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High-throughput assays have been developed to measure DNA methylation, among which bisulfite-based sequencing (BS-seq) and microarray technologies are the most popular for genome-wide profiling. A major goal in DNA methylation analysis is the detection of differentially methylated genomic regions under two different conditions. To accomplish this, many state-of-the-art methods have been proposed in the past few years; only a handful of these methods are capable of analyzing both types of data (BS-seq and microarray), though. On the other hand, covariates, such as sex and age, are known to be potentially influential on DNA methylation; and thus, it would be important to adjust for their effects on differential methylation analysis. In this chapter, we describe a Bayesian curve credible bands approach and the accompanying software, BCurve, for detecting differentially methylated regions for data generated from either microarray or BS-Seq. The unified theme underlying the analysis of these two different types of data is the model that accounts for correlation between DNA methylation in nearby sites, covariates, and between-sample variability. The BCurve R software package also provides tools for simulating both microarray and BS-seq data, which can be useful for facilitating comparisons of methods given the known "gold standard" in the simulated data. We provide detailed description of the main functions in BCurve and demonstrate the utility of the package for analyzing data from both platforms using simulated data from the functions provided in the package. Analyses of two real datasets, one from BS-seq and one from microarray, are also furnished to further illustrate the capability of BCurve.

The rate of coal mine accidents in China is still high and most coal mine accidents are caused by human unsafe behavior, and the correction of the behavior is, therefore, paramount. In this article, a group dynamics field model and a hierarchical index system of the group dynamics factors of the unsafe behavior of coal miners are established. The internal and external dynamics of groups are analyzed and the importance of each factor is calculated and determined. On this basis, suggested correction measures are put forward. Then, in combination with a questionnaire, the corrective measures of unsafe behaviors are determined and simulated. The results show that, while the correction of unsafe behaviors both in progress and after implementation can achieve good results, the former is more effective than the latter. Via the present research, both unsafe behaviors and the occurrence of coal mine accidents can be effectively prevented, and the safety of coal mine production can be ensured.

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

The prevalence of dropout events is a serious problem for single-cell Hi-C (scHiC) data due to insufficient sequencing depth and data coverage, which brings difficulties in downstream studies such as clustering and structural analysis. Complicating things further is the fact that dropouts are confounded with structural zeros due to underlying properties, leading to observed zeros being a mixture of both types of events. Although a great deal of progress has been made in imputing dropout events for single cell RNA-sequencing (RNA-seq) data, little has been done in identifying structural zeros and imputing dropouts for scHiC data. In this paper, we adapted several methods from the single-cell RNA-seq literature for inference on observed zeros in scHiC data and evaluated their effectiveness. Through an extensive simulation study and real data analysis, we have shown that a couple of the adapted single-cell RNA-seq algorithms can be powerful for correctly identifying structural zeros and accurately imputing dropout values. Downstream analysis using the imputed values showed considerable improvement for clustering cells of the same types together over clustering results before imputation.

Whole genome profiling of differential DNA methylation between diseased and normal samples has significant implications in research to understand the role of epigenetic regulations of cells. In recent years, the development of bisulfite sequencing (BS-seq) based molecular technology has enabled the measurement of DNA methylation at a nucleotide resolution throughout the genome. Given the availability of this new type of DNA methylation data with certain features challenging traditional analytical methods such as the Fisher exact rest, a number of new statistical methods have recently been proposed for analyzing them. However, the relative performances of many of these methods are still not fully understood, despite a couple of recent review papers. To arm researchers with the knowledge so that they can select the best suited method and software for analyzing their data, in this paper, we selected and evaluated four methods that are able to account for between-sample variability, a prominent feature of DNA methylation data. The commonality and differences of these methods in terms of several important characteristics, including modeling, data filtering, statistical testing, and covariates handling, were reviewed. Simulation studies as well as applications to two datasets were carried out to compare and contrast the methods. To be as unbiased as possible, our simulation modeling took into account characteristics of real data but did not use any of the specific models in these four methods.