问题
I'm fairly new at coding (completely self taught), and have started using it at at my job as a research assistant in a cancer lab. I need some help setting up a few line graphs in matplot lab.
I have a dataset that includes nextgen sequencing data for about 80 patients. on each patient, we have different timepoints of analysis, different genes detected (out of 40), and the associated %mutation for the gene.
My goal is to write two scripts, one that will generate a "by patient" plot, that will be a linegraph with y-%mutation, x-time of measurement, and will have a different color line for all lines made by each of the patient's associated genes. The second plot will be a "by gene", where I will have one plot contain different color lines that represent each of the different patient's x/y values for that specific gene.
Here is an example dataframe for 1 genenumber for the above script:
gene yaxis xaxis pt# gene#
ASXL1-3 34 1 3 1
ASXL1-3 0 98 3 1
IDH1-3 24 1 3 11
IDH1-3 0 98 3 11
RUNX1-3 38 1 3 21
RUNX1-3 0 98 3 21
U2AF1-3 33 1 3 26
U2AF1-3 0 98 3 26
I have setup a groupby script that when I iterate over it, gives me a dataframe for every gene-timepoint for each patient.
grouped = df.groupby('pt #')
for groupObject in grouped:
group = groupObject[1]
For patient 1, this gives the following output:
y x gene patientnumber patientgene genenumber dxtotransplant \
0 40.0 1712 ASXL1 1 ASXL1-1 1 1857
1 26.0 1835 ASXL1 1 ASXL1-1 1 1857
302 7.0 1835 RUNX1 1 RUNX1-1 21 1857
I need help writing a script that will create either of the plots described above. using the bypatient example, my general idea is that I need to create a different subplot for every gene a patient has, where each subplot is the line graph represented by that one gene.
Using matplotlib this is about as far as I have gotten:
plt.figure()
grouped = df.groupby('patient number')
for groupObject in grouped:
group = groupObject[1]
df = group #may need to remove this
for element in range(len(group)):
xs = np.array(df[df.columns[1]]) #"x" column
ys= np.array(df[df.columns[0]]) #"y" column
gene = np.array(df[df.columns[2]])[element] #"gene" column
plt.subplot(1,1,1)
plt.scatter(xs,ys, label=gene)
plt.plot(xs,ys, label=gene)
plt.legend()
plt.show()
This produces the following output:
In this output, the circled line is not supposed to be connected to the other 2 points. In this case, this is patient 1, who has the following datapoint:
x y gene
1712 40 ASXL1
1835 26 ASXL1
1835 7 RUNX1
Using seaborn I have gotten close to my desired graph using this code:
grouped = df.groupby(['patientnumber'])
for groupObject in grouped:
group = groupObject[1]
g = sns.FacetGrid(group, col="patientgene", col_wrap=4, size=4, ylim=(0,100))
g = g.map(plt.scatter, "x", "y", alpha=0.5)
g = g.map(plt.plot, "x", "y", alpha=0.5)
plt.title= "gene:%s"%element
Using this code I get the following:
If I adjust the line:
g = sns.FacetGrid(group, col="patientnumber", col_wrap=4, size=4, ylim=(0,100))
I get the following result:
As you can see in the 2d example, the plot is treating every point on my plot as if they are from the same line (but they are actually 4 separate lines).
How I can tweak my iterations so that each patient-gene is treated as a separate line on the same graph?
回答1:
I wrote a subplot function that may give you a hand. I modified the data a tad to help illustrate the plotting functionality.
