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	Chr	Start	End	Strand	Length	10B.sam	10D_minus.sam	1p2-1.sam	1p2-2.sam	2-10B_minus.sam
 gene_0001_thrL	AP012030.1	190	255	+	66	1119.230093	2070.357664	5212.719939	4193.551146	1955.183064
 gene_0003_thrA	AP012030.1	338	2800	+	2463	1872.865147	4391.896215	5773.99795	6352.945352	3971.157253
 gene_0005_thrB	AP012030.1	2802	3734	+	933	1925.903487	4443.130625	3779.116205	4639.666182	3926.988291
 gene_0007_thrC	AP012030.1	3735	5021	+	1287	2288.887037	3590.528484	3897.465673	4230.197519	3276.631145
 gene_0008_yaaX	AP012030.1	5235	5531	+	297	186.9824878	553.2073517	293.9131871	321.6221759	585.3916812
 gene_0009_yaaA	AP012030.1	5684	6460	-	777	42.27207978	45.02772533	73.69652755	49.63567721	49.63092301
 gene_0011_yaaJ	AP012030.1	6530	7960	-	1431	29.68188822	18.17449013	19.74707008	17.02170195	21.92416
 gene_0014_talB	AP012030.1	8239	9192	+	954	3197.624287	397.0260641	1580.352153	1586.773069	443.3769857
 gene_0016_mog	AP012030.1	9307	9894	+	588	218.27842	87.23291626	137.3536608	138.6934099	94.32622154
 gene_0017_yaaH	AP012030.1	9929	10495	-	567	146.5658456	50.05540317	89.97128851	78.75914474	61.42555542



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gene̾¤Ç¸«¤Æ½ÅÊ£¤Î¤¢¤ëCDS¤ò¼è½Ð¤·¤Æ¤ß¤ë¤È

