<jupyter_start><jupyter_text>Table of Contents This is the table of contents  Load Data<jupyter_code>df = pd.Dataframe("")<jupyter_output><empty_output><jupyter_text>Test Data<jupyter_code>import string
import random

origin = [(random.choice(string.ascii_uppercase[17:19])) for i in range(100)]
ID = [(random.choice(string.ascii_uppercase)*6) for i in range(100)]

df_test = pd.read_csv('~/Downloads/testRNAseqData.csv', nrows= 100)
# df_test['ReqSeq ID'] = ID
df_test['Origin'] = origin<jupyter_output><empty_output><jupyter_text>View Data<jupyter_code>df_test.head()<jupyter_output><empty_output><jupyter_text>TSNE<jupyter_code>df_data = df_test[df_test.columns.difference(['gene_ID','Origin'])]

tsne = TSNE(n_components=3).fit_transform(df_data)

df_test['tsne1'] = tsne[:,0]
df_test['tsne2'] = tsne[:,1]

fg = sns.FacetGrid(data=df_test, hue='Origin', aspect=1.61)
from sklearn.decomposition import PCA
fg.map(plt.scatter, 'tsne1', 'tsne2').add_legend()<jupyter_output><empty_output><jupyter_text>PCA<jupyter_code>pca = PCA(n_components=3).fit_transform(df_data)

df_test['pca1'] = pca[:,0]
df_test['pca2'] = pca[:,1]

fg2 = sns.FacetGrid(data=df_test, hue='Origin', aspect=1.61)
fg2.map(plt.scatter, 'pca1', 'pca2').add_legend()<jupyter_output><empty_output>
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{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '237276', 'license': 'MIT', 'n_tokens_mistral': 552, 'n_tokens_neox': 525, 'n_words': 68, 'path': 'Visualization/QC for BioTuring Test.ipynb', 'repo_name': 'krobasky/RNA-Seq-in-the-Cloud', 'size': '1265'}