MOTIVATION: Morphological analysis of dendritic spines is critical to understanding the function and dysfunction of neural circuits. The growing trends of the large-scale electron microscopy (EM) imaging systems and automatic cellular reconstruction provide unprecedented opportunities to investigate the ultrastructure of dendrites. This morphometric analysis of dendritic spines requires accurate compartment segmentation methods as well as meaningful quantification methods. However, most existing methods rely on surface or volumetric information alone, which may not deliver accurate segmentation results. RESULTS: We developed VSOT, a method based on Volume-Surface Optimization, designed for the accurate structural analysis of dendritic reconstruction. VSOT accurately segments dendritic reconstructions into compartments, including spine, spine head, and spine neck, by leveraging advanced optimization techniques that integrate local surface and global volumetric information. Our tests on public datasets of spine segmentation, as well as on a first-of-its-kind dataset of head-neck segmentation that we manually constructed, show that VSOT offers more accurate results than peer methods. When applied to a large EM dataset of different brain layers, VSOT reveals how the structure of dendrites varies across brain areas. Furthermore, we explored the structural relationships between neurons and astrocytes at tripartite synapses. With the newly developed computation methods, neuroscientists can exploit the large-scale volumetric EM data to address various scientific questions and advance the understanding of neural circuits. AVAILABILITY AND IMPLEMENTATION: VSOT is available at https://github.com/yu-lab-vt/VSOT. The data and codes in this study are available at Zenodo (https://doi.org/10.5281/zenodo.15115542).

To address many of the deficiencies in optical neuroimaging technologies, such as poor tempo-spatial resolution, low penetration depth, contact-based measurement, and time-consuming image reconstruction, a novel, noncontact, portable, time-resolved laser speckle contrast imaging (TR-LSCI) technique has been developed for continuous, fast, and high-resolution 2D mapping of cerebral blood flow (CBF) at different depths of the head. TR-LSCI illuminates the head with picosecond-pulsed, coherent, widefield near-infrared light and synchronizes a fast, high-resolution, gated single-photon avalanche diode camera to selectively collect diffuse photons with longer pathlengths through the head, thus improving the accuracy of CBF measurement in the deep brain. The reconstruction of a CBF map was dramatically expedited by incorporating convolution functions with parallel computations. The performance of TR-LSCI was evaluated using head-simulating phantoms with known properties and in-vivo rodents with varied hemodynamic challenges to the brain. TR-LSCI enabled mapping CBF variations at different depths with a sampling rate of up to 1 Hz and spatial resolutions ranging from tens/hundreds of micrometers on rodent head surfaces to 1-2 millimeters in deep brains. With additional improvements and validation in larger populations against established methods, we anticipate offering a noncontact, fast, high-resolution, portable, and affordable brain imager for fundamental neuroscience research in animals and for translational studies in humans.

MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

3D segmentation of nuclei images is a fundamental task for many biological studies. Despite the rapid advances of large-volume 3D imaging acquisition methods and the emergence of sophisticated algorithms to segment the nuclei in recent years, a benchmark with all cells completely annotated is still missing, making it hard to accurately assess and further improve the performance of the algorithms. The existing nuclei segmentation benchmarks either worked on 2D only or annotated a small number of 3D cells, perhaps due to the high cost of 3D annotation for large-scale data. To fulfill the critical need, we constructed NIS3D, a 3D, high cell density, large-volume, and completely annotated Nuclei Image Segmentation benchmark, assisted by our newly designed semi-automatic annotation software. NIS3D provides more than 22,000 cells across multiple most-used species in this area. Each cell is labeled by three independent annotators, so we can measure the variability of each annotation. A confidence score is computed for each cell, allowing more nuanced testing and performance comparison. A comprehensive review on the methods of segmenting 3D dense nuclei was conducted. The benchmark was used to evaluate the performance of several selected state-of-the-art segmentation algorithms. The best of current methods is still far away from human-level accuracy, corroborating the necessity of generating such a benchmark. The testing results also demonstrated the strength and weakness of each method and pointed out the directions of further methodological development. The dataset can be downloaded here: https://github.com/yu-lab-vt/NIS3D.

