This module does is very similar to Module 3 from the course. With this module, and the ones to follow, you can see how we can use Python to do the analysis we have already done in R.

First we need to import our modules. We then read in the data as a csv file. In order to make the data the same as the data we used while working in R, we are going to drop the columns below (brca_clin_df.drop).

import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
brca_expr_norm_names_df = pd.read_csv('/home/alex/brca_expr_norm_names_df.csv')
brca_expr_norm_names_df = brca_expr_norm_names_df.drop(columns = ["Unnamed: 0"])
print("The gene expression data frame has " + str(np.shape(brca_expr_norm_names_df)[0]) + " rows and " + str(np.shape(brca_expr_norm_names_df)[1]) + " columns.")
## The gene expression data frame has 18351 rows and 1083 columns.

We can see that the sentence created when you run the code chunk above is the same sentence we created in R.

When we look at the data below, we can see that the data is the same as it is in Module 3.

brca_expr_norm_names_df.head(10)
##      symbol  TCGA-3C-AAAU  ...  TCGA-Z7-A8R5  TCGA-Z7-A8R6
## 0    TSPAN6      188.1840  ...     1319.4800      677.1640
## 1      TNMD        0.3447  ...        4.7785        1.7399
## 2      DPM1      524.2260  ...      596.7210      645.8460
## 3     SCYL3      325.1410  ...      498.1330      520.9010
## 4  C1orf112      124.2940  ...      138.6080      554.0010
## 5       FGR       75.1356  ...      551.3250      126.6640
## 6       CFH      348.1050  ...     1467.0100      330.5790
## 7     FUCA2      563.1730  ...     1199.4100      672.2920
## 8      GCLC      578.6820  ...      421.7070      417.5730
## 9      NFYA     1408.6200  ...      762.7750      959.7220
## 
## [10 rows x 1083 columns]

If we use .set_index in Python, we can use one of the column names from the data frame as the names of the rows. We are doing this in the code chunk below.

brca_expr_norm_names_df = brca_expr_norm_names_df.set_index("symbol")
brca_expr_norm_names_df
##             TCGA-3C-AAAU  TCGA-3C-AALI  ...  TCGA-Z7-A8R5  TCGA-Z7-A8R6
## symbol                                  ...                            
## TSPAN6          188.1840      207.7220  ...     1319.4800      677.1640
## TNMD              0.3447        1.0875  ...        4.7785        1.7399
## DPM1            524.2260      809.1350  ...      596.7210      645.8460
## SCYL3           325.1410     1558.9400  ...      498.1330      520.9010
## C1orf112        124.2940      274.6660  ...      138.6080      554.0010
## ...                  ...           ...  ...           ...           ...
## ZNF595          151.3050      179.4450  ...      241.3160      194.1710
## AC091738.3      283.3100      205.0030  ...       38.8257      270.3780
## C2ORF15         103.0530      112.9580  ...       89.3767      168.9600
## GRIN2B            0.0000        0.0000  ...        0.0000        0.0000
## ZBTB8B            1.0581        0.0000  ...        4.7785        0.6960
## 
## [18351 rows x 1082 columns]

We can use round() in Python to round the values in a data frame. We are using this below. Notice that we make sure brca_expr_norm_names_df is a data frame.

df1 = pd.DataFrame(brca_expr_norm_names_df)
df = round(df1, 1)
df
##             TCGA-3C-AAAU  TCGA-3C-AALI  ...  TCGA-Z7-A8R5  TCGA-Z7-A8R6
## symbol                                  ...                            
## TSPAN6             188.2         207.7  ...        1319.5         677.2
## TNMD                 0.3           1.1  ...           4.8           1.7
## DPM1               524.2         809.1  ...         596.7         645.8
## SCYL3              325.1        1558.9  ...         498.1         520.9
## C1orf112           124.3         274.7  ...         138.6         554.0
## ...                  ...           ...  ...           ...           ...
## ZNF595             151.3         179.4  ...         241.3         194.2
## AC091738.3         283.3         205.0  ...          38.8         270.4
## C2ORF15            103.1         113.0  ...          89.4         169.0
## GRIN2B               0.0           0.0  ...           0.0           0.0
## ZBTB8B               1.1           0.0  ...           4.8           0.7
## 
## [18351 rows x 1082 columns]

In the code chunks below, we are taking the first (line 56) and second (line 61) rows in the data frame and finding the mean of those genes. Remember, row 1 is denoted 0 in Python and row 2 is denoted 1 in Python.

tspan = brca_expr_norm_names_df.iloc[0, ]
print("Average expression of TSPAN6: " + str(round(tspan.mean())))
## Average expression of TSPAN6: 996
tnmd = brca_expr_norm_names_df.iloc[1, ]
print("Average expression of TNMD: " + str(round(tnmd.mean())))
## Average expression of TNMD: 24

To find the mean expression level for each gene, we have to take the transpose of brca_expr_norm_names_df. Remember, when we take the tranpose of a matrix or data frame, the rows become the columns and the columns become the rows. We then take the mean of every gene and round that to 0 decimal places.

