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## NOTE: This is Python 3 code.
import pandas as pd
import numpy as np
import random as rd
from sklearn.decomposition import PCA
from sklearn import preprocessing
import matplotlib.pyplot as plt # NOTE: This was tested with matplotlib v. 2.1.0
#########################
#
# Data Generation Code
#
#########################
## In this example, the data is in a data frame called data.
## Columns are individual samples (i.e. cells)
## Rows are measurements taken for all the samples (i.e. genes)
## Just for the sake of the example, we'll use made up data...
genes = ['gene' + str(i) for i in range(1,101)]
wt = ['wt' + str(i) for i in range(1,6)]
ko = ['ko' + str(i) for i in range(1,6)]
data = pd.DataFrame(columns=[*wt, *ko], index=genes)
for gene in data.index:
data.loc[gene,'wt1':'wt5'] = np.random.poisson(lam=rd.randrange(10,1000), size=5)
data.loc[gene,'ko1':'ko5'] = np.random.poisson(lam=rd.randrange(10,1000), size=5)
print(data.head())
print(data.shape)
#########################
#
# Perform PCA on the data
#
#########################
# First center and scale the data
scaled_data = preprocessing.scale(data.T)
pca = PCA() # create a PCA object
pca.fit(scaled_data) # do the math
pca_data = pca.transform(scaled_data) # get PCA coordinates for scaled_data
#########################
#
# Draw a scree plot and a PCA plot
#
#########################
#The following code constructs the Scree plot
per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)
labels = ['PC' + str(x) for x in range(1, len(per_var)+1)]
plt.bar(x=range(1,len(per_var)+1), height=per_var, tick_label=labels)
plt.ylabel('Percentage of Explained Variance')
plt.xlabel('Principal Component')
plt.title('Scree Plot')
plt.show()
#the following code makes a fancy looking plot using PC1 and PC2
pca_df = pd.DataFrame(pca_data, index=[*wt, *ko], columns=labels)
plt.scatter(pca_df.PC1, pca_df.PC2)
plt.title('My PCA Graph')
plt.xlabel('PC1 - {0}%'.format(per_var[0]))
plt.ylabel('PC2 - {0}%'.format(per_var[1]))
for sample in pca_df.index:
plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample]))
plt.show()
#########################
#
# Determine which genes had the biggest influence on PC1
#
#########################
## get the name of the top 10 measurements (genes) that contribute
## most to pc1.
## first, get the loading scores
loading_scores = pd.Series(pca.components_[0], index=genes)
## now sort the loading scores based on their magnitude
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
# get the names of the top 10 genes
top_10_genes = sorted_loading_scores[0:10].index.values
## print the gene names and their scores (and +/- sign)
print(loading_scores[top_10_genes])
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