Empowered by advances in single-cell RNA sequencing techniques (scRNA-seq), discovering new cell types or new subsets of a cell type has become an increasingly popular research interest. This type of study, by nature, requires assessment of similarity between cell groups. However, so far there is no quantitative metric for accurate and objective evaluation of such similarity; while current practice suffers from quite a few challenges including subjectivity. In this work, we propose a novel quantitative metric of single-cell-to-target-cell-type similarity, on the basis of scRNA-seq data and the signatures or differentially expressed gene (DEG) list of the target cell type. The proposed similarity score, TySim, evaluates the statistical significance of joint differential expression of the given DEGs in the cell to be tested. For this statistical test, the null distribution is established upon full consideration of complex factors causing heterogeneous sequencing efficiency of genes/cells. The design of TySim avoids the needs for clustering and for batch effect removal on cross-platform data, detouring the accompanying risks and burdens. Being the first quantitative metric of similarity to target cell type at a single-cell resolution, TySim has the potential to facilitate and enable a variety of biological studies. We validated the effectiveness of TySim and explored the possible directions of application through three example study cases of real datasets. Experimental results demonstrate TySim’s superior performance and great potential in making contributions to biological studies.

Multiple time series data occur in many real applications and the alignment among them is usually a fundamental step of data analysis. Frequently, these multiple time series are inter-dependent, which provides extra information for the alignment task and this information cannot be well utilized in the conventional pairwise alignment methods. Recently, the joint alignment was modeled as a max-flow problem, in which both the profile similarity between the aligned time series and the distance between adjacent warping functions are jointly optimized. However, despite the new model having elegant mathematical formulation and superior alignment accuracy, the long computation time and large memory usage, due to the use of the existing general-purpose max-flow algorithms, limit significantly its well-deserved wide use. In this report, we present BIdirectional pushing with Linear Component Operations (BILCO), a novel algorithm that solves the joint alignment max-flow problems efficiently and exactly. We develop the strategy of linear component operations that integrates dynamic programming technique and the push-relabel approach. This strategy is motivated by the fact that the joint alignment max-flow problem is a generalization of dynamic time warping (DTW) and numerous individual DTW problems are embedded. Further, a bidirectional-pushing strategy is proposed to introduce prior knowledge and reduce unnecessary computation, by leveraging another fact that good initialization can be easily computed for the joint alignment max-flow problem. We demonstrate the efficiency of BILCO using both synthetic and real experiments. Tested on thousands of datasets under various simulated scenarios and in three distinct application categories, BILCO consistently achieves at least 10 and averagely 20-folds increase in speed, and uses at most 1/8 and averagely 1/10 memory compared with the best existing max-flow method. Our source code can be found at https://github.com/yu-lab-vt/BILCO.

MOTIVATION: Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. RESULTS: We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. AVAILABILITY AND IMPLEMENTATION: The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.

The variance-stabilizing transformation (VST) problem is to transform heteroscedastic data to homoscedastic data so that they are more tractable for subsequent analysis. However, most of the existing approaches focus on finding an analytical solution for a certain parametric distribution, which severely limits the applications, because simple distributions cannot faithfully describe the real data while more complicated distributions cannot be analytically solved. In this paper, we converted the VST problem into a convex optimization problem, which can always be efficiently solved, identified the specific structure of the convex problem, which further improved the efficiency of the proposed algorithm, and showed that any finite discrete distributions and the discretized version of any continuous distributions from real data can be variance-stabilized in an easy and nonparametric way. We demonstrated the new approach on bioimaging data and achieved superior performance compared to peer algorithms in terms of not only the variance homoscedasticity but also the impact on subsequent analysis such as denoising. Source codes are available at https://github.com/yu-lab-vt/ConvexVST.

MOTIVATION: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. RESULTS: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. AVAILABILITY AND IMPLEMENTATION: Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. RESULTS: Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. AVAILABILITY AND IMPLEMENTATION: An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Min-cost flow has been a widely used paradigm for solving data association problems in multi-object tracking (MOT). However, most existing methods of solving min-cost flow problems in MOT are either direct adoption or slight modifications of generic min-cost flow algorithms, yielding sub-optimal computation efficiency and holding the applications back from larger scale of problems. In this paper, by exploiting the special structures and properties of the graphs formulated in MOT problems, we develop an efficient min-cost flow algorithm, namely, minimum-update Successive Shortest Path (muSSP). muSSP is proved to provide exact optimal solution and we demonstrated its efficiency through 40 experiments on five MOT datasets with various object detection results and a number of graph designs. muSSP is always the most efficient in each experiment compared to the three peer solvers, improving the efficiency by 5 to 337 folds relative to the best competing algorithm and averagely 109 to 4089 folds to each of the three peer methods.