||	gene|	Start|	Length|	Anc|
|gene_3114|	arpB|	1786475|	474|	7.176203468|
|gene_3115|	arpB|	1786959|	1416|	1.441322222|
|gene_3782|	gatR|	2153495|	447|	6.087732338|
|gene_3786|	gatR|	2155197|	339|	163.2194667|
|gene_2015|	icd|	1172515|	1251|	1782.60336|
|gene_2058|	icd|	1190066|	165|	12.36916525|
|gene_6818|	ilvG|	3929665|	984|	1955.874255|
|gene_6819|	ilvG|	3930728|	582|	942.1393394|
|gene_0495|	insA|	 290296|	276|	0|
|gene_1774|	insA|	1040161|	276|	6.572986365|
|gene_6204|	insA|	3561389|	276|	0.821623296|
|gene_7778|	insA|	4498297|	276|	37.7946716|
|gene_0035|	insB|	  19812|	504|	3.149555967|
|gene_0475|	insB|	 278403|	504|	0|
|gene_0494|	insB|	 289874|	504|	2.6996194|
|gene_1775|	insB|	1040355|	504|	64.34092903|
|gene_7637|	insB|	4413646|	504|	3.599492533|
|gene_7779|	insB|	4498491|	294|	30.85279314|
|gene_7781|	insB|	4498785|	210|	78.82888648|
|gene_0647|	insC|	 381730|	411|	0|
|gene_1727|	insC|	1016493|	411|	0|
|gene_2221|	insC|	1280354|	411|	0|
|gene_5485|	insC|	3164049|	411|	1.103494061|
|gene_7744|	insC|	4477997|	411|	0|
|gene_0648|	insD|	 382080|	924|	0|
|gene_1728|	insD|	1016861|	906|	0|
|gene_2222|	insD|	1280722|	906|	0|
|gene_2529|	insD|	1450103|	906|	0|
|gene_3625|	insD|	2052755|	906|	0|
|gene_5486|	insD|	3164417|	906|	0|
|gene_7745|	insD|	4478347|	924|	2.6996194|
|gene_0535|	insE|	 315706|	309|	0|
|gene_0536|	insE|	 315715|	300|	0|
|gene_2021|	insE|	1177320|	309|	0|
|gene_3785|	insE|	2154038|	300|	0|
|gene_0467|	insH|	 273326|	981|	0|
|gene_0505|	insH|	 294457|	981|	0|
|gene_0973|	insH|	 566399|	981|	0|
|gene_1163|	insH|	 678257|	981|	0|
|gene_1843|	insH|	1081651|	1017|	0|
|gene_2218|	insH|	1278220|	981|	0|
|gene_2394|	insH|	1377059|	1017|	0.222977414|
|gene_2401|	insH|	1380609|	1017|	0.222977414|
|gene_2466|	insH|	1409928|	981|	16.18120497|
|gene_3011|	insH|	1725750|	981|	0|
|gene_3316|	insH|	1893378|	981|	0|
|gene_3623|	insH|	2050108|	1017|	28.31813152|
|gene_3690|	insH|	2085698|	1017|	0|
|gene_3792|	insH|	2158195|	1017|	5.351457925|
|gene_3983|	insH|	2274059|	1017|	0|
|gene_5382|	insH|	3108121|	981|	0|
|gene_5806|	insH|	3343608|	981|	0|
|gene_6318|	insH|	3630088|	1017|	0|
|gene_6713|	insH|	3868728|	1017|	0|
|gene_0463|	insI|	 269828|	1152|	16.53516882|
|gene_2530|	insI|	1451540|	1152|	1.181083487|
|gene_7761|	insI|	4487236|	1152|	11.22029313|
|gene_0028|	insL|	  15440|	1119|	0|
|gene_1028|	insL|	 598319|	1119|	0|
|gene_4345|	insL|	2499206|	1119|0|
|gene_0462|	insN|	 269467|	405|	9.518658032|
|gene_7760|	insN|	4486967|	267|	40.76728622|
|gene_0464|	insO|	 271055|	426|	117.11025|
|gene_7764|	insO|	4488728|	597|	20.89152701|
|gene_6038|	kefG|	3458512|	555|	0|
|gene_6039|	kefG|	3458512|	552|	0|
|gene_2164|	ldrB|	1247821|	135|	0|
|gene_2167|	ldrB|	1248356|	135|	68.87029047|
|gene_2169|	ldrB|	1248891|	135|	0|
|gene_2465|	lomR|	1409640|	156|	15.99005337|
|gene_2469|	lomR|	1410996|	171|	1.326128828|
|gene_3836|	molR|	2181468|	825|	3.573314406|
|gene_3838|	molR|	2182404|	1938|	8.658841171|
|gene_3840|	molR|	2184304|	960|	4.251900555|
|gene_7426|	phnE|	4301437|	369|	4.916380045|
|gene_7427|	phnE|	4301655|	621|	5.842654547|
|gene_1997|	potA|	1161851|	1137|	0|
|gene_1998|	potA|	1161851|	1119|	0|
|gene_6273|	rhsB|	3597098|	4236|	3.051411163|
|gene_6498|	rhsB|	3746869|	294|	13.11243709|
|gene_1713|	sulA|	1009402|	516|	0|
|gene_1714|	sulA|	1009402|	510|	0|
|gene_3689|	wbbL|	2085199|	282|	99.71360166|
|gene_3691|	wbbL|	2086719|	474|	189.4517716|
|gene_1830|	ycdN|	1071684|	111|	4.085910443|
|gene_1832|	ycdN|	1071794|	720|	11.65335708|
|gene_2077|	ycgH|	1198254|	1521|	32.05465244|
|gene_2078|	ycgH|	1199859|	1017|	105.0223618|
|gene_2212|	ychG|	1273079|	591|	44.50946097|
|gene_2213|	ychG|	1273621|	231|	54.97406778|
|gene_2284|	yciX|	1316143|	129|	54.49464277|
|gene_2285|	yciX|	1316253|	189|	278.3607559|
|gene_2528|	ydbA|	1447574|	2559|	13.20376569|
|gene_2532|	ydbA|	1452872|	3324|	120.0697148|
|gene_3511|	yedN|	1995151|	192|	1.181083487|
|gene_3512|	yedN|	1995352|	321|	0.70644246|
|gene_3570|	yedS|	2017854|	486|	5.132609723|
|gene_3572|	yedS|	2018349|	210|	16.1977164|
|gene_3574|	yedS|	2018642|	405|	19.03731606|
|gene_3597|	yeeL|	2036079|	327|	3.467401064|
|gene_3599|	yeeL|	2036427|	705|	0.964970339|
|gene_4039|	yfaS|	2314679|	486|	15.39782917|
|gene_4041|	yfaS|	2315180|	4104|	11.65888261|
|gene_4169|	yfcC|	2401962|	1521|	0.298182813|
|gene_4171|	yfcC|	2402004|	1479|	0|
|gene_7747|	yjgX|	4479813|	432|	58.26678538|
|gene_7748|	yjgX|	4480202|	360|	30.23573728|
|gene_0530|	ykgM|	312798|	141|	3697.444681|
|gene_0531|	ykgM|	312938|	267|	2342.420321|
|gene_0940|	ylbE|	548781|	1260|	0.179974627|
|gene_0941|	ylbE|	549039|	1002|	0|
|gene_2630|	yncI|	1512768|	747|	21.55358782|
|gene_2632|	yncI|	1513558|	201|	15.79478813|
|gene_2579|	yncK|	1485325|	168|	31.0456231|
|gene_2580|	yncK|	1485544|	288|	37.7946716|
|gene_2084|	ypjA|	1202348|	213|	5.323193183|
|gene_4790|	ypjA|	2756055|	4500|	23.98701824|

