我正在尝试使用Python的Dash-plotly库生成ManhattanPlot:https://dash.plotly.com/dash-bio/manhattanplot
我有小麦等植物的 SNP 结果数据，这些植物的染色体名称带有字母，例如: 3A、3B、3D。
是否可以在plotly/python中处理此类标识符(带字母后缀)？
在文档中有以下关于染色体 ID 的注释:
chrm (string; default 'CHR'): A string denoting the column name for the chromosome. This column must be float or integer. Minimum number of chromosomes required is 1. If you have X, Y, or MT chromosomes, be sure to renumber these 23, 24, 25, etc.
我有许多植物基因组的数据，其中染色体名称中包含字母。 看来 CHR 列不能有字符串值，这从用户的角度来看很奇怪。
最佳答案
这看起来像是一个不幸的案例，dashbio.ManhattanPlot 对象的开发者对 chrm 参数做出了假设。
查看 dash-bio 存储库中的 _manhattan.py，您可以首先注释掉 these lines这样 CHRM 列不需要是数字。
编辑:其他更改是将 self.ticksLabels 更新为字符串而不是整数，并在决定何时显示每个其他刻度标签而不是所有刻度标签时将阈值从 10 个刻度增加到 20 个刻度。我在下面添加了本地版本的 _manhattan.py 文件:
from __future__ import absolute_import
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
import plotly.graph_objects as go
from .utils import _get_hover_text
SUGGESTIVE_LINE_LABEL = "suggestive line"
GENOMEWIDE_LINE_LABEL = "genomewide line"
def ManhattanPlot(
dataframe,
chrm="CHR",
bp="BP",
p="P",
snp="SNP",
gene="GENE",
annotation=None,
logp=True,
title="Manhattan Plot",
showgrid=True,
xlabel=None,
ylabel='-log10(p)',
point_size=5,
showlegend=True,
col=None,
suggestiveline_value=-np.log10(1e-8),
suggestiveline_color='#636efa',
suggestiveline_width=1,
genomewideline_value=-np.log10(5e-8),
genomewideline_color='#EF553B',
genomewideline_width=1,
highlight=True,
highlight_color="red",
):
"""Returns a figure for a manhattan plot.
Keyword arguments:
- dataframe (dataframe; required): A pandas dataframe which must contain at
least the following three columns:
- the chromosome number
- genomic base-pair position
- a numeric quantity to plot such as a p-value or zscore
- chrm (string; default 'CHR'): A string denoting the column name for
the chromosome. This column must be float or integer. Minimum
number of chromosomes required is 1. If you have X, Y, or MT
chromosomes, be sure to renumber these 23, 24, 25, etc.
- bp (string; default 'BP'): A string denoting the column name for the
chromosomal position.
- p (string; default 'P'): A string denoting the column name for the
float quantity to be plotted on the y-axis. This column must be
numeric. It does not have to be a p-value. It can be any numeric
quantity such as peak heights, Bayes factors, test statistics. If
it is not a p-value, make sure to set logp = False.
- snp (string; default 'SNP'): A string denoting the column name for
the SNP names (e.g., rs number). More generally, this column could
be anything that identifies each point being plotted. For example,
in an Epigenomewide association study (EWAS), this could be the
probe name or cg number. This column should be a character. This
argument is optional, however it is necessary to specify it if you
want to highlight points on the plot, using the highlight argument
in the figure method.
- gene (string; default 'GENE'): A string denoting the column name for
the GENE names. This column could be a string or a float. More
generally, it could be any annotation information that you want
to include in the plot.
- annotation (string; optional): A string denoting the column to use
as annotations. This column could be a string or a float. It
could be any annotation information that you want to include in
the plot (e.g., zscore, effect size, minor allele frequency).
- logp (bool; optional): If True, the -log10 of the p-value is
plotted. It isn't very useful to plot raw p-values; however,
plotting the raw value could be useful for other genome-wide plots
(e.g., peak heights, Bayes factors, test statistics, other
"scores", etc.)
- title (string; default 'Manhattan Plot'): The title of the graph.
- showgrid (bool; default true): Boolean indicating whether gridlines
should be shown.
- xlabel (string; optional): Label of the x axis.
- ylabel (string; default '-log10(p)'): Label of the y axis.
