Visualize single-cell developmental trajectories (pseudotime) to show how cells differentiate from stem cells to mature cells. Includes trajectory inference, pseudotime calculation, and publication-ready visualizations.
Install with Tessl CLI
npx tessl i github:aipoch/medical-research-skills --skill pseudotime-trajectory-viz72
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
CLI usage and output file structure
CLI entry point used
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100%
analysis_report.json exists
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report has method field
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report has n_cells field
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report has root_cell field
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pseudotime_range structure
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lineages as dict
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pseudotime_values.csv exists
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csv has cell_id column
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csv has pseudotime column
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csv has lineage column
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trajectory_plot file
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Without context: $0.4337 · 2m 32s · 21 turns · 24 in / 6,424 out tokens
With context: $0.8867 · 3m · 35 turns · 245 in / 7,068 out tokens
Gene expression dynamics along trajectories
genes flag used
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plot-genes flag used
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gene_trends directory exists
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individual gene trend files
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requested genes in trend files
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gene expression heatmap
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scripts/main.py entry point
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trajectory_plot exists
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analysis_report.json exists
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Without context: $0.6140 · 3m 23s · 29 turns · 82 in / 9,750 out tokens
With context: $0.9437 · 3m 17s · 37 turns · 478 in / 7,775 out tokens
PAGA method and alternative trajectory inference
method paga flag
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100%
analysis_report method field
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paga_graph file exists
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start-cell-type flag used
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trajectory_plot exists
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pseudotime_values.csv exists
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analysis_report.json structure
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50%
scripts/main.py entry point
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output directory used
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100%
Without context: $0.6680 · 3m 30s · 30 turns · 37 in / 10,109 out tokens
With context: $1.5617 · 5m 55s · 44 turns · 9,037 in / 17,730 out tokens
Table of Contents
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