<jupyter_start><jupyter_text>Analyse: comparison between liver cells and  HepG2 cells (human liver cancer cells)  HepG2 HepG2 is a human liver cancer cell line. Hep G2 is an immortal cell line which was derived in 1975 from the liver tissue of a 15-year-old Caucasian male from Argentina with a well-differentiated hepatocellular carcinoma. These cells are epithelial in morphology, have a modal chromosome number of 55, and are not tumorigenic in nude mice.<jupyter_code>from tfcomb import CombObj
genome_path="../testdaten/hg19_masked.fa"
motif_path="../testdaten/HOCOMOCOv11_HUMAN_motifs.txt"
result_path="./results/"<jupyter_output><empty_output><jupyter_text>Using saved objects from market basket analysis from pkl files for liver and hepG2 cells using complete results of TF. Saving results from market basket analysis in objects.<jupyter_code>liver_object= CombObj().from_pickle(f"{result_path}Liver_enhancers_complete.pkl")
liver_object.prefix = "liver"

hepG2_object = CombObj().from_pickle(f"{result_path}HepG2_enhancers_complete.pkl")
hepG2_object.prefix = "HepG2"<jupyter_output><empty_output><jupyter_text>Showing found rules of TF in each cell line<jupyter_code>print(f"liver: {liver_object}")
print(f"HepG2: {hepG2_object}")<jupyter_output>liver: <CombObj: 92371 TFBS (401 unique names) | Market basket analysis: 156517 rules>
HepG2: <CombObj: 1526600 TFBS (401 unique names) | Market basket analysis: 160799 rules><jupyter_text>Comparing rules of TF of cell line objects<jupyter_code>compare_objliver_hepG2 = liver_object.compare(hepG2_object)<jupyter_output>INFO: Calculating foldchange for contrast: liver / HepG2
INFO: The calculated log2fc's are found in the rules table (<DiffCombObj>.rules)<jupyter_text>Results Differential analysis of liver and hepG2 Results of differential analysis are found in compare_objCD4_CD8.rules. The table shows the rules of TF that distinguish strongly in occurence. The duplicates in the results are removed with simplify rules<jupyter_code>compare_objliver_hepG2.simplify_rules()
compare_objliver_hepG2.rules
compare_objliver_hepG2.plot_heatmap()
selectionliver_hepG2 = compare_objliver_hepG2.select_rules()
selectionliver_hepG2.plot_network()
selectionliver_hepG2.rules.head(10)
selectionliver_hepG2.rules.tail(10)<jupyter_output><empty_output>
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{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '276527', 'license': 'MIT', 'n_tokens_mistral': 812, 'n_tokens_neox': 718, 'n_words': 211, 'path': 'wp6/analyse/sl_enhancer_analyses_liver_hepG2.ipynb', 'repo_name': 'loosolab/Datenanalyse-2021', 'size': '2300'}