Plotly Interactive Plots

jaechang-hits/plotly-interactive-plots

Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib for publication figures.

This is a copy. The original lives at biotender-max/plotly-interactive-plots.

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Install

one command, takes just this skill from the repository

npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill plotly-interactive-plots

The instruction itself

23 sections, as written by the author

Plotly Interactive Plots

Overview

Plotly is a Python library for producing interactive, web-ready figures backed by HTML and JavaScript. It exposes two complementary APIs: plotly.express (px) provides a high-level, DataFrame-oriented interface for generating common chart types in one line, while plotly.graph_objects (go) offers fine-grained control over every trace, axis, and layout property. Figures are fully interactive by default — supporting hover tooltips, zoom, pan, and click events — and can be embedded in web pages, Jupyter notebooks, or built into web applications using the Dash framework.

When to Use

You need hover tooltips that display gene names, p-values, or sample metadata without cluttering the static figure.
You are building a multi-panel interactive dashboard for dose-response curves, patient cohorts, or multi-condition comparisons.
You want to share figures as self-contained HTML files that non-programmers can explore in a browser.
You need 3D scatter or surface plots for structural biology, conformational landscapes, or PCA of high-dimensional data.
You are creating heatmaps of gene expression or correlation matrices where users need to zoom into specific gene clusters.
You require animation frames to show time-series or treatment-response trajectories.
Use seaborn instead when you need automatic statistical aggregation (confidence intervals, regression fits) with minimal code.
Use matplotlib when you need fine-grained control over every axis element for print-ready publication figures at exact journal specifications.

Prerequisites

Python packages: plotly, kaleido (static image export), pandas, numpy
Data requirements: pandas DataFrames or NumPy arrays; long-form (tidy) data works best with px
Environment: Jupyter Lab/Notebook (inline rendering), or save as HTML for browser display

pip install plotly kaleido pandas numpy

For Jupyter Lab inline rendering (if not automatic):

pip install "jupyterlab>=3" ipywidgets

Quick Start

import plotly.express as px
import pandas as pd

# Gene expression scatter with hover info
df = pd.DataFrame({
    "log2FC": [-3.1, 0.2, 1.8, 2.5, -0.5, 4.1],
    "neg_log10_padj": [8.2, 0.4, 2.1, 6.8, 0.1, 9.3],
    "gene": ["BRCA1", "MYC", "TP53", "EGFR", "CDKN1A", "KRAS"],
    "significance": ["sig", "ns", "ns", "sig", "ns", "sig"],
})

fig = px.scatter(
    df, x="log2FC", y="neg_log10_padj",
    color="significance", hover_name="gene",
    title="Volcano Plot — Treatment vs Control",
)
fig.show()

Core API

Module 1: px Scatter and Line — Relational Plots

px.scatter() and px.line() map DataFrame columns to visual encodings (color, symbol, size) and automatically populate hover tooltips from hover_data.

import plotly.express as px
import pandas as pd
import numpy as np

# Dose-response scatter: color by drug, symbol by cell line
np.random.seed(42)
df = pd.DataFrame({
    "dose_uM": np.tile([0.01, 0.1, 1, 10, 100], 4),
    "viability": np.clip(np.random.normal(
        [100, 90, 70, 40, 10] * 4, 5), 0, 110),
    "drug": ["DrugA"] * 5 + ["DrugA"] * 5 + ["DrugB"] * 5 + ["DrugB"] * 5,
    "cell_line": ["HCT116"] * 10 + ["MCF7"] * 10,
    "replicate": np.tile([1, 2, 3, 4, 5], 4),
})

fig = px.scatter(
    df, x="dose_uM", y="viability",
    color="drug", symbol="cell_line",
    log_x=True,
    hover_data={"replicate": True, "dose_uM": ":.2f"},
    labels={"viability": "Cell Viability (%)", "dose_uM": "Dose (µM)"},
    title="Dose-Response by Drug and Cell Line",
)
fig.show()
print(f"Figure has {len(fig.data)} traces")

# Time-course gene expression line plot
time_df = pd.DataFrame({
    "hour": list(range(0, 25, 4)) * 3,
    "expression": [1.0, 1.8, 3.2, 4.5, 3.8, 2.1, 1.2,
                   1.0, 2.5, 5.1, 6.8, 5.5, 3.2, 1.8,
                   1.0, 1.1, 1.0, 1.2, 1.1, 1.0, 0.9],
    "gene": ["MYC"] * 7 + ["EGFR"] * 7 + ["GAPDH"] * 7,
})

fig = px.line(
    time_df, x="hour", y="expression",
    color="gene", markers=True,
    labels={"expression": "Relative Expression (log2)", "hour": "Time (h)"},
    title="Time-Course Gene Expression",
)
fig.update_traces(line=dict(width=2.5), marker=dict(size=8))
fig.show()

