Browsing by Subject "generalized linear model"
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Item Open Access A Probabilistic Approach to Receptive Field Mapping in the Frontal Eye Fields.(Frontiers in systems neuroscience, 2016-01) Mayo, J Patrick; Morrison, Robert M; Smith, Matthew AStudies of the neuronal mechanisms of perisaccadic vision often lack the resolution needed to determine important changes in receptive field (RF) structure. Such limited analytical power can lead to inaccurate descriptions of visuomotor processing. To address this issue, we developed a precise, probabilistic technique that uses a generalized linear model (GLM) for mapping the visual RFs of frontal eye field (FEF) neurons during stable fixation (Mayo et al., 2015). We previously found that full-field RF maps could be obtained using 1-8 dot stimuli presented at frame rates of 10-150 ms. FEF responses were generally robust to changes in the number of stimuli presented or the rate of presentation, which allowed us to visualize RFs over a range of spatial and temporal resolutions. Here, we compare the quality of RFs obtained over different stimulus and GLM parameters to facilitate future work on the detailed mapping of FEF RFs. We first evaluate the interactions between the number of stimuli presented per trial, the total number of trials, and the quality of RF mapping. Next, we vary the spatial resolution of our approach to illustrate the tradeoff between visualizing RF sub-structure and sampling at high resolutions. We then evaluate local smoothing as a possible correction for situations where under-sampling occurs. Finally, we provide a preliminary demonstration of the usefulness of a probabilistic approach for visualizing full-field perisaccadic RF shifts. Our results present a powerful, and perhaps necessary, framework for studying perisaccadic vision that is applicable to FEF and possibly other visuomotor regions of the brain.Item Open Access Generalized admixture mapping for complex traits.(G3 (Bethesda), 2013-07-08) Zhu, Bin; Ashley-Koch, Allison E; Dunson, David BAdmixture mapping is a popular tool to identify regions of the genome associated with traits in a recently admixed population. Existing methods have been developed primarily for identification of a single locus influencing a dichotomous trait within a case-control study design. We propose a generalized admixture mapping (GLEAM) approach, a flexible and powerful regression method for both quantitative and qualitative traits, which is able to test for association between the trait and local ancestries in multiple loci simultaneously and adjust for covariates. The new method is based on the generalized linear model and uses a quadratic normal moment prior to incorporate admixture prior information. Through simulation, we demonstrate that GLEAM achieves lower type I error rate and higher power than ANCESTRYMAP both for qualitative traits and more significantly for quantitative traits. We applied GLEAM to genome-wide SNP data from the Illumina African American panel derived from a cohort of black women participating in the Healthy Pregnancy, Healthy Baby study and identified a locus on chromosome 2 associated with the averaged maternal mean arterial pressure during 24 to 28 weeks of pregnancy.Item Open Access Novel Methods to Identify Chromatin Accessibility Differences Across Primates(2019) Edsall, Lee ElizabethOne of the aims of evolutionary biology is to identify gene regulatory regions (and the resulting level of expression) that evolved between species. The conventional method of analysis for this is to perform pairwise comparisons on data generated for each species. Software programs for this approach are mature and work well when there are only two species of interest. These same programs can be used when there are three species of interest. However, the analysis becomes more cumbersome and the statistical significance (p-value) difficult to calculate. Performing pairwise comparisons when there are more than three species have significant limitations. One is the exponential increase in the number of tests performed, greatly reducing the sensitivity after false discovery rate correction. For n species, (n-1) tests are performed on each region. Another limitation is the lack of a principled way to identify and classify genes (or regulatory regions) containing changes in multiple species.
To address these limitations, we developed a novel method of jointly modelling the data from all of the species using a negative binomial generalized linear model. In addition to providing a principled way of identifying and classifying sites with multiple changes, our method is more sensitive largely due to a substantial decrease in the number of tests performed. Our method jointly models all of the data in a single test, regardless of the number of species. As a result, the correction for number of independent tests performed is (n-1) times larger for the multiple pairwise method than for the joint modelling approach.
We applied this joint modelling approach to DNase-seq data generated from skin fibroblast cells from five primate species; human, chimpanzee, gorilla, orangutan, and rhesus macaque. We identified 89,744 DNase I Hypersensitive sites (DHS sites) that were comparable across all species, of which 41% (36,666) were classified as differential in one or more species. 30% of the differential sites (11,095) are likely due to a single change in chromatin accessibility in one species. Changes that likely occurred on the internal human-chimpanzee branch or human-chimpanzee-gorilla branch account for 15% (5,385) of the differential sites. 16% (6,034) of the differential sites contain changes that happened on either the human-chimpanzee-gorilla-orangutan internal branch or the rhesus macaque species branch. 32% (11,698) of the differential sites are due to multiple changes in chromatin accessibility (e.g., independent changes on the human and orangutan species branches).
The accuracy of this new approach was demonstrated by a high degree of concordance with an earlier study from our laboratory that analyzed data from human, chimpanzee, and rhesus macaque. Additionally, we performed a conventional pairwise analysis of the DHS sites from the five species and classified only 33% as differential, indicating decreased sensitivity compared to the joint modelling approach. Together, these results indicate that this novel joint modelling approach provides an improved method for comparative analysis of DNase-seq data.
Although we developed this method for DNase-seq data, we expect that it can be applied to other count-based data types such as ChIP-seq, ATAC-seq, and RNA-seq. We also expect that it can be applied to other experimental designs such as time-series, multi-tissue comparisons, and multiple developmental stage comparisons. The R script for performing the joint modelling analysis and instructions for modifying the script for use by other investigators are available in a GitHub repository (http://github.com/ledsall/2019primate).
Item Open Access SINGLE UNIT AND ENSEMBLE RESPONSE PROPERTIES OF THE GUSTATORY CORTEX IN THE AWAKE RAT(2007-08-10) Stapleton, Jennifer RebeccaMost studies of gustatory coding have been performed in either anesthetized or awake, passively stimulated rats. In this dissertation the influences of behavioral state on gustatory processing in awake rats are described. In the first set of experiments, the effects of non-contingent tastant delivery on the chemical tuning of single neurons were explored. Tastants were delivered non-contingently through intra-oral cannulas to restrained, non water-deprived rats while single unit responses were recorded from the gustatory cortex (GC). As the subjects' behavior progressed from acceptance to rejection of the tastants, the chemical tuning of the neurons changed as well. This suggests that the subjects' behavioral state powerfully influences gustatory processing. In the second set of experiments, rats were trained to lick for fluid reinforcement on an FR5 schedule while single unit activity was recorded from GC. In this case, the chemical tuning was much more stable. Under this paradigm, chemosensory responses were rapid (~ 150 ms) and broadly tuned. In the third study, it was found that ensembles of GC neurons could discriminate between tastants and their concentrations on a single trial basis, and such discrimination was accomplished with a combination of rate and temporal coding. Ensembles of GC neurons also anticipated the identity of the upcoming stimulus when the tastant delivery was predictable. Finally, it was found that ensembles of GC neurons could discriminate between the bitter stimuli nicotine and quinine. Nicotine is both a bitter tastant and a trigeminal stimulant, and when the acetylcholine receptors in the lingual epithelium were blocked with mecamylamine, the ensembles failed to discriminate nicotine from quinine.