scPlantLLM¶

⚠️ Status: partial | Version: v1.0

Overview¶

Plant-specific single-cell model, handles polyploidy and plant gene nomenclature

!!! tip “When to choose scPlantLLM”

User has plant single-cell data (Arabidopsis, rice, maize, etc.) or mentions polyploidy

Specifications¶

Property	Value
Model	scPlantLLM
Version	v1.0
Tasks	`embed`, `integrate`
Modalities	RNA
Species	plant (Arabidopsis, rice, maize, etc.)
Gene IDs	symbol
Embedding Dim	512
GPU Required	Yes
Min VRAM	16 GB
Recommended VRAM	32 GB
CPU Fallback	No
Adapter Status	⚠️ partial

Quick Start¶

import omicverse as ov

# 1. Check model spec
info = ov.fm.describe_model("scplantllm")

# 2. Profile your data
profile = ov.fm.profile_data("your_data.h5ad")

# 3. Validate compatibility
check = ov.fm.preprocess_validate("your_data.h5ad", "scplantllm", "embed")

# 4. Run inference
result = ov.fm.run(
    task="embed",
    model_name="scplantllm",
    adata_path="your_data.h5ad",
    output_path="output_scplantllm.h5ad",
    device="auto",
)

# 5. Interpret results
metrics = ov.fm.interpret_results("output_scplantllm.h5ad", task="embed")

Input Requirements¶

Requirement	Detail
Gene ID scheme	symbol
Preprocessing	Standard preprocessing with plant gene nomenclature. Model handles polyploidy-specific challenges.
Data format	AnnData (`.h5ad`)
Batch key	`.obs` column for batch integration (optional)

Output Keys¶

After running ov.fm.run(), results are stored in the AnnData object:

Key	Location	Description
`X_scplantllm`	`adata.obsm`	Cell embeddings (512-dim)

import scanpy as sc

adata = sc.read_h5ad("output_scplantllm.h5ad")
embeddings = adata.obsm["X_scplantllm"]  # shape: (n_cells, 512)

# Downstream analysis
sc.pp.neighbors(adata, use_rep="X_scplantllm")
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color=["leiden"])

Resources¶

Repository / Checkpoint: https://github.com/scPlantLLM/scPlantLLM
License: Check upstream LICENSE

Hands-On Tutorial¶

For a step-by-step walkthrough with code, see the scPlantLLM Tutorial Notebook.