Single Cell Technologies and Analysis: Seurat and friends

Clarice S Groeneveld

2025-02-14

Why Single Cell?

Identify biomarkers of cell types and states

Capture full heterogeneity in a sample

Track changes in states: development, disease progression, immune responses

Explore cell-to-cell interactions

Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020).

Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020).

10x Chromium had the strongest consistent performance: high sensitivity, fastest
Lowest costs: Drop-seq, Seq-Well and inDrops
High sensitivity: both low-throughput methods (Smart-seq2 and CEL-Seq2)
Smart-seq2: full-length transcript allows for splicing isoforms and variant detection

Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020).

For 10X: Cell Ranger (https://www.10xgenomics.com/support/software/cell-ranger/latest/algorithms-overview/cr-3p-cellplex-algorithm)

Others: Alevin (Salmon), Kallisto BUStools (kb), STARsolo

CMO: Cell Multiplexing Oligo tags

GEX: Gene expression

Expectation-Maximization (EM) algorithm of the parameters of the Gaussians and state assignments

	Seurat	Bioconductor	scverse
Programming Language	R	R	Python
Core Data Structure	Seurat object	SingleCellExperiment	AnnData
Ecosystem	Integrated all-in-one “pipeline”	Modular via Bioconductor packages	Modular, interoperable toolkit
Visualization	Extensive built-in plotting functions	Visualization via scatter + ggplot2	scanpy + MPL / seaborn
Community & Support	Large, active community	Bioconductor project	Growing, active Python 3community