omicverse-single-cell-lda-topic-clustering

OmicVerse Single-Cell LDA Topic Clustering

Goal

Turn the notebook's MIRA-backed topic modeling section into a reusable job: fit an LDA topic model on count-like single-cell data, predict topic usage, and optionally derive hard cluster labels or RFC-based labels.

Quick Workflow

1. Inspect whether the data still has a count layer and a highly-variable-feature flag.
2. Choose feature_type, learning_rate, and whether to run ondisk=False or ondisk=True.
3. Construct LDA_topic(...), then decide how many topics to fit.
4. Run predicted(num_topics=...) to create LDA_cluster.
5. If the user wants harder classification on an embedding, run get_results_rfc(...) with explicit use_rep and LDA_threshold.

Interface Summary

• ov.utils.LDA_topic(adata, feature_type='expression', highly_variable_key='highly_variable_features', layers='counts', batch_key=None, learning_rate=0.001, ondisk=False) constructs the wrapper.
• The documented feature_type branch is modality-sensitive; current docs describe at least expression and accessibility.
• ondisk=False keeps training data in memory. ondisk=True writes local training and test datasets before tuning and fitting.
• predicted(num_topics=6) fits the model with the chosen topic count, predicts topic usage, and writes LDA_cluster.
• get_results_rfc(adata, use_rep='X_pca', LDA_threshold=0.5, num_topics=6) trains tree-based classifiers on high-confidence topic assignments and writes LDA_cluster_rfc and LDA_cluster_clf.

Stage Selection

• Use the plain predicted(...) path when the user wants one topic-to-cluster projection without extra classifiers.
• Use the RFC path only when the user explicitly wants classifier-derived hard labels from the topic representation.
• Use ondisk=True only when dataset size makes in-memory execution impractical.
• Treat the notebook's embedding panels as optional reporting, not the reusable contract.

Input Contract

• Start from AnnData.
• Keep a count-like layer available under the selected layers key.
• Ensure the selected highly-variable flag exists in adata.var.
• Ensure the embedding named by use_rep exists before the RFC path.

Minimal Execution Patterns

import omicverse as ov

lda = ov.utils.LDA_topic(
    adata,
    feature_type="expression",
    highly_variable_key="highly_variable_features",
    layers="counts",
    batch_key=None,
    learning_rate=1e-3,
    ondisk=False,
)
lda.predicted(num_topics=13)

lda.get_results_rfc(
    adata,
    use_rep="scaled|original|X_pca",
    LDA_threshold=0.4,
    num_topics=13,
)

Constraints

• Do not use this skill on data that no longer has a usable count-like layer for the selected layers key.
• Do not treat the notebook's topic count as a universal default.
• Do not use the RFC path without a real embedding in obsm.
• ondisk=True creates local training/test datasets and is a runtime mode choice, not part of the reusable semantic contract.
• Keep smoke and acceptance commands shell-agnostic.

Validation

• Check that the wrapper constructed successfully with the intended feature_type, layers, and ondisk mode.
• After predicted(...), check that topic columns and LDA_cluster exist.
• After the RFC path, check that LDA_cluster_rfc and LDA_cluster_clf exist.
• If only a bounded smoke path was run, state whether model fitting was skipped or reduced.

Resource Map

• Use the branch selection notes when choosing feature_type, ondisk, or the RFC path.
• Use the source grounding notes for current signatures, PyTorch compatibility behavior, and output labels.
• Use the notebook mapping notes to trace the notebook's topic-modeling section into this reusable skill.
• Use the compatibility notes for dependency, GPU, and filesystem-sensitive behavior.