11–26 Nov 2021
Europe/Budapest timezone

Life beyond the pixels: single-cell analysis using machine learning and image analysis methods

Not scheduled
20m
Online lecture

Speaker

Dr Péter Horvath (BRC Szeged, Hungary)

Description

In this talk I will give an overview of the computational steps in the analysis of a single cell-based large-scale microscopy experiments using deep learning techniques. First, I will present a novel microscopic image
correction method designed to eliminate illumination and uneven background effects. New single-cell image segmentation
methods will be presented using differential geometry, energy minimization and deep learning
methods (www.nucleaizer.org). I will discuss the Advanced Cell Classifier (ACC) (www.cellclassifier.org), a
machine learning software tool capable of identifying cellular phenotypes based on features
extracted from the image. It provides an interface for a user to efficiently train machine
learning methods to predict various phenotypes. For cases where discrete cell-based decisions
are not suitable, we propose a method to use multi-parametric regression to analyze
continuous biological phenomena. To improve the learning speed and accuracy, we propose
an active learning scheme that selects the most informative cell samples.
Our recently developed single-cell isolation methods, based on laser-microcapturing and
patch clamping, utilize the selection and extraction of specific cell(s) using the above machine
learning models. I will show that we successfully performed DNA and RNA sequencing,
dPCR, and targeted electrophysiology measurements on the selected cells.
Finally I will show our results in the COVID-19 fight using deep learning methods (Daly etal Science).

Primary author

Dr Péter Horvath (BRC Szeged, Hungary)

Presentation materials

There are no materials yet.