[[¥Î¡¼¥È>¥Î¡¼¥È/¥Î¡¼¥È]]~ ˬÌä¼Ô¿ô¡¡&counter();¡¡¡¡¡¡¡¡¡¡¡¡ºÇ½ª¹¹¿·¡¡&lastmod(); *RNAseq¡Á´ßËܥǡ¼¥¿¤Î¸å³ʬÀÏ [#gcc5a764] TPMÊäÀµ¤¬½ª¤ï¤Ã¤¿¼Â¸³¥Ç¡¼¥¿¤ò¤É¤¦Ê¬ÀϤ¹¤ë¤«¹Í¤¨¤ë¡£ ***¥Ç¡¼¥¿ [#sfe06552] TPMÊäÀµºÑ¤ß¥Ç¡¼¥¿¡Êȯ¸½ÃͤÎÊäÀµºÑ¤ß¥Ç¡¼¥¿¡Ë¤Ï 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 ¤Î¤è¤¦¤Ë¤Ê¤Ã¤Æ¤¤¤ë¡£ Ãí°Õ¡§¡¡Æ±¤¸gene̾¤Ç¡¢Ê£¿ô¤Î°Û¤Ê¤ëCDS¤Ë¤Ê¤Ã¤Æ¤¤¤ë¾ì¹ç¤¬¤¢¤ë¡£¤Ä¤Þ¤êgene̾¤Ç¤Ï¥æ¥Ë¡¼¥¯¤Ç¤Ï¤Ê¤¯¡¢CDS°ÌÃ֤ǥæ¥Ë¡¼¥¯¤Ë¤Ê¤ë¡£¤³¤ì¤é¤Ï¥È¥é¥ó¥¹¥Ý¥¾¥ó¡ÊinsA¡ÁN¤Ê¤É¡Ë¤Ê¤É¥²¥Î¥àÆâ¤òÈô¤Ó²ó¤ë°äÅÁ»Ò¤é¤·¤¤¡£ 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| ¤³¤ì¤é¤Ë¤Ä¤¤¤Æ¤Ï¡¢È¯¸½Î̲òÀϤÎÅö½é¤Ç¤ÏÊüÃÖ¤·¤Æ¤ª¤¤¤¿¡ÊCDS°ÌÃ֤ǶèÊ̤·¤Æ½èÍý¤·¤Æ ¤¤¤¿¡Ë¤¬¡¢°äÅÁ»Ò¤Îȯ¸½Î̤Ȥ·¤Æ¤ß¤ë¤È²¿¤é¤«¤ÎȽÃǤò¤·¤Ê¤±¤ì¤Ð¤Ê¤é¤Ê¤¤¡£ ÆâÍÆ¤ò¸«¤ë¤È¡Ê´ßËÜÀèÀ¸¤Ë¤è¤ë¡Ë¡¢Æ±¤¸ÇÛÎ󤬥³¥Ô¡¼¤µ¤ì¤Æ¤¤¤ë¾ì¹ç¤È¡¢£±¤Ä¤ÎÇÛÎó¤¬ÅÓÃæ¤Ë;ʬ¤ÊÔó»¨Êª¤¬Æþ¤Ã¤ÆÀÚ¤ì¤Æ¤¤¤ë¤è¤¦¤Ë¸«¤¨¤ë¾ì¹ç¤¬¤¢¤ë¡£¤µ¤é¤Ë°Üư¤ÎÅÓÃæ¤Çû¤¯¤Ê¤Ã¤¿¤êŤ¯¤Ê¤Ã¤¿¤ê¤·¤Æ¤¤¤ë¥±¡¼¥¹¤¬¤¢¤ë¡£¤³¤ì¤é¤ÎȽÃÇ¤ÏÆñ¤·¤¤¤¬¡¢ -¾¤Î»ñÎÁ¡ÊEcoCyc¤Ê¤É¡Ë¤«¤é¡Ö¸µ¡×¤¬Ê¬¤«¤ë¤È¤¡¢ÉôʬX¤ÈÉôʬY¤¬¤½¤ì¤¾¤ì¤Î°ìÉô¤Ç¤¢¤ê¡¢·ë¹ç¤¹¤ë¤È¤Û¤Ü¡Ö¸µ¡×¤ÈƱ¤¸¤Ë¤Ê¤ë¾ì¹ç¡¢ 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- 0¡¡¤Ä¤Þ¤ê°ìÀÚȯ¸½Ìµ¤·¡¢¤¬¤¢¤êÆÀ¤ë¡£¤³¤ì¤Ï¤ª¤½¤é¤¯Ê¬ÀÏÂоݤ«¤é½ü³°¤·¤Æ¤è¤¤¤À¤í¤¦¡£ 0 - ÅÓÃæ¤Ënon-0¤¬¤¢¤ë - ...