sc-cell-annotation

Annotate cell types from normalized scRNA-seq data using marker scoring, CellTypist, PopV-style reference mapping, lightweight KNNPredict-style mapping, SingleR, or scmap through shared Python/R backends.

Aller à l'installation

Dépôt: mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Dernière activité de la source: 30 avril 2026 à 21:29
Langue détectée de SKILL.md: anglais
Étoiles: 31
Forks: 8

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.

npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill sc-cell-annotation

La commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.

Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

SKILL.md

Instructions source · Aperçu en lecture seule

Retour à la recherche Retour à l’installation

medsam2-3d-segmentation

Operate MedSAM2 for promptable segmentation of 3D medical images and medical videos, including CT lesion propagation, MRI volumes, RECIST-guided prompts, efficient CPU-oriented variants, training, and 3D Slicer integration. Use when generating or validating volumetric masks from sparse prompts or propagating masks through image slices or video frames.

2026-06-18

monai-medical-imaging

Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.

2026-06-18

txgemma-therapeutics

Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.

2026-06-18

Tout voir dans ce dépôt

name	sc-cell-annotation
description	Annotate cell types from normalized scRNA-seq data using marker scoring, CellTypist, PopV-style reference mapping, lightweight KNNPredict-style mapping, SingleR, or scmap through shared Python/R backends.
version	0.9.0
author	OmicsClaw
license	MIT
tags	["singlecell","annotation","celltypist","popv","knnpredict","singler","scmap"]
metadata	{"omicsclaw":{"domain":"singlecell","allowed_extra_flags":["--manual-map","--manual-map-file","--cluster-key","--method","--model","--reference","--marker-file","--celltypist-majority-voting","--no-celltypist-majority-voting","--r-enhanced"],"param_hints":{"manual":"[Truncated]","markers":"[Truncated]","celltypist":"[Truncated]","popv":"[Truncated]","knnpredict":"[Truncated]","singler":"[Truncated]","scmap":"[Truncated]"},"legacy_aliases":["sc-annotate"],"saves_h5ad":true,"requires_preprocessed":true}}

Single-Cell Cell Annotation

Why This Exists

Without it: users hand-label clusters inconsistently or trust opaque defaults.
With it: annotation method, reference/model choice, label outputs, and figures are standardized.
Why OmicsClaw: one wrapper unifies marker-based and reference-style annotation while preserving an AnnData-first workflow.

Scope Boundary

Implemented methods:

manual
markers
celltypist
popv
knnpredict
singler
scmap

This skill annotates cells or clusters. It does not replace marker discovery or replicate-aware DE.

Input Expectations

Expected state: normalized expression in adata.X
Typical upstream step: sc-clustering
Typical downstream steps: sc-markers, sc-de, or interpretation/reporting
Marker mode needs an existing cluster/label column; it will not auto-cluster anymore

Public Parameters

--method
--manual-map
--manual-map-file
--cluster-key
--model
--reference
--marker-file
--celltypist-majority-voting

Output Contract

Successful runs write:

processed.h5ad
report.md
result.json
figures/embedding_cell_type.png
figures/embedding_cluster_vs_cell_type.png
figures/cluster_to_cell_type_heatmap.png
figures/cell_type_counts.png
figures/embedding_annotation_score.png when scores are available
figures/manifest.json
figure_data/manifest.json
tables/annotation_summary.csv

Scenario	Recommended method	Example
PBMC / immune cells (human)	`markers` (default) or `celltypist`	`--method markers`
Immune cells (any tissue, human)	`celltypist`	`--method celltypist --model Immune_All_Low.pkl`
Mouse data	`celltypist` with mouse model, or custom markers	`--method celltypist --model Mouse_Isocortex_Hippocampus.pkl`
Specialized tissue (brain, lung, etc.)	`celltypist` with tissue model, or `--marker-file`	`--method celltypist --model Human_Lung_Atlas.pkl`
Have a labeled reference dataset	`knnpredict` or `popv`	`--method knnpredict --reference ref.h5ad`
R environment available	`singler` or `scmap`	`--method singler --reference HPCA`

