurate forecast of link traffic than the traditional baseline DL frameworks.

Robust Low Rank and Sparse Representation for Multiple Kernel Dimensionality Reduction

In the fields of pattern recognition and data mining, two problems need to be addressed. First, the curse of dimensionality degrades the performance of many practical data processing techniques. Second, due to the existence of noise and outliers, feature extraction on corrupted data cannot be effectively achieved. Recently, some representation based methods have produced promising results. However, these methods cannot handle the case in which nonlinear similarity exists and have failed to provide the quantized interpretability for the importance of features. In this paper, we propose a novel low rank and sparse representation method to realize dimensionality reduction and robustly extract latent low dimensional discriminative features. Specifically, we first adopt multiple kernel learning to map the original data into an embedded reproducing kernel Hilbert space (RKHS) and then kernel based similarity discriminative projection is learned to explore the within-class and between-class variability. Notably, this low dimensional feature learning strategy is definitely integrated into the low rank matrix recovery of the kernel matrix. Next, we introduce the regularization of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> norm on error matrix to eliminate noise and on projection matrix to lead the selected features to be more compact and interpretable. The non-convex optimization problem is effectively solved by the alternating direction method of multipliers (ADMM) methods. Extensive experiments on seven benchmark datasets are conducted to demonstrate the effectiveness of our method.

SLISEMAP: supervised dimensionality reduction through local explanations

Existing methods for explaining black box learning models often focus on building local explanations of the models’ behaviour for particular data items. It is possible to create global explanations for all data items, but these explanations generally have low fidelity for complex black box models. We propose a new supervised manifold visualisation method, slisemap, that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. We provide a mathematical derivation of our problem and an open source implementation implemented using the GPU-optimised PyTorch library. We compare slisemap to multiple popular dimensionality reduction methods and find that slisemap is able to utilise labelled data to create embeddings with consistent local white box models. We also compare slisemap to other model-agnostic local explanation methods and show that slisemap provides comparable explanations and that the visualisations can give a broader understanding of black box regression and classification models.

Association and functional analysis of angiotensin-converting enzyme 2 genetic variants with the pathogenesis of pre-eclampsia.

The aim of this study was to investigate the relationship between potential functional single-nucleotide polymorphisms (SNPs) of the angiotensin-converting enzyme 2 (ACE2) gene and the pathogenesis of pre-eclampsia (PE) in Guangxi, China. A case-control study was conducted involving 327 PE cases and 591 age-matched, normal, singleton pregnant women. Potential functional ACE2 gene variants (rs2106809 A>G, rs6632677 G>C, and rs2074192 C>T) were selected and genotyped using kompetitive allele-specific PCR. The strength of the associations between the studied genetic variants and the risk of PE were evaluated using odds ratios (ORs) and corresponding 95% confidence intervals (CIs). After adjusting for age and body mass index (BMI), unconditional logistic regression analysis showed that rs2106809 A>G was significantly associated with PE risk (AG vs. AA, OR = 1.43, 95% CI = 1.03-1.99, p = 0.034; AG/GG vs. AA, OR = 1.45, 95% CI = 1.06-1.99, p = 0.019), especially with severe PE (AG vs. AA, adjusted OR = 1.70, 95% CI = 1.10-2.61; AG/GG vs. AA, adjusted OR = 1.71, 95% CI = 1.14-2.57). Further stratified analysis showed that rs2106809 was even more pronounced in subjects in the pre-pregnancy BMI (pre-BMI) >23 kg/m2 (adjusted OR = 2.14, 95% CI = 1.32-3.45) and triglyceride (TG) >2.84 mmol/L subgroups (adjusted OR = 1.81, 95% CI = 1.09-3.01) under the dominant genetic model. We also found that rs2106809 interacted with pre-BMI (p interaction = 0.040), thereby affecting an individual's genetic susceptibility to PE. Multiple dimension reduction analysis demonstrated that rs2106809 made the best one-locus model, and the three-locus model was the best interaction model for predicting PE risk. Functional analysis suggested that rs2106809 A>G causes a change in the reliability of classifications of two putative splice sites in the ACE2 gene, potentially regulating the expression of functional genes (PIR, ACE2, and CLTRN) in multiple tissues and cell lines (p< 0.05). The ACE2 gene rs2106809 A>G variant is significantly associated with the risk of PE via individual locus effects and/or complex gene-gene and gene-environment interactions. Regulating the expression of functional genes such as PIR, ACE2, and CLTRN may be the molecular mechanism by which rs2106809 increases an individual's susceptibility to PE.

Assessment of Tennessee's county-level vulnerability to hepatitis C virus and HIV outbreaks using socioeconomic, healthcare, and substance use indicators.

BackgroundHuman immunodeficiency virus (HIV), hepatitis C virus (HCV), and injection drug use are syndemic in the central Appalachian states. In Tennessee (TN), declines in HIV among persons who inject drugs (PWID) stalled, and HCV infection rates increased significantly from 2013–2017. To better target strategies to address the syndemic, county-level socioeconomic, opioid use, access to healthcare, and health factors were modeled to identify indicators predictive of vulnerability to an HIV/HCV outbreak among PWID in TN.MethodsNewly reported chronic HCV cases among persons aged 13–39 years in 2016–2017 were used as a proxy for county-level HIV/HCV vulnerability among TN’s 95 counties. Seventy-five publicly available county-level measures from 2016–2017 were collected and reduced through multiple dimension reduction techniques. Negative binomial regression identified indicators associated with HCV which were used to calculate county-level vulnerability to a local HIV/HCV outbreak.ResultsThirteen county-level indicators were identified as strongly predictive of HIV/HCV vulnerability with the statistically significant indicators being percentage of the population aged 20–44 years, per capita income, teen birth rate, percentage of clients in TDMHSAS-funded opioid treatment and recovery, syphilis case rate, and percentage of homes with at least one vehicle. Based on the 13 indicators, we identified the distribution of vulnerability to an HIV/HCV outbreak among TN’s counties. Eleven high vulnerability counties were identified, with the preponderance located in east and middle TN.ConclusionThis analysis identified the county-level factors most associated with vulnerability to an HIV/HCV outbreak among PWID in TN. These results, alongside routine surveillance, will guide targeted prevention and linkage to care efforts for the most vulnerable communities.

