Single-Cell Packages

Purpose

This page is for collecting packages encountered while learning and doing single-cell analysis.

Keep it practical: package name, ecosystem, task, and when it becomes relevant.

Packages

Package	Ecosystem	Task	Notes
Seurat	R	General workflow	Common entry point for scRNA-seq case analysis
patchwork	R	Plot composition	Combine ggplot figures into one layout
SingleCellExperiment	R / Bioconductor	Data object	Core Bioconductor container
zellkonverter	R / Bioconductor	h5ad import/export	Bridges AnnData and SingleCellExperiment
Scanpy	Python	General workflow	Main AnnData-based analysis framework
AnnData	Python	Data object	Common .h5ad object format

Package Notes

Seurat

Seurat is the main R package I will encounter for practical scRNA-seq analysis. It is often used as a full workflow framework rather than a single-purpose tool.

Typical uses:

• create a single-cell object from count matrices
• store cell metadata, gene metadata, assays, reductions, graphs, and cluster labels
• run QC, normalization, feature selection, PCA, UMAP, clustering, marker detection, and integration
• handle common multimodal extensions such as CITE-seq, spatial data, and multi-assay objects

What to remember:

• Seurat is convenient because many steps live in one ecosystem.
• A Seurat object is not just an expression matrix; it also stores metadata and analysis results.
• For condition-level differential expression, do not blindly use cell-level marker tests as final evidence. For multi-sample disease comparisons, pseudobulk is usually the safer direction.
• When importing from .h5ad, check whether raw counts, normalized data, layers, reductions, and metadata survived conversion.

patchwork

patchwork is an R package for combining multiple ggplot2 plots into one figure layout.

In single-cell work, it is useful when comparing related plots side by side, such as QC plots, UMAPs, feature plots, dot plots, or condition-specific panels.

Common pattern:

p1 + p2
p1 / p2
(p1 + p2) / p3

What to remember:

• + places plots side by side.
• / stacks plots vertically.
• Parentheses control the layout.
• It is mainly for figure assembly, not data analysis.

Open Questions

• Should data objects have separate pages?
• Should this stay as a package list or become a workflow map?
• Should R and Python ecosystems be separated later?