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 motifpvalcorr_pvalmotifpvalcorr_pval
8Man0.0251980.2705128Man0.0251980.270512
4GlcNAc0.0297840.2705124GlcNAc0.0297840.270512
25Man(a1-?)Man0.0300570.27051225Man(a1-?)Man0.0300570.270512
10Man(a1-3)Man0.0460550.31087110Man(a1-3)Man0.0460550.310871
11Man(a1-6)Man0.0917520.35389911Man(a1-6)Man0.0917520.353899
13Man(b1-4)GlcNAc0.0917520.35389913Man(b1-4)GlcNAc0.0917520.353899
14GlcNAc(b1-4)GlcNAc0.0917520.35389914GlcNAc(b1-4)GlcNAc0.0917520.353899
16Man(a1-2)Man0.1308260.44153716Man(a1-2)Man0.1308260.441537
3GlcN0.1628530.4885603GlcN0.1628530.488560
0Fuc0.3613940.7505870Fuc0.3613940.750587
\n" @@ -1081,142 +1081,142 @@ " \n", " 1\n", " GlcNAc(b1-4)GlcNAc\n", - " 3.528458\n", - " 1.969106\n", - " 0.000004\n", - " 0.000050\n", - " 0.733257\n", - " 36.265595\n", + " 3.488110\n", + " 1.948240\n", + " 0.000028\n", + " 0.000199\n", + " 0.823898\n", + " 39.952624\n", " \n", " \n", " 13\n", " Glc\n", - " 21.362005\n", - " -1.188865\n", - " 0.000022\n", - " 0.000153\n", - " 0.733257\n", - " -27.579095\n", + " 21.625130\n", + " -1.131727\n", + " 0.000017\n", + " 0.000199\n", + " 0.988829\n", + " -20.516420\n", " \n", " \n", " 3\n", " Man(a1-3)Man\n", - " 4.508876\n", - " 1.719222\n", - " 0.000247\n", - " 0.001151\n", - " 0.584688\n", - " 23.339466\n", + " 4.459483\n", + " 1.698465\n", + " 0.000372\n", + " 0.001735\n", + " 0.823898\n", + " 23.365422\n", " \n", " \n", " 0\n", " core_fucose(a1-3)\n", - " 1.277706\n", - " 2.192932\n", - " 0.002588\n", - " 0.004252\n", - " 0.584688\n", - " 11.090423\n", - " \n", - " \n", - " 2\n", - " Man(a1-2)Man\n", - " 3.231170\n", - " 1.553464\n", - " 0.002423\n", - " 0.004252\n", - " 0.584688\n", - " 8.435493\n", - " \n", - " \n", - " 6\n", - " GlcNAc\n", - " 8.715115\n", - " 1.014227\n", - " 0.002247\n", - " 0.004252\n", - " 0.584688\n", - " 14.294328\n", + " 1.264427\n", + " 2.174537\n", + " 0.000606\n", + " 0.002123\n", + " 0.988829\n", + " 8.942929\n", " \n", " \n", - " 7\n", - " Man(a1-?)Man\n", - " 11.268504\n", - " 1.744974\n", - " 0.002733\n", - " 0.004252\n", - " 0.584688\n", - " 13.760687\n", + " 5\n", + " Kdo(a2-?)Kdo\n", + " 3.312295\n", + " -2.816328\n", + " 0.001246\n", + " 0.002492\n", + " 0.988829\n", + " -6.677979\n", " \n", " \n", " 9\n", " Man\n", - " 14.796962\n", - " 1.796669\n", - " 0.002328\n", - " 0.004252\n", - " 0.584688\n", - " 16.063347\n", + " 14.630760\n", + " 1.775643\n", + " 0.001171\n", + " 0.002492\n", + " 0.823898\n", + " 15.688657\n", " \n", " \n", " 10\n", " Kdo\n", - " 4.974599\n", - " -2.802028\n", - " 0.001785\n", - " 0.004252\n", - " 0.630760\n", - " -9.395584\n", + " 4.968442\n", + " -2.816328\n", + " 0.000921\n", + " 0.002492\n", + " 0.988829\n", + " -7.286003\n", " \n", " \n", - " 11\n", - " Glc(b1-3)Glc\n", - " 5.114861\n", - " -6.978624\n", - " 0.003783\n", - " 0.005297\n", - " 0.584688\n", - " -12.092472\n", + " 6\n", + " GlcNAc\n", + " 8.632368\n", + " 0.993599\n", + " 0.002163\n", + " 0.003028\n", + " 0.823898\n", + " 14.130929\n", " \n", " \n", - " 5\n", - " Kdo(a2-?)Kdo\n", - " 3.316399\n", - " -2.802028\n", - " 0.004302\n", - " 0.005475\n", - " 0.584688\n", - " -8.746932\n", + " 7\n", + " Man(a1-?)Man\n", + " 11.142650\n", + " 1.723896\n", + " 0.002123\n", + " 0.003028\n", + " 0.823898\n", + " 13.650820\n", " \n", " \n", " 8\n", " betaGlucan\n", - " 2.557430\n", - " -6.978624\n", - " 0.006985\n", - " 0.008149\n", - " 0.584688\n", - " -8.257750\n", + " 2.674773\n", + " -4.206173\n", + " 0.001969\n", + " 0.003028\n", + " 0.988829\n", + " -6.023846\n", + " \n", + " \n", + " 11\n", + " Glc(b1-3)Glc\n", + " 5.349546\n", + " -4.206173\n", + " 0.002908\n", + " 0.003701\n", + " 0.988829\n", + " -6.123911\n", " \n", " \n", " 