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Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.
Passed 21 of 25 test cases with the skill, and 19 of 25 without it.
2 fixed19 passed either way4 still failing0 broke
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One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
When running CellTypist on a human PBMC dataset to obtain granular sub-type annotations like naive CD4+ T cells and memory B cells, a teammate suggests leaving the model parameter unset so it defaults to a general tissue model. Which specific pre-trained model parameter should be passed to celltypist.annotate() for high-resolution human immune cell sub-types?
| Checked | Without skill | With skill |
|---|---|---|
| Specify model='Immune_All_Low.pkl' for high-resolution human immune cell sub-type annotation. | Missed | Passed |
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Short Description: Best practices for annotating cell types in single-cell RNA-seq data using marker-based, automated, and reference-based approaches.
Authors: Distilled from "Single-cell best practices" by Luecken, M.D. et al.
Affiliations: Helmholtz Munich, Wellcome Sanger Institute, Harvard Medical School, and contributors
Version: 1.0
Last Updated: January 2025
License: CC BY 4.0
Commercial Use: ✅ Allowed
Source: https://www.sc-best-practices.org/cellular_structure/annotation.html
Citation: Luecken, M.D., Theis, F.J. et al. (2023). Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology.
Cell type annotation is the process of assigning cell type labels to clusters or individual cells in single-cell RNA-seq data. This guide covers three main approaches and their practical implementation.
A cell type is a stable identity defined by a developmental trajectory and core marker gene program (e.g., CD4+ T cell, hepatocyte). A cell state is a transient condition (activated, cycling, stressed) overlaid on a cell type. Annotation should target cell types first; states are attributes that may further subdivide a type but should not be conflated with type identity.
Marker genes are genes whose expression is enriched in a specific cell type relative to other cells in the same tissue context. Reliable annotation uses panels of multiple markers (typically 3-5 per type) rather than a single gene, because expression is noisy in droplet-based scRNA-seq and many markers are shared across related types. Markers come in two flavors: canonical (literature-derived, e.g., CD3D for T cells) and data-derived (from differential expression on the dataset).
A reference atlas is a previously annotated dataset (e.g., Human Cell Atlas, Tabula Sapiens) used to project labels onto a new "query" dataset. Label transfer methods (scArches, scANVI, Azimuth, SingleR) align query cells into the reference latent space and assign the nearest neighbor's label. Quality of transfer depends on tissue match, technology match (e.g., 10x v3 vs. Smart-seq2), and species match.
test cases passed, vs 19 / 25 without
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