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 http://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

Resources:

Z. Lu avatar
Z. Lu
Data scientist, bioinformatician, retro fan and web lover.
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