A novel data fusion method for the effective analysis of multiple panels of flow cytometry data

Abstract

Multicolour flow cytometry (MFC) is used to measure multiple cellular markers at the single-cell level. Cellular markers may be coloured with different panels of fluorescently-labelled antibodies to enable cell identification or the detection of activated cells in pre-defined, ‘gated’ specific cell subsets. The number of markers that can be used per measurement is technologically limited however, requiring every panel to be analysed in a separate aliquot measurement. The combined analyses of these dedicated panels may enhance the predictive ability of these measurements and could enrich the interpretation of the immunological information. Here we introduce a fusion method for MFC data, based on DAMACY (Discriminant Analysis of Multi-Aspect Cytometry data), which can combine information from complementary panels. This approach leads to both enhanced predictions and clearer interpretations in comparison with the analysis of separate measurements. We illustrate this method using two datasets: the response of neutrophils evoked by a systemic endotoxin challenge and the activated immune status of the innate cells, T cells and B cells in obese versus lean individuals. The data fusion approach was able to detect cells that do not individually show a difference between clinical phenotypes but do play a role in combination with other cells.

Flow cytometric evaluation of the neutrophil compartment in COVID-19 at hospital presentation: A normal response to an abnormal situation

“…Discriminant analysis of multi-aspect cytometry (DAMACY) fusion analysis was performed as described earlier. 33,34 DAMACY first describes the cellular distribution using 2D smoothed histograms of the first two principal components using 100 bins per principal component. The 2D smoothed histograms were created separately for fNLF − and for fNLF + and then fused together.…”

“…Variables are presented as frequencies and percentages for categor- Multidimensional analysis was performed by application of the DAMACY algorithm as described by us earlier. 33 The results of all statistical analyses are shown in Supporting Information Table S1.…”

High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning

“…Parallel to these experimental developments, computational techniques have also matured and found new applications in biology. Numerous studies have shown that independent single-cell experiments can be combined into a single augmented dataset, suggesting that multiple and partially overlapping datasets can be leveraged to obtain a single integrated and informative data matrix (4)(5)(6)(7)(8)(9). Machine learning techniques have also substantially improved in the last decade, with well-known applications to fields such as genomics, computer vision, or speech recognition (10,11).…”

Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

“…Parallel to these experimental developments, computational techniques have also matured and found new applications in biology. Numerous studies have shown that independent single-cell experiments can be combined into a single augmented dataset, suggesting that multiple and partially-overlapping datasets can be leveraged to obtain a single integrated and informative data matrix (Abdelaal et al 2019;Pedreira et al 2008;Leite Pereira et al 2019;Tinnevelt et al 2019;Haghverdi et al 2018;Stuart et al 2019). Machine learning techniques have also significantly improved in the last decade, with wellknown applications to fields such as genomics, computer vision or speech recognition (Eraslan et al 2019;LeCun, Bengio, and Hinton 2015).…”