Flag	Type	Default	Description	Validation
`--input`	str	—	Input `.h5ad` file	required unless `--demo`
`--output`	str	—	Output directory	required
`--demo`	flag	off	Run with bundled PBMC3k data	—
`--method`	str	`markers`	Annotation method: `manual`, `markers`, `celltypist`, `popv`, `knnpredict`, `singler`, `scmap`	validated against METHOD_REGISTRY
`--model`	str	`Immune_All_Low`	CellTypist model name or file stem	celltypist only
`--reference`	str	`HPCA`	SingleR/scmap atlas key or path to labeled H5AD (popv/knnpredict)	—
`--cluster-key`	str	None	Cluster/label column for marker summaries; auto-detected when omitted	—
`--manual-map`	str	None	Inline cluster-to-label mapping, e.g. `0=T cell;1,2=Myeloid`	manual only
`--manual-map-file`	str	None	Path to mapping file (json/csv/tsv/txt)	manual only
`--marker-file`	str	None	Path to custom marker gene file (JSON or CSV)	markers only
`--celltypist-majority-voting`	flag	off	Enable CellTypist neighborhood majority-voting smoothing	celltypist only
`--species`	str	`Human`	SCSA species filter (`Human`/`Mouse`)	scsa only
`--tissue`	str	`All`	SCSA tissue filter (e.g. `Blood`, `Brain`, `All`)	scsa only
`--scsa-foldchange`	float	1.5	SCSA DE fold-change threshold	scsa only
`--scsa-pvalue`	float	0.05	SCSA DE p-value threshold	scsa only
`--r-enhanced`	flag	off	Also render R Enhanced ggplot2 figures	—

Renderer	Output file	figure_data CSV	Plot description	Required R packages
`plot_embedding_discrete`	`r_embedding_discrete.png`	`annotation_embedding_points.csv`	UMAP/embedding colored by annotated cell types	ggplot2
`plot_embedding_feature`	`r_embedding_feature.png`	`annotation_embedding_points.csv`	UMAP/embedding colored by annotation confidence score	ggplot2
`plot_cell_barplot`	`r_cell_barplot.png`	`cell_type_counts.csv`	Bar chart of cell type counts	ggplot2
`plot_cell_proportion`	`r_cell_proportion.png`	`cell_type_counts.csv`	Stacked proportion bar chart per sample or cluster	ggplot2
`plot_cell_sankey`	`r_cell_sankey.png`	`cluster_annotation_matrix.csv`	Sankey/alluvial diagram from cluster to cell type	ggplot2, ggalluvial

sc-cell-annotation

Options d'installation

Vérifiez les fichiers source

Single-Cell Cell Annotation

Why This Exists

Scope Boundary

Input Expectations

Public Parameters

Output Contract

Single-Cell Cell Annotation

Why This Exists

Scope Boundary

Input Expectations

Public Parameters

Output Contract

What Users Should Inspect First

Reference Data Guide

Which method should I use?

Custom marker file format

CellTypist models

Reference H5AD for knnpredict / popv

Species and gene naming

Guardrails

CLI Parameters

R Enhanced Plots

Workflow Position

sc-cell-annotation

Informations de source

Options d'installation

Vérifiez les fichiers source

Plus depuis ce dépôt

Plus depuis ce dépôt

Single-Cell Cell Annotation

Why This Exists

Scope Boundary

Input Expectations

Public Parameters

Output Contract

Single-Cell Cell Annotation

Why This Exists

Scope Boundary

Input Expectations

Public Parameters

Output Contract

What Users Should Inspect First

Reference Data Guide

Which method should I use?

Custom marker file format

CellTypist models

Reference H5AD for knnpredict / popv

Species and gene naming

Guardrails

CLI Parameters

R Enhanced Plots

Workflow Position