4\n", " GalNAc(a1-4)GlcNAcA\n", - " 1.658200\n", - " -2.802028\n", - " 0.014605\n", - " 0.015728\n", - " 0.584688\n", - " -6.607927\n", + " 1.656147\n", + " -2.816328\n", + " 0.003293\n", + " 0.003842\n", + " 0.988829\n", + " -5.134776\n", + " \n", + " \n", + " 2\n", + " Man(a1-2)Man\n", + " 3.195056\n", + " 1.531678\n", + " 0.004655\n", + " 0.005014\n", + " 0.823898\n", + " 8.671388\n", " \n", " \n", " 12\n", " GlcN\n", - " 13.689714\n", - " -0.165373\n", - " 0.101152\n", - " 0.101152\n", - " 0.584688\n", - " -2.280319\n", + " 13.600811\n", + " -0.185387\n", + " 0.097643\n", + " 0.097643\n", + " 0.823898\n", + " -2.296375\n", " \n", " \n", "\n", @@ -1224,36 +1224,36 @@ ], "text/plain": [ " Glycan Mean abundance Log2FC p-val corr p-val \\\n", - "1 GlcNAc(b1-4)GlcNAc 3.528458 1.969106 0.000004 0.000050 \n", - "13 Glc 21.362005 -1.188865 0.000022 0.000153 \n", - "3 Man(a1-3)Man 4.508876 1.719222 0.000247 0.001151 \n", - "0 core_fucose(a1-3) 1.277706 2.192932 0.002588 0.004252 \n", - "2 Man(a1-2)Man 3.231170 1.553464 0.002423 0.004252 \n", - "6 GlcNAc 8.715115 1.014227 0.002247 0.004252 \n", - "7 Man(a1-?)Man 11.268504 1.744974 0.002733 0.004252 \n", - "9 Man 14.796962 1.796669 0.002328 0.004252 \n", - "10 Kdo 4.974599 -2.802028 0.001785 0.004252 \n", - "11 Glc(b1-3)Glc 5.114861 -6.978624 0.003783 0.005297 \n", - "5 Kdo(a2-?)Kdo 3.316399 -2.802028 0.004302 0.005475 \n", - "8 betaGlucan 2.557430 -6.978624 0.006985 0.008149 \n", - "4 GalNAc(a1-4)GlcNAcA 1.658200 -2.802028 0.014605 0.015728 \n", - "12 GlcN 13.689714 -0.165373 0.101152 0.101152 \n", + "1 GlcNAc(b1-4)GlcNAc 3.488110 1.948240 0.000028 0.000199 \n", + "13 Glc 21.625130 -1.131727 0.000017 0.000199 \n", + "3 Man(a1-3)Man 4.459483 1.698465 0.000372 0.001735 \n", + "0 core_fucose(a1-3) 1.264427 2.174537 0.000606 0.002123 \n", + "5 Kdo(a2-?)Kdo 3.312295 -2.816328 0.001246 0.002492 \n", + "9 Man 14.630760 1.775643 0.001171 0.002492 \n", + "10 Kdo 4.968442 -2.816328 0.000921 0.002492 \n", + "6 GlcNAc 8.632368 0.993599 0.002163 0.003028 \n", + "7 Man(a1-?)Man 11.142650 1.723896 0.002123 0.003028 \n", + "8 betaGlucan 2.674773 -4.206173 0.001969 0.003028 \n", + "11 Glc(b1-3)Glc 5.349546 -4.206173 0.002908 0.003701 \n", + "4 GalNAc(a1-4)GlcNAcA 1.656147 -2.816328 0.003293 0.003842 \n", + "2 Man(a1-2)Man 3.195056 1.531678 0.004655 0.005014 \n", + "12 GlcN 13.600811 -0.185387 0.097643 0.097643 \n", "\n", " corr Levene p-val Effect size \n", - "1 0.733257 36.265595 \n", - "13 0.733257 -27.579095 \n", - "3 0.584688 23.339466 \n", - "0 0.584688 11.090423 \n", - "2 0.584688 8.435493 \n", - "6 0.584688 14.294328 \n", - "7 0.584688 13.760687 \n", - "9 0.584688 16.063347 \n", - "10 0.630760 -9.395584 \n", - "11 0.584688 -12.092472 \n", - "5 0.584688 -8.746932 \n", - "8 0.584688 -8.257750 \n", - "4 0.584688 -6.607927 \n", - "12 0.584688 -2.280319 " + "1 0.823898 39.952624 \n", + "13 0.988829 -20.516420 \n", + "3 0.823898 23.365422 \n", + "0 0.988829 8.942929 \n", + "5 0.988829 -6.677979 \n", + "9 0.823898 15.688657 \n", + "10 0.988829 -7.286003 \n", + "6 0.823898 14.130929 \n", + "7 0.823898 13.650820 \n", + "8 0.988829 -6.023846 \n", + "11 0.988829 -6.123911 \n", + "4 0.988829 -5.134776 \n", + "2 0.823898 8.671388 \n", + "12 0.823898 -2.296375 " ] }, "execution_count": null, @@ -1351,7 +1351,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1521,7 +1521,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -1627,7 +1627,8 @@ "### get_glycanova\n", "\n", "> get_glycanova (df, groups, impute=True, motifs=False,\n", - "> feature_set=['exhaustive', 'known'], min_samples=None)\n", + "> feature_set=['exhaustive', 'known'], min_samples=None,\n", + "> posthoc=True)\n", "\n", "Calculate an ANOVA for each glycan (or motif) in