Module 2: px Statistical Plots — Distributions and Categories

px.box(), px.violin(), px.histogram(), and px.strip() produce publication-ready distribution summaries with built-in grouping.

import plotly.express as px
import pandas as pd
import numpy as np

# Violin + strip overlay: expression by cell type
np.random.seed(7)
n = 60
cell_data = pd.DataFrame({
    "expression": np.concatenate([
        np.random.normal(4.2, 0.8, n),
        np.random.normal(6.5, 1.2, n),
        np.random.normal(2.8, 0.6, n),
    ]),
    "cell_type": ["T cell"] * n + ["B cell"] * n + ["NK cell"] * n,
    "patient_id": np.tile([f"P{i:02d}" for i in range(1, 11)], 18),
})

fig = px.violin(
    cell_data, x="cell_type", y="expression",
    color="cell_type", box=True, points="all",
    hover_data=["patient_id"],
    labels={"expression": "CD3E Expression (log2 CPM)"},
    title="CD3E Expression Across Cell Types",
)
fig.update_traces(jitter=0.3, pointpos=-1.5)
fig.show()
print(f"Cells per type: {cell_data.groupby('cell_type').size().to_dict()}")

# Histogram with rug: distribution of fold changes
fc_df = pd.DataFrame({
    "log2FC": np.concatenate([
        np.random.normal(0.1, 0.8, 500),   # not DE genes
        np.random.normal(2.5, 0.4, 50),    # upregulated
        np.random.normal(-2.3, 0.4, 40),   # downregulated
    ]),
    "category": ["background"] * 500 + ["up"] * 50 + ["down"] * 40,
})

fig = px.histogram(
    fc_df, x="log2FC", color="category",
    nbins=60, barmode="overlay", opacity=0.7,
    marginal="rug",
    labels={"log2FC": "log2 Fold Change", "count": "Gene Count"},
    title="Distribution of Fold Changes (DESeq2 Results)",
    color_discrete_map={"background": "gray", "up": "crimson", "down": "steelblue"},
)
fig.show()

Module 3: px Heatmap and Matrix — Gene Expression and Correlations

px.imshow() renders 2D arrays or DataFrames as color-encoded matrices, ideal for expression heatmaps and correlation matrices.

import plotly.express as px
import pandas as pd
import numpy as np

# Gene expression heatmap (genes × samples)
np.random.seed(12)
genes = [f"Gene_{g}" for g in ["BRCA1", "TP53", "EGFR", "MYC", "KRAS",
                                 "CDKN1A", "RB1", "PTEN", "VHL", "APC"]]
samples = [f"S{i:02d}" for i in range(1, 9)]

expr_matrix = pd.DataFrame(
    np.random.normal(0, 1.5, (10, 8)) +
    np.array([2, -1, 3, -2, 1, -3, 0, 2, -1, 3]).reshape(-1, 1),
    index=genes, columns=samples,
)

fig = px.imshow(
    expr_matrix,
    color_continuous_scale="RdBu_r",
    color_continuous_midpoint=0,
    aspect="auto",
    labels={"color": "log2 Expression (z-score)"},
    title="Gene Expression Heatmap",
)
fig.update_xaxes(side="top")
fig.update_layout(width=600, height=500)
fig.show()
print(f"Heatmap shape: {expr_matrix.shape} (genes × samples)")

# Correlation matrix heatmap
from itertools import combinations

markers = ["IL6", "TNF", "CXCL10", "IFNg", "IL10", "IL1B", "CCL2", "IL17A"]
np.random.seed(3)
raw = np.random.multivariate_normal(
    mean=np.zeros(8),
    cov=np.eye(8) * 0.3 + 0.7,
    size=80,
)
corr_df = pd.DataFrame(raw, columns=markers).corr()

fig = px.imshow(
    corr_df,
    color_continuous_scale="RdBu_r",
    color_continuous_midpoint=0,
    zmin=-1, zmax=1,
    text_auto=".2f",
    title="Cytokine Correlation Matrix (n=80 patients)",
)
fig.update_traces(textfont_size=10)
fig.show()

Module 4: go Graph Objects — Full Trace Control

plotly.graph_objects provides fine-grained access to every trace property: marker symbols, error bars, fill areas, and multi-trace layouts. Essential when px lacks the flexibility you need.

