Automated Mapping of Phenotype Space with Single-Cell Data

By N. Samusik, Zinaida Good, M. Spitzer, K. Davis, Garry P. Nolan. Nature Methods, 2016

375 citations recorded by the source corpus.

Accurate identification of cell subsets in complex populations is key to discovering novelty in multidimensional single-cell experiments. We present X-shift (http://web.stanford.edu/~samusik/vortex/), an algorithm that processes data sets using fast k-nearest-neighbor estimation of cell event density and arranges populations by marker-based classification. X-shift enables automated cell-subset clustering and access to biological insights that 'prior knowledge' might prevent the researcher from discovering.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/13a704ba8d8c7c20be14d397f0bc901c783f0fe9

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Citations in the corpus

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types (2017, background): “We found that Cytosplore +HSNE replicates the previously identi ﬁ ed hierarchy in the immune-system-wide single-cell data 4,5,14 , i.e., we can immediately identify major lineages at the highest overview level, while acquiring more information by dissecting the immune system at the deeper levels of…”
UMAP does not preserve global structure any better than t-SNE when using the same initialization (2019, methodology): “We used all three datasets analyzed in the original publication (sample sizes from 320 000 to 820 000 cells) [7, 8, 9].”
Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging (2017, positive): “Moreover, application of X-shift phenotype-mapping algorithm (Samusik et al., 2016) tailored to parsing the multidimensional single-cell data enabled the detection of rare cells types (such as CD4 MHCII [Lti] cells, CD11c B cells [ABC cells]) and simultaneous placement of those cells in the tissue a”
Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data (2016, methodology): “A number of recent studies have provided overviews of available clustering meth-ods for high-dimensional cytometry data (1,2,14–16), performance comparisons against a subset of existing methods while introducing a new method (17,18), or performance comparisons using simulated data (19).”
Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front (2019, methodology): “to unsupervised hierarchical X-shift clustering using an angular distance algorithm (Samusik et al., 2016).”
Evaluation of UMAP as an alternative to t-SNE for single-cell data (2018, methodology): “UMAP better represents the multi-branched trajectory of hematopoietic development To investigate how UMAP handles continuity of cell phenotypes we applied it alongside tSNE on the well-documented topic of bone-marrow hematopoiesis using both a masscytometry (>86,000 events, 25 parameters, 24 cell po”
Fundamental limits on dynamic inference from single-cell snapshots (2017, background): “Interestingly, several recent studies (8, 15, 43) have developed k-nearest neighbor graph-based representations of single-cell data, and others have suggested embedding cells in diffusion maps (24, 44) on the basis of other similarity kernels.”