the DataFrame\n", "\n", @@ -1639,10 +1640,12 @@ "| motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False\n", "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is ['exhaustive','known']; options are: 'known' (hand-crafted glycan features), 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), and 'chemical' (molecular properties of glycan)\n", "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: at least half per group\n", + "| posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True\n", "\n", "| Returns:\n", "| :-\n", - "| Returns a pandas DataFrame with an F statistic and corrected p-value for each glycan." + "| (i)a pandas DataFrame with an F statistic and corrected p-value for each glycan.\n", + "| (ii) a dictionary of type glycan : pandas DataFrame, with post-hoc results for each glycan with a significant ANOVA." ], "text/plain": [ "---\n", @@ -1650,7 +1653,8 @@ "### get_glycanova\n", "\n", "> get_glycanova (df, groups, impute=True, motifs=False,\n", - "> feature_set=['exhaustive', 'known'], min_samples=None)\n", + "> feature_set=['exhaustive', 'known'], min_samples=None,\n", + "> posthoc=True)\n", "\n", "Calculate an ANOVA for each glycan (or motif) in the DataFrame\n", "\n", @@ -1662,10 +1666,12 @@ "| motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False\n", "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is ['exhaustive','known']; options are: 'known' (hand-crafted glycan features), 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), and 'chemical' (molecular properties of glycan)\n", "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: at least half per group\n", + "| posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True\n", "\n", "| Returns:\n", "| :-\n", - "| Returns a pandas DataFrame with an F statistic and corrected p-value for each glycan." + "| (i)a pandas DataFrame with an F statistic and corrected p-value for each glycan.\n", + "| (ii) a dictionary of type glycan : pandas DataFrame, with post-hoc results for each glycan with a significant ANOVA." ] }, "execution_count": null, @@ -1713,86 +1719,86 @@ " \n", " 7\n", " GlcNAc(b1-4)GlcNAc\n", - " 471.482512\n", - " 0.000002\n", - " \n", - " \n", - " 11\n", - " Glc\n", - " 581.527268\n", - " 0.000002\n", + " 450.486541\n", + " 0.000004\n", " \n", " \n", " 8\n", " Man(a1-3)Man\n", - " 284.103597\n", - " 0.000005\n", + " 265.131405\n", + " 0.000007\n", " \n", " \n", - " 1\n", - " Glc(b1-3)Glc\n", - " 224.154344\n", - " 0.000008\n", + " 11\n", + " Glc\n", + " 268.489613\n", + " 0.000007\n", " \n", " \n", " 9\n", " GlcNAc\n", - " 200.616336\n", - " 0.000009\n", - " \n", - " \n", - " 5\n", - " core_fucose(a1-3)\n", - " 159.237436\n", - " 0.000015\n", + " 191.173401\n", + " 0.000013\n", " \n", " \n", " 13\n", " Man\n", - " 131.802267\n", - " 0.000022\n", + " 130.904360\n", + " 0.000031\n", " \n", " \n", " 12\n", " Man(a1-?)Man\n", - " 121.383262\n", - " 0.000025\n", + " 121.161867\n", + " 0.000033\n", " \n", " \n", - " 0\n", - " betaGlucan\n", - " 113.603319\n", - " 0.000026\n", + " 5\n", + " core_fucose(a1-3)\n", + " 90.894891\n", + " 0.000065\n", + " \n", + " \n", + " 6\n", + " Man(a1-2)Man\n", + " 75.596239\n", + " 0.000097\n", " \n", " \n", " 4\n", " Kdo\n", - " 86.722259\n", - " 0.000052\n", + " 55.797979\n", + " 0.000207\n", " \n", " \n", - " 3\n", - " Kdo(a2-?)Kdo\n", - " 73.125735\n", - " 0.000073\n", + " 0\n", + " betaGlucan\n", + " 52.855100\n", + " 0.000217\n", " \n", " \n", - " 6\n", - " Man(a1-2)Man\n", - " 72.576887\n", - " 0.000073\n", + " 1\n", + " Glc(b1-3)Glc\n", + " 47.781166\n", + " 0.000246\n", + " \n", + " \n", + " 3\n", + " Kdo(a2-?)Kdo\n", + " 47.382481\n", + " 0.000246\n", " \n", " \n", " 2\n", " GalNAc(a1-4)GlcNAcA\n", - " 