import plotly.graph_objects as go
import numpy as np

# Volcano plot built from scratch with go.Scatter
np.random.seed(99)
n_genes = 5000
log2fc = np.random.normal(0, 1.2, n_genes)
pval = np.random.uniform(0, 1, n_genes) ** 2  # skew toward low p-values
neg_log10_p = -np.log10(pval + 1e-300)
gene_names = [f"Gene_{i:04d}" for i in range(n_genes)]

# Classify genes
sig_mask = (np.abs(log2fc) > 1.5) & (neg_log10_p > 3)
up_mask = sig_mask & (log2fc > 0)
down_mask = sig_mask & (log2fc < 0)
ns_mask = ~sig_mask

fig = go.Figure()

# Non-significant background
fig.add_trace(go.Scatter(
    x=log2fc[ns_mask], y=neg_log10_p[ns_mask],
    mode="markers",
    name="Not significant",
    marker=dict(color="lightgray", size=4, opacity=0.5),
    text=[gene_names[i] for i in np.where(ns_mask)[0]],
    hovertemplate="<b>%{text}</b><br>log2FC: %{x:.2f}<br>-log10(p): %{y:.2f}<extra></extra>",
))

# Upregulated
fig.add_trace(go.Scatter(
    x=log2fc[up_mask], y=neg_log10_p[up_mask],
    mode="markers",
    name=f"Up ({up_mask.sum()} genes)",
    marker=dict(color="crimson", size=7, opacity=0.8),
    text=[gene_names[i] for i in np.where(up_mask)[0]],
    hovertemplate="<b>%{text}</b><br>log2FC: %{x:.2f}<br>-log10(p): %{y:.2f}<extra></extra>",
))

# Downregulated
fig.add_trace(go.Scatter(
    x=log2fc[down_mask], y=neg_log10_p[down_mask],
    mode="markers",
    name=f"Down ({down_mask.sum()} genes)",
    marker=dict(color="steelblue", size=7, opacity=0.8),
    text=[gene_names[i] for i in np.where(down_mask)[0]],
    hovertemplate="<b>%{text}</b><br>log2FC: %{x:.2f}<br>-log10(p): %{y:.2f}<extra></extra>",
))

# Threshold lines
fig.add_hline(y=3, line_dash="dash", line_color="black", line_width=1)
fig.add_vline(x=1.5, line_dash="dash", line_color="black", line_width=1)
fig.add_vline(x=-1.5, line_dash="dash", line_color="black", line_width=1)

fig.update_layout(
    title="Volcano Plot (Treatment vs Control, n=5000 genes)",
    xaxis_title="log2 Fold Change",
    yaxis_title="-log10(adjusted p-value)",
    legend=dict(x=0.01, y=0.99),
    width=750, height=550,
)
fig.show()
print(f"Up: {up_mask.sum()}, Down: {down_mask.sum()}, NS: {ns_mask.sum()}")

# Bar chart with error bars: mean ± SEM per treatment group
groups = ["Vehicle", "DrugA 1µM", "DrugA 10µM", "DrugB 1µM", "DrugB 10µM"]
means = [100.0, 82.3, 54.7, 91.2, 68.5]
sems = [3.2, 4.1, 3.8, 3.5, 4.7]

fig = go.Figure(go.Bar(
    x=groups, y=means,
    error_y=dict(type="data", array=sems, visible=True),
    marker_color=["gray", "lightsalmon", "crimson", "lightblue", "steelblue"],
    hovertemplate="%{x}<br>Mean: %{y:.1f}%<br>SEM: ±%{error_y.array:.1f}%<extra></extra>",
))

fig.update_layout(
    title="Cell Viability by Treatment (Mean ± SEM, n=6)",
    yaxis_title="Viability (%)", yaxis_range=[0, 120],
    xaxis_title="Treatment Group",
    showlegend=False,
)
fig.show()

Module 5: 3D and Specialized Charts

Plotly supports 3D scatter, surface plots, parallel coordinates, and treemaps — chart types unavailable in seaborn or standard matplotlib.

