FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.

Motivation: Flow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference. Current FCM data analysis mainly relies on subjective manual gating analysis, which is difficult to be directly integrated with other automated computational methods.

A system-view of Bordetella pertussis booster vaccine responses in adults primed with whole-cell versus acellular vaccine in infancy.

The increased incidence of whooping cough worldwide suggests that current vaccination against Bordetella pertussis infection has limitations in quality and duration of protection. The resurgence of infection has been linked to the introduction of acellular vaccines (aP), which have an improved safety profile compared with the previously used whole-cell (wP) vaccines. To determine immunological differences between aP and wP priming in infancy, we performed a systems approach of the immune response to booster vaccination.

Unbiased analysis of peripheral blood mononuclear cells reveals CD4 T cell response to RSV matrix protein.

Respiratory syncytial virus (RSV) is the most important cause of respiratory tract illness especially in young infants that develop severe disease requiring hospitalization, and accounting for 74,000-126,000 admissions in the United States (Rezaee et al., 2017; Resch, 2017). Observations of neonatal and infant T cells suggest that they may express different immune markers compared to T-cells from older children.

Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.

High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive.