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?
;-)