import plotly.express as px
import numpy as np
import pandas as pd

# 3D PCA scatter: cell clusters in embedding space
np.random.seed(42)
n_per_cluster = 80
cluster_centers = {"T cell": [3, 2, 1], "B cell": [-3, 1, 2], "Monocyte": [0, -3, -1]}

records = []
for ctype, center in cluster_centers.items():
    coords = np.random.normal(center, 0.8, (n_per_cluster, 3))
    for row in coords:
        records.append({
            "PC1": row[0], "PC2": row[1], "PC3": row[2],
            "cell_type": ctype,
            "score": np.random.uniform(0.5, 1.0),
        })

pca_df = pd.DataFrame(records)

fig = px.scatter_3d(
    pca_df, x="PC1", y="PC2", z="PC3",
    color="cell_type", size="score", opacity=0.7,
    hover_data={"score": ":.3f"},
    title="3D PCA — Single-Cell Transcriptomics",
)
fig.update_traces(marker=dict(sizeref=0.04))
fig.show()
print(f"Total cells: {len(pca_df)}, clusters: {pca_df['cell_type'].nunique()}")

import plotly.graph_objects as go
import numpy as np
import pandas as pd

# Parallel coordinates: multi-parameter drug screen
np.random.seed(5)
n_compounds = 200
drug_df = pd.DataFrame({
    "MW": np.random.normal(380, 60, n_compounds),
    "logP": np.random.uniform(-1, 6, n_compounds),
    "HBA": np.random.randint(2, 10, n_compounds),
    "HBD": np.random.randint(0, 6, n_compounds),
    "IC50_nM": np.random.lognormal(4, 1.5, n_compounds),
    "selectivity": np.random.uniform(1, 100, n_compounds),
})

fig = px.parallel_coordinates(
    drug_df,
    color="IC50_nM",
    color_continuous_scale="RdYlGn_r",
    dimensions=["MW", "logP", "HBA", "HBD", "IC50_nM", "selectivity"],
    labels={
        "MW": "MW (Da)", "logP": "logP",
        "HBA": "H-Bond Acceptors", "HBD": "H-Bond Donors",
        "IC50_nM": "IC50 (nM)", "selectivity": "Selectivity Index",
    },
    title="Drug Candidate Properties — Parallel Coordinates",
)
fig.show()

Module 6: Subplots and Export

make_subplots() creates multi-panel layouts with shared axes, mixed chart types, and independent traces per panel. fig.write_html() exports interactive figures; fig.write_image() exports static files via kaleido.

from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np

# Two-panel: raw data + summary statistics
np.random.seed(77)
doses = [0.01, 0.1, 1, 10, 100]
drugs = {"DrugA": {"EC50": 1.0, "hill": 1.5}, "DrugB": {"EC50": 8.0, "hill": 0.9}}

def hill_curve(dose, ec50, hill, top=100, bottom=0):
    return bottom + (top - bottom) / (1 + (ec50 / dose) ** hill)

fig = make_subplots(
    rows=1, cols=2,
    subplot_titles=["Dose-Response Curves", "IC50 Comparison"],
    shared_yaxes=False,
)

colors = {"DrugA": "crimson", "DrugB": "steelblue"}
ic50_values = []

for drug, params in drugs.items():
    # Smooth fit curve
    x_fit = np.logspace(-2, 2, 200)
    y_fit = hill_curve(x_fit, params["EC50"], params["hill"])
    fig.add_trace(go.Scatter(
        x=x_fit, y=y_fit, mode="lines",
        name=f"{drug} fit", line=dict(color=colors[drug], width=2.5),
    ), row=1, col=1)

    # Noisy data points
    y_data = [hill_curve(d, params["EC50"], params["hill"]) +
              np.random.normal(0, 4) for d in doses]
    fig.add_trace(go.Scatter(
        x=doses, y=y_data, mode="markers",
        name=f"{drug} data", marker=dict(color=colors[drug], size=9),
        showlegend=False,
    ), row=1, col=1)
    ic50_values.append(params["EC50"])

# Bar chart of IC50 values
fig.add_trace(go.Bar(
    x=list(drugs.keys()), y=ic50_values,
    marker_color=list(colors.values()),
    showlegend=False,
    hovertemplate="%{x}<br>IC50: %{y:.2f} µM<extra></extra>",
), row=1, col=2)

fig.update_xaxes(type="log", title_text="Dose (µM)", row=1, col=1)
fig.update_yaxes(title_text="Viability (%)", row=1, col=1)
fig.update_xaxes(title_text="Drug", row=1, col=2)
fig.update_yaxes(title_text="IC50 (µM)", row=1, col=2)
fig.update_layout(title="Dose-Response Dashboard", height=450, width=850)
fig.show()
print(f"Subplots: {len(fig.data)} traces across 2 panels")