BACKGROUND: Genome-wide DNA copy number changes are the hallmark events in the initiation and progression of cancers. Quantitative analysis of somatic copy number alterations (CNAs) has broad applications in cancer research. With the increasing capacity of high-throughput sequencing technologies, fast and efficient segmentation algorithms are required when characterizing high density CNAs data. RESULTS: A fast and informative segmentation algorithm, DBS (Deviation Binary Segmentation), is developed and discussed. The DBS method is based on the least absolute error principles and is inspired by the segmentation method rooted in the circular binary segmentation procedure. DBS uses point-by-point model calculation to ensure the accuracy of segmentation and combines a binary search algorithm with heuristics derived from the Central Limit Theorem. The DBS algorithm is very efficient requiring a computational complexity of O(n*log n), and is faster than its predecessors. Moreover, DBS measures the change-point amplitude of mean values of two adjacent segments at a breakpoint, where the significant degree of change-point amplitude is determined by the weighted average deviation at breakpoints. Accordingly, using the constructed binary tree of significant degree, DBS informs whether the results of segmentation are over- or under-segmented. CONCLUSION: DBS is implemented in a platform-independent and open-source Java application (ToolSeg), including a graphical user interface and simulation data generation, as well as various segmentation methods in the native Java language.

BACKGROUND: Conventional differential gene expression analysis by methods such as student's t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups. RESULTS: Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method. CONCLUSIONS: The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.

Recent advancements in near-infrared diffuse correlation techniques and instrumentation have opened the path for versatile deep tissue microvasculature blood flow imaging systems. Despite this progress there remains a need for a completely noncontact, noninvasive device with high translatability from small/testing (animal) to large/target (human) subjects with trivial application on both. Accordingly, we discuss our newly developed setup which meets this demand, termed noncontact speckle contrast diffuse correlation tomography (nc_scDCT). The nc_scDCT provides fast, continuous, portable, noninvasive, and inexpensive acquisition of 3-D tomographic deep (up to 10 mm) tissue blood flow distributions with straightforward design and customization. The features presented include a finite-element-method implementation for incorporating complex tissue boundaries, fully noncontact hardware for avoiding tissue compression and interactions, rapid data collection with a diffuse speckle contrast method, reflectance-based design promoting experimental translation, extensibility to related techniques, and robust adjustable source and detector patterns and density for high resolution measurement with flexible regions of interest enabling unique application-specific setups. Validation is shown in the detection and characterization of both high and low contrasts in flow relative to the background using tissue phantoms with a pump-connected tube (high) and phantom spheres (low). Furthermore, in vivo validation of extracting spatiotemporal 3-D blood flow distributions and hyperemic response during forearm cuff occlusion is demonstrated. Finally, the success of instrument feasibility in clinical use is examined through the intraoperative imaging of mastectomy skin flap.

Liquid (or gas) chromatography coupled with mass spectrometry (LC-MS or GC-MS) allows quantitative comparison of biomolecular abundance in biological samples to help with the discovery of candidate biomarkers for complex diseases such as cancer. A fundamental challenge in using quantitative analysis of biomolecules by LC-MS or GC-MS for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Various contaminations present in cancerous tissues or adjacent non-cancerous constituents confound the characterization of molecular expression profiles and thus hinder the discovery of reliable biomarkers. We previously applied probabilistic purification model on a relatively small sample-size metabolomic data. In this study, we further apply probabilistic purification models on larger sample-size and multi-group metabolomic datasets acquired by analysis of liver tissues using both LC-MS and GC-MS. We demonstrate the advantages of incorporating purification models in retrieving underlying sources and reduce noise in metabolomic data. Furthermore, we investigate the benefit of the proposed models in improving our ability to detect changes in the level of metabolites among liver tissue from multiple groups (tumor, liver cirrhosis, and normal).

