Comparison of Batch Effect Removal Methods for High Dimensional Mass Cytometry Data

Suwalska, Aleksandra; Plessis, Nelita du; Spuy, Gian D. van der; Polańska, Joanna

doi:10.1007/978-3-031-07802-6_34

Cited by 1 publication

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The publicly available dataset [ 9 ] was preprocessed and visualized as a mISO plot [ 17 ] in two-dimensional space ( Figure 1 A). The appropriate cell types, identified by the experts, are indicated in the visualization with different colors.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry

Suwalska,

Polanska

2023

IJMS

Self Cite

View full text Add to dashboard Cite

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker’s intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

show abstract

Section: Resultsmentioning

confidence: 99%

“…A mISO plot [ 17 ] was generated to understand the data better. The mISO plot is a proposition of visualizing high-dimensional labeled datasets in the two-dimensional UMAP projection.…”

Section: Methodsmentioning

confidence: 99%