- point_size (number; default 5): Size of the points of the Scatter
plot.
- showlegend (bool; default true): Boolean indicating whether legends
should be shown.
- col (string; optional): A string representing the color of the
points of the scatter plot. Can be in any color format accepted by
plotly.graph_objects.
- suggestiveline_value (bool | float; default 8): A value which must
be either False to deactivate the option, or a numerical value
corresponding to the p-value at which the line should be drawn.
The line has no influence on the data points.
- suggestiveline_color (string; default 'grey'): Color of the suggestive
line.
- suggestiveline_width (number; default 2): Width of the suggestive
line.
- genomewideline_value (bool | float; default -log10(5e-8)): A boolean
which must be either False to deactivate the option, or a numerical value
corresponding to the p-value above which the data points are
considered significant.
- genomewideline_color (string; default 'red'): Color of the genome-wide
line. Can be in any color format accepted by plotly.graph_objects.
- genomewideline_width (number; default 1): Width of the genome-wide
line.
- highlight (bool; default True): turning on/off the highlighting of
data points considered significant.
- highlight_color (string; default 'red'): Color of the data points
highlighted because they are significant. Can be in any color
format accepted by plotly.graph_objects.
# ...
Example 1: Random Manhattan Plot
'''
dataframe = pd.DataFrame(
np.random.randint(0,100,size=(100, 3)),
columns=['P', 'CHR', 'BP'])
fig = create_manhattan(dataframe, title='XYZ Manhattan plot')
plotly.offline.plot(fig, image='png')
'''
"""
mh = _ManhattanPlot(
dataframe,
chrm=chrm,
bp=bp,
p=p,
snp=snp,
gene=gene,
annotation=annotation,
logp=logp
)
return mh.figure(
title=title,
showgrid=showgrid,
xlabel=xlabel,
ylabel=ylabel,
point_size=point_size,
showlegend=showlegend,
col=col,
suggestiveline_value=suggestiveline_value,
suggestiveline_color=suggestiveline_color,
suggestiveline_width=suggestiveline_width,
genomewideline_value=genomewideline_value,
genomewideline_color=genomewideline_color,
genomewideline_width=genomewideline_width,
highlight=highlight,
highlight_color=highlight_color
)
class _ManhattanPlot():
def __init__(
self,
x,
chrm="CHR",
bp="BP",
p="P",
snp="SNP",
gene="GENE",
annotation=None,
logp=True
):
"""
Keyword arguments:
- dataframe (dataframe; required): A pandas dataframe which
must contain at least the following three columns:
- the chromosome number
- genomic base-pair position
- a numeric quantity to plot such as a p-value or zscore
- chrm (string; default 'CHR'): A string denoting the column name for the
chromosome. This column must be float or integer. Minimum number
of chromosomes required is 1. If you have X, Y, or MT chromosomes,
be sure to renumber these 23, 24, 25, etc.
- bp (string; default 'BP'): A string denoting the column name for the
chromosomal position.
- p (string; default 'P'): A string denoting the column name for the
float quantity to be plotted on the y-axis. This column must be
numeric. This does not have to be a p-value. It can be any
numeric quantity such as peak heights, bayes factors, test
statistics. If it is not a p-value, make sure to set logp = FALSE.
- snp (string; default 'SNP'): A string denoting the column name for the
SNP names (e.g. rs number). More generally, this column could be
anything that identifies each point being plotted. For example, in
an Epigenomewide association study (EWAS) this could be the probe
name or cg number. This column should be a character. This
argument is optional, however it is necessary to specify if you
want to highlight points on the plot using the highlight argument
in the figure method.
- gene (string; default 'GENE'): A string denoting the column name for the
GENE names. This column could be a string or a float. More
generally, it could be any annotation information that you want
to include in the plot.
- annotation (string; optional): A string denoting the column name for
an annotation. This column could be a string or a float. This
could be any annotation information that you want to include in
the plot (e.g. zscore, effect size, minor allele frequency).
- logp (bool; default True): If True, the -log10 of the p-value is
plotted. It isn't very useful to plot raw p-values; however,
plotting the raw value could be useful for other genome-wide plots
(e.g., peak heights, Bayes factors, test statistics, other
"scores", etc.).
Returns:
- A ManhattanPlot object."""
