Publications by authors named "Anna C Gilbert"

9 Publications

  • Page 1 of 1

A rank-based marker selection method for high throughput scRNA-seq data.

BMC Bioinformatics 2020 Oct 23;21(1):477. Epub 2020 Oct 23.

Department of Mathematics, Yale University, 10 Hillhouse Ave, New Haven, 06511, USA.

Background: High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.

Results: We introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells.

Conclusions: According to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03641-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585212PMC
October 2020

White matter correlates of cognitive flexibility in youth with bipolar disorder and typically developing children and adolescents.

Psychiatry Res Neuroimaging 2020 Aug 29;305:111169. Epub 2020 Aug 29.

Pediatric Mood, Imaging, and NeuroDevelopment (PediMIND) Program, Emma Pendleton Bradley Hospital, East Providence, RI, USA; Division of Child Psychiatry, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Prior studies using behavioral tasks and neuroimaging have shown that children and adolescents with bipolar disorder (BD) have deficits in cognitive flexibility (CF)-defined as adaptation to changing rewards and punishments. However, no study, to our knowledge, has examined the white matter microstructural correlates of CF in youth with BD. To address this gap, we examined the relationship between CF assessed with the Cambridge Neuropsychological Testing Automated Battery (CANTAB)'s Intra-Extra Dimensional Set Shift task (ID/ED) and diffusion tensor imaging analyzed with FSL's preprocessing tools and Tract-Based Spatial Statistics (TBSS). We found a significantly different relationship between microstructural integrity of multiple white matter regions and CF performance in BD (n=28) and age-matched typically developing control (TDC) youths (n=26). Evaluation of the slopes of linear regressions in BD vs. TDC (ID/ED Simple Reversal error rate vs. fractional anisotropy) revealed significantly different slopes across the groups, indicating an aberrant relationship between CF and underlying white matter microstructure in youth with BD. These results underscore the importance of examining specific CF-neuroimaging relationships in BD youth. Future longitudinal studies could seek to define the white matter microstructural trajectories in BD vs. TDC, and relative to CF deficits and BD illness course.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pscychresns.2020.111169DOI Listing
August 2020

White matter correlates of cognitive flexibility in youth with bipolar disorder and typically developing children and adolescents.

Psychiatry Res Neuroimaging 2020 Aug 29;305:111169. Epub 2020 Aug 29.

Pediatric Mood, Imaging, and NeuroDevelopment (PediMIND) Program, Emma Pendleton Bradley Hospital, East Providence, RI, USA; Division of Child Psychiatry, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Prior studies using behavioral tasks and neuroimaging have shown that children and adolescents with bipolar disorder (BD) have deficits in cognitive flexibility (CF)-defined as adaptation to changing rewards and punishments. However, no study, to our knowledge, has examined the white matter microstructural correlates of CF in youth with BD. To address this gap, we examined the relationship between CF assessed with the Cambridge Neuropsychological Testing Automated Battery (CANTAB)'s Intra-Extra Dimensional Set Shift task (ID/ED) and diffusion tensor imaging analyzed with FSL's preprocessing tools and Tract-Based Spatial Statistics (TBSS). We found a significantly different relationship between microstructural integrity of multiple white matter regions and CF performance in BD (n=28) and age-matched typically developing control (TDC) youths (n=26). Evaluation of the slopes of linear regressions in BD vs. TDC (ID/ED Simple Reversal error rate vs. fractional anisotropy) revealed significantly different slopes across the groups, indicating an aberrant relationship between CF and underlying white matter microstructure in youth with BD. These results underscore the importance of examining specific CF-neuroimaging relationships in BD youth. Future longitudinal studies could seek to define the white matter microstructural trajectories in BD vs. TDC, and relative to CF deficits and BD illness course.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pscychresns.2020.111169DOI Listing
August 2020

White matter correlates of cognitive flexibility in youth with bipolar disorder and typically developing children and adolescents.

Psychiatry Res Neuroimaging 2020 Aug 29;305:111169. Epub 2020 Aug 29.

