MOTIVATION: Hi-C technology has been developed to profile genome-wide chromosome conformation. So far Hi-C data have been generated from a large compendium of different cell types and different tissue types. Among different chromatin conformation units, chromatin loops were found to play a key role in gene regulation across different cell types. While many different loop calling algorithms have been developed, most loop callers identified shared loops as opposed to cell-type-specific loops. RESULTS: We propose SSSHiC, a new loop calling algorithm based on significance in scale space, which can be used to understand data at different levels of resolution. By applying SSSHiC to neuronal and glial Hi-C data, we detected more loops that are potentially engaged in cell-type-specific gene regulation. Compared with other loop callers, such as Mustache, these loops were more frequently anchored to gene promoters of cellular marker genes and had better APA scores. Therefore, our results suggest that SSSHiC can effectively capture loops that contain more gene regulatory information. AVAILABILITY AND IMPLEMENTATION: The Hi-C data used in this study can be accessed through the PsychENCODE Knowledge Portal at https://www.synapse.org/#! Synapse: syn21760712. The code utilized for Curvature SSS cited in this study is available at https://github.com/jsmarron/MarronMatlabSoftware/blob/master/Matlab9/Matlab9Combined.zip. All custom code used in this research can be found in the GitHub repository: https://github.com/jerryliu01998/HiC. The code has also been submitted to Code Ocean with the doi: 10.24433/CO.1912913.v1.

High throughput -omics technologies facilitate the investigation of regulatory mechanisms of complex diseases. Along this line, scientists develop promising tools and methods to extend our understanding at the molecular and functional levels. To this end, miRcorrNet tool performs integrative analysis of microRNA (miRNA) and gene expression profiles via machine learning (ML) approach to identify significant miRNA groups and their associated target genes. In this study, we propose miRcorrNetPro tool, which extends miRcorrNet by tracking group scoring, ranking and other information through the cross-validation iterations. Heatmap visualizations enable deep novel insights into the collective behavior of clusters of groups in cellular signaling and hence facilitate detection of potential biomarkers for the disease under investigation. Although miRcorrNetPro is designed as a generic tool, here we present our findings and potential miRNA biomarkers for Breast Cancer (BRCA). The miRcorrNetPro tool and all other supplementary files are available at https://github.com/Miray-Unlu/miRcorrNetPro.

BACKGROUND: Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-inflated or over-dispersed models are based on aggregations at either the gene or the cell level. However, they typically lose accuracy due to a too crude aggregation at those two levels. RESULTS: We avoid the crude approximations entailed by such aggregation through proposing an independent Poisson distribution (IPD) particularly at each individual entry in the scRNA-seq data matrix. This approach naturally and intuitively models the large number of zeros as matrix entries with a very small Poisson parameter. The critical challenge of cell clustering is approached via a novel data representation as Departures from a simple homogeneous IPD (DIPD) to capture the per-gene-per-cell intrinsic heterogeneity generated by cell clusters. Our experiments using real data and crafted experiments show that using DIPD as a data representation for scRNA-seq data can uncover novel cell subtypes that are missed or can only be found by careful parameter tuning using conventional methods. CONCLUSIONS: This new method has multiple advantages, including (1) no need for prior feature selection or manual optimization of hyperparameters; (2) flexibility to combine with and improve upon other methods, such as Seurat. Another novel contribution is the use of crafted experiments as part of the validation of our newly developed DIPD-based clustering pipeline. This new clustering pipeline is implemented in the R (CRAN) package scpoisson.

A general asymptotic framework is developed for studying consistency properties of principal component analysis (PCA). Our framework includes several previously studied domains of asymptotics as special cases and allows one to investigate interesting connections and transitions among the various domains. More importantly, it enables us to investigate asymptotic scenarios that have not been considered before, and gain new insights into the consistency, subspace consistency and strong inconsistency regions of PCA and the boundaries among them. We also establish the corresponding convergence rate within each region. Under general spike covariance models, the dimension (or number of variables) discourages the consistency of PCA, while the sample size and spike information (the relative size of the population eigenvalues) encourage PCA consistency. Our framework nicely illustrates the relationship among these three types of information in terms of dimension, sample size and spike size, and rigorously characterizes how their relationships affect PCA consistency.

UNLABELLED: R/DWD is an extensible package for classification. It is built based on a recently developed powerful classification method called distance weighted discrimination (DWD). DWD is related to, and has been shown to be superior to, the support vector machine in situations that are fundamental to bioinformatics, such as very high dimensional data. DWD has proven to be very useful for several fundamental bioinformatics tasks, including classification, data visualization and removal of biases, such as batch effects. Earlier DWD implementations, however, relied on Matlab, which is not free and requires a license. The major contribution of the R/DWD package is an implementation that is completely in R and thus can be used without any requirements for licensing or software purchase. In addition, R/DWD also provides efficient solvers for second-order-cone-programming and quadratic programming. AVAILABILITY AND IMPLEMENTATION: The package is freely available from cran.r-project.org.

BACKGROUND: Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. RESULTS: Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. CONCLUSION: ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration.

BACKGROUND: Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without the need to establish absolute standard curves for each and every target. To achieve reliable quantification all primer pairs or array probes must perform with the same efficiency. RESULTS: Our results indicate that QPCR primer-pairs differ significantly both in reliability and efficiency. They can only be used in an array format if the raw data (so called CT values for real-time QPCR) are transformed to take these differences into account. We developed a novel method to obtain efficiency-adjusted CT values. We introduce transformed confidence intervals as a novel measure to identify unreliable primers. We introduce a robust clustering algorithm to combine efficiencies of groups of probes, and our results indicate that using n < 10 cluster-based mean efficiencies is comparable to using individually determined efficiency adjustments for each primer pair (N = 96-1024). CONCLUSIONS: Careful estimation of primer efficiency is necessary to avoid significant measurement inaccuracies. Transformed confidence intervals are a novel method to assess and interpret the reliability of an efficiency estimate in a high throughput format. Efficiency clustering as developed here serves as a compromise between the imprecision in assuming uniform efficiency, and the computational complexity and danger of over-fitting when using individually determined efficiencies.

One goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with single objects, analysis of multi-object complexes presents new challenges related to alignment and pose. In this paper, we present a methodology for discriminant analysis of multiple objects represented by sampled medial manifolds. Non-euclidean metrics that describe geodesic distances between sets of sampled representations are used for alignment and discrimination. Our choice of discriminant method is the distance-weighted discriminant because of its generalization ability in high-dimensional, low sample size settings. Using an unbiased, soft discrimination score, we associate a statistical hypothesis test with the discrimination results. We explore the effectiveness of different choices of features as input to the discriminant analysis, using measures like volume, pose, shape, and the combination of pose and shape. Our method is applied to a longitudinal pediatric autism study with 10 subcortical brain structures in a population of 70 subjects. It is shown that the choices of type of global alignment and of intrinsic versus extrinsic shape features, the latter being sensitive to relative pose, are crucial factors for group discrimination and also for explaining the nature of shape change in terms of the application domain.

An important goal of statistical shape analysis is the discrimination between populations of objects, exploring group differences in morphology not explained by standard volumetric analysis. Certain applications additionally require analysis of objects in their embedding context by joint statistical analysis of sets of interrelated objects. In this paper, we present a framework for discriminant analysis of populations of 3-D multi-object sets. In view of the driving medical applications, a skeletal object parametrization of shape is chosen since it naturally encodes thickening, bending and twisting. In a multi-object setting, we not only consider a joint analysis of sets of shapes but also must take into account differences in pose. Statistics on features of medial descriptions and pose parameters, which include rotational frames and distances, uses a Riemannian symmetric space instead of the standard Euclidean metric. Our choice of discriminant method is the distance weighted discriminant (DWD) because of its generalization ability in high dimensional, low sample size settings. Joint analysis of 10 subcortical brain structures in a pediatric autism study demonstrates that multi-object analysis of shape results in a better group discrimination than pose, and that the combination of pose and shape performs better than shape alone. Finally, given a discriminating axis of shape and pose, we can visualize the differences between the populations.

Numerous studies have shown that the process of packet arrivals from Web traffic exhibits strong long-range dependence, which makes it not amenable to be described using the convenient but necessarily short-range dependent framework of Poisson modeling. However, Web traffic is ultimately driven by independent human behavior, so it seems natural to search for an underlying "seed process", consistent with Poissonity, indirectly driving the packet arrivals of Web traffic. Our study examines Web traffic at different levels of packet aggregation, using powerful statistical analysis tools for identifying the finest level that can be effectively modeled using a homogeneous Poisson process. We show that the arrivals of HTTP responses, TCP connections and Web pages do not provide a satisfactory seed process. However, we find Poissonity in the arrivals of "navigation bursts". A navigation burst is a tightly-spaced sequence of Web pages downloaded by the same Web client, which can be explained by fast navigation through several pages before reaching relevant content. Our analysis suggests that the start times of such navigation bursts, which we identify by detecting user think times between 12 and 30 seconds, can be effectively modeled as a homogeneous Poisson process. We believe that our methodology can be extended to other complex modeling problems where finding Poissonity can greatly simplify parsimonious modeling.

MOTIVATION: Systematic differences due to experimental features of microarray experiments are present in most large microarray data sets. Many different experimental features can cause biases including different sources of RNA, different production lots of microarrays or different microarray platforms. These systematic effects present a substantial hurdle to the analysis of microarray data. RESULTS: We present here a new method for the identification and adjustment of systematic biases that are present within microarray data sets. Our approach is based on modern statistical discrimination methods and is shown to be very effective in removing systematic biases present in a previously published breast tumor cDNA microarray data set. The new method of 'Distance Weighted Discrimination (DWD)' is shown to be better than Support Vector Machines and Singular Value Decomposition for the adjustment of systematic microarray effects. In addition, it is shown to be of general use as a tool for the discrimination of systematic problems present in microarray data sets, including the merging of two breast tumor data sets completed on different microarray platforms. AVAILABILITY: Matlab software to perform DWD can be retrieved from https://genome.unc.edu/pubsup/dwd/

This paper presents a multiscale framework based on a medial representation for the segmentation and shape characterization of anatomical objects in medical imagery. The segmentation procedure is based on a Bayesian deformable templates methodology in which the prior information about the geometry and shape of anatomical objects is incorporated via the construction of exemplary templates. The anatomical variability is accommodated in the Bayesian framework by defining probabilistic transformations on these templates. The transformations, thus, defined are parameterized directly in terms of natural shape operations, such as growth and bending, and their locations. A preliminary validation study of the segmentation procedure is presented. We also present a novel statistical shape analysis approach based on the medial descriptions that examines shape via separate intuitive categories, such as global variability at the coarse scale and localized variability at the fine scale. We show that the method can be used to statistically describe shape variability in intuitive terms such as growing and bending.

A central feature of the evolution of large software systems is that change-which is necessary to add new functionality, accommodate new hardware, and repair faults-becomes increasingly difficult over time. We approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteen-plus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed.

This paper is an attempt to understand the processes by which software ages. We define code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: For instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is, on the average, a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight.