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

• scRNAseqDB (human single cell)

Resources:

• awesome-single-cell
• Analysis of single cell RNA-seq data (Hemberg Lab from the Sanger Inst)