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***ÅÓÃæ¤ÎÆ»Áð [#t554de2c]
º£¤Ïlog(tpm/Anc)¤ò¼è¤Ã¤¿É½¡£

|	|gene	|Start	|Length	|Anc	|43B	|45a_minus	|45A_minus	|45L	|1_2-1	|2_5-1|
|gene_0001	|thrL	|190	|66	|0	|0.518801371	|0.120822012	|0.130553092	|0.662278199	|0.736981706	|0.782980429|
|gene_0003	|thrA	|338	|2463	|0	|0.363545812	|0.217293869	|0.207090028	|0.462360214	|0.457629592	|0.265097062|
|gene_0005	|thrB	|2802	|933	|0	|0.375643354	|0.338447083	|0.313037592	|0.533665319	|0.35669746	|0.17628472|
|gene_0007	|thrC	|3735	|1287	|0	|0.351946181	|0.178209151	|0.160556929	|0.449396654	|0.309469379	|0.092155761|
|gene_0008	|yaaX	|5235	|297	|0	|0.361804821	|0.497406189	|0.565856219	|0.483361196	|0.254979922	|0.141397537|
|gene_0009	|yaaA	|5684	|777	|0	|0.127790066	|-0.16041128	|-0.046404433	|0.015105221	|0.04435137	|-0.083863423|
|gene_0011	|yaaJ	|6530	|1431	|0	|-0.061913911	|0.120802403	|0.215477533	|0.132366925	|0.108788741	|-0.028453563|
|gene_0014	|talB	|8239	|954	|0	|0.025617922	|-0.296533334	|-0.408331467	|0.077236367	|0.241795445	|0.203333867|
|gene_0016	|mog	|9307	|588	|0	|-0.18900972	|-0.26035833	|-0.255735167	|-0.114590533	|-0.140329364	|-0.159416034|
|gene_0017	|yaaH	|9929	|567	|0	|-0.151212522	|-0.346290801	|-0.274411814	|-0.177810069	|-0.161112427	|-0.103483574|

¾åµ­¥Ç¡¼¥¿¤ËÂФ·¤Æclustering¤ò¹Ô¤Ã¤¿·ë²Ì¡ÊÁ´Éô¹Ô¤¦¤Èµ÷Î¥·×»»¤Ë»þ´Ö¤¬Èó¾ï¤Ë¤«¤«¤ë¤Î¤Ç100¸Ä¤À¤±¡Ë

&ref(CompareTPM.pdf);