¡¡¤³¤ì¤é¤Î¥±¡¼¥¹¤Ï¡¢Îã³°»ë¤¹¤ëɬÍפϤʤ¯¡¢£±¤Ä¤Î¥Ñ¥¿¡¼¥ó¤È¹Í¤¨¤Æ¤è¤«¤í¤¦¡£Anc¤¬0¤Ç¤¢¤ë¥±¡¼¥¹¤Ï¡¢¡Ö¤Ê¤«¤Ã¤¿¤â¤Î¤¬½Ð¤Æ¤¯¤ë¤è¤¦¤Ë¤Ê¤Ã¤¿¡×¤È¤¤¤¦°ÕÌ£¤Ç¾¯¤·¹Í¤¨¤ëɬÍפ¬¤¢¤ë¤«¤âÃΤì¤Ê¤¤¤¬¡¢¤È¤ê¤¢¤¨¤º£±¤Ä¤Î¥Ñ¥¿¡¼¥ó¤È¤·¤Æ¹Í¤¨¤ë¤³¤È¤Ë¤¹¤ë¡£ ¤Ê¤ª¡¢¸å½Ò¤Î¤è¤¦¤Ë¡¢ÊÑÆ°¤È¤·¤Æ·ÏÎó¾å¤ÎÁ°¤ÎÃͤȤÎÈæ¤ò¹Í¤¨¤ë¤È¡¢0¤Ç³ä¤ë¤³¤È¤Ë¤Ê¤ë¤Î¤Ç¡¢Èù¾®ÃͤËÃÖ¤´¹¤¨¤ë¤Ê¤É¤Î¹©Éפ¬É¬Íפˤʤ뤷¡¢Èù¾®Ãͤˤ¹¤ë¤Èlog10¤ò¼è¤ë¤ÈÉé¤ÎÂ礤ÊÃͤˤʤäÆÂ¿¾¯»ÏËö¤¬°¤¤¡£ Ʊ¤¸gene̾¤¬Ê£¿ô¤ÎCDS¤Ë¸½¤ì¤ë·ï¤Ë¤Ä¤¤¤Æ¡£¡©¡© ***ÊÑÆ°¤Î¿ôÃÍ»ØÉ¸¤Ë¤Ä¤¤¤Æ [#s91230ce] ¡Àµµ¬²½¡§~ ȯ¸½Î̤ÎÀäÂÐÃͤϡ¢º£²¾¤Ë°ÕÌ£¤¬Ìµ¤¤¤È¹Í¤¨¤ë¡£Íߤ·¤¤¤Î¤Ï¥µ¥ó¥×¥ë´Ö¤Ç¸«¤¿¤È¤¤ÎÁý¸º¤Î¥Ñ¥¿¡¼¥ó¡ÊÁý¸º¤ÎÊý¸þ¤È¿²É¡¢¤½¤ì¤¬°ìÏ¢¤Î¥µ¥ó¥×¥ë´Ö¤Ç¤É¤¦¤¤¤¦Ï¢º¿¤«¡Ë¤Ê¤Î¤Ç¡¢¿ôÃͤÏgene´Ö¤ÇÀµµ¬²½¤¹¤ëɬÍפ¬¤¢¤ë¤¬¡¢£±¤Ä¤ÎÊýË¡¤È¤·¤ÆÆ±°ìgeneÆâ¤Ç¤ÎÊÑÆ°ÈæÎ¨¤ò¼è¤Ã¤Æ¤·¤Þ¤¦¡£~ £²¤Ä¤ÎÈæÎ¨¤¬¹Í¤¨¤é¤ì¤ë¡§£±ÈÖÌܤϴð½àÃ͡ʤ¿¤È¤¨¤ÐAnc¤«¡¢¤Þ¤¿¤ÏÁ´ÂΤÎÊ¿¶ÑÃÍ¡¢ºÇÉÑÃͤΤ褦¤Ê¤â¤Î¡Ë¤ËÂФ¹¤ëÈæÎ¨¡¢£²ÈÖÌܤϥµ¥ó¥×¥ë´Ö¤ÎÈæÎ¨¡£ ¢ÃͤÎlog¡Êlog10¡Ë¤Ë¤è¤ë°µ½Ì~ 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|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¤ËÍÑ°Õ # #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() ·ëÏÀ¤Ï¡¢ 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