Featured image of post Clustering of single cells

Clustering of single cells

PCA is the most commonly used clustering method. Through machine learning some new methods were also reported.

Title image from https://satijalab.org/seurat/get_started_v1_2.html.

Methods for dimensionality reductions

  • Linear: PCA
  • Non-linear: t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • shared k-nearest neighbor (KNN), e.g., SNN-Clip
  • Centered Pearson’s correlation (e.g., SINCERA)
  • cell similarity matrix (e.g., SIMLR)
  • zero-inflated factor analysis (ZIFA)
  • Discriminant Analysis
  • Neural networks (e.g., Using neural networks for reducing the dimensions of single-cell RNA-Seq data)

Clustering methods (examples here)

  • Hierarchical clustering
  • k-means
  • graph-based

Analysis pipelines:

  • Scater
  • Seurat
  • simpleSingleCell

scRNAseq databases


comments powered by Disqus
CC-BY-NC 4.0
Built with Hugo Theme Stack