Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures.

MOTIVATION: Non-canonical (or non-B) DNA are genomic regions whose three-dimensional conformation deviates from the canonical double helix. Non-B DNA play an important role in basic cellular processes and are associated with genomic instability, gene regulation, and oncogenesis. Experimental methods are low-throughput and can detect only a limited set of non-B DNA structures, while computational methods rely on non-B DNA base motifs, which are necessary but not sufficient indicators of non-B structures. Oxford Nanopore sequencing is an efficient and low-cost platform, but it is currently unknown whether nanopore reads can be used for identifying non-B structures. RESULTS: We build the first computational pipeline to predict non-B DNA structures from nanopore sequencing. We formalize non-B detection as a novelty detection problem and develop the GoFAE-DND, an autoencoder that uses goodness-of-fit (GoF) tests as a regularizer. A discriminative loss encourages non-B DNA to be poorly reconstructed and optimizing Gaussian GoF tests allows for the computation of P-values that indicate non-B structures. Based on whole genome nanopore sequencing of NA12878, we show that there exist significant differences between the timing of DNA translocation for non-B DNA bases compared with B-DNA. We demonstrate the efficacy of our approach through comparisons with novelty detection methods using experimental data and data synthesized from a new translocation time simulator. Experimental validations suggest that reliable detection of non-B DNA from nanopore sequencing is achievable. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/bayesomicslab/ONT-nonb-GoFAE-DND.

A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.

Traditional bulk RNA-sequencing of human pancreatic islets mainly reflects transcriptional response of major cell types. Single-cell RNA sequencing technology enables transcriptional characterization of individual cells, and thus makes it possible to detect cell types and subtypes. To tackle the heterogeneity of single-cell RNA-seq data, powerful and appropriate clustering is required to facilitate the discovery of cell types. In this paper, we propose a new clustering framework based on a graph-based model with various types of dissimilarity measures. We take the compositional nature of single-cell RNA-seq data into account and employ log-ratio transformations. The practical merit of the proposed method is demonstrated through the application to the centered log-ratio-transformed single-cell RNA-seq data for human pancreatic islets. The practical merit is also demonstrated through comparisons with existing single-cell clustering methods. The R-package for the proposed method can be found at https://github.com/Zhang-Data-Science-Research-Lab/LrSClust.

Network meta-regression for ordinal outcomes: Applications in comparing Crohn's disease treatments.

Crohn's disease (CD) is a life-long condition associated with recurrent relapses characterized by abdominal pain, weight loss, anemia, and persistent diarrhea. In the US, there are approximately 780 000 CD patients and 33 000 new cases added each year. In this article, we propose a new network meta-regression approach for modeling ordinal outcomes in order to assess the efficacy of treatments for CD. Specifically, we develop regression models based on aggregate covariates for the underlying cut points of the ordinal outcomes as well as for the variances of the random effects to capture heterogeneity across trials. Our proposed models are particularly useful for indirect comparisons of multiple treatments that have not been compared head-to-head within the network meta-analysis framework. Moreover, we introduce Pearson residuals and construct an invariant test statistic to evaluate goodness-of-fit in the setting of ordinal outcome data. A detailed case study demonstrating the usefulness of the proposed methodology is carried out using aggregate ordinal outcome data from 16 clinical trials for treating CD.

BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES.

Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a large number of ties. Under a general wear process model, we derive the likelihood of parameters. When the wear process is a Gamma process, the likelihood has a semi-closed form that allows posterior sampling to be carried out for the parameters, hence achieving model selection using Bayesian deviance information criterion. An innovative simulation algorithm via direct forward sampling and Gibbs sampling is developed to sample event times that may have ties in the presence of arbitrary covariates; this provides a tool to assess the precision of inference. An extensive simulation study is reported and a data set is used to further illustrate the proposed methodology.

Neuronal stability and drift across periods of sleep: premotor activity patterns in a vocal control nucleus of adult zebra finches.

