Label Transfer

Reference-atlas annotation with HLCA and related notebooks

What it does

This workflow groups together atlas-based label transfer notebooks for annotating new scRNA-seq datasets. The committed materials emphasize HLCA-based lung annotation and a more general reference-to-query transfer pattern using Seurat- or SCANVI-style tooling.

When to use it

Use this workflow when clustering is complete but biological labels still need to be transferred from a curated reference atlas. It is most useful for lung datasets or for projects that already have a trusted annotated reference object and want a branch notebook focused on transferring labels rather than redoing preprocessing from scratch.

Prerequisites

Steps

Choose the notebook that matches your reference setup

This folder contains two parallel entry points rather than one monolithic tutorial:

  • HLCA_atlas_annotation.ipynb for Human Lung Cell Atlas-based annotation
  • scRNAseq_label_transfer.ipynb for a more general reference-to-query transfer pattern

Both expect a processed query dataset plus a curated annotated reference. The HLCA notes also call out metadata with sample and batch information.

Use the HLCA notebook for lung-specific annotation

The HLCA branch is intended for lung datasets and the committed README summarizes its workflow as:

load and preprocess the query dataset
align query data to the HLCA reference with PCA and Harmony integration
transfer labels with scANVI or Symphony
generate visualization plots

The short HLCA introduction file reinforces the same scope: query scRNA-seq input, a pre-annotated HLCA reference, supporting metadata, and per-cell cell type annotations as output.

Use the general notebook for non-HLCA references

The second notebook covers a broader label-transfer pattern that is not specific to lung atlases. The committed README describes it as:

normalize and log-transform the query data
integrate query and reference with PCA/CCA (Seurat) or variational inference (SCANVI)
transfer labels and assess confidence
visualize the combined embedding

Review the transferred labels, confidence summaries, and embeddings

Across both notebooks, the intended deliverables are the same: predicted cell type labels on the query dataset plus visualization outputs that let you inspect whether the reference mapping looks plausible. This page stays at the routing-and-rationale level because the implementation details live inside the committed Jupyter notebooks rather than in rendered markdown.

Gotchas / notes

  • The committed source material for this workflow is thinner than some other scrna folders: the README is high-level, and most implementation detail lives inside Jupyter notebooks.
  • There are no committed local figure assets in this folder.
  • The HLCA note is lung-focused, so projects outside that assay/tissue context will likely rely more heavily on the general notebook.
  • The HLCA introduction file is very brief and partially incomplete, so the notebooks themselves remain the main source for execution detail.

📄 View source on GitHub