# Export to HTML (interactive) and PNG (static)
# Requires: pip install kaleido

fig.write_html("dose_response_dashboard.html")
print("Saved: dose_response_dashboard.html (interactive, shareable)")

fig.write_image("dose_response_dashboard.png", width=1200, height=600, scale=2)
print("Saved: dose_response_dashboard.png (300 DPI equivalent with scale=2)")

fig.write_image("dose_response_dashboard.svg")
print("Saved: dose_response_dashboard.svg (vector, editable in Inkscape/Illustrator)")

Common Workflows

Workflow 1: Interactive Volcano Plot with Gene Annotations

Goal: Build a fully annotated volcano plot from DESeq2 results, with gene-name hover tooltips, threshold lines, and highlighted hit labels for sharing as HTML.

import plotly.graph_objects as go
import pandas as pd
import numpy as np

# Simulate DESeq2 output (replace with pd.read_csv("deseq2_results.csv"))
np.random.seed(42)
n = 3000
df = pd.DataFrame({
    "gene": [f"GENE_{i:04d}" for i in range(n)],
    "log2FC": np.random.normal(0, 1.0, n),
    "padj": np.clip(np.random.exponential(0.1, n), 1e-20, 1.0),
    "baseMean": np.random.lognormal(5, 1.5, n),
})
# Inject some hits
df.loc[:20, "log2FC"] = np.random.uniform(2.5, 5, 21)
df.loc[:20, "padj"] = np.random.uniform(1e-15, 1e-5, 21)
df.loc[21:35, "log2FC"] = np.random.uniform(-4, -2, 15)
df.loc[21:35, "padj"] = np.random.uniform(1e-12, 1e-4, 15)

df["neg_log10_padj"] = -np.log10(df["padj"].clip(1e-300))

# Classify
FC_THRESH, P_THRESH = 1.5, 2.0   # |log2FC| > 1.5, -log10(padj) > 2
df["category"] = "NS"
df.loc[(df["log2FC"] > FC_THRESH) & (df["neg_log10_padj"] > P_THRESH), "category"] = "Up"
df.loc[(df["log2FC"] < -FC_THRESH) & (df["neg_log10_padj"] > P_THRESH), "category"] = "Down"

color_map = {"NS": "lightgray", "Up": "crimson", "Down": "steelblue"}
size_map = {"NS": 4, "Up": 7, "Down": 7}
opacity_map = {"NS": 0.4, "Up": 0.85, "Down": 0.85}

fig = go.Figure()

for cat in ["NS", "Up", "Down"]:
    sub = df[df["category"] == cat]
    fig.add_trace(go.Scatter(
        x=sub["log2FC"], y=sub["neg_log10_padj"],
        mode="markers",
        name=f"{cat} (n={len(sub)})",
        marker=dict(
            color=color_map[cat],
            size=size_map[cat],
            opacity=opacity_map[cat],
        ),
        customdata=sub[["gene", "padj", "baseMean"]].values,
        hovertemplate=(
            "<b>%{customdata[0]}</b><br>"
            "log2FC: %{x:.3f}<br>"
            "padj: %{customdata[1]:.2e}<br>"
            "baseMean: %{customdata[2]:.1f}<extra></extra>"
        ),
    ))

# Threshold lines
fig.add_hline(y=P_THRESH, line_dash="dot", line_color="black", line_width=1.2,
              annotation_text=f"padj=0.01", annotation_position="right")
fig.add_vline(x=FC_THRESH, line_dash="dot", line_color="black", line_width=1.2)
fig.add_vline(x=-FC_THRESH, line_dash="dot", line_color="black", line_width=1.2)

# Label top 5 upregulated hits by significance
top_up = df[df["category"] == "Up"].nlargest(5, "neg_log10_padj")
for _, row in top_up.iterrows():
    fig.add_annotation(
        x=row["log2FC"], y=row["neg_log10_padj"],
        text=row["gene"], showarrow=True,
        arrowhead=2, arrowsize=1, arrowcolor="crimson",
        font=dict(size=9, color="crimson"),
        xshift=8, yshift=5,
    )

fig.update_layout(
    title="Volcano Plot — Treatment vs Control (DESeq2)",
    xaxis_title="log2 Fold Change",
    yaxis_title="-log10(adjusted p-value)",
    legend=dict(x=0.01, y=0.99, bordercolor="lightgray", borderwidth=1),
    width=800, height=560,
    plot_bgcolor="white",
)
fig.update_xaxes(showgrid=True, gridcolor="lightgray", zeroline=True, zerolinecolor="darkgray")
fig.update_yaxes(showgrid=True, gridcolor="lightgray")

fig.write_html("volcano_interactive.html")
print(f"Up: {(df.category=='Up').sum()}, Down: {(df.category=='Down').sum()}")
print("Saved: volcano_interactive.html")

