@booklet {67218,
	title = {Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model},
	year = {2016},
	abstract = {<p>Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles. While the single-cell techniques are extremely powerful and hold great promise, they are currently still labor intensive, have a high cost per cell, and, most importantly, do not provide information on spatial distribution of cell types in specific regions of the brain. We propose a complementary approach that uses computational methods to infer the cell types and their gene expression profiles through analysis of brain-wide single-cell resolution in situ hybridization (ISH) imagery contained in the Allen Brain Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH image for each gene and model it as a spatial point process mixture, whose mixture weights are given by the cell types which express that gene. By fitting a point process mixture model jointly to the ISH images, we infer both the spatial point process distribution for each cell type and their gene expression profile. We validate our predictions of cell type-specific gene expression profiles using single cell RNA sequencing data, recently published for the mouse somatosensory cortex. Jointly with the gene expression profiles, cell features such as cell size, orientation, intensity and local density level are inferred per cell type.</p>
},
	author = {Furong Huang and Animashree Anandkumar and Christian Borgs and Jennifer Chayes and Ernest Fraenkel and Michael Hawrylycz and Ed Lein and Alessandro Ingrosso and Srinivas Turaga}
}
@article {46353,
	title = {Cluster analysis and robust use of full-field models for sonar beamforming},
	journal = {Journal of  Acoustical Society of America },
	volume = {120},
	year = {2006},
	month = {11/2006},
	pages = { 2635{\textendash}2647},
	type = {Journal Article},
	chapter = {2635},
	abstract = {<p>Multipath propagation in shallow water can lead to mismatch losses when single-path replicas are\&nbsp;usedfor horizontal array\&nbsp;beamforming.Matched field processing(MFP) seeks to remedy this by\&nbsp;using\&nbsp;full-fieldacoustic propagationmodels to predict the multipath arrival structure. Ideally MFP can give source localization in range and depth as well as detection gains but\&nbsp;robustly\&nbsp;estimating range and depth is difficult in practice. The approach described here seeks to collapse\&nbsp;full-field\&nbsp;replica outputs to bearing which is\&nbsp;robustly\&nbsp;estimated while retaining any signal gains provided by the\&nbsp;full-field\&nbsp;model.Clusteranalysis\&nbsp;is\&nbsp;used\&nbsp;to group together\&nbsp;full-field\&nbsp;replicas with similar responses. This yields a less redundant {\textquotedblleft}sampled field{\textquotedblright} describing a set of representative multipath structures for each bearing. A detection algorithm is introduced that\&nbsp;uses\&nbsp;clustering\&nbsp;to collapse\&nbsp;beamformer\&nbsp;outputs to bearing such that signal gains are retained while increases in the noise floor are minimized. Horizontal array data from SWELLEX-96 are\&nbsp;used\&nbsp;to demonstrate the detection benefits of sampled field as compared to single-pathbeamforming.</p>
},
	issn = {0001-4966},
	doi = {10.1121/1.2346128},
	author = {Brian Tracey and  Nigel Lee and Srinivas Turaga}
}
