数据与复现单细胞与空间组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
BI

bio-single-cell-trajectory-inference

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Infer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime.

OpenClawNanoClaw分析处理复现实验bio-single-cell-trajectory-inference🧬 bioinformatics (gptomics bio-* suite)bioinformatics — single-cell & spatial omicsinfer

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-trajectory-inference

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • cds <- as.cell_data_set(seurat_obj)。
  • cds <- preprocess_cds(cds,num_dim = 50) cds <- reduce_dimension(cds,reduction_method = 'UMAP')。

原始文档

SKILL.md 摘录

Monocle3 (R)

Goal: Infer developmental trajectories and pseudotime ordering using Monocle3's principal graph approach.

Approach: Learn a principal graph through the data manifold, order cells along the graph from a root state, and extract pseudotime values.

"Find the developmental trajectory in my data" → Construct a tree-like graph through the cell embedding, assign pseudotime from a root population, and identify branch points.

library(monocle3)

## Order cells (select root interactively or programmatically)

cds <- order_cells(cds, root_cells = root_cell_ids)

## Plot trajectory with pseudotime

plot_cells(cds, color_cells_by = 'pseudotime', label_branch_points = TRUE, label_leaves = TRUE)

适用场景

  • 适合在inferring developmental trajectories 或 pseudotime时使用。

不适用场景

  • Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。

上游相关技能

  • single-cell/clustering - Prerequisite clustering
  • single-cell/cell-communication - Downstream signaling analysis
  • differential-expression/deseq2-basics - DE along trajectory

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