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Deg And Marker Gene Heatmap With Viridis Col Clustering

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deg-and-marker-gene-heatmap-with-viridis-col-clustering

Deg And Marker Gene Heatmap With Viridis Col Clustering is an Agent Skill for documents published in the ECNU-ICALK/AutoSkill repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Generates a publication-ready heatmap for differentially expressed genes (DEGs) or marker genes using viridis colormap, column-only hierarchical clustering, and Arial font — applicable to any normalized gene expression matrix with genes as rows and samples/subclusters as columns.

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Inspect the Agent Skill at https://github.com/ECNU-ICALK/AutoSkill/blob/HEAD/SkillBank/Users/u39/deg-and-marker-gene-heatmap-with-viridis-col-clustering/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/deg-and-marker-gene-heatmap-with-viridis-col-clustering, but wait for my approval before writing files or running scripts.

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deg-and-marker-gene-heatmap-with-viridis-col-clustering

Generates a publication-ready heatmap for differentially expressed genes (DEGs) or marker genes using viridis colormap, column-only hierarchical clustering, and Arial font — applicable to any normalized gene expression matrix with genes as rows and samples/subclusters as columns.

Prompt

Goal

Generate a seaborn-based heatmap for differentially expressed or marker genes, accepting a pandas DataFrame with genes as rows and samples/subclusters as columns.

Constraints & Style

  • Use cmap="viridis" exclusively; do not use RdBu_r, center, or any other colormap or symmetry setting.
  • Enable only column-wise hierarchical clustering: set col_cluster=True and row_cluster=False.
  • Use Arial font for all text elements (title, axis labels, tick labels, colorbar label); enforce via plt.rcParams["font.sans-serif"] = ["Arial", "DejaVu Sans", "Liberation Sans"] and plt.rcParams["axes.unicode_minus"] = False; explicitly annotate plot elements if seaborn does not inherit font settings.
  • Apply row-wise z-score normalization (per gene) before plotting: df.T.apply(lambda x: (x - x.mean()) / x.std(ddof=0)).T.
  • Use robust=True in sns.heatmap for outlier resilience.
  • Set linewidths=0.3 and linecolor='lightgray' for subtle cell borders.
  • Set figure size to (8, 10); include colorbar labeled "Z-score" with shrink=0.6.
  • Title: "Differentially Expressed Genes (Z-score normalized)" or "Marker Genes (Z-score normalized)" (bold, 14pt); adapt label based on context but retain consistent phrasing.
  • Axis labels: "Samples" or "Subclusters" (x), "Genes" (y); no rotation of tick labels.
  • Call plt.tight_layout() before plt.show(); ensure no clipping.

Workflow

  1. Accept input DataFrame with gene-indexed rows and sample/subcluster-labeled columns.
  2. Apply row-wise z-score normalization.
  3. Configure matplotlib font settings for Arial compatibility.
  4. Generate heatmap with specified clustering, colormap, robust scaling, layout, and labeling.
  5. Display the plot.

Triggers

  • 生成差异表达基因热图
  • 画DEG热图
  • 单细胞亚群标志基因热图
  • scRNA-seq marker gene heatmap
  • viridis列聚类热图