text 蛋白质组 - Seth.ipynb

Posted 2021-04-29

{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport itertools\nfrom beakerx import *","execution_count":17,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Setting up"},{"metadata":{"trusted":true},"cell_type":"code","source":"peptideFile = \"/mnt/c/.../raw_data/Peptide Report for proteOMZ_st4to14_metzymepeptides2.xls\"","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Will need to skip the first few lines of metadata\n \nfileOpen = open(peptideFile, 'r')\nfileRead = fileOpen.readlines()\nstart = 0\ni = 0\nfor line in fileRead:\n    if line.startswith(\"Experiment name\"):\n        start = i\n        break\n    else:\n        i+=1\nfileOpen.close()\n \n#Skipping the first few lines when opening file for dataframe\ndf_peptide = pd.read_csv(peptideFile, delimiter='\\t', skiprows=i, low_memory=False)\n \n#Need to drop the last line of the dataframe as it's a footer, cannot use 'skipfooter' in pandas call as it conflicts with low_memory call\ndf_peptide = df_peptide[:-1]","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_peptide.head()","execution_count":21,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f220d1b4bdbe4ef8b0add54e8dc3588d"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_peptide.tail()","execution_count":6,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"128a84453f80419297d496b68ab65eec"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_peptide.columns","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"Index(['Experiment name', 'Biological sample category',\n       'Biological sample name', 'MS/MS sample name', 'Protein name',\n       'Protein accession numbers', 'Database sources',\n       'Protein molecular weight (Da)', 'Protein identification probability',\n       'Exclusive unique peptide count', 'Exclusive unique spectrum count',\n       'Exclusive spectrum count', 'Percentage of total spectra',\n       'Percentage sequence coverage', 'Peptide sequence',\n       'Previous amino acid', 'Next amino acid',\n       'Best Peptide identification probability',\n       'Best Sequest (XCorr Only) deltaCn', 'Best Sequest (XCorr Only) XCorr',\n       'Best Sequest (XCorr Only) Sp', 'Best Sequest (XCorr Only) SpRank',\n       'Best Sequest (XCorr Only) Peak Ratio',\n       'Number of identified +1H spectra', 'Number of identified +2H spectra',\n       'Number of identified +3H spectra', 'Number of identified +4H spectra',\n       'Number of enzymatic termini', 'Calculated +1H Peptide Mass (AMU)',\n       'Median Retention Time', 'Total Precursor Intensity', 'Total TIC',\n       'Peptide start index', 'Peptide stop index', 'Star Category',\n       'Assigned', 'Other Proteins'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Multiple entries assigned \"TRUE\""},{"metadata":{"trusted":true},"cell_type":"code","source":"gb_peptide = df_peptide.groupby([\"Peptide sequence\"])","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(df_peptide)","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"1884177"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(gb_peptide)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"110507"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"gb_peptide.get_group('ALAEEAATGTK')","execution_count":19,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"022af90c24984c48b2216fb4b2f43cb0"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"gb_peptide.get_group('SQSDSNLDGILESVK')","execution_count":15,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"519bcd7cc44140309161c0bd869a5bf5"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"gb_peptide.get_group('LVNAEADYK')","execution_count":16,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"02983c3c7b124c44a7e6c1f02591dcb4"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 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