gene,yaxis,xaxis,pt #,gene #
ASXL1-3,34,1,3,1
ASXL1-3,3,98,3,1
IDH1-3,24,1,3,11
IDH1-3,7,98,3,11
RUNX1-3,38,1,3,21
RUNX1-3,2,98,3,21
U2AF1-3,33,1,3,26
U2AF1-3,0,98,3,26
ASXL1-3,39,1,4,1
ASXL1-3,8,62,4,1
ASXL1-3,0,119,4,1
IDH1-3,27,1,4,11
IDH1-3,12,62,4,11
IDH1-3,1,119,4,11
RUNX1-3,42,1,4,21
RUNX1-3,3,62,4,21
RUNX1-3,1,119,4,21
U2AF1-3,16,1,4,26
U2AF1-3,1,62,4,26
U2AF1-3,0,119,4,26
This is the subplotting function...with some extra bells and whistles :)
def plotByGroup(df, group, xCol, yCol, title = "", xLabel = "", yLabel = "", lineColors = ["red", "orange", "yellow", "green", "blue", "purple"], lineWidth = 2, lineOpacity = 0.7, plotStyle = 'ggplot', showLegend = False):
"""
Plot multiple lines from a Pandas Data Frame for each group using DataFrame.groupby() and MatPlotLib PyPlot.
@params
df - Required - Data Frame - Pandas Data Frame
group - Required - String - Column name to group on
xCol - Required - String - Column name for X axis data
yCol - Required - String - Column name for y axis data
title - Optional - String - Plot Title
xLabel - Optional - String - X axis label
yLabel - Optional - String - Y axis label
lineColors - Optional - List - Colors to plot multiple lines
lineWidth - Optional - Integer - Width of lines to plot
lineOpacity - Optional - Float - Alpha of lines to plot
plotStyle - Optional - String - MatPlotLib plot style
showLegend - Optional - Boolean - Show legend
@return
MatPlotLib Plot Object
"""
# Import MatPlotLib Plotting Function & Set Style
from matplotlib import pyplot as plt
matplotlib.style.use(plotStyle)
figure = plt.figure() # Initialize Figure
grouped = df.groupby(group) # Set Group
i = 0 # Set iteration to determine line color indexing
for idx, grp in grouped:
colorIndex = i % len(lineColors) # Define line color index
lineLabel = grp[group].values[0] # Get a group label from first position
xValues = grp[xCol] # Get x vector
yValues = grp[yCol] # Get y vector
plt.subplot(1,1,1) # Initialize subplot and plot (on next line)
plt.plot(xValues, yValues, label = lineLabel, color = lineColors[colorIndex], lw = lineWidth, alpha = lineOpacity)
# Plot legend
if showLegend:
plt.legend()
i += 1
# Set title & Labels
axis = figure.add_subplot(1,1,1)
axis.set_title(title)
axis.set_xlabel(xLabel)
axis.set_ylabel(yLabel)
# Return plot for saving, showing, etc.
return plt
And to use it...
import pandas
# Load the Data into Pandas
df = pandas.read_csv('data.csv')
#
# Plotting - by Patient
#
# Create Patient Grouping
patientGroup = df.groupby('pt #')
# Iterate Over Groups
for idx, patientDF in patientGroup:
# Let's give them specific titles
plotTitle = "Gene Frequency over Time by Gene (Patient %s)" % str(patientDf['pt #'].values[0])
# Call the subplot function
plot = plotByGroup(patientDf, 'gene', 'xaxis', 'yaxis', title = plotTitle, xLabel = "Days", yLabel = "Gene Frequency")
# Add Vertical Lines at Assay Timepoints
timepoints = set(patientDf.xaxis.values)
[plot.axvline(x = timepoint, linewidth = 1, linestyle = "dashed", color='gray', alpha = 0.4) for timepoint in timepoints]
# Let's see it
plot.show()
And of course, we can do the same by gene.
#
# Plotting - by Gene
#
# Create Gene Grouping
geneGroup = df.groupby('gene')
# Generate Plots for Groups
for idx, geneDF in geneGroup:
plotTitle = "%s Gene Frequency over Time by Patient" % str(geneDf['gene'].values[0])
plot = plotByGroup(geneDf, 'pt #', 'xaxis', 'yaxis', title = plotTitle, xLab = "Days", yLab = "Frequency")
plot.show()
If this isn't what you're looking for, provide a clarification and I'll take another crack at it.
来源：https://stackoverflow.com/questions/38340855/how-to-make-multiline-graph-with-matplotlib-subplots-and-pandas