44.101059\n", - " 0.000278\n", + " 31.131360\n", + " 0.000731\n", " \n", " \n", " 10\n", " GlcN\n", - " 10.391379\n", - " 0.011243\n", + " 10.585124\n", + " 0.010769\n", " \n", " \n", "\n", @@ -1800,20 +1806,20 @@ ], "text/plain": [ " Glycan F statistic corr p-val\n", - "7 GlcNAc(b1-4)GlcNAc 471.482512 0.000002\n", - "11 Glc 581.527268 0.000002\n", - "8 Man(a1-3)Man 284.103597 0.000005\n", - "1 Glc(b1-3)Glc 224.154344 0.000008\n", - "9 GlcNAc 200.616336 0.000009\n", - "5 core_fucose(a1-3) 159.237436 0.000015\n", - "13 Man 131.802267 0.000022\n", - "12 Man(a1-?)Man 121.383262 0.000025\n", - "0 betaGlucan 113.603319 0.000026\n", - "4 Kdo 86.722259 0.000052\n", - "3 Kdo(a2-?)Kdo 73.125735 0.000073\n", - "6 Man(a1-2)Man 72.576887 0.000073\n", - "2 GalNAc(a1-4)GlcNAcA 44.101059 0.000278\n", - "10 GlcN 10.391379 0.011243" + "7 GlcNAc(b1-4)GlcNAc 450.486541 0.000004\n", + "8 Man(a1-3)Man 265.131405 0.000007\n", + "11 Glc 268.489613 0.000007\n", + "9 GlcNAc 191.173401 0.000013\n", + "13 Man 130.904360 0.000031\n", + "12 Man(a1-?)Man 121.161867 0.000033\n", + "5 core_fucose(a1-3) 90.894891 0.000065\n", + "6 Man(a1-2)Man 75.596239 0.000097\n", + "4 Kdo 55.797979 0.000207\n", + "0 betaGlucan 52.855100 0.000217\n", + "1 Glc(b1-3)Glc 47.781166 0.000246\n", + "3 Kdo(a2-?)Kdo 47.382481 0.000246\n", + "2 GalNAc(a1-4)GlcNAcA 31.131360 0.000731\n", + "10 GlcN 10.585124 0.010769" ] }, "execution_count": null, @@ -1826,7 +1832,8 @@ "test_df['label_8'] = [0.177, 0.009, 6.105, 5.549, 0.278]\n", "test_df['label_9'] = [0.511, 0.011, 4.998, 7.005, 0.414]\n", "\n", - "get_glycanova(test_df, [1,1,1,2,2,2,3,3,3], motifs = True)" + "anv, ph = get_glycanova(test_df, [1,1,1,2,2,2,3,3,3], motifs = True)\n", + "anv" ] }, { @@ -1842,7 +1849,8 @@ "\n", "### get_meta_analysis\n", "\n", - "> get_meta_analysis (effect_sizes, variances, model='fixed')\n", + "> get_meta_analysis (effect_sizes, variances, model='fixed', filepath='',\n", + "> study_names=[])\n", "\n", "Fixed-effects model or random-effects model for meta-analysis of glycan effect sizes\n", "\n", @@ -1851,6 +1859,8 @@ "| effect_sizes (array-like): Effect sizes (e.g., Cohen's d) from each study\n", "| variances (array-like): Corresponding effect size variances from each study\n", "| model (string): Whether to use 'fixed' or 'random' effects model\n", + "| filepath (string): absolute path including full filename allows for saving the Forest plot\n", + "| study_names (list): list of strings indicating the name of each study\n", "\n", "| Returns:\n", "| :-\n", @@ -1862,7 +1872,8 @@ "\n", "### get_meta_analysis\n", "\n", - "> get_meta_analysis (effect_sizes, variances, model='fixed')\n", + "> get_meta_analysis (effect_sizes, variances, model='fixed', filepath='',\n", + "> study_names=[])\n", "\n", "Fixed-effects model or random-effects model for meta-analysis of glycan effect sizes\n", "\n", @@ -1871,6 +1882,8 @@ "| effect_sizes (array-like): Effect sizes (e.g., Cohen's d) from each study\n", "| variances (array-like): Corresponding effect size variances from each study\n", "| model (string): Whether to use 'fixed' or 'random' effects model\n", + "| filepath (string): absolute path including full filename allows for saving the Forest plot\n", + "| study_names (list): list of strings indicating the name of each study\n", "\n", "| Returns:\n", "| :-\n", @@ -2014,16 +2027,16 @@ " \n", " \n", " \n", - " 0\n", + " 1\n", " Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga...\n", - " 5.326856\n", + " 5.326852\n", " 0.002697\n", " 0.005394\n", " \n", " \n", - " 1\n", + " 0\n", " Fuc(a1-2)Gal(b1-3)GalNAc\n", - " -2.030522\n", + " -2.030518\n", " 0.328428\n", " 0.328428\n", " \n", @@ -2033,12 +2046,12 @@ ], "text/plain": [ " Glycan Change p-val \\\n", - "0 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 5.326856 0.002697 \n", - "1 Fuc(a1-2)Gal(b1-3)GalNAc -2.030522 0.328428 \n", + "1 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 5.326852 0.002697 \n", + "0 Fuc(a1-2)Gal(b1-3)GalNAc -2.030518 0.328428 \n", "\n", " corr p-val \n", - "0 0.005394 \n", - "1 0.328428 " + "1 0.005394 \n", + "0 0.328428 " ] }, "execution_count": null, @@ -2361,644 +2374,644 @@ "data": { "text/html": [ "\n", - "\n", + "