**Á´ÂΤò¥¯¥é¥¹¥¿²½ [#y7bd23d4]

 %matplotlib inline
 #
 import pandas as pd
 import numpy as np
 from scipy.spatial import distance
 from scipy.cluster.hierarchy import linkage, dendrogram
 import matplotlib.pyplot as plt
 import math
 import os
 import pickle
 
 def dfnormalize(row):  # Anc¤¬0¤Ê¤é¥ª¡¼¥ë0¡¢¤½¤¦¤Ç¤Ê¤±¤ì¤Ðlog(u/Anc)
     Anc = row['Anc']
     rest = row.to_list()[3:]
     #print('rest\n', rest)
     if Anc==0:
         result = [0] * len(rest)
     else:
         result = [math.log10(u/Anc) if u>0.000001 else math.log10(0.000001/Anc) for u in rest] 
     #print('result\n', result)
     output = pd.Series(result, index=(row.index.to_list()[3:]))
     output['gene'] = row['gene']
     output['Anc'] = 0 if Anc==0 else 0
     output['Start'] = row['Start']
     output['Length'] = row['Length']
     #print('output\n', output)
     return(output)
 
 #def myeuc(u):   # Euclideanµ÷Î¥¤ò·×»»¤¹¤ë´Ø¿ô¡Ámap¤¹¤ë¤¿¤á¤ËÍѰÕ
 #    #print('u\n', u, '\ndfl.loc[target]\n', dfl.loc[target])
 #    result = distance.euclidean(u, dfl[['Anc', '43B', '45a_minus', '45A_minus', '45L', '1_2-1', '2_5-1']].loc[target])
 #    #print('result:', result)
 #    return(result)
 
 picklefname = 'DistanceTest.pickle'
 slist = ['Anc', '43B', '45a_minus', '45A_minus', '45L', '1_2-1', '2_5-1']
     
 if not os.path.exists(picklefname):
     fname = 'count_tpm.tsv'
     df = pd.read_csv(fname, sep='\t', index_col=0)
     #print(df.columns.to_list())
     df = df.rename(columns=
     {'10B.sam': '45a_2-10Bplus', 
      '10D_minus.sam': '45a_10D_minus', 
      '1p2-1.sam': '1_2-1', 
      '1p2-2.sam': '1_2-2', 
      '2-10B_minus.sam': '45a_2-10B_minus', 
      '2p5-1.sam': '2_5-1', 
      '2p6-1.sam': '2_6-1', 
      '43B.sam': '43B', 
      '45A_minus.sam': '45A_minus', 
      '45A_plus.sam': '45A_plus', 
      '45L.sam': '45L', 
      '45a10D_plus.sam': '45a_10D_plus', 
      '45aIII6c_plus.sam': '45a_III6c_plus', 
      '45a_minus.sam': '45a_minus', 
      '45a_plus.sam': '45a_plus', 
      '45alll6c_minus.sam': '45a_III6c_minus', 
      '45b_minus.sam': '45b_minus', 
      '45b_plus.sam': '45b_plus', 
      '45c_minus.sam': '45c_minus', 
      '45c_plus.sam': '45c_plus', 
      '45d7B_minus.sam': '45d_7B_minus', 
      '45d7B_plus.sam': '45d_7B_plus', 
      'Anc.sam': 'Anc', 
      'PwOw_minus.sam': 'PwOw_minus', 
      'PwOw_plus.sam': 'PwOw_plus'           
     })
     df['gene'] = [u[10:] for u in df.index.to_list()]
     df.index = [u[:9] for u in df.index.to_list()]
      
     dfdup = df[df.duplicated(subset='gene', keep=False)]\
     [['gene', 'Start', 'Length', 'Anc', '43B', '45A_minus', '45L', \
           '1_2-1', '2_5-1']].sort_values(['gene', 'Start'])
     dfdup.to_excel('DuplicatedCDS.xlsx')
     print(dfdup)
     
     df1 = df.copy()[['gene', 'Start', 'Length', \
           'Anc', '43B', '45a_minus', '45b_minus', '45c_minus', '45A_minus', '45L', \
           '1_2-1', '2_5-1']]
 
     df1 = df1[df1['Anc']!=0]   # Anc¤¬0¤Î¤â¤Î¤ò½ü¤¯¡ÊAnc¤Ç³ä¤ë¤«¤é¡Ë
     df1x = df1[:].apply(dfnormalize, axis=1)
     df1x = df1x[['gene', 'Start', 'Length', \
              'Anc', '43B', '45a_minus', '45A_minus', '45L', '1_2-1', '2_5-1']]
     df1x.to_excel('CompareTPM.xlsx')
 
