Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images

06/24/2018
by   Mousumi Roy, et al.
0

Cellular phenotypic features derived from histopathology images are the basis of pathologic diagnosis and are thought to be related to underlying molecular profiles. Due to overwhelming cell numbers and population heterogeneity, it remains challenging to quantitatively compute and compare features of cells with distinct molecular signatures. In this study, we propose a self-reliant and efficient analysis framework that supports quantitative analysis of cellular phenotypic difference across distinct molecular groups. To demonstrate efficacy, we quantitatively analyze astrocytomas that are molecularly characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype (WT) using imaging data from The Cancer Genome Atlas database. Representative cell instances that are phenotypically different between these two groups are retrieved after segmentation, feature computation, data pruning, dimensionality reduction, and unsupervised clustering. Our analysis is generic and can be applied to a wide set of cell-based biomedical research.

READ FULL TEXT

page 2

page 3

page 4

research
08/17/2022

Deep Learning Enabled Time-Lapse 3D Cell Analysis

This paper presents a method for time-lapse 3D cell analysis. Specifical...
research
08/20/2023

Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures

Crohn's disease (CD) is a chronic and relapsing inflammatory condition t...
research
02/26/2018

DropLasso: A robust variant of Lasso for single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to mea...
research
02/06/2018

Cellular Cohomology in Homotopy Type Theory

We present a development of cellular cohomology in homotopy type theory....
research
08/11/2021

Predicting Molecular Phenotypes with Single Cell RNA Sequencing Data: an Assessment of Unsupervised Machine Learning Models

According to the National Cancer Institute, there were 9.5 million cance...
research
06/15/2023

Multi-omics Prediction from High-content Cellular Imaging with Deep Learning

High-content cellular imaging, transcriptomics, and proteomics data prov...
research
09/29/2022

A canonical correlation-based framework for performance analysis of radio access networks

Data driven optimization and machine learning based performance diagnost...

Please sign up or login with your details

Forgot password? Click here to reset