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 Apif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]GlcAra(a1-2)Ara(a1-6)GlcNAcAra(a1-2)Glc(b1-2)AraAra(a1-2)GlcAAra(a1-2)[Glc(b1-6)]GlcAra(a1-6)GlcAraf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)ArafAraf(a1-3)Gal(b1-6)GalD-Apif(b1-2)GlcD-Apif(b1-2)GlcAD-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]GlcD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)AraD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-FucD-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-FucD-Apif(b1-6)GlcD-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeD-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMeFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6AcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)Glc3Ac6AcFruf(b2-1)Glc4Ac6AcFruf(b2-1)Glc6AcFruf(b2-1)[Glc(b1-2)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc3Ac(b1-2)]GlcFruf(b2-1)[Glc6Ac(b1-2)]GlcFruf1Ac(b2-1)Glc2Ac4Ac6AcFuc(a1-2)Gal(b1-2)Xyl(a1-6)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-4)XylFuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcFuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(a1-4)GalGal(a1-6)GalGal(a1-6)Gal(a1-6)GalGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]GlcGal(a1-6)Gal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Glc(a1-2)FrufGal(a1-6)ManGal(a1-6)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)[Gal(a1-6)]ManGal(b1-2)GlcAGal(b1-2)GlcA6MeGal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)[Xyl(b1-3)]GlcAGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(a1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Gal(b1-3)GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc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1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-olXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcXyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-olXyl(b1-2)Ara(a1-6)GlcXyl(b1-2)Ara(a1-6)GlcNAcXyl(b1-2)Ara(a1-6)[Glc(b1-2)]GlcXyl(b1-2)Ara(a1-6)[Glc(b1-4)]GlcNAcXyl(b1-2)D-Fuc(b1-6)GlcXyl(b1-2)D-Fuc(b1-6)GlcNAcXyl(b1-2)D-Fuc(b1-6)[Glc(b1-2)]GlcXyl(b1-2)Fuc(a1-6)GlcXyl(b1-2)Fuc(a1-6)GlcNAcXyl(b1-2)Gal(b1-2)GlcA6MeXyl(b1-2)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)Rha(a1-2)AraXyl(b1-2)[Glc(b1-3)]AraXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcNXyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAcXyl(b1-2)[Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcXyl(b1-2)[Rha(a1-3)]GlcAXyl(b1-3)AraXyl(b1-3)Xyl(b1-2)[Rha(a1-6)]Glc(b1-2)GlcXyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]GlcXyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]Glc(b1-2)GlcXyl(b1-4)Rha(a1-2)AraXyl(b1-4)Rha(a1-2)D-FucXyl(b1-4)Rha(a1-2)D-FucOMeXyl(b1-4)Rha(a1-2)[Rha(a1-6)]GlcXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl3Ac(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl(b1-4)XylXyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl3Ac(b1-4)XylXyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)XylXyl(b1-4)[GlcAOMe(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)XylXyl2Ac3Ac4Ac(b1-3)AraXylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-olXylOMe(b1-3)XylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-olXylOMe(b1-4)RhaOMe(a1-2)D-FucOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-olXylf(b1-2)Xyl(b1-3)[Rha(b1-2)Rha(b1-4)]Xyl[Araf(a1-3)Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Araf(a1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(a1-4)Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Man(b1-4)Man(b1-4)Man(b1-4)Gal(a1-6)]Man(b1-2)[Gal(a1-6)]Man(b1-2)[Gal(1-4)Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Gal(b1-3)Gal(b1-6)[Araf(a1-3)]Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)]Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)GlcApif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]GlcAra(a1-2)Ara(a1-6)GlcNAcAra(a1-2)Glc(b1-2)AraAra(a1-2)GlcAAra(a1-2)[Glc(b1-6)]GlcAra(a1-6)GlcAraf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)ArafAraf(a1-3)Gal(b1-6)GalD-Apif(b1-2)GlcD-Apif(b1-2)GlcAD-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]GlcD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)AraD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-FucD-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-FucD-Apif(b1-6)GlcD-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeD-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMeFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6AcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)Glc3Ac6AcFruf(b2-1)Glc4Ac6AcFruf(b2-1)Glc6AcFruf(b2-1)[Glc(b1-2)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc3Ac(b1-2)]GlcFruf(b2-1)[Glc6Ac(b1-2)]GlcFruf1Ac(b2-1)Glc2Ac4Ac6AcFuc(a1-2)Gal(b1-2)Xyl(a1-6)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-4)XylFuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcFuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(a1-4)GalGal(a1-6)GalGal(a1-6)Gal(a1-6)GalGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]GlcGal(a1-6)Gal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Glc(a1-2)FrufGal(a1-6)ManGal(a1-6)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)[Gal(a1-6)]ManGal(b1-2)GlcAGal(b1-2)GlcA6MeGal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)[Xyl(b1-3)]GlcAGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(a1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Gal(b1-3)GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-6)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-4)Gal(b1-4)ManGal(b1-4)Gal(b1-4)ManOMeGal(b1-4)GlcAGal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGal(b1-4)Man(b1-4)ManGal(b1-4)Man(b1-4)Man(b1-4)GalGal(b1-4)Xyl(b1-4)Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1FerOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucOMeOSinGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)[Fuc(a1-6)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGalA(a1-2)[Araf(a1-5)Araf(a1-4)]Rha(b1-4)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalAGalOMe(b1-2)[XylOMe(b1-3)]GlcAOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalOMe(b1-4)XylOMe(b1-4)[D-ApifOMe(b1-3)]RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalf(b1-2)[Galf(b1-4)]ManGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNGlc(a1-2)Rha(a1-6)GlcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-4)Glc(a1-2)Rha(a1-6)GlcGlc(a1-4)Glc(a1-4)Glc(a1-6)GlcGlc(a1-4)Glc(a1-4)GlcAGlc(a1-4)GlcA(b1-2)GlcAGlc(b1-2)AraGlc(b1-2)Ara(a1-2)GlcAGlc(b1-2)Gal(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcA(b1-3)[Glc(b1-3)]AraGlc(b1-2)GlcGlc(b1-2)Glc(a1-2)FrufOBzOCinGlc(b1-2)Glc(b1-2)GlcGlc(b1-2)GlcAGlc(b1-2)[Ara(a1-3)]GlcA6MeGlc(b1-2)[Ara(a1-3)]GlcAOMeGlc(b1-2)[Ara(a1-6)]GlcGlc(b1-2)[Glc(b1-3)]Glc(a1-2)FrufGlc(b1-2)[Glc(b1-3)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-2)[Glc6Ac(b1-3)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