How stable are neural activity patterns compared across periods of sleep? We evaluated this question in adult zebra finches, whose premotor neurons in the nucleus robustus arcopallialis (RA) exhibit sequences of bursts during daytime singing that are characterized by precise timing relative to song syllables. Each burst has a highly regulated pattern of spikes. We assessed these spike patterns in singing that occurred before and after periods of sleep. For about half of the neurons, one or more premotor bursts had changed after sleep, an average of 20% of all bursts across all RA neurons. After sleep, modified bursts were characterized by a discrete, albeit modest, loss of spikes with compensatory increases in spike intervals, but not changes in timing relative to the syllable. Changes in burst structure followed both interrupted bouts of sleep (1.5-3 h) and full nights of sleep, implicating sleep and not circadian cycle as mediating these effects. Changes in burst structure were also observed during the day, but far less frequently. In cases where multiple bursts in the sequence changed in a single cell, the sequence position of those bursts tended to cluster together. Bursts that did not show discrete changes in structure also showed changes in spike counts, but not biased toward losses. We hypothesize that changes in burst patterns during sleep represent active sculpting of the RA network, supporting auditory feedback-mediated song maintenance.

Representations of conspecific song by starling secondary forebrain auditory neurons: toward a hierarchical framework.

The functional organization giving rise to stimulus selectivity in higher-order auditory neurons remains under active study. We explored the selectivity for motifs, spectrotemporally distinct perceptual units in starling song, recording the responses of 96 caudomedial mesopallium (CMM) neurons in European starlings (Sturnus vulgaris) under awake-restrained and urethane-anesthetized conditions. A subset of neurons was highly selective between motifs. Selectivity was correlated with low spontaneous firing rates and high spike timing precision, and all but one of the selective neurons had similar spike waveforms. Neurons were further tested with stimuli in which the notes comprising the motifs were manipulated. Responses to most of the isolated notes were similar in amplitude, duration, and temporal pattern to the responses elicited by those notes in the context of the motif. For these neurons, we could accurately predict the responses to motifs from the sum of the responses to notes. Some notes were suppressed by the motif context, such that removing other notes from motifs unmasked additional excitation. Models of linear summation of note responses consistently outperformed spectrotemporal receptive field models in predicting responses to song stimuli. Tests with randomized sequences of notes confirmed the predictive power of these models. Whole notes gave better predictions than did note fragments. Thus in CMM, auditory objects (motifs) can be represented by a linear combination of excitation and suppression elicited by the note components of the object. We hypothesize that the receptive fields arise from selective convergence by inputs responding to specific spectrotemporal features of starling notes.

Template-based spike pattern identification with linear convolution and dynamic time warping.

Pattern identification for spiking activity, which is central to neurophysiological analysis, is complicated by variability in spiking at multiple timescales. Incorporating likelihood tests on the variability at two timescales, we developed an approach to identifying segments from continuous neurophysiological recordings that match preselected spike "templates." At smaller timescales, each component of the preselected pattern is represented by a linear filter. Local scores to measure the similarities between short data segments and the pattern components are computed as filter responses. At larger timescales, overall scores to measure the similarities between relatively long data segments and the entire pattern are computed by dynamic time warping, which combines the local similarity scores associated with the pattern components, optimizing over a range of intercomponent time intervals. Occurrences of the pattern are identified by local peaks in the overall similarity scores. This approach is developed for point process representations and binary representations of spiking activity, both deriving from a single underlying statistical model. Point process representations are suitable for highly reliable single-unit responses, whereas binary representations are preferred for more variable single-unit responses and multiunit responses. Testing with single units recorded from individual electrodes within the robust nucleus of the arcopallium of zebra finches and with recordings from an array placed within the motor cortex of macaque monkeys demonstrates that the approach can identify occurrences of specified patterns with good time precision in a broad range of neurophysiological data.

Pattern filtering for detection of neural activity, with examples from HVc activity during sleep in zebra finches.

The detection of patterned spiking activity is important in the study of neural coding. A pattern filtering approach is developed for pattern detection under the framework of point processes, which offers flexibility in combining temporal details and firing rates. The detection combines multiple steps of filtering in a coarse-to-fine manner. Under some conditional Poisson assumptions on the spiking activity, each filtering step is equivalent to classifying by likelihood ratios all the data segments as targets or as background sequences. Unlike previous studies, where global surrogate data were used to evaluate the statistical significance of the detected patterns, a localized p-test procedure is developed, which better accounts for firing modulation and nonstationarity in spiking activity. Common temporal structures of patterned activity are learned using an entropy-based alignment procedure, without relying on metrics or pair-wise alignment. Applications of pattern filtering to single, presumptive interneurons recorded in the nucleus HVc of zebra finch are illustrated. These demonstrate a match between the auditory-evoked response to playback of the individual bird's own song and spontaneous activity during sleep. Small temporal compression or expansion, or both, is required for optimal matching of spontaneous patterns to stimulus-evoked activity.