# checking the validity of the arguments
# Make sure you have chrm, bp and p columns and that they are of
# numeric type
if chrm not in x.columns.values:
raise KeyError("Column %s not found in 'x' data.frame" % chrm)
# else:
# if not is_numeric_dtype(x[chrm].dtype):
# raise TypeError("%s column should be numeric. Do you have "
# "'X', 'Y', 'MT', etc? If so change to "
# "numbers and try again." % chrm)
if bp not in x.columns.values:
raise KeyError("Column %s not found in 'x' data.frame" % bp)
else:
if not is_numeric_dtype(x[bp].dtype):
raise TypeError("%s column should be numeric type" % bp)
if p not in x.columns.values:
raise KeyError("Column %s not found in 'x' data.frame" % p)
else:
if not is_numeric_dtype(x[p].dtype):
raise TypeError("%s column should be numeric type" % p)
# Create a new DataFrame with columns named after chrm, bp, and p.
self.data = pd.DataFrame(data=x[[chrm, bp, p]])
if snp is not None:
if snp not in x.columns.values:
# Warn if you don't have a snp column
raise KeyError(
"snp argument specified as %s but column not found in "
"'x' data.frame" % snp)
else:
# If the input DataFrame has a snp column, add it to the new
# DataFrame
self.data[snp] = x[snp]
if gene is not None:
if gene not in x.columns.values:
# Warn if you don't have a gene column
raise KeyError(
"gene argument specified as %s but column not found in "
"'x' data.frame" % gene)
else:
# If the input DataFrame has a gene column, add it to the new
# DataFrame
self.data[gene] = x[gene]
if annotation is not None:
if annotation not in x.columns.values:
# Warn if you don't have an annotation column
raise KeyError(
"annotation argument specified as %s but column not "
"found in 'x' data.frame" % annotation
)
else:
# If the input DataFrame has a gene column, add it to the new
# DataFrame
self.data[annotation] = x[annotation]
self.xlabel = ""
self.ticks = []
self.ticksLabels = []
self.nChr = len(x[chrm].unique())
self.chrName = chrm
self.pName = p
self.snpName = snp
self.geneName = gene
self.annotationName = annotation
self.logp = logp
# Set positions, ticks, and labels for plotting
self.index = 'INDEX'
self.pos = 'POSITION'
# Fixes the bug where one chromosome is missing by adding a sequential
# index column.
idx = 0
for i in self.data[chrm].unique():
idx = idx + 1
self.data.loc[self.data[chrm] == i, self.index] = int(idx)
# Set the type to be the same as provided for chrm column
self.data[self.index] = \
self.data[self.index].astype(self.data[chrm].dtype)
# This section sets up positions and ticks. Ticks should be placed in
# the middle of a chromosome. The new pos column is added that keeps
# a running sum of the positions of each successive chromosome.
# For example:
# chrm bp pos
# 1 1 1
# 1 2 2
# 2 1 3
# 2 2 4
# 3 1 5
if self.nChr == 1:
# For a single chromosome
self.data[self.pos] = self.data[bp]
self.ticks.append(int(len(self.data[self.pos]) / 2.) + 1)
self.xlabel = "Chromosome %s position" % (self.data[chrm].unique())
self.ticksLabels = self.ticks
else:
# For multiple chromosomes
lastbase = 0
for i in self.data[self.index].unique():
if i == 1:
self.data.loc[self.data[self.index] == i, self.pos] = \
self.data.loc[self.data[self.index] == i, bp].values
else:
prevbp = self.data.loc[self.data[self.index] == i - 1, bp]
# Shift the basepair position by the largest bp of the
# current chromosome
lastbase = lastbase + prevbp.iat[-1]
self.data.loc[self.data[self.index] == i, self.pos] = \
self.data.loc[self.data[self.index] == i, bp].values \
+ lastbase
tmin = min(self.data.loc[self.data[self.index] == i, self.pos])
tmax = max(self.data.loc[self.data[self.index] == i, self.pos])
self.ticks.append(int((tmin + tmax) / 2.) + 1)
self.xlabel = 'Chromosome'
self.data[self.pos] = self.data[self.pos].astype(
self.data[bp].dtype)
if self.nChr > 20: # To avoid crowded labels
self.ticksLabels = [
chrm if np.mod(int(t+1), 2) # Only every two ticks
else ''
for t,chrm in enumerate(self.data[chrm].unique())
]
else:
self.ticksLabels = self.data[chrm].unique() # All the ticks
def figure(
self,
title="Manhattan Plot",
showgrid=True,
xlabel=None,
ylabel='-log10(p)',
point_size=5,
showlegend=True,
col=None,
suggestiveline_value=-np.log10(1e-8),
suggestiveline_color='blue',
suggestiveline_width=1,
genomewideline_value=-np.log10(5e-8),
genomewideline_color='red',
genomewideline_width=1,
highlight=True,
highlight_color="red",
):
"""Keyword arguments:
- title (string; default 'Manhattan Plot'): The title of the
graph.
