Browsing by Author "Chang, Andersen"

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    An automated respiratory data pipeline for waveform characteristic analysis
    (Wiley, 2023) Lusk, Savannah; Ward, Christopher S.; Chang, Andersen; Twitchell-Heyne, Avery; Fattig, Shaun; Allen, Genevera; Jankowsky, Joanna L.; Ray, Russell S.
    Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2-year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open-source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. Key points Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face-value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication-worthy graphs with statistics. We validate Breathe Easy with a terabyte-scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.
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    A CRISPR toolbox for generating intersectional genetic mouse models for functional, molecular, and anatomical circuit mapping
    (Springer Nature, 2022) Lusk, Savannah J.; McKinney, Andrew; Hunt, Patrick J.; Fahey, Paul G.; Patel, Jay; Chang, Andersen; Sun, Jenny J.; Martinez, Vena K.; Zhu, Ping Jun; Egbert, Jeremy R.; Allen, Genevera; Jiang, Xiaolong; Arenkiel, Benjamin R.; Tolias, Andreas S.; Costa-Mattioli, Mauro; Ray, Russell S.
    The functional understanding of genetic interaction networks and cellular mechanisms governing health and disease requires the dissection, and multifaceted study, of discrete cell subtypes in developing and adult animal models. Recombinase-driven expression of transgenic effector alleles represents a significant and powerful approach to delineate cell populations for functional, molecular, and anatomical studies. In addition to single recombinase systems, the expression of two recombinases in distinct, but partially overlapping, populations allows for more defined target expression. Although the application of this method is becoming increasingly popular, its experimental implementation has been broadly restricted to manipulations of a limited set of common alleles that are often commercially produced at great expense, with costs and technical challenges associated with production of intersectional mouse lines hindering customized approaches to many researchers. Here, we present a simplified CRISPR toolkit for rapid, inexpensive, and facile intersectional allele production.
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    Graphical Models for Functional Neuronal Connectivity
    (2022-09-29) Chang, Andersen; Allen, Genevera I
    With modern calcium imaging technology, activities of thousands of neurons can be recorded in vivo. These experiments can potentially provide new insights into intrinsic functional neuronal connectivity, defined as contemporaneous correlations between neuronal activities. As a common tool for estimating conditional dependencies in high-dimensional settings, graphical models are a natural choice for estimating functional connectivity networks. However, raw neuronal activity data presents several statistical challenges when applying graphical models. In this project, we develop new methods to estimate scientifically meaningful functional neuronal connectivity networks using the graphical model framework. One unique facet of calcium imaging data is that the important information lies in rare extreme value observations that indicate neuronal firing, rather than in the observations near the mean. Thus, a graphical modeling technique which finds conditional dependencies between the extreme values of features is required in order to estimate scientifically meaningful functional connectivity networks from calcium imaging data. To address this, we develop a novel class of graphical models, called the Subbotin graphical model, which can be used to find sparse conditional dependency structures for extreme values. We first derive the form of the Subbotin graphical model and show the conditions under which it is normalizable. We then study the empirical performance of the Subbotin graphical model on simulations as well as real-world data. Additionally, in many modern calcium imaging data sets, the complete data set is often comprised of multiple individual recording sessions of partially overlapping subsets of neurons. Thus, in order to estimate a graph on the full data, conditional dependencies in the missing portion of the covariance must be inferred; this is known as the graph quilting problem. We introduce several graph quilting methods that can be applied to for calcium imaging data, which utilize a low-rankness assumption to impute the full covariance matrix. Through several empirical studies, we investigate the efficacy of these methods for estimating graphical models for functional connectivity in the presence of missing joint observations. We also develop new methods for covariate and dynamic latent variable adjustment for functional neuronal data, which can arise from the presence of stimuli, unobserved neurons, and physical activity. We first introduce two models to infer functional connectivity from neuronal activity data after adjusting for dynamic latent brain states, and we use simulation studies to compare their performance to traditional, unconditional graphical models. We then propose a new method for sparse high-dimensional linear regression for extreme values, called the Extreme Lasso. We prove consistency and variable selection consistency for our regression method, and we analyze the theoretical impact of extreme value observations on the model parameter estimates using the concept of influence functions. We then study the empirical performance of the Extreme Lasso for selecting features associated with extreme values in high-dimensional regression. In our work, we demonstrate the applicability of each of our developed methods to finding functional connectivity networks through studies on several real-world calcium imaging data sets. In particular, we compare these network estimates to those from existing methods from both the graphical model and neuroscience literature, and we show that our methods can provide more scientifically sensible functional connectivity estimates.