Workflow 2: Multi-Panel Dose-Response Dashboard with make_subplots

Goal: Display dose-response curves for multiple drugs across cell lines in a grid layout with a shared color scale and consistent formatting.

from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np
import pandas as pd

# Simulated IC50 data for 3 drugs × 3 cell lines
np.random.seed(10)
drugs = ["DrugA", "DrugB", "DrugC"]
cell_lines = ["HCT116", "MCF7", "A549"]
doses = np.logspace(-2, 2, 7)  # 0.01 to 100 µM

def hill(x, ec50, hill_n, top=100, bottom=0):
    return bottom + (top - bottom) / (1 + (ec50 / x) ** hill_n)

ec50_table = {
    ("DrugA", "HCT116"): 0.5, ("DrugA", "MCF7"): 2.0, ("DrugA", "A549"): 8.0,
    ("DrugB", "HCT116"): 5.0, ("DrugB", "MCF7"): 0.8, ("DrugB", "A549"): 15.0,
    ("DrugC", "HCT116"): 12.0, ("DrugC", "MCF7"): 6.0, ("DrugC", "A549"): 1.2,
}

palette = px_colors = ["#EF553B", "#636EFA", "#00CC96", "#AB63FA", "#FFA15A",
                        "#19D3F3", "#FF6692", "#B6E880", "#FF97FF"]

fig = make_subplots(
    rows=len(drugs), cols=len(cell_lines),
    subplot_titles=[f"{d} / {c}" for d in drugs for c in cell_lines],
    shared_xaxes=True, shared_yaxes=True,
    vertical_spacing=0.08, horizontal_spacing=0.04,
)

for r, drug in enumerate(drugs, start=1):
    for c, cell_line in enumerate(cell_lines, start=1):
        ec50 = ec50_table[(drug, cell_line)]
        x_fit = np.logspace(-2, 2, 200)
        y_fit = hill(x_fit, ec50, hill_n=1.5)

        # Noisy replicate data
        y_data = np.array([hill(d, ec50, 1.5) + np.random.normal(0, 5) for d in doses])

        color = palette[(r - 1) * len(cell_lines) + (c - 1)]
        show_legend = (c == 1 and r == 1)

        fig.add_trace(go.Scatter(
            x=x_fit, y=y_fit, mode="lines",
            line=dict(color=color, width=2),
            name=f"{drug}/{cell_line}",
            showlegend=False,
            hovertemplate=f"{drug} in {cell_line}<br>Dose: %{{x:.2f}} µM<br>Viability: %{{y:.1f}}%<extra></extra>",
        ), row=r, col=c)

        fig.add_trace(go.Scatter(
            x=doses, y=np.clip(y_data, 0, 110), mode="markers",
            marker=dict(color=color, size=7, opacity=0.8),
            showlegend=False,
            hovertemplate=f"Measured<br>Dose: %{{x:.2f}} µM<br>Viability: %{{y:.1f}}%<extra></extra>",
        ), row=r, col=c)

        # IC50 annotation
        fig.add_annotation(
            x=np.log10(ec50), y=50,
            text=f"IC50={ec50:.1f}µM",
            font=dict(size=8), showarrow=False,
            xref=f"x{(r-1)*len(cell_lines)+c if (r-1)*len(cell_lines)+c > 1 else ''}",
            yref=f"y{(r-1)*len(cell_lines)+c if (r-1)*len(cell_lines)+c > 1 else ''}",
        )

# Apply log scale to all x-axes
for i in range(1, len(drugs) * len(cell_lines) + 1):
    axis_key = f"xaxis{i if i > 1 else ''}"
    fig.layout[axis_key].update(type="log", title_text="Dose (µM)" if i > 6 else "")

for i in range(1, len(drugs) * len(cell_lines) + 1):
    axis_key = f"yaxis{i if i > 1 else ''}"
    fig.layout[axis_key].update(range=[-5, 115],
                                 title_text="Viability (%)" if i in [1, 4, 7] else "")

fig.update_layout(
    title="Dose-Response Dashboard — 3 Drugs × 3 Cell Lines",
    height=700, width=900,
)
fig.write_html("dose_response_dashboard.html")
print("Saved: dose_response_dashboard.html")
print(f"Grid: {len(drugs)} drugs × {len(cell_lines)} cell lines = {len(drugs)*len(cell_lines)} panels")

Key Parameters

|-----------|------------------|---------|-----------------|--------|

| hover_data | px.* | {} | Dict or list of column names | Extra columns shown in hover tooltip |

Best Practices

Prefer px for DataFrame data, fall back to go for multi-trace composition. Use px.scatter() and its siblings for 80% of plots. Switch to go when you need traces with different types in the same figure (e.g., scatter + filled area) or need fine-grained per-trace control.