- showgrid (bool; default True): Boolean indicating whether
gridlines should be shown.
- xlabel (string; optional): Label of the x axis.
- ylabel (string; default '-log10(p)'): Label of the y axis.
- point_size (number; default 5): Size of the points of the
scatter plot.
- showlegend (bool; default True): Boolean indicating whether
legends should be shown.
- col (string; optional): A string representing the color of the
points of the Scatter plot. Can be in any color format
accepted by plotly.graph_objects.
- suggestiveline_value (bool | float; default 8): A value which
must be either False to deactivate the option, or a numerical value
corresponding to the p-value at which the line should be
drawn. The line has no influence on the data points.
- suggestiveline_color (string; default 'grey'): Color of the
suggestive line.
- suggestiveline_width (number; default 2): Width of the
suggestive line.
- genomewideline_value (bool | float; default -log10(5e-8)): A
boolean which must be either False to deactivate the option, or a
numerical value corresponding to the p-value above which the
data points are considered significant.
- genomewideline_color (string; default 'red'): Color of the
genome-wide line. Can be in any color format accepted by
plotly.graph_objects.
- genomewideline_width (number; default 1): Width of the genome
wide line.
- highlight (bool; default True): Whether to turn on or off the
highlighting of data points considered significant.
- highlight_color (string; default 'red'): Color of the data
points highlighted because they are significant. Can be in any
color format accepted by plotly.graph_objects.
Returns:
- A figure formatted for plotly.graph_objects.
"""
xmin = min(self.data[self.pos].values)
xmax = max(self.data[self.pos].values)
horizontallines = []
if suggestiveline_value:
suggestiveline = go.layout.Shape(
name=SUGGESTIVE_LINE_LABEL,
type="line",
fillcolor=suggestiveline_color,
line=dict(
color=suggestiveline_color,
width=suggestiveline_width
),
x0=xmin, x1=xmax, xref="x",
y0=suggestiveline_value, y1=suggestiveline_value, yref="y"
)
horizontallines.append(suggestiveline)
if genomewideline_value:
genomewideline = go.layout.Shape(
name=GENOMEWIDE_LINE_LABEL,
type="line",
fillcolor=genomewideline_color,
line=dict(
color=genomewideline_color,
width=genomewideline_width
),
x0=xmin, x1=xmax, xref="x",
y0=genomewideline_value, y1=genomewideline_value, yref="y"
)
horizontallines.append(genomewideline)
data_to_plot = [] # To contain the data traces
tmp = pd.DataFrame() # Empty DataFrame to contain the highlighted data
if highlight:
if not isinstance(highlight, bool):
if self.snpName not in self.data.columns.values:
raise KeyError(
"snp argument specified for highlight as %s but "
"column not found in the data.frame" % self.snpName
)
else:
if not genomewideline_value:
raise Warning(
"The genomewideline_value you entered is not a "
"positive value, or False, you cannot set highlight "
"to True in that case.")
tmp = self.data
# Sort the p-values (or -log10(p-values) above the line
if genomewideline_value:
if self.logp:
tmp = tmp.loc[-np.log10(tmp[self.pName])
> genomewideline_value]
else:
tmp = tmp.loc[tmp[self.pName] > genomewideline_value]
highlight_hover_text = _get_hover_text(
tmp,
snpname=self.snpName,
genename=self.geneName,
annotationname=self.annotationName
)
if not tmp.empty:
data_to_plot.append(
go.Scattergl(
x=tmp[self.pos].values,
y=-np.log10(tmp[self.pName].values) if self.logp
else tmp[self.pName].values,
mode="markers",
text=highlight_hover_text,
marker=dict(
color=highlight_color,
size=point_size
),
name="Point(s) of interest"
)
)
# Remove the highlighted data from the DataFrame if not empty
if tmp.empty:
data = self.data
else:
data = self.data.drop(self.data.index[tmp.index])
if self.nChr == 1:
if col is None:
col = ['black']