df1 = brca_expr_norm_names_df.transpose()
mean_expr = round(df1.mean(), 0)
mean_expr
## symbol
## TSPAN6        996.0
## TNMD           24.0
## DPM1          699.0
## SCYL3         624.0
## C1orf112      320.0
##               ...  
## ZNF595        230.0
## AC091738.3    106.0
## C2ORF15       119.0
## GRIN2B          1.0
## ZBTB8B          6.0
## Length: 18351, dtype: float64

In the code chunk below, we are creating a histogram of the mean expression values of the genes. To create a histogram we use the matplotlib.pyplot module.

plt.hist(mean_expr, bins = [0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000])
## (array([13621.,  2613.,   914.,   397.,   212.,   136.,   114.,    63.,
##           42.,    38.]), array([    0,  1000,  2000,  3000,  4000,  5000,  6000,  7000,  8000,
##         9000, 10000]), <BarContainer object of 10 artists>)
plt.title("Distribution of Gene Expression Values")
plt.xlabel("Mean Expression")
plt.ylabel("Frequency")
plt.show()

Similar to length in R, we use len in Python to find the length of an object. We use this number to find the values below.

print("Total values: " + str(len(mean_expr)))
## Total values: 18351
print("Number of values > 10,000: " + str(sum(mean_expr > 10000)))
## Number of values > 10,000: 201
print("Percentage of values > 10,000: " + str(round(sum(mean_expr>10000)/len(mean_expr)*100, 1)))
## Percentage of values > 10,000: 1.1

To create the log data frame of brca_expr_norm_names_df, we add 1 to our data frame and then take the log base 2 of it. The 2 in np.log2 corresponds to the base of the log that we are taking.

dfadd = df + 1
brca_expr_norm_names_df_log = np.log2(dfadd)
brca_expr_norm_names_df_log.head()
##           TCGA-3C-AAAU  TCGA-3C-AALI  ...  TCGA-Z7-A8R5  TCGA-Z7-A8R6
## symbol                                ...                            
## TSPAN6        7.563768      7.705287  ...     10.366869      9.405567
## TNMD          0.378512      1.070389  ...      2.536053      1.432959
## DPM1          9.036723      9.661956  ...      9.223278      9.337176
## SCYL3         8.349171     10.607238  ...      8.963185      9.027630
## C1orf112      6.969243      8.106955  ...      7.125155      9.116344
## 
## [5 rows x 1082 columns]

We now create a histogram of brca_expr_norm_names_df_log.

plt.hist(brca_expr_norm_names_df_log, bins = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
## (array([[2749.,  780.,  642., ...,  527.,  172.,   57.],
##        [2380.,  786.,  654., ...,  539.,  176.,   67.],
##        [2966.,  548.,  546., ...,  539.,  179.,   77.],
##        ...,
##        [2479.,  557.,  554., ...,  493.,  178.,   58.],
##        [2522.,  600.,  637., ...,  494.,  162.,   82.],
##        [2816.,  795.,  683., ...,  458.,  185.,   70.]]), array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]), <a list of 1082 BarContainer objects>)
plt.title("Distribution of Log_2 Gene Expression Values")
plt.xlabel("Mean Log_2 Expression")
plt.ylabel("Frequency")
plt.show()

Similar to what we did in R, we create a list of every fourth number between 0 and 1081. In R, we are able to pick our interval. In Python, we aren’t able to do this, so we have to tell Python which number to start with, which number to stop at, and how many numbers you would like. We then subset brca_expr_norm_names_df_log to contain those columns. Lastly, we create a boxplot of these values.

I = np.linspace(start = 0, stop = 1081, num = 108)
newdat = brca_expr_norm_names_df_log.iloc[0:18350,I]
plt.boxplot(newdat)
## {'whiskers': [<matplotlib.lines.Line2D object at 0x7fd17966aef0>, <matplotlib.lines.Line2D object at 0x7fd1792b0c18>, <matplotlib.lines.Line2D object at 0x7fd1ac791780>, <matplotlib.lines.Line2D object at 0x7fd1a86289e8>, <matplotlib.lines.Line2D object at 0x7fd1a87a8438>, <matplotlib.lines.Line2D object at 0x7fd1a87a8358>, <matplotlib.lines.Line2D object at 0x7fd1a8a447f0>, <matplotlib.lines.Line2D object at 0x7fd1a8a44f98>, <matplotlib.lines.Line2D object at 0x7fd1a8a29588>, <matplotlib.lines.Line2D object at 0x7fd1a88b26a0>, <matplotlib.lines.Line2D object at 0x7fd1a86257b8>, <matplotlib.lines.Line2D object at 0x7fd1a8625358>, <matplotlib.lines.Line2D object at 0x7fd1a89b5828>, <matplotlib.lines.Line2D object at 0x7fd1a89b5438>, <matplotlib.lines.Line2D object at 0x7fd1a8904160>, <matplotlib.lines.Line2D object at 0x7fd1a8904a20>, <matplotlib.lines.Line2D object at 0x7fd1a86b8780>, <matplotlib.lines.Line2D object at 0x7fd1a86b8ba8>, <matplotlib.lines.Line2D object at 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plt.xlabel("Samples")
plt.ylabel("Log_2 Gene Expression")
plt.show()