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In many applications, such as in metabolomics and image series analysis, alignment is simultaneously needed for multiple pairs. Because the underlying warping functions are often related, independent application of DTW to each pair is a sub-optimal solution. Yet, it is largely unknown how to efficiently conduct a joint alignment with all warping functions simultaneously considered, since any given warping function is constrained by the others and dynamic programming cannot be applied. In this paper, we show that the joint alignment problem can be transformed into a network flow problem and thus can be exactly and efficiently solved by the max flow algorithm, with a guarantee of global optimality. We name the proposed approach graphical time warping (GTW), emphasizing the graphical nature of the solution and that the dependency structure of the warping functions can be represented by a graph. Modifications of DTW, such as windowing and weighting, are readily derivable within GTW. We also discuss optimal tuning of parameters and hyperparameters in GTW. We illustrate the power of GTW using both synthetic data and a real case study of an astrocyte calcium movie.

Spintronics is regarded as a promising solution for resolving the major challenging issues related to the scaling of Si-based complementary metal–oxide–semiconductor (CMOS) technology as it offers the advantages of combing the spin and charge degrees of freedom. After decades of progress, the quintessence to achieve practical low-dissipation applications lies in the ability to manipulate magnetic states by electric field via several different physical mechanisms. Among them, the emergence of the spin-orbit coupling engineering has been shown to dramatically reduce energy dissipation and improve the performance as well as to multiply spintronic device possibilities and functionalities for a new generation of ultralow-power nonvolatile spintronic systems. This article provides a review of the current development including fundamental physics and experimental implementations of electric-field-controlled ferromagnetism in dilute magnetic semiconductors, voltage control of magnetic anisotropy, spin-orbit-torque-assisted magnetization switching, and antiferromagnetic (AFM) material-based spin-orbitronic systems. We provide an assessment in terms of scaling of energy, speed, and size. Finally, we offer an outlook of electric-field-controlled spintronic applications, particularly in view of their integration with CMOS to form hybrid spintronic circuits.

Studies associating changes in the levels of multiple biomolecules including proteins, glycans, glycoproteins, and metabolites with the onset of cancer have been widely investigated to identify clinically relevant diagnostic biomarkers. Advances in liquid or gas chromatography mass spectrometry (LC-MS, GC-MS) have enabled high-throughput qualitative and quantitative analysis of these biomolecules. While results from separate analyses of different biomolecules have been reported widely, the mutual information obtained by partly or fully combining them has been relatively unexplored. In this study, we investigate integrative analysis of proteins, N-glycans, and metabolites to take advantage of complementary information to improve the ability to distinguish cancer cases from controls. Specifically, support vector machine-recursive feature elimination algorithm is utilized to select a panel of proteins, N-glycans, and metabolites based on LC-MS and GC-MS data previously acquired by the analysis of blood samples from two cohorts in a liver cancer study. Improved performances are observed by integrative analysis compared to separate proteomic, glycomic, and metabolomic studies in distinguishing liver cancer cases from patients with liver cirrhosis.

Liquid (or gas) chromatography coupled with mass spectrometry (LC/GC-MS) allows quantitative comparison of biomolecular abundance in clinical samples to help with the discovery of candidate biomarkers for complex diseases such as cancer. A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of clinical samples. Various contaminations from related disease tissues or adjacent non-cancerous constituents in a sample confound the characterization of molecular expression profiles and thus hinder the discovery of reliable biomarkers. This issue has been raised and discussed in analysis of microarray and RNA-seq data in cancer genomics studies. To the best of our knowledge, the issue has not yet been rigorously addressed in analyzing LC/GC-MS data that are generated in a variety of omic studies including proteomics and metabolomics. Purification of LC/GC-MS based biomolecular expression profiles is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in clinical samples. In this study, we applied a topic model to computationally deconvolute each of LC/GC-MS based cancer expression profiles and infer the underlying sample-specific pure cancer profiles. We demonstrated the capability of the model in capturing mixture proportions of contaminants and cancer profiles on a synthetic LC-MS dataset. Improved performances were also achieved on experimental LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC).