 ############
 # Line Graphs
 ############
     #df1g = df1x[['Anc', '43B', '45a_minus', '45A_minus', '45L', '1_2-1', '2_5-1']]
     #min = 0.4  # ÀäÂÐÃͤ¬0.4°Ê¾å¤Î¥Ç¡¼¥¿ÅÀ¤À¤±¥×¥í¥Ã¥È
     #df1g = df1g[(abs(df1g['45a_minus'])>min) & (abs(df1g['45A_minus'])>min) &\
     #            (abs(df1g['45L'])>min) & (abs(df1g['1_2-1'])>min) & (abs(df1g['2_5-1'])>min) ]
     #
     #df1g.T.plot()
     #plt.show()
 
     df2 = df.copy()[slist]
     # df2 = df2 + 1  # ÃÍ0¤òÈò¤±¤ë¤¿¤á ¢Í¡¡¤¹¤Ù¤­¤Ç¤Ï¤Ê¤¤¡£¤à¤·¤íÈù¾®¤ÊÀµ¿ô¤Ë¤¹¤Ù¤­¤À¤í¤¦¡£
     df2 = df2 + 0.00000001
     dfl = np.log10(df2)   # Àè¤Ëlog10¤ò¼è¤ë
     dfl_t = dfl.T
 
     # ¥ª¡¼¥ë0¤Î¹Ô¤Ï½ü¤«¤Ê¤±¤ì¤Ð¤Ê¤é¤Ê¤¤
     dfl_copy = dfl.replace(0.0, np.nan).dropna(how='all', axis=0)
     dfl = dfl.loc[dfl_copy.index]
     print('dfl\n', dfl.head()); print()
     dfl.to_pickle(picklefname)
 else:
     dfl = pd.read_pickle(picklefname)
 
 pickle2fname = 'CompareTPM2.pickle'
 if not os.path.exists(pickle2fname):
     #dfl = dfl.head(100)
 
     # target¤È¾¤Îgene¤È¤ÎÂФε÷Î¥¤ò·×»»¤¹¤ë
     genenamelist = dfl.T.columns.to_list()
     print(genenamelist)
     #for target in genenamelist[:50]:
     for target in genenamelist:
         #print('dfl[slist].loc[target]\n', dfl[slist].loc[target])
         dfl['D_'+target] = dfl[slist].apply(lambda x: \
             distance.euclidean(x, dfl[slist].loc[target]), axis=1)
 
         print('target:', target)
         #dfx = dfl.sort_values('d', ascending=True)[:10]
         #print('dfx\n', dfx)
         #dfg = dfx.drop('d', axis=1).T
         #dfg.plot()
         #plt.ylabel('$log_{10}(TPM)$')
         #plt.title(target)
         #plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
         #plt.show()
        
     print(dfl.drop(columns=slist).head(), '\n')
     dfl.to_pickle('CompareTPM2.pickle')
 else:
     dfl = pd.read_pickle(pickle2fname)
 
 pickleLinkagefname = 'CompareTPMLinkage.pickle'
 if not os.path.exists(pickleLinkagefname):
     dArray = distance.squareform(dfl.drop(columns=slist))
     result = linkage(dArray, method = 'average')
     node_labels = [u[2:] for u in dfl.drop(columns=slist).columns.to_list()]
     
     with open(pickleLinkagefname, 'wb') as pfw:
         pickle.dump([result, node_labels], pfw)
 else:
     with open(pickleLinkagefname, 'rb') as pf:
         result, node_labels = pickle.load(pf)
 
 plt.figure(figsize=(100,100), dpi=200, facecolor='w', edgecolor='k')
 dendrogram(result, labels=node_labels)
 plt.savefig('CompareTPM.pdf')
 plt.show()
 print('complete')

