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

id	7161407a-1575-427e-9989-d784d2376a0c
name	deg-and-marker-gene-heatmap-with-viridis-col-clustering
description	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.
version	0.1.1
tags	["bioinformatics","single-cell","scRNA-seq","heatmap","seaborn","gene-expression","marker-genes","visualization"]
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"]

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