Systems biology aims at unravelling the mechanisms of complex diseases by investigating how individual elements of the cell (e.g., genes, proteins, metabolites, etc.) interact with each other. Network-based methods provide an intuitive framework to model, characterize, and understand these interactions. To reconstruct a biological network, one can either query public databases for known interactions (knowledge-driven approach) or build a mathematical model to measure the associations from data (data-driven approach). In this paper, we propose a new network inference method, integrating knowledge and data-driven approaches. The method integrates prior biological knowledge (i.e., protein-protein interactions from BioGRID database) and a Gaussian graphical model (i.e., graphical LASSO algorithm) to construct robust and biologically relevant network. The network is then utilized to extract differential sub-networks between case and control groups using the result from a statistical analysis (e.g., logistic regression). We applied the proposed method on a proteomic dataset acquired by analysis of sera from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The differential sub-networks led to the identification of hub proteins and key pathways, whose relevance to HCC study has been confirmed by literature survey.

Recent technological advances have enabled the generation of various omic data (e.g., genomics, proteomics, metabolomics and glycomics) in a high-throughput manner. The integration of multi-omic data sets is desirable to unravel the complexity of a biological system. In this paper, we propose a new approach to investigate both inter and intra relationships for multi-omic data sets by using regularized canonical correlation analysis and graphical lasso. The application of this novel approach on real multi-omic data sets helps identify hub proteins and their neighbors that may be missed by typical statistical analysis to serve as biomarker candidates. Also, the integration of data from various cellular components (i.e., proteins, metabolites and glycans) offers the potential to discover more reliable biomarker candidates for complex disease.

UNLABELLED: Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal cell contamination and random background aberrations in tumor samples. We describe a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches to address the aforementioned challenges in DNA copy number analyses. In addition, the package provides a command-line interface for users with scripting and programming needs to incorporate or extend AISAIC to their customized analysis pipelines. This open-source multiplatform software offers several attractive features: (i) it implements a user friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects deletion types directly from copy number signals using a Bayes hypothesis test; (iii) it estimates the fraction of normal contamination for each sample; (iv) it produces unbiased null distribution of random background alterations by iterative aberration-exclusive permutations; and (v) it identifies significant consensus regions and the percentage of homozygous/hemizygous deletions across multiple samples. AISAIC also provides users with a parallel computing option to leverage ubiquitous multicore machines. AVAILABILITY AND IMPLEMENTATION: AISAIC is available as a Java application, with a user's guide and source code, at https://code.google.com/p/aisaic/.

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

Glycomics helps investigate the role glycosylation plays in complex diseases. Liquid chromatography (LC) coupled with mass spectrometry (MS) is routinely used to profile the glycans released from proteins in a biological sample. This enables us to compare observed glycans and their abundances among different biological samples to discover candidate biomarkers. One of the challenges in label-free LC/MS-based glycan profiling is the presence of various charge states and derived adduct ions. We propose a novel Glycan Profile Annotation (GPA) algorithm to automatically cluster and annotate these ions using a graphical model. Specifically, GPA aims to generate a list of unique neutral masses representing putative glycan composition derived from various charge states and multiple adducts. We demonstrate the performance of GPA in recognizing ions derived from the same glycan through analysis of LC/MS data from a serum biomarker discovery study. In addition, a simulation study is carried out to evaluate GPA's performance against existing tools in handling ambiguous cases.

One major challenge of systems biology is to infer biological networks. Classical graphical modeling methods that measure full conditional relationships between random variables may lead to unreliable results. This is partly due to the singular matrix problem when the number of variables exceeds the number of the samples. In this paper, we propose a low order partial correlation method to address this problem, trading off the small bias introduced by the low order constraint for the more reliable approximation of the network structure. Simulation results show that our proposed method works well under various conditions commonly seen in real applications and the inferred network faithfully uncovers the conditional independence relations among variables.

UNLABELLED: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases. AVAILABILITY: http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm.

MOTIVATION: Identification of somatic DNA copy number alterations (CNAs) and significant consensus events (SCEs) in cancer genomes is a main task in discovering potential cancer-driving genes such as oncogenes and tumor suppressors. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale with high resolution. However, existing copy number analysis methods are oblivious to normal cell contamination and cannot distinguish between contributions of cancerous and normal cells to the measured copy number signals. This contamination could significantly confound downstream analysis of CNAs and affect the power to detect SCEs in clinical samples. RESULTS: We report here a statistically principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletion type and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on two simulated datasets, two prostate cancer datasets and The Cancer Genome Atlas high-grade ovarian dataset, and obtained very promising results supported by the ground truth and biological plausibility. Moreover, based on a large number of comparative simulation studies, the proposed method gives significantly improved power to detect SCEs after in silico correction of normal tissue contamination. We develop a cross-platform open-source Java application that implements the whole pipeline of copy number analysis of heterogeneous cancer tissues including relevant processing steps. We also provide an R interface, bacomR, for running BACOM within the R environment, making it straightforward to include in existing data pipelines. AVAILABILITY: The cross-platform, stand-alone Java application, BACOM, the R interface, bacomR, all source code and the simulation data used in this article are freely available at authors' web site: http://www.cbil.ece.vt.edu/software.htm.

