Methods for analyzing tellurium imaging mass cytometry data

A Beginner's Guide to Analyzing and Visualizing Mass Cytometry Data.

Mass cytometry has revolutionized the study of cellular and phenotypic diversity, significantly expanding the number of phenotypic and functional characteristics that can be measured at the single-cell level. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. Many of these algorithms circumvent traditional approaches used in flow cytometric...

An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data

Azad, Ariful Ph.D., Purdue University, May 2014. An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data. Major Professor: Alex Pothen. Flow cytometry (FC) is a single-cell profiling platform for measuring the phenotypes (protein expressions) of individual cells from millions of cells in biological samples. In the last several years, FC has begun to employ high-throughput tec...

Signal and image processing methods for imaging mass spectrometry data

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.

Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by "manual gating" can be inefficient and unreliable in these high-dimensional settings, wh...

Gating mass cytometry data by deep learning

Motivation Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types...