# If single chromosome, ticks and labels automatic.
layout = go.Layout(
title=title,
xaxis={
'title': self.xlabel if xlabel is None else xlabel,
'showgrid': showgrid,
'range': [xmin, xmax],
},
yaxis={'title': ylabel},
hovermode='closest'
)
hover_text = _get_hover_text(
data,
snpname=self.snpName,
genename=self.geneName,
annotationname=self.annotationName
)
data_to_plot.append(
go.Scattergl(
x=data[self.pos].values,
y=-np.log10(data[self.pName].values) if self.logp
else data[self.pName].values,
mode="markers",
showlegend=showlegend,
marker={
'color': col[0],
'size': point_size,
'name': "chr%i" % data[self.chrName].unique()
},
text=hover_text
)
)
else:
# if multiple chrms, use the ticks and labels you created above.
layout = go.Layout(
title=title,
xaxis={
'title': self.xlabel if xlabel is None else xlabel,
'showgrid': showgrid,
'range': [xmin, xmax],
'tickmode': "array",
'tickvals': self.ticks,
'ticktext': self.ticksLabels,
'ticks': "outside"
},
yaxis={'title': ylabel},
hovermode='closest'
)
icol = 0
if col is None:
col = [
'black' if np.mod(i, 2)
else 'grey' for i in range(self.nChr)
]
for i in data[self.index].unique():
tmp = data[data[self.index] == i]
chromo = tmp[self.chrName].unique()[0] # Get chromosome name
hover_text = _get_hover_text(
data,
snpname=self.snpName,
genename=self.geneName,
annotationname=self.annotationName
)
data_to_plot.append(
go.Scattergl(
x=tmp[self.pos].values,
y=-np.log10(tmp[self.pName].values) if self.logp
else tmp[self.pName].values,
mode="markers",
showlegend=showlegend,
name=f"Chr{chromo}",
marker={
'color': col[icol],
'size': point_size
},
text=hover_text
)
)
icol = icol + 1
layout.shapes = horizontallines
return go.Figure(data=data_to_plot, layout=layout)
然后，我修改了plotly-dash示例here使用修改后的 df，其中 "CHR" 列具有诸如 "1A","1B",..."9A","9B"之类的字符串值 而不是整数，并测试了 Dash 应用程序是否正确呈现这些字符串。
import numpy as np
import pandas as pd
import dash
from dash.dependencies import Input, Output
import dash_bio as dashbio
from dash import html, dcc
app = dash.Dash(__name__)
df = pd.read_csv('https://git.io/manhattan_data.csv')
## create some sample data where "CHR" column
## contains strings of the format "{number}{letter}"
## where {letter} is one of "A","B"
np.random.seed(42)
df_test = df[df["CHR"] < 10].copy()
for _, df_group in df_test.groupby("CHR"):
start, end = df_group.index[0], df_group.index[-1]
midpt = (start + end) // 2
df_test['CHR'].loc[start:midpt] = df_group['CHR'].loc[start:midpt].astype(str) + 'A'
df_test['CHR'].loc[midpt:end] = df_group['CHR'].loc[midpt:end].astype(str) + 'B'
app.layout = html.Div([
'Threshold value',
dcc.Slider(
id='default-manhattanplot-input',
min=1,
max=10,
marks={
i: {'label': str(i)} for i in range(10)
},
value=6
),
html.Br(),
html.Div(
dcc.Graph(
id='default-dashbio-manhattanplot',
figure=dashbio.ManhattanPlot(
dataframe=df_test
)
)
)
])
@app.callback(
Output('default-dashbio-manhattanplot', 'figure'),
Input('default-manhattanplot-input', 'value')
)
def update_manhattanplot(threshold):
return dashbio.ManhattanPlot(
dataframe=df_test,
genomewideline_value=threshold
)
if __name__ == '__main__':
app.run_server(debug=True)
关于python - 带有字符串染色体名称的交互式曼哈顿图，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/72147771/