Dataset Information

Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm.

ABSTRACT:

Background

The Flow-Self Organizing Maps (FlowSOM) artificial intelligence (AI) program, available within the Bioconductor open-source R-project, allows for an unsupervised visualization and interpretation of multiparameter flow cytometry (MFC) data.

Methods

Applied to a reference merged file from 11 normal bone marrows (BM) analyzed with an MFC panel targeting erythropoiesis, FlowSOM allowed to identify six subpopulations of erythropoietic precursors (EPs). In order to find out how this program would help in the characterization of abnormalities in erythropoiesis, MFC data from list-mode files of 16 patients (5 with non-clonal anemia and 11 with myelodysplastic syndrome [MDS] at diagnosis) were analyzed.

Results

Unsupervised FlowSOM analysis identified 18 additional subsets of EPs not present in the merged normal BM samples. Most of them involved subtle unexpected and previously unreported modifications in CD36 and/or CD71 antigen expression and in side scatter characteristics. Three patterns were observed in MDS patient samples: i) EPs with decreased proliferation and abnormal proliferating precursors, ii) EPs with a normal proliferating fraction and maturation defects in late precursors, and iii) EPs with a reduced erythropoietic fraction but mostly normal patterns suggesting that erythropoiesis was less affected. Additionally, analysis of sequential samples from an MDS patient under treatment showed a decrease of abnormal subsets after azacytidine treatment and near normalization after allogeneic hematopoietic stem-cell transplantation.

Conclusion

Unsupervised clustering analysis of MFC data discloses subtle alterations in erythropoiesis not detectable by cytology nor FCM supervised analysis. This novel AI analytical approach sheds some new light on the pathophysiology of these conditions.

SUBMITTER: Porwit A

PROVIDER: S-EPMC9306598 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.