A Novel Multilabel Classification Framework for Coupling Faults in Hot Rolling Processes

Compared with a single fault, the occurrence, evolution, and composition of coupling faults have more uncertainties and diversities, which make coupling fault classification a challenging topic in academic research and industrial application areas. This brief addresses the classification problems of coupling faults from a new perspective. Specifically, the main innovations are: 1) a classification framework for coupling faults is first proposed, which integrates multiple kernel learning and multilabel dimensionality reduction; 2) label correlations and nonlinear characteristics among coupling faults are fully explored aiming at improving classification performance; and 3) a trace ratio form of l_1 norm-based objective function is designed for improving the robustness of multilabel classifier. Extensive experiments on the hot rolling process (HRP) are finally given to validate the effectiveness of the proposed scheme.

A Visual Analytics Method for Time-Series Log Data Using Multiple Dimensionality Reduction

A Visual Analytics Method for Time-Series Log Data Using Multiple Dimensionality Reduction

Analyzing the Behavior of Multiple Dimensionality Reduction Algorithms to Obtain Better Accuracy using Benchmark KDD CUP Dataset

In the ubiquitously connected world of IT infrastructure, Intrusion Detection System (IDS) plays vital role. IDS is considered as a critical component of security infrastructure and is implemented either through hardware or software devices and can detect malicious activities in a networked environment. To detect or prevent network attacks, Network Intrusion Detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. Analyzing different attacks effectively through Dimensionality Reduction Algorithms is an efficient mechanism. The significance of these algorithms is they improvise feature selection from huge datasets. Also through this the learning speed is enhanced. Speed is a crucial parameter in the success of network intrusion detection systems for defending reactions. In this paper open source datasets Knowledge Discovery in Databases (KDD CUP) dataset and 10% KDD CUP dataset are employed for experimentation. These datasets are provided to Dimensionality Reduction Algorithms like Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA) and Kernel PCA with different kernels and classified with Logistic Regression classification algorithm for procuring accurate results. Further to boost up the accuracy achieved so far K-fold algorithm is utilized. Finally a comparative study of different accuracy results is done by using K-fold algorithm and also without the usage of this algorithm. The empirical study on KDD CUP data confirms the effectiveness of the proposed scheme. In this paper we discovered the combination of multiple dimensionality reduction algorithm such as PCA , LDA and Kernel PCA with classification algorithm and this combination of algorithm gives best result. Our study will help out the researchers to uncover critical area such as intrusion detection in network traffic environment. The results what we identified will be very much helpful for researchers for their future research on KDD CUP dataset. In this the new theory will be arrived by this research that the best accuracy achieved by PCA with 10% KDD CUP dataset experimental results without KFold attained 98% and with KFold attained 99%. LDA with 10% KDD CUP Dataset experimental results without KFold attained 98% and with KFold attained 99%.

Analysis of rs1864182 and rs1864183 variants in ATG10 gene and antineutrophil cytoplasmic autoantibody‐associated vasculitis in Chinese Guangxi population

ObjectivesTo investigate the association of autophagy‐associated gene 10 (ATG10) gene polymorphisms (rs1864182 and rs1864183) with antineutrophil cytoplasmic autoantibody (ANCA)‐associated vasculitis (AAV) in Chinese Guangxi population.MethodsThe single nucleotide polymorphisms (SNPs) of ATG10 rs1864182 and rs1864183 in 395 participants (195 AAVs and 200 healthy controls) were genotyped. Generalized multiple dimensionality reduction (GMDR) was used to analyze the SNP‐SNP interactions among two SNPs of ATG10 gene and other SNPs of autophagy gene previously studied by our research team.ResultsIn this study, we found that the two ATG10 SNPs were not associated with AAV risk in Chinese Guangxi population. However, there were statistically significant differences in the incidence of hemoptysis, hematuria, and proteinuria among the three genotypes of ATG10 rs1864182 and rs1864183 (p < 0.05). Moreover, permutation test of GMDR suggested that immunity‐related GTPase M(IRGM) rs4958847, autophagy‐associated gene 7 (ATG7) rs6442260, ATG7 rs2594966, ATG10 rs1864183, protein kinase B(AKT2) rs3730051, and AKT2 rs11552192 might interact with each other in the process of developing AAV (p < 0.05).ConclusionsOur results indicated that there existed no association between ATG10 SNPs and AAV, and SNP‐SNP interactions among IRGM rs4958847, ATG7 rs6442260, ATG7 rs2594966, ATG10 rs1864183, AKT2 rs3730051, and AKT2 rs11552192 may confer AAV risk in the Chinese Guangxi population.

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

AbstractLarge-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.

MLG: multilayer graph clustering for multi-condition scRNA-seq data.

Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present multilayer graph clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale benchmarking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.