Discover latent transcriptional programs in scRNA-seq data using AmortizedLDA.
npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill scvi-lda
Please help me install this Agent Skill. Skill: scvi-lda Source: https://github.com/tony-zhelonkin/SciAgent-toolkit/tree/main/skills/scvi-lda Command: npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill scvi-lda
AmortizedLDA provides a scalable topic modeling approach to discover latent transcriptional programs across single-cell RNA-seq data, treating cells as documents and genes as words.
Remove MT genes from counts, set up anndata with a counts layer, train AmortizedLDA, and retrieve per-cell topic proportions and gene-topic distributions.
High-intent search queries and answers about installing and using this skill.
Latent transcriptional programs in scRNA-seq data are discovered using AmortizedLDA topic modeling, which treats cells as documents and genes as words to identify shared gene modules and intermediate cell states across large datasets.
Intermediate cell states in scRNA-seq are identified by training AmortizedLDA to extract per-cell topic proportions, providing an interpretable topic-based dimensionality reduction space that reveals transitional cellular states.
Yes, you must remove mitochondrial genes from raw counts before topic modeling scRNA-seq data, and you need to set up a dedicated counts layer in anndata as a prerequisite for configuring and training AmortizedLDA.
Yes, you can compare topic structures across conditions or batches in scRNA-seq using scvi-tools, which supports AmortizedLDA for scalable unsupervised gene-module discovery and interpretable topic-based comparisons.
AmortizedLDA provides interpretable dimensionality reduction for scRNA-seq by treating cells as documents and genes as words, yielding per-cell topic proportions and gene-topic distributions that serve as a transparent latent space.
PharMolixApply scvi-tools probabilistic models to single-cell multi-omics data.
chenyhvvvvEstimate cell type proportions in spatial transcriptomics spots using an scRNA-seq reference.
tony-zhelonkinPerform unsupervised integration and batch correction for scRNA-seq data using scVI.
rubenslivAnalyze single-cell RNA-seq data with Scanpy for clustering and trajectory inference.
