Diffusion maps for high-dimensional single-cell analysis of differentiation data

By Laleh Haghverdi, Florian Buettner, Fabian J Theis. Bioinform., 2015

665 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/41e1046b42f2cfd905d6a69ebf667485b3cd687d

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In Molecular Biology

Citations in the corpus

Pan-cancer single-cell landscape of tumor-infiltrating T cells (2021, background): “First, on a global scale, the diffusionmap (25) and RNA velocity (26) showed that CD8 T cells could develop from naïve T cells to either Temra or Tex cells (fig.”
SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “…techniques and bioinformatics methods have been optimized for single-cell RNA-seq 4 , including methods for expression normalization 5,6 , differential expression analysis 7–12 , clustering 13–16 , dimensionality reduction 17,18 , trajectory inference 19 , and rare cell type identification 17,20 .”
Data-analysis strategies for image-based cell profiling (2017, neutral): “We therefore recommend the following options to make code and data available publicly online.”
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis (2016, methodology): “Diffusion map (27) is a dimension reduction technique used to define differentiation trajectories.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Two popular dimensionality reduction techniques that are principally summarization methods are principal component analysis (PCA; Pearson, 1901) and diffusion maps (Coifman et al , 2005) which were popularized for single-cell analysis by Haghverdi et al (2015).”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, positive): “This graph was used as input to the destiny package, which reduces the dimensionality of the data using diffusion maps 113 .”
A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. (2016, methodology): “Diffusion maps have recently emerged as a dimensionality reduction procedure particularly suited to displaying continuous differentiation processes from single-cell snapshot data.(11,31,37) When applied to the 1656 cells profiled here (Figure 2A), an intuitive graphical representation of the early p”