"""
Module for plotting quantitative proteomics data.
"""
from typing import Dict
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.figure_factory import create_distplot
# ! only has static methods, so scoping in the module should be enough
# import plotldatapoint
# plotdatapoint.function
[docs]
class PlotDataPoint:
"""
Class for plotting data points.
"""
[docs]
@staticmethod
def plot_fold_change_histogram(
result_df: pd.DataFrame, species_ratio: Dict[str, Dict[str, str]], hide_annot: bool = False
) -> go.Figure:
"""
Plot smooth shaded density distributions of log2 fold changes using Plotly, color-coded by species.
Parameters
----------
result_df : pd.DataFrame
The results DataFrame containing fold changes and species data.
species_ratio : Dict[str, Dict[str, str]]
A dictionary mapping species to their respective colors and ratios.
Returns
-------
go.Figure
A Plotly figure object representing the shaded density plots.
"""
species_list = list(species_ratio.keys())
# Filter to include only rows where any of the species columns are True
result_df = result_df[result_df[species_list].any(axis=1)]
result_df["kind"] = result_df[species_list].apply(lambda x: species_list[np.argmax(x)], axis=1)
# Prepare lists for create_distplot
data = []
group_labels = []
colors = []
for species in species_list:
data.append(result_df.loc[result_df["kind"] == species, "log2_A_vs_B"].dropna().tolist())
group_labels.append(species)
colors.append(species_ratio[species]["color"])
# Create the distplot without histogram or rug
fig = create_distplot(data, group_labels, colors=colors, show_hist=False, show_rug=False)
# Update each density trace to fill under the curve
for trace in fig.data:
if trace.mode == "lines":
trace.update(fill="tozeroy", opacity=0.4) # adjust opacity as needed
# Customize layout
fig.update_layout(
width=700,
height=700,
xaxis=dict(title="log2_A_vs_B", color="black", gridwidth=1, linecolor="black", range=[-4, 4]),
yaxis=dict(title="Density", color="black", gridwidth=1, linecolor="black"),
)
# Add vertical lines for expected ratios
ratio_map = {species: np.log2(data["A_vs_B"]) for species, data in species_ratio.items()}
for species, ratio in ratio_map.items():
fig.add_vline(
x=ratio, line_dash="dash", line_color=species_ratio[species]["color"], annotation_text=species
)
fig.add_annotation(
x=0.5,
y=0.5,
xref="paper",
yref="paper",
text="-Beta-" if not hide_annot else "",
font=dict(size=50, color="rgba(0,0,0,0.1)"),
showarrow=False,
)
return fig
[docs]
@staticmethod
def plot_metric(
benchmark_metrics_df: pd.DataFrame,
metric: str = "Median",
software_colors: Dict[str, str] = {
"MaxQuant": "#8bc6fd",
"AlphaPept": "#17212b",
"ProlineStudio": "#8b26ff",
"MSAngel": "#C0FA7D",
"FragPipe": "#F89008",
"i2MassChroQ": "#108E2E",
"Sage": "#E43924",
"WOMBAT": "#663200",
"DIA-NN": "#d42f2f",
"AlphaDIA": "#1D2732",
"Custom": "#000000",
"Spectronaut": "#007548",
"FragPipe (DIA-NN quant)": "#F89008",
"MSAID": "#bfef45",
"MetaMorpheus": "#637C7A",
"Proteome Discoverer": "#911eb4",
"PEAKS": "#f032e6",
"quantms": "#f5e830",
},
mapping: Dict[str, int] = {"old": 10, "new": 20},
highlight_color: str = "#d30067",
label: str = "None",
legend_name_map: Dict[str, str] = {
"AlphaPept": "AlphaPept (legacy tool)",
},
hide_annot: bool = False,
) -> go.Figure:
"""
Plot mean metrics in a scatter plot with Plotly, highlighting specific data points.
Parameters
----------
benchmark_metrics_df : pd.DataFrame
The DataFrame containing benchmark metrics data.
metric : str, optional
The metric to plot, either "Median" or "Mean", by default "Median".
software_colors : Dict[str, str], optional
A dictionary mapping software names to their colors, by default predefined colors.
mapping : Dict[str, int], optional
A dictionary mapping categories to scatter plot sizes, by default {"old": 10, "new": 20}.
highlight_color : str, optional
The color used for highlighting certain points, by default "#d30067".
label : str, optional
The column name for labeling data points, by default "None".
legend_name_map : Dict[str, str], optional
A dictionary mapping software names to legend names, by default None.
