Flow cytometry research.

Look through our collection of publications to catch up on the newest trends, approaches, and innovations in the flow cytometry field.

Latest News

Automated cell detection poster

This poster from the ESCCA conference outlines the ECLIPSE algorithm, which automates the detection of abnormal cells in clinical samples. It improves diagnostic accuracy and speed by eliminating normal cells, allowing for the exact detection of minimal residual disease (MRD) in multiple myeloma.
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Advanced MRD Detection by ECLIPSE poster

This poster introduces ECLIPSE, an automated method for detecting minimal residual disease (MRD) in multiple myeloma. It enhances accuracy by filtering out normal cells, processing large datasets quickly and effectively.
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Eclipse Algorithm: Automated Disease Diagnosis in Flow Cytometry

The Eclipse algorithm is making flow cytometry automated. Eclipse can accurately identify disease-specific cells. It also analyzes unique marker expressions that are improving our understanding of immune responses. This innovation also improves diagnostics and insights into diseases like asthma and systemic endotoxin challenges.
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Flow Cytometry analysis: Approach to data pre-processing

This article brings to light a preprocessing technique for multicolor flow cytometry data. It addresses the problem of analyzing samples with different cell counts. This approach is making sure that every sample is valued equally, no matter the number of cells it contains. It means it is improving our understanding of the immune system and boosting the accuracy of classifications. It also indicates the critical need to factor in the multi-set nature of data for superior results in both immunological research and diagnostics.
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Flow Cytometry data analysis with OTflow

The rapid development of flow cytometry is capable of determining up to 50 parameters per cell. This requires sophisticated analysis techniques. This study uncover OTflow which is an innovative algorithm designed for the optimal transformation of data. OTflow points at avoiding typical pitfalls such as peak splitting in multivariate analysis. By ensuring a more accurate read of complex flow cytometry data, OTflow increases the precision of diagnostics.
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Single cell analysis: A guide to advanced multivariate techniques

This latest research shows the key steps for multivariate analysis of single cell data. It is the spotlight on white blood cells. Our study walks you through the crucial phases of study design, data preparation, and analytical methods, including clustering and discriminant analysis. This approach allows us to reveal the identity and function of cells, setting the stage for discovery in immunology and other fields.
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