   # Correct: px for simple grouped plots
   fig = px.box(df, x="treatment", y="expression", color="genotype")

   # Correct: go when px cannot express the structure
   fig = go.Figure()
   fig.add_trace(go.Scatter(x=x, y=y_upper, fill="tonexty", ...))
   fig.add_trace(go.Scatter(x=x, y=y_lower, ...))

Always include hovertemplate for scientific figures. The default tooltip shows raw coordinates without units or gene names. A custom template with customdata provides full biological context.

   fig.add_trace(go.Scatter(
       customdata=df[["gene", "padj"]].values,
       hovertemplate="<b>%{customdata[0]}</b><br>padj: %{customdata[1]:.2e}<extra></extra>",
   ))

Export HTML for sharing, PNG/SVG for journals. fig.write_html() produces a self-contained file with no external dependencies. Use scale=2 or higher with write_image() to achieve sufficient resolution for print.

Don't use fig.show() in batch scripts. In non-interactive contexts (CI, HPC, cron jobs), fig.show() may open a browser window or fail. Use write_html() or write_image() exclusively.

Use color_continuous_midpoint=0 for diverging palettes on z-score data. Without it, the midpoint color defaults to the data midpoint, not zero, misrepresenting symmetric fold changes or correlations.

   fig = px.imshow(corr_matrix, color_continuous_scale="RdBu_r",
                   color_continuous_midpoint=0, zmin=-1, zmax=1)

Set plot_bgcolor="white" for publication figures. Plotly defaults to a light-gray grid background. White background with subtle gridlines is cleaner for most scientific contexts.

   fig.update_layout(plot_bgcolor="white")
   fig.update_xaxes(showgrid=True, gridcolor="lightgray")
   fig.update_yaxes(showgrid=True, gridcolor="lightgray")

Common Recipes

When to use: Overlay multiple conditions in one figure with a dropdown button to show/hide individual traces cleanly.

import plotly.graph_objects as go
import numpy as np

conditions = ["Untreated", "DrugA", "DrugB"]
colors = ["gray", "crimson", "steelblue"]
np.random.seed(1)
x = np.linspace(0, 24, 49)

fig = go.Figure()
for i, (cond, color) in enumerate(zip(conditions, colors)):
    y = np.sin(x / 4 + i * 0.5) * (1 - i * 0.2) + np.random.normal(0, 0.05, len(x))
    fig.add_trace(go.Scatter(
        x=x, y=y, mode="lines+markers",
        name=cond, line=dict(color=color, width=2),
        visible=(i == 0),  # only first trace visible initially
    ))

# One button per condition (shows only that trace)
buttons = []
for i, cond in enumerate(conditions):
    visibility = [j == i for j in range(len(conditions))]
    buttons.append(dict(label=cond, method="update",
                        args=[{"visible": visibility}, {"title": f"Gene Expression — {cond}"}]))

# "Show All" button
buttons.append(dict(label="Show All", method="update",
                    args=[{"visible": [True] * len(conditions)}, {"title": "Gene Expression — All Conditions"}]))

fig.update_layout(
    updatemenus=[dict(type="dropdown", x=0.01, y=1.15, showactive=True, buttons=buttons)],
    title="Gene Expression — Untreated",
    xaxis_title="Time (h)", yaxis_title="Relative Expression",
)
fig.show()

Recipe: Annotating Specific Hits with Arrows

When to use: Label outliers, drug hits, or significant genes directly on the figure without cluttering non-annotated points.

import plotly.graph_objects as go
import numpy as np
import pandas as pd

np.random.seed(33)
df = pd.DataFrame({
    "x": np.random.normal(0, 1.5, 300),
    "y": np.random.normal(0, 1.5, 300),
    "gene": [f"G{i:03d}" for i in range(300)],
})
# Inject top hits
hits = pd.DataFrame({
    "x": [3.2, -2.8, 2.5, -3.5],
    "y": [4.1, 3.8, -3.2, -2.9],
    "gene": ["BRCA1", "TP53", "EGFR", "KRAS"],
})

