A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations

Roca, Carlos P.; Burton, Oliver T.; Neumann, Julika; Tareen, Samar; Whyte, Carly E.; Gergelits, Václav; Veiga, Rafael Valente; Humblet-Baron, Stéphanie; Liston, Adrian

doi:10.1016/j.crmeth.2022.100390

Cited by 16 publications

(12 citation statements)

References 32 publications

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“…Hence, we examined to what extent this already defined artificial microglia subpopulation was induced in our pipeline and whether other immune cells were affected. To determine whether the embeddings of cells differed based on inhibitor exposure, we performed a cross-entropy test 29 on immune cell subsets and the non-immune cell subset. This analysis revealed that granulocytes (composed of the neutrophil, basophil, and mast cell populations), macrophages, microglia, and monocytes were affected by enzymatic dissociation ( Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…All other populations were subset individually. A cross entropy test 29 was conducted in each dataset to determine whether the UMAP embeddings of inhibitor-treated cells and vehicle-treated cells differed significantly from one another, and the Kullback-Leibler divergences and Holm-adjusted p- values are shown for each comparison. Artificial activation modules were taken from differential expression analyses from pseudobulked single-cell datasets in ref.…”

Section: Methodsmentioning

confidence: 99%

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Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Boles,

Uriarte Huarte,

Tansey

2023

Preprint

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Inflammation is a realized detriment to brain health in a growing number of neurological diseases, but querying neuroinflammation in its cellular complexity remains a challenge. This manuscript aims to provide a reliable and accessible strategy for examining the brain’s immune system. We compare the efficacy of cell isolation methods in producing ample and pure immune samples from mouse brains. Then, with the high-input single-cell genomics platform PIPseq, we generate a rich neuroimmune dataset containing microglia and many peripheral immune populations. To demonstrate this strategy’s utility, we interrogate the well-established model of LPS-induced neuroinflammation with single-cell resolution. We demonstrate the activation of crosstalk between microglia and peripheral phagocytes and highlight the unique contributions of microglia and peripheral immune cells to neuroinflammation. Our approach enables the high-depth evaluation of inflammation in longstanding rodent models of neurological disease to reveal novel insight into the contributions of the immune system to brain health.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Boles,

Uriarte Huarte,

Tansey

2023

Preprint

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“…Using the integrated dataset, we split the dataset based on the conditions by the built-in function of the Seurat package. Then, we computed a cross-entropy test for the UMAP projections by applying a two-sided Kolmogorov-Smirnov test ( Roca et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Decoding the complexity of delayed wound healing following Enterococcus faecalis infection

Celik,

Lee,

Tanoto

et al. 2024

eLife

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Wound infections are highly prevalent, and can lead to delayed or failed healing, causing significant morbidity and adverse economic impacts. These infections occur in various contexts, including diabetic foot ulcers, burns, and surgical sites. Enterococcus faecalis is often found in persistent non-healing wounds, but its contribution to chronic wounds remains understudied. To address this, we employed single-cell RNA sequencing (scRNA-seq) on infected wounds in comparison to uninfected wounds in a mouse model. Examining over 23,000 cells, we created a comprehensive single-cell atlas that captures the cellular and transcriptomic landscape of these wounds. Our analysis revealed unique transcriptional and metabolic alterations in infected wounds, elucidating the distinct molecular changes associated with bacterial infection compared to the normal wound healing process. We identified dysregulated keratinocyte and fibroblast transcriptomes in response to infection, jointly contributing to an anti-inflammatory environment. Notably, E. faecalis infection prompted a premature, incomplete epithelial-to-mesenchymal transition in keratinocytes. Additionally, E. faecalis infection modulated M2-like macrophage polarization by inhibiting pro-inflammatory resolution in vitro , in vivo, and in our scRNA-seq atlas. Furthermore, we discovered macrophage crosstalk with neutrophils, which regulates chemokine signaling pathways, while promoting anti-inflammatory interactions with endothelial cells. Overall, our findings offer new insights into the immunosuppressive role of E. faecalis in wound infections. Wound infections, including diabetic foot ulcers, burns, or surgical sites, often lead to prolonged healing and significant health and economic burdens. Among the bacteria implicated in these persistent wounds, Enterococcus faecalis remains a relatively enigmatic player. To unravel its role in non-healing wounds, we used single-cell RNA sequencing in a mouse model, scrutinizing over 23,000 cells to construct a comprehensive single-cell map of infected wounds compared to uninfected wounds. Our investigation revealed distinct genetic and metabolic alterations in infected wounds, in which infection resulted in a perturbed inflammatory environment delayed wound healing signatures. Specifically, E. faecalis infection induces a premature and incomplete transition in keratinocytes, impeding their healing function. Furthermore, infection influences the behavior of immune cells like macrophages, affecting the body’s response to the infection. These findings not only shed light on E. faecalis ’s role in delayed wound healing but also offer potential avenues for future treatments, providing valuable insights into the challenging realm of wound infections.

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“…We proceed by asking to what extent a t-SNE-based assessment can be quantified. Based on quantitative evaluations already proposed [44,45], we suggest an analysis in terms of the Pearson correlation coefficient r (A, B), a well-established measure for the similarity of two pictures labelled A and B. The two pictures are composed of N pixels each, with pixel density A j and B j , respectively.…”

Section: Quantification Of the T-sne Representationmentioning

confidence: 99%

Characterization of CD34+ Cells from Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) Using a t-Distributed Stochastic Neighbor Embedding (t-SNE) Protocol

Nollmann,

Moskorz,

Wimmenauer

et al. 2024

Cancers

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Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic cells from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to obtain deeper insight and segregation between leukemic cells and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were “leukemia-related” containing a great proportion of CD34+/CD38− hematopoietic stem cells (HSCs) or CD34+ cells with a strong co-expression of CD45RA/CD123, respectively. CD34+ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

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A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations

Cited by 16 publications

References 32 publications

Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Decoding the complexity of delayed wound healing following Enterococcus faecalis infection

Characterization of CD34+ Cells from Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) Using a t-Distributed Stochastic Neighbor Embedding (t-SNE) Protocol

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