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Cytometry plays essential roles in immunology and oncology. Recent advancements in cellular imaging allow more detailed characterization of cells by labeling each cell with multiple protein markers. The increase of dimensionality makes manual analysis challenging. Clustering algorithms provide a means for phenotyping high-dimensional cell populations in an unsupervised manner for downstream analysis. The choice and usability of the methods are critical in practice. Literature provided comprehensive studies on those topics using publicly available flow cytometry data, which validated cell phenotypes by those methods against manual gated cell populations. In order to extend the knowledge for identification of cell phenotypes including unknown cell populations in our dataset, we conducted an exploratory study using clinical relevant tissue types as reference standard. Using our in-house database of multiplexed immunofluorescence images of breast cancer tissue microarrays (TMAs), we experimented with two commonly used algorithms (PhenoGraph and FlowSOM). Our pipeline includes: 1) cell phenotyping using Phenograph/FlowSOM; 2) clustering TMA cores into four groups using the percentage of each cell phenotypes with the algorithms (PhenoGraph/Spectral/K-means); 3) comparing the tissue groups to clinically relevant subtypes that were manually assigned based on the immunohistochemistry scores of serial sections. We experimented with different hyperparameter settings and input markers. Cell phenotypes using Phenograph with 10 markers and tissue clustering using Spectral yielded the highest mean F-measure (average over four tissue subtypes) of 0.71. In general, our results showed that cell phenotypes by Phenograph yielded better performance with larger variations than FlowSOM, which gives very consistent results.
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