Heatmap for Single Gene



In [1]:

    
import numpy as np
import numpy.random
import matplotlib.pyplot as plt

# Generate some test data
x = np.random.randn(8873)
y = np.random.randn(8873)

heatmap, xedges, yedges = np.histogram2d(x, y, bins=50)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]

plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.show()

The previous bit of code is to make sure that the Heatmap plots in my Python 3 notebook.



In [38]:

    
import numpy as np
import numpy.random
import matplotlib.pyplot as plt
import csv

aortaData = []
aa1 = []
aa2 = []
aa3 = []
aa4 = []
aa5 = []
with open ("genomicdata.csv") as csvfile:
    readCSV = csv.reader(csvfile, delimiter= '\t') #gives access to the CSV file
    for col in readCSV:
        if col[7] == '':
            aortaData.append('0')
        else:
            aortaData.append(col[7]) #save the data from the side 
    #need smaller arrays
    #Each array needs 5 elements
    aa1 = list(map(float, aortaData[1:6]))
    aa2 = list(map(float, aortaData[6:11]))
    aa3 = list(map(float, aortaData[11:16]))
    aa4 = list(map(float, aortaData[16:21]))
    aa5 = list(map(float, aortaData[21:26]))

# Generate some test data
x = np.random.randn(8873)
y = np.random.randn(8873)

heatmap, xedges, yedges = np.histogram2d(x, y, bins=5) #if array is 5x5 (has 25 components), need 5 bins
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]

plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.colorbar()
plt.show()

In the code above, I created a heatmap using the code from the heatmap above ( http://stackoverflow.com/questions/2369492/generate-a-heatmap-in-matplotlib-using-a-scatter-data-set ) and from the data of some of the expression vectors for genes in cells in the aorta.



In [28]:

    
import matplotlib.pyplot as plt
import numpy as np

aortaData = []
aa1 = []
aa2 = []
aa3 = []
aa4 = []
aa5 = []
with open ("genomicdata.csv") as csvfile:
    readCSV = csv.reader(csvfile, delimiter= '\t') #gives access to the CSV file
    for col in readCSV:
        if col[7] == '':
            aortaData.append('0')
        else:
            aortaData.append(col[7]) #save the data from the side 
    #need smaller arrays
    #Each array needs 5 elements
    aa1 = list(map(float, aortaData[1:6]))
    aa2 = list(map(float, aortaData[6:11]))
    aa3 = list(map(float, aortaData[11:16]))
    aa4 = list(map(float, aortaData[16:21]))
    aa5 = list(map(float, aortaData[21:26]))

x = [1, 2, 3, 4, 5]
y = [0.1, 0.2, 0.3, 0.4, 0.5]

#take the 5 small arrays
intensity = [
    aa1,
    aa2,
    aa3,
    aa4,
    aa5
]

#setup the 2D grid with Numpy
x, y = np.meshgrid(x, y)

#convert intensity (list of lists) to a numpy array for plotting
intensity = np.array(intensity)

plt.pcolormesh(x, y, intensity) #plots the intensity on the grid with colors
plt.colorbar() #colors are set from the colorbar. The colorbar shows the intensity 
plt.show()

https://stackoverflow.com/questions/33282368/plotting-a-2d-heatmap-with-matplotlib



In [20]:

    
import matplotlib.pyplot as plt
import numpy as np

#here's our data to plot, all normal Python lists
x = [1, 2, 3, 4, 5]
y = [0.1, 0.2, 0.3, 0.4, 0.5]

intensity = [
    [3, 0, 20, 2, .6],
    [4, 33, 13, 2, 5],
    [.6, 1, 9, 10, 10],
    [0, 7, 0, 6, 4],
    [18, 15, 94, 0, 0]
]

#setup the 2D grid with Numpy
x, y = np.meshgrid(x, y)

#convert intensity (list of lists) to a numpy array for plotting
intensity = np.array(intensity)

#now just plug the data into pcolormesh, it's that easy!
plt.pcolormesh(x, y, intensity)
plt.colorbar() #need a colorbar to show the intensity scale
plt.show() #boom

This is a test to manually see how the heat map should be expressed. I took the array from the previous example, and wrote it out manually to test how the data will be expressed. http://seaborn.pydata.org/generated/seaborn.heatmap.html



In [30]:

    
import matplotlib.pyplot as plt
import numpy as np

aortaData = []
aa1 = []
aa2 = []
aa3 = []
aa4 = []
aa5 = []
aa6 = []
aa7 = []
aa8 = []
aa9 = []
aa10 = []
with open ("genomicdata.csv") as csvfile:
    readCSV = csv.reader(csvfile, delimiter= '\t') #gives access to the CSV file
    for col in readCSV:
        if col[7] == '':
            aortaData.append('0')
        else:
            aortaData.append(col[7]) #save the data from the side 
    #need smaller arrays
    #Each array needs 5 elements
    aa1 = list(map(float, aortaData[1:11]))
    aa2 = list(map(float, aortaData[11:21]))
    aa3 = list(map(float, aortaData[21:31]))
    aa4 = list(map(float, aortaData[31:41]))
    aa5 = list(map(float, aortaData[41:51]))
    aa6 = list(map(float, aortaData[51:61]))
    aa7 = list(map(float, aortaData[61:71]))
    aa8 = list(map(float, aortaData[71:81]))
    aa9 = list(map(float, aortaData[81:91]))
    aa10 = list(map(float, aortaData[91:101]))

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]

#take the 10 small arrays
intensity = [
    aa1,
    aa2,
    aa3,
    aa4,
    aa5,
    aa6,
    aa7,
    aa8,
    aa9,
    aa10
]

#setup the 2D grid with Numpy
x, y = np.meshgrid(x, y)

#convert intensity (list of lists) to a numpy array for plotting
intensity = np.array(intensity)

plt.pcolormesh(x, y, intensity) #plots the intensity on the grid with colors
plt.colorbar() #colors are set from the colorbar. The colorbar shows the intensity 
plt.show()



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