viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

By E. Amir, K. Davis, Michelle D. Tadmor, Erin F. Simonds, Jacob H. Levine, Sean Bendall, Daniel K. Shenfeld, Smita Krishnaswamy, Garry P. Nolan, Dana Pe'er. 2013

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New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.

Subfields: Biophysics, Molecular Biology

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Citations in the corpus

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization (2020, methodology): “This limitation was improved in subsequent papers (see, for example, Amir et al., 2013; van der Maaten, 2014; Linderman et al., 2017, as discussed earlier, using only 3·Perplexity attraction points).”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “Specifically, S CANPY provides preprocessing comparable to S EURAT [10] and C ELL R ANGER [6], visualization through T SNE [11, 12], graph-drawing [13– 15] and diffusion maps [11, 16, 17], clustering similar”
Genome-wide RNA Tomography in the zebrafish embryo. (2014, background): “t-SNE maps project high-dimensional data onto a 2D surface while retaining distance information between individual objects (Amir et al., 2013; van der Maaten and Hinton, 2008).”
Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade (2017, positive): “t-Distributed Stochastic Neighbor Embedding (t-SNE) dimension reduction and PhenoGraph clustering analyses were performed using the tool Cyt run inMATLAB or in Python(Amir et al., 2013; Levine et al., 2015).”
Diffusion maps for high-dimensional single-cell analysis of differentiation data (2015, methodology): “, 2011) and t-SNE (Van der Maaten and Hinton, 2008) as used in viSNE (Amir et al., 2013) are examples of clustering methods.”
Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes (2018, methodology): “The viSNE implementation of t-SNE and EMGM algorithms from the CYT single-cell analysis package were obtained from the Pe’er laboratory at Columbia University (Amir et al., 2013).”