DeepAI AI Chat
Log In Sign Up

Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model

02/04/2016
by   Furong Huang, et al.
University of California, Irvine
Allen Institute
MIT
Microsoft
Politecnico di Torino
Howard Hughes Medical Institute
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/09/2022

Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics

A key problem in computational biology is discovering the gene expressio...
10/25/2022

A single-cell gene expression language model

Gene regulation is a dynamic process that connects genotype and phenotyp...
06/13/2018

Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks

Understanding cell identity is an important task in many biomedical area...
10/05/2020

Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data

A central goal in neurobiology is to relate the expression of genes to t...
03/03/2022

From local to global gene co-expression estimation using single-cell RNA-seq data

In genomics studies, the investigation of the gene relationship often br...
07/20/2020

Joint Learning of Discrete and Continuous Variability with Coupled Autoencoding Agents

Jointly identifying discrete and continuous factors of variability can h...
04/29/2022

Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics

The absence of a conventional association between the cell-cell cohabita...