DNA copy number change is an important form of structural variations in human genomes. Detecting copy number changes using DNA array data is a challenging task due to high density genomic loci, low signal to noise ratios, and normal tissue contamination. We propose fused margin regression (FMR) method that combines a variable fusion rule and robust epsilon-insensitive loss criterion to approximate piecewise constant segments of the underlying copy number profile. We tested FMR on both simulation and real CGH and SNP array datasets, and observed competitively improved performance as compared to several widely-adopted existing methods.

Copy number change is an important form of structural variation in human genomes. Somatic copy number alterations can cause the acquisition of oncogenes and loss of tumor suppressor genes in tumorigenesis. Recent development of SNP array technology facilitates studies on copy number changes in a genome-wide scale with high resolution. However, tumor samples often consist of mixed cancer and normal cells. Such tissue heterogeneity poses as a serious hurdle to analyzing copy number changes and could confound subsequent marker identification and diagnostic classification rooted in specific cells. We report here a statistically-principled in silico approach to accurately estimate genomic deletions and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on three simulation and one real datasets and obtained highly promising results validated by the ground truth and figure of merit. We expect this newly developed method to be a useful tool in routine copy number analysis of heterogeneous tissues.

MOTIVATION: In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models. RESULTS: We address these objectives via maximum entropy conditional probability modeling (MECPM), coupled with a novel model structure search. Unlike neural networks and support vector machines (SVMs), MECPM makes explicit and is determined by the interactions that confer phenotype-predictive power. Our method identifies both a marker subset and the multiple k-way interactions between these markers. Additional key aspects are: (i) evaluation of a select subset of up to five-way interactions while retaining relatively low complexity; (ii) flexible single nucleotide polymorphism (SNP) coding (dominant, recessive) within each interaction; (iii) no mathematical interaction form assumed; (iv) model structure and order selection based on the Bayesian Information Criterion, which fairly compares interactions at different orders and automatically sets the experiment-wide significance level; (v) MECPM directly yields a phenotype-predictive model. MECPM was compared with a panel of methods on datasets with up to 1000 SNPs and up to eight embedded penetrance function (i.e. ground-truth) interactions, including a five-way, involving less than 20 SNPs. MECPM achieved improved sensitivity and specificity for detecting both ground-truth markers and interactions, compared with previous methods. AVAILABILITY: http://www.cbil.ece.vt.edu/ResearchOngoingSNP.htm

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data.

Switching dynamic linear models are commonly used methods to describe change in an evolving time series, where the switching ARIMA (autoregressive integrated moving average) model is a special case. Short-term forecasting of traffic flows is an essential part of intelligent traffic systems (ITS). We apply the switching ARIMA model to a traffic flow series. We have observed that the conventional switching model is inappropriate to describe the pattern changing. Thus, the variable of duration is introduced and we use the sigmoid function to describe the influence of duration to the transition probability of the patterns. Based on the switching ARIMA model, a forecasting algorithm is presented. We apply the proposed model to real data obtained from UTC/SCOOT systems in Beijing's traffic management bureau. The experiments show that our proposed model is applicable and effective.

This paper introduces a novel networked traffic parameter forecasting method. Based on the detailed analysis of the literature, the paper describes the fundamental ideas. Then we select a typical traffic network in Beijing City. In order to simplify the problem, we classify the links using clustering analysis and find the representative links in each group. Furthermore, we introduce the Markov chain model to predict the traffic parameter. EM algorithm is applied to estimate the parameters of mixed Gaussian distributions, i.e., means, covariances and mixing coefficients. According to the regression equations between the representative links and the other links in the same group, we can obtain all the predicted traffic parameters of all the link in the road network. The case studies using real data from UTC-SCOOT system in Beijing have proved the effectiveness and applicability of the proposed method.