If None, the software names will be used as legend names.
Returns
-------
go.Figure
A Plotly figure object representing the scatter plot.
"""
all_median_abs_epsilon = [
v2["median_abs_epsilon"] for v in benchmark_metrics_df["results"] for v2 in v.values()
]
all_mean_abs_epsilon = [v2["mean_abs_epsilon"] for v in benchmark_metrics_df["results"] for v2 in v.values()]
all_nr_prec = [v2["nr_prec"] for v in benchmark_metrics_df["results"] for v2 in v.values()]
# Add hover text with detailed information for each data point
hover_texts = []
for idx, _ in benchmark_metrics_df.iterrows():
datapoint_text = ""
if benchmark_metrics_df.is_temporary[idx] == True:
datapoint_text = (
f"ProteoBench ID: {benchmark_metrics_df.id[idx]}<br>"
+ f"Software tool: {benchmark_metrics_df.software_name[idx]} {benchmark_metrics_df.software_version[idx]}<br>"
)
if "comments" in benchmark_metrics_df.columns:
comment = benchmark_metrics_df.comments[idx]
if isinstance(comment, str):
datapoint_text = (
datapoint_text
+ f"Comment (private submission): {comment[:10] + '...' if len(comment) > 10 else comment}..."
)
else:
# TODO: Determine parameters based on module
datapoint_text = (
f"ProteoBench ID: {benchmark_metrics_df.id[idx]}<br>"
+ f"Software tool: {benchmark_metrics_df.software_name[idx]} {benchmark_metrics_df.software_version[idx]}<br>"
+ f"Search engine: {benchmark_metrics_df.search_engine[idx]} {benchmark_metrics_df.search_engine_version[idx]}<br>"
+ f"FDR psm: {benchmark_metrics_df.ident_fdr_psm[idx]}<br>"
+ f"MBR: {benchmark_metrics_df.enable_match_between_runs[idx]}<br>"
+ f"Precursor Tolerance: {benchmark_metrics_df.precursor_mass_tolerance[idx]}<br>"
+ f"Fragment Tolerance: {benchmark_metrics_df.fragment_mass_tolerance[idx]}<br>"
+ f"Enzyme: {benchmark_metrics_df.enzyme[idx]} <br>"
+ f"Missed Cleavages: {benchmark_metrics_df.allowed_miscleavages[idx]}<br>"
+ f"Min peptide length: {benchmark_metrics_df.min_peptide_length[idx]}<br>"
+ f"Max peptide length: {benchmark_metrics_df.max_peptide_length[idx]}<br>"
)
if "submission_comments" in benchmark_metrics_df.columns:
comment = benchmark_metrics_df.submission_comments[idx]
if isinstance(comment, str):
datapoint_text = (
datapoint_text
+ f"Comment (public submission): {comment[:10] + '...' if len(comment) > 10 else comment}..."
)
hover_texts.append(datapoint_text)
scatter_size = [mapping[item] for item in benchmark_metrics_df["old_new"]]
if "Highlight" in benchmark_metrics_df.columns:
scatter_size = [
item * 2 if highlight else item
for item, highlight in zip(scatter_size, benchmark_metrics_df["Highlight"])
]
# Color plot based on software tool
colors = [software_colors[software] for software in benchmark_metrics_df["software_name"]]
if "Highlight" in benchmark_metrics_df.columns:
colors = [
highlight_color if highlight else item
for item, highlight in zip(colors, benchmark_metrics_df["Highlight"])
]
benchmark_metrics_df["color"] = colors
benchmark_metrics_df["hover_text"] = hover_texts
benchmark_metrics_df["scatter_size"] = scatter_size
if metric == "Median":
layout_xaxis_range = [
min(all_median_abs_epsilon) - min(all_median_abs_epsilon) * 0.05,
max(all_median_abs_epsilon) + max(all_median_abs_epsilon) * 0.05,
]
layout_xaxis_title = (
"Median absolute difference between measured and expected log2-transformed fold change."