Pediatric Mood, Imaging, and NeuroDevelopment (PediMIND) Program, Emma Pendleton Bradley Hospital, East Providence, RI, USA; Division of Child Psychiatry, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Prior studies using behavioral tasks and neuroimaging have shown that children and adolescents with bipolar disorder (BD) have deficits in cognitive flexibility (CF)-defined as adaptation to changing rewards and punishments. However, no study, to our knowledge, has examined the white matter microstructural correlates of CF in youth with BD. To address this gap, we examined the relationship between CF assessed with the Cambridge Neuropsychological Testing Automated Battery (CANTAB)'s Intra-Extra Dimensional Set Shift task (ID/ED) and diffusion tensor imaging analyzed with FSL's preprocessing tools and Tract-Based Spatial Statistics (TBSS). We found a significantly different relationship between microstructural integrity of multiple white matter regions and CF performance in BD (n=28) and age-matched typically developing control (TDC) youths (n=26). Evaluation of the slopes of linear regressions in BD vs. TDC (ID/ED Simple Reversal error rate vs. fractional anisotropy) revealed significantly different slopes across the groups, indicating an aberrant relationship between CF and underlying white matter microstructure in youth with BD. These results underscore the importance of examining specific CF-neuroimaging relationships in BD youth. Future longitudinal studies could seek to define the white matter microstructural trajectories in BD vs. TDC, and relative to CF deficits and BD illness course.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pscychresns.2020.111169DOI Listing
August 2020

Imaging from the inside out: inverse scattering with photoactivated internal sources.

Opt Lett 2018 Jun;43(12):3005-3008

We propose a method to reconstruct the optical properties of a scattering medium with subwavelength resolution. The method is based on the solution to the inverse scattering problem with internal sources. Applications to photoactivated localization microscopy are described.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1364/OL.43.003005DOI Listing
June 2018

Fast joint design method for parallel excitation radiofrequency pulse and gradient waveforms considering off-resonance.

Magn Reson Med 2012 Jul 3;68(1):278-85. Epub 2012 May 3.

Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.

A fast parallel excitation pulse design algorithm to select and to order phase-encoding (PE) locations (also known as "spokes") of an Echo-Volumar excitation k-space trajectory considering B(0) field inhomogeneity is presented. Recently, other groups have conducted research to choose optimal PE locations, but the potential benefit of considering B(0) field inhomogeneity during PE location selection or their ordering has not been fully investigated. This article introduces a novel fast greedy algorithm to determine PE locations and their order that takes into account the off-resonance effects. Computer simulations of the proposed algorithm for B(1) field inhomogeneity correction demonstrate that it not only improves excitation accuracy but also provides an effective ordering of the PE locations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mrm.24311DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939078PMC
July 2012

Gradient-based image recovery methods from incomplete Fourier measurements.

IEEE Trans Image Process 2012 Jan 16;21(1):94-105. Epub 2011 Jun 16.

University of Maryland, College Park, MD 20742, USA.

A major problem in imaging applications such as magnetic resonance imaging and synthetic aperture radar is the task of trying to reconstruct an image with the smallest possible set of Fourier samples, every single one of which has a potential time and/or power cost. The theory of compressive sensing (CS) points to ways of exploiting inherent sparsity in such images in order to achieve accurate recovery using sub-Nyquist sampling schemes. Traditional CS approaches to this problem consist of solving total-variation (TV) minimization programs with Fourier measurement constraints or other variations thereof. This paper takes a different approach. Since the horizontal and vertical differences of a medical image are each more sparse or compressible than the corresponding TV image, CS methods will be more successful in recovering these differences individually. We develop an algorithm called GradientRec that uses a CS algorithm to recover the horizontal and vertical gradients and then estimates the original image from these gradients. We present two methods of solving the latter inverse problem, i.e., one based on least-square optimization and the other based on a generalized Poisson solver. After a thorough derivation of our complete algorithm, we present the results of various experiments that compare the effectiveness of the proposed method against other leading methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TIP.2011.2159803DOI Listing
January 2012

poolMC: smart pooling of mRNA samples in microarray experiments.

BMC Bioinformatics 2010 Jun 2;11:299. Epub 2010 Jun 2.

Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Background: Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements.

Results: A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment.

Conclusions: The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2105-11-299DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2900278PMC
June 2010