-2)[Rha(a1-3)]GlcAGlc(b1-2)[Xyl(b1-2)Ara(a1-6)]GlcGlc(b1-2)[Xyl(b1-2)D-Fuc(b1-6)]GlcGlc(b1-3)AraGlc(b1-3)GlcGlc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)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-4)RhaOMe(a1-2)D-FucOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMeXylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-olXylf(b1-2)Xyl(b1-3)[Rha(b1-2)Rha(b1-4)]Xyl[Araf(a1-3)Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Araf(a1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(a1-4)Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Man(b1-4)Man(b1-4)Man(b1-4)Gal(a1-6)]Man(b1-2)[Gal(a1-6)]Man(b1-2)[Gal(1-4)Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Gal(b1-3)Gal(b1-6)[Araf(a1-3)]Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)]Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc
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Quillajaceae0000000000001100001100000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000100000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000110000000000100000000000000000000000000000000Quillajaceae0000000000001100001100000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000100000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000110000000000100000000000000000000000000000000
\n" diff --git a/build/lib/glycowork/motif/analysis.py b/build/lib/glycowork/motif/analysis.py index c100a6bc..a51d78c6 100644 --- a/build/lib/glycowork/motif/analysis.py +++ b/build/lib/glycowork/motif/analysis.py @@ -10,6 +10,7 @@ from scipy.stats import ttest_ind, ttest_rel, f, norm, levene from statsmodels.formula.api import ols from statsmodels.stats.multitest import multipletests +from statsmodels.stats.multicomp import pairwise_tukeyhsd from sklearn.manifold import TSNE from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA @@ -690,7 +691,7 @@ def get_volcano(df_res, y_thresh = 0.05, x_thresh = 1.0, plt.show() -def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exhaustive', 'known'], min_samples = None): +def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exhaustive', 'known'], min_samples = None, posthoc = True): """Calculate an ANOVA for each glycan (or motif) in the DataFrame\n | Arguments: | :- @@ -699,12 +700,15 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh | impute (bool): replaces zeroes with draws from left-shifted distribution or KNN-Imputer; default:True | motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False | feature_set (list): which feature set to use for annotations, add more to list to expand; default is ['exhaustive','known']; options are: 'known' (hand-crafted glycan features), 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), and 'chemical' (molecular properties of glycan) - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: at least half per group\n + | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: at least half per group + | posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True\n | Returns: | :- - | Returns a pandas DataFrame with an F statistic and corrected p-value for each glycan. + | (i)a pandas DataFrame with an F statistic and corrected p-value for each glycan. + | (ii) a dictionary of type glycan : pandas DataFrame, with post-hoc results for each glycan with a significant ANOVA. """ results = [] + posthoc_results = {} df.fillna(0, inplace = True) groups_unq = sorted(set(groups)) df = impute_and_normalize(df, [[df.columns.tolist()[i+1] for i, x in enumerate(groups) if x == g] for g in groups_unq], impute = impute, @@ -735,11 +739,14 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh f_value = anova_table["F"]["C(Group)"] p_value = anova_table["PR(>F)"]["C(Group)"] results.append((glycan, f_value, p_value)) + if p_value < 0.05 and posthoc: + posthoc = pairwise_tukeyhsd(endog = data['Abundance'], groups = data['Group'], alpha = 0.05) + posthoc_results[glycan] = pd.DataFrame(data = posthoc._results_table.data[1:], columns = posthoc._results_table.data[0]) results_df = pd.DataFrame(results, columns=["Glycan", "F statistic", "corr p-val"]) results_df['corr p-val'] = multipletests(results_df['corr p-val'].values.tolist(), method = 'fdr_bh')[1] - return results_df.sort_values(by = 'corr p-val') + return results_df.sort_values(by = 'corr p-val'), posthoc_results def get_meta_analysis(effect_sizes, variances, model = 'fixed', filepath = '', diff --git a/glycowork/motif/analysis.py b/glycowork/motif/analysis.py index c100a6bc..a51d78c6 100644 --- a/glycowork/motif/analysis.py +++ b/glycowork/motif/analysis.py @@ -10,6 +10,7 @@ from scipy.stats import ttest_ind, ttest_rel, f, norm, levene from statsmodels.formula.api import ols from statsmodels.stats.multitest import multipletests +from statsmodels.stats.multicomp import pairwise_tukeyhsd from sklearn.manifold import TSNE from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA @@ -690,7 +691,7 @@ def get_volcano(df_res, y_thresh = 0.05, x_thresh = 1.0, plt.show() -def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exhaustive', 'known'], min_samples = None): +def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exhaustive', 'known'], min_samples = None, posthoc = True): """Calculate an ANOVA for each glycan (or motif) in the DataFrame\n | Arguments: | :- @@ -699,12 +700,15 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh | impute (bool): replaces zeroes with draws from left-shifted distribution or KNN-Imputer; default:True | motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False | feature_set (list): which feature set to use for annotations, add more to list to expand; default is ['exhaustive','known']; options are: 'known' (hand-crafted glycan features), 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), and 'chemical' (molecular properties of glycan) - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: at least half per group\n + | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: at least half per group + | posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True\n | Returns: | :- - | Returns a pandas DataFrame with an F statistic and corrected p-value for each glycan. + | (i)a pandas DataFrame with an F statistic and corrected p-value for each glycan. + | (ii) a dictionary of type glycan : pandas DataFrame, with post-hoc results for each glycan with a significant ANOVA. """ results = [] + posthoc_results = {} df.fillna(0, inplace = True) groups_unq = sorted(set(groups)) df = impute_and_normalize(df, [[df.columns.tolist()[i+1] for i, x in enumerate(groups) if x == g] for g in groups_unq], impute = impute, @@ -735,11 +739,14 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh f_value = anova_table["F"]["C(Group)"] p_value = anova_table["PR(>F)"]["C(Group)"] results.append((glycan, f_value, p_value)) + if p_value < 0.05 and posthoc: + posthoc = pairwise_tukeyhsd(endog = data['Abundance'], groups = data['Group'], alpha = 0.05) + posthoc_results[glycan] = pd.DataFrame(data = posthoc._results_table.data[1:], columns = posthoc._results_table.data[0]) results_df = pd.DataFrame(results, columns=["Glycan", "F statistic", "corr p-val"]) results_df['corr p-val'] = multipletests(results_df['corr p-val'].values.tolist(), method = 'fdr_bh')[1] - return results_df.sort_values(by = 'corr p-val') + return results_df.sort_values(by = 'corr p-val'), posthoc_results def get_meta_analysis(effect_sizes, variances, model = 'fixed', filepath = '',