Automated B-cell and plasma cell identification using unsupervised clustering by FlowSOM and an excel-based classification model.

Manual gating for plasma cell (PC) identification in multiparametric flow cytometry (MFC) is time-consuming and operator-dependent, especially when PCs are scarce. Artificial intelligence approaches such as unsupervised clustering (e.g., FlowSOM) map high-dimensional data that still require expert interpretation. The primary aim of this work was to develop a lightweight, transparent, spreadsheet-based algorithm for a classification model that integrates with automated clustering outputs for standardized B-cell and PC identification in research flow cytometry datasets. Bone marrow aspirates were stained with a standard BD OneFlow™ PC screening tube (CD38, CD56, β2-microglobulin, CD19, cyIgκ, cyIgλ, CD45, CD138) and acquired on a BD FACSLyric™ flow cytometer. FCS files were exported to CellEngine cytometry software and singlet nucleated events underwent FlowSOM clustering (8 clusters/sample). Cluster-level median fluorescence intensities (MFIs) were exported to an Excel "PC Trainer Classifier" that (i) normalizes markers to an in-sample B-cell anchor, (ii) computes a PCscore with CD138 as a hard gate and CD38 as a soft gate, plus secondary features (CD19↓, CD45↓, CD56↑), (iii) applies a forced core fallback (highest CD38/CD138 core score) when strict criteria yield no PCs, and (iv) derives a NEOscore (CD56↑, CD19↓, CD45↓) for neoplastic phenotype. The rule-based classifier was trained on expert assigned PC and B-cell clusters from 40 samples (30 clonal and 10 polyclonal). Validation was done on a new set of 52 samples, independent of the model. Elements of the Excel formula design and error-proofing were co-developed with ChatGPT (OpenAI); all outputs were verified by the authors. Across 52 validation cases (8 clusters/case; 416 clusters total), B-cell detection achieved: Sensitivity 0.902 (0.79-0.96), Specificity 0.984 (0.96-0.99), Precision 0.885 (0.77-0.94), Accuracy 0.973 (0.95-0.99) and F1 0.893. PC identification achieved: Sensitivity 0.651 (0.54-0.75), Specificity 0.828 (0.79-0.86), Precision 0.458 (0.37-0.55), Accuracy 0.796 (0.76-0.83) and F1 0.537. A transparent Excel-based classifier integrated with FlowSOM clustering enables highly reproducible B-cell identification and provides a structured approach to PC classification in research flow cytometry datasets. While the B-cell classifier demonstrated excellent discriminatory performance, PC identification yielded moderate sensitivity and precision, likely reflecting underlying biological and phenotypic heterogeneity. Consequently, the PC classification component is best interpreted as a triage or augmented-intelligence tool intended to support, rather than replace, expert assessment. This approach provides a structured and auditable framework that reduces operator dependency and improves inter-case harmonization. Its interpretability, low cost and portability make it particularly suited to research laboratories operating in resource-variable settings. Further optimisation and prospective validation may refine PC classification performance.