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=df["x"], y=df["y"], mode="markers",
    marker=dict(color="lightgray", size=5, opacity=0.6),
    text=df["gene"],
    hovertemplate="<b>%{text}</b><br>x: %{x:.2f}, y: %{y:.2f}<extra></extra>",
    name="Background",
))
fig.add_trace(go.Scatter(
    x=hits["x"], y=hits["y"], mode="markers",
    marker=dict(color="crimson", size=10, symbol="diamond"),
    text=hits["gene"],
    hovertemplate="<b>%{text}</b> [HIT]<br>x: %{x:.2f}, y: %{y:.2f}<extra></extra>",
    name="Hits",
))

for _, row in hits.iterrows():
    fig.add_annotation(
        x=row["x"], y=row["y"],
        text=f"<b>{row['gene']}</b>",
        showarrow=True, arrowhead=2, arrowwidth=1.5,
        arrowcolor="crimson", font=dict(size=11, color="crimson"),
        ax=25, ay=-30,  # arrow offset in pixels
        bgcolor="rgba(255,255,255,0.7)", bordercolor="crimson", borderwidth=1,
    )

fig.update_layout(
    title="Hit Identification with Arrow Annotations",
    xaxis_title="Score A", yaxis_title="Score B",
    plot_bgcolor="white",
)
fig.show()

Troubleshooting

| Problem | Cause | Solution |

|---------|-------|----------|

| ValueError: kaleido is required for static image export | kaleido not installed | pip install kaleido; verify with import kaleido |

| Blank figure in Jupyter Notebook | Renderer not configured | Run import plotly.io as pio; pio.renderers.default = "notebook" or upgrade JupyterLab to ≥3 |

| fig.show() opens blank browser tab | No data in figure or offline renderer issue | Check len(fig.data) > 0; use pio.renderers.default = "browser" |

| Hover tooltips show wrong values | customdata index mismatch in hovertemplate | Verify customdata column order matches %{customdata[N]} indices in template |

| Colors not assigned consistently across traces | px re-orders palette when category counts differ | Use color_discrete_map={"Cat1": "#color1", ...} to pin colors explicitly |

| write_image produces blurry PNG | Default scale=1 is too low for print | Use fig.write_image("fig.png", scale=2) for 150 DPI or scale=4 for 300 DPI |

| Subplots x-axes not all log-scaled after shared_xaxes=True | Shared axis only synchronizes range, not type | Iterate over all xaxis keys in fig.layout and set type="log" explicitly |

| Large datasets slow to render in browser | Too many individual points in a single scatter trace | Downsample background noise points; keep labeled hits as a separate, smaller trace |

| fig.update_layout does not apply to subplot axes | Multi-panel figures use indexed axes (xaxis2, xaxis3) | Use fig.update_xaxes() (applies to all) or target fig.layout["xaxis2"] explicitly |

seaborn-statistical-plots — use for statistical aggregation (confidence intervals, regression), publication-quality static figures with minimal code, and when matplotlib-level output is required
matplotlib-scientific-plotting — use for full control over every figure element, custom layouts, embedded text rendering, and journal-specification figure preparation
pydeseq2-differential-expression — volcano plot outputs from DESeq2 results are a primary input for the interactive volcano workflow above
scanpy-scrna-seq — Scanpy's UMAP embeddings can be visualized interactively in 3D with px.scatter_3d

References

Plotly Python Documentation — official API reference, examples gallery, and getting-started guides
Plotly Express API Reference — complete px function signatures and parameters
Plotly Graph Objects Reference — full go trace and layout attribute reference
Plotly GitHub Repository — source code, issue tracker, changelog
Kaleido Static Image Export — dependency for write_image() PNG/SVG/PDF export

How to use it

Copy the folder

Take jaechang-hits/plotly-interactive-plots from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.

Plotly Interactive Plots

Install

The instruction itself

Plotly Interactive Plots

Overview

When to Use

Prerequisites

Quick Start

Core API

Module 1: px Scatter and Line — Relational Plots

Module 2: px Statistical Plots — Distributions and Categories

Module 3: px Heatmap and Matrix — Gene Expression and Correlations

Module 4: go Graph Objects — Full Trace Control

Module 5: 3D and Specialized Charts

Module 6: Subplots and Export

Common Workflows

Workflow 1: Interactive Volcano Plot with Gene Annotations

Workflow 2: Multi-Panel Dose-Response Dashboard with make_subplots

Key Parameters

Best Practices

Common Recipes

Recipe: Dropdown Menu to Toggle Between Conditions

Recipe: Annotating Specific Hits with Arrows

Troubleshooting

Related Skills

References

How to use it