¹¹¤Ë¡¢½ÐÎϤÎCompareTPMLinkage.pickle¤òÆÉ¤ó¤Ç¡¢fcluster¤Ç¥¯¥é¥¹¥¿¤ò½ÐÎÏ

 %matplotlib inline
 # ºÇ¸å¤ÎÉôʬ¡ÊLinkage·×»»¤è¤ê¸å¤í¤ÎÉôʬ¡Ë¤À¤±
 import pandas as pd
 import numpy as np
 from scipy.spatial import distance
 from scipy.cluster.hierarchy import linkage, dendrogram
 import matplotlib.pyplot as plt
 import os
 import pickle
 
 # gene_number¤«¤égene_name¤Ø¤ÎÊÑ´¹¼­½ñ
 fname = 'count_tpm.tsv'
 df = pd.read_csv(fname, sep='\t', index_col=0)
 gene_name_dict = {u[:9]: u[10:] for u in df.index.to_list()}
 #print(gene_name_dict)
 
 pickleLinkagefname = 'CompareTPMLinkage.pickle'
 with open(pickleLinkagefname, 'rb') as pf:
     result, node_labels = pickle.load(pf)
 # print(result[:10])
 NUM_CLUSTERS = 10
 for num in range(10, NUM_CLUSTERS+1):
     labels = fcluster(result, t=num, criterion='maxclust')
     #fcluster¤Ï¡¢ÆþÎϤ¬¤É¤Î¥¯¥é¥¹¥¿¤Ë°¤¹¤ë¤«¡Ê¥¯¥é¥¹¥¿ÈÖ¹æ labels¡Ë¤òÊÖ¤¹
     #print(num, labels)
     # ¥¯¥é¥¹¥¿¤´¤È¤Ë¡¢¤½¤ì¤Ë°¤¹¤ëÆþÎϤò¥ê¥¹¥È¤È¤·¤ÆÉ½¼¨
     clusters = []
     for cl_id in range(1, num+1):
         l = [gene_name_dict[ node_labels[n] ] for n in range(0,len(labels)) if labels[n]==cl_id]
         #print(' ', cl_id, l)
         clusters.append([cl_id, l])
     with open('clusters_'+str(num)+'.pickle', 'wb') as pwf:
         pickle.dump(clusters, pwf)
 print('complete')

¥¯¥é¥¹¥¿¤´¤È¤Ë¡¢¤½¤ì¤¾¤ì¤Ë°¤¹¤ëgene¤Îȯ¸½ÊÑÆ°¤ò¥°¥é¥Õɽ¼¨¤¹¤ë¡£

 %matplotlib inline
 # ¥¯¥é¥¹¥¿¤Ë°¤¹¤ëgene¤Î¥°¥é¥Õ¤òÉÁ¤¯
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import pickle
 
 fname = 'count_tpm.tsv'
 df = pd.read_csv(fname, sep='\t', index_col=0)
 gene_name_dict = {u[:9]: u[10:] for u in df.index.to_list()}
 #print(gene_name_dict)
 
 NUM = 10
 with open('clusters_'+str(NUM)+'.pickle', 'rb') as pf:
     clusters = pickle.load(pf)
 
 picklefname = 'DistanceTest.pickle'
 dfl = pd.read_pickle(picklefname)
 #print(dfl.index.to_list())
 for ucl_id, l in clusters:
     print(l)
     ############
     # Line Graphs
     dfl['gene'] = [gene_name_dict[u] for u in dfl.index]
     #print(l, dfl[dfl['gene'].isin(l)])
     dfg = dfl[dfl['gene'].isin(l)]
     dfg = dfg[['Anc', '43B', '45a_minus', '45A_minus', '45L', '1_2-1', '2_5-1']]
     dfg = dfg.iloc[0:10, :]
     dfg.T.plot()
     plt.show()

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|&ref(./cluster_1.png,250x180);|&ref(./cluster_2.png,250x180);|&ref(./cluster_3.png,250x180);|&ref(./cluster_4.png,250x180);|&ref(./cluster_5.png,250x180);|
|&ref(./cluster_6.png,250x180);|&ref(./cluster_7.png,250x180);|&ref(./cluster_8.png,250x180);|&ref(./cluster_9.png,250x180);|&ref(./cluster_10.png,250x180);|

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