Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis

TL;DR

This paper introduces Information Preserving Component Analysis (IPCA), a machine learning method that finds low-dimensional projections of flow cytometry data to better visualize and analyze high-dimensional relationships for cancer diagnosis and research.

Contribution

IPCA provides a novel linear projection technique that preserves high-dimensional data relationships, enhancing visualization and variable selection in flow cytometry analysis.

Findings

Enables visualization of high-dimensional data in low dimensions

Maintains relationships between different oncological data sets

Aids in cancer diagnosis and exploratory research

Abstract

Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma -- cancers of the hematopoietic system. By only viewing a series of 2-dimensional projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e. information) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just 2 at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow cytometric research. We refer to our method as Information Preserving Component Analysis (IPCA).