)
elif metric == "Mean":
layout_xaxis_range = [
min(all_mean_abs_epsilon) - min(all_mean_abs_epsilon) * 0.05,
max(all_mean_abs_epsilon) + max(all_mean_abs_epsilon) * 0.05,
]
layout_xaxis_title = "Mean absolute difference between measured and expected log2-transformed fold change."
fig = go.Figure(
layout_yaxis_range=[
min(all_nr_prec) - min(max(all_nr_prec) * 0.05, 2000),
max(all_nr_prec) + min(max(all_nr_prec) * 0.05, 2000),
],
layout_xaxis_range=layout_xaxis_range,
)
# Get all unique color-software combinations (necessary for highlighting)
color_software_combinations = benchmark_metrics_df[["color", "software_name"]].drop_duplicates()
benchmark_metrics_df["enable_match_between_runs"] = benchmark_metrics_df["enable_match_between_runs"].astype(
str
)
# plot the data points, one trace per software tool
for _, row in color_software_combinations.iterrows():
color = row["color"]
software = row["software_name"]
tmp_df = benchmark_metrics_df[
(benchmark_metrics_df["color"] == color) & (benchmark_metrics_df["software_name"] == software)
]
# to do: remove this line as soon as parameters are homogeneous, see #380
# tmp_df["enable_match_between_runs"] = tmp_df["enable_match_between_runs"].astype(str)
fig.add_trace(
go.Scatter(
x=tmp_df["{}_abs_epsilon".format(metric.lower())],
y=tmp_df["nr_prec"],
mode="markers" if label == "None" else "markers+text",
hovertext=tmp_df["hover_text"],
text=tmp_df[label] if label != "None" else None,
marker=dict(color=tmp_df["color"], showscale=False),
marker_size=tmp_df["scatter_size"],
name=legend_name_map.get(tmp_df["software_name"].iloc[0], tmp_df["software_name"].iloc[0]),
)
)
fig.update_layout(
width=700,
height=700,
xaxis=dict(
title=layout_xaxis_title,
gridcolor="white",
gridwidth=2,
linecolor="black",
),
yaxis=dict(
title="Total number of precursor ions quantified in the selected number of raw files",
gridcolor="white",
gridwidth=2,
linecolor="black",
),
)
fig.update_xaxes(showgrid=True, gridcolor="lightgray", gridwidth=1)
fig.update_yaxes(showgrid=True, gridcolor="lightgray", gridwidth=1)
fig.add_annotation(
x=0.5,
y=0.5,
xref="paper",
yref="paper",
text="-Beta-" if not hide_annot else "",
font=dict(size=50, color="rgba(0,0,0,0.1)"),
showarrow=False,
)
fig.update_layout(clickmode="event+select")
return fig
[docs]
@staticmethod
def plot_CV_violinplot(result_df: pd.DataFrame) -> go.Figure:
"""
Plot the coefficient of variation (CV) for A and B groups using a violin plot.
Parameters
----------
result_df : pd.DataFrame
The results DataFrame containing the CV data.
Returns
-------
go.Figure:
A Plotly figure object representing the violin plot.
"""
fig = px.violin(result_df, y=["CV_A", "CV_B"], box=True, title=None, points=False)
fig.update_layout(
xaxis_title="Condition",
yaxis_title="CV",
xaxis=dict(linecolor="black"), # Set the X axis line color to black
yaxis=dict(linecolor="black"), # Set the Y axis line color to black
)
return fig
[docs]
@staticmethod
def plot_ma_plot(result_df: pd.DataFrame, species_ratio: Dict[str, Dict[str, str]]) -> go.Figure:
"""
Plot a MA plot using Plotly.
Parameters
----------
result_df : pd.DataFrame
The results DataFrame containing the MA plot data.
species_ratio : Dict[str, Dict[str, str]]
A dictionary mapping species to their respective colors and ratios.
Returns
-------
go.Figure
A Plotly figure object representing the MA plot.
"""
color_map = {species: data["color"] for species, data in species_ratio.items()}
# take mean of log intensity mean a and log intensity mean b
result_df["logIntensityMean"] = (result_df["log_Intensity_mean_A"] + result_df["log_Intensity_mean_B"]) / 2
fig = px.scatter(
result_df,
x="log2_A_vs_B",
y="logIntensityMean",
color="species",
color_discrete_map=color_map,
labels={"log2_A_vs_B": "log2_FC(A:B)", "logIntensityMean": "log2_Intensity_Mean", "species": "Organism"},
title="log2FC vs logIntensityMean",
size_max=10,
opacity=0.6,
)
# Add vertical lines for expected ratios
ratio_map = {species: np.log2(data["A_vs_B"]) for species, data in species_ratio.items()}
for species, ratio in ratio_map.items():
fig.add_vline(
x=ratio, line_dash="dash", line_color=species_ratio[species]["color"], annotation_text=species
)
fig.update_traces(marker=dict(size=6)) # Marker size approx. equivalent to s=10 in seaborn
return fig
