diff --git a/assays/canal-1_Phenotyping_2019/dataset/canal-1_yield_data.xlsx b/assays/canal-1_Phenotyping_2019/dataset/canal-1_yield_data.xlsx
deleted file mode 100644
index 04fab0fd8d9f30827bd95147a8a1eede84431b98..0000000000000000000000000000000000000000
--- a/assays/canal-1_Phenotyping_2019/dataset/canal-1_yield_data.xlsx
+++ /dev/null
@@ -1,3 +0,0 @@
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-oid sha256:3bc80d6593d3bd97a204ded64ecb2ec569149d38ed14592fe529491647f74221
-size 9944
diff --git a/runs/Met_whole/Bubble_comb.png b/runs/Met_whole/Bubble_comb.png
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diff --git a/runs/Met_whole/asparagine.png b/runs/Met_whole/asparagine.png
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diff --git a/runs/Met_whole/cana1_gl_pathway_results.csv b/runs/Met_whole/cana1_gl_pathway_results.csv
deleted file mode 100644
index cba40447cd40e9bdce5060ef4e7bfda893db1363..0000000000000000000000000000000000000000
--- a/runs/Met_whole/cana1_gl_pathway_results.csv
+++ /dev/null
@@ -1,59 +0,0 @@
-,Total Cmpd,Hits,Raw p,#NAME?,Holm adjust,FDR,Impact
-Fructose and mannose metabolism,20,1,0.00029287,3.5333,0.016986,0.016986,0
-"Alanine, aspartate and glutamate metabolism",22,10,0.0010156,2.9933,0.05789,0.023646,0.84892
-Glutathione metabolism,26,7,0.0014774,2.8305,0.082733,0.023646,0.13362
-Cyanoamino acid metabolism,29,6,0.0016307,2.7876,0.089691,0.023646,0.35593
-Selenocompound metabolism,13,1,0.004896,2.3102,0.26438,0.056793,0
-Nitrogen metabolism,12,2,0.006858,2.1638,0.36348,0.057737,0
-Glyoxylate and dicarboxylate metabolism,29,11,0.007169,2.1445,0.37279,0.057737,0.50324
-Aminoacyl-tRNA biosynthesis,46,19,0.0079637,2.0989,0.40615,0.057737,0.11111
-Arginine biosynthesis,18,8,0.0093094,2.0311,0.46547,0.059994,0.34079
-Pyrimidine metabolism,38,3,0.010468,1.9801,0.51293,0.060714,0.05766
-Purine metabolism,63,4,0.011995,1.921,0.57578,0.063248,0.07925
-Arginine and proline metabolism,34,8,0.0155,1.8097,0.72852,0.074919,0.5247
-"Glycine, serine and threonine metabolism",33,8,0.018263,1.7384,0.84011,0.078969,0.60216
-Cysteine and methionine metabolism,46,6,0.019406,1.7121,0.87326,0.078969,0.16135
-Sulfur metabolism,15,3,0.020423,1.6899,0.89862,0.078969,0.10774
-Sphingolipid metabolism,17,1,0.026431,1.5779,1,0.090635,0
-"Valine, leucine and isoleucine biosynthesis",22,6,0.026565,1.5757,1,0.090635,0.10727
-Galactose metabolism,27,7,0.029971,1.5233,1,0.095385,0.29394
-Porphyrin and chlorophyll metabolism,48,1,0.031247,1.5052,1,0.095385,0
-Phosphatidylinositol signaling system,26,2,0.039039,1.4085,1,0.11321,0.03285
-Inositol phosphate metabolism,28,3,0.044463,1.352,1,0.1228,0.12552
-Carbon fixation in photosynthetic organisms,21,5,0.050027,1.3008,1,0.13189,0.10075
-Butanoate metabolism,17,5,0.05963,1.2245,1,0.15037,0.13636
-"Valine, leucine and isoleucine degradation",37,3,0.071051,1.1484,1,0.17171,0
-Glycerophospholipid metabolism,37,2,0.08671,1.0619,1,0.19859,0.09832
-Glycerolipid metabolism,21,4,0.089437,1.0485,1,0.19859,0.02235
-"Phenylalanine, tyrosine and tryptophan biosynthesis",22,4,0.092521,1.0338,1,0.19859,0.1016
-Pantothenate and CoA biosynthesis,23,4,0.095883,1.0183,1,0.19859,0.08423
-Nicotinate and nicotinamide metabolism,13,2,0.099296,1.0031,1,0.19859,0
-Glucosinolate biosynthesis,65,6,0.10452,0.98082,1,0.20206,0
-beta-Alanine metabolism,18,4,0.11245,0.94905,1,0.21039,0.32937
-Monobactam biosynthesis,8,2,0.12424,0.90576,1,0.22518,0
-Ascorbate and aldarate metabolism,18,5,0.1425,0.84619,1,0.2476,0.40299
-Citrate cycle (TCA cycle),20,7,0.14515,0.83819,1,0.2476,0.32413
-Pyruvate metabolism,22,4,0.16038,0.79485,1,0.24955,0.32193
-Phenylalanine metabolism,11,1,0.16098,0.79322,1,0.24955,0.47059
-"Tropane, piperidine and pyridine alkaloid biosynthesis",8,1,0.16098,0.79322,1,0.24955,0
-Lysine biosynthesis,9,3,0.16758,0.77578,1,0.24955,0
-Isoquinoline alkaloid biosynthesis,6,2,0.1678,0.77521,1,0.24955,0.5
-Propanoate metabolism,20,1,0.17295,0.76207,1,0.25078,0
-Starch and sucrose metabolism,22,7,0.33099,0.48019,1,0.46823,0.72431
-Tyrosine metabolism,16,4,0.34913,0.45701,1,0.48214,0.27703
-Lysine degradation,18,3,0.36048,0.44312,1,0.48622,0
-Biosynthesis of secondary metabolites - unclassified,5,1,0.40582,0.39166,1,0.52567,1
-Ubiquinone and other terpenoid-quinone biosynthesis,38,2,0.40785,0.3895,1,0.52567,0.00097
-Histidine metabolism,15,1,0.41804,0.37878,1,0.52709,0.04264
-Phenylpropanoid biosynthesis,46,3,0.46851,0.32928,1,0.57816,0.05295
-Amino sugar and nucleotide sugar metabolism,50,5,0.51068,0.29185,1,0.60855,0.10791
-Fatty acid biosynthesis,56,2,0.51412,0.28894,1,0.60855,0
-Glycolysis / Gluconeogenesis,26,3,0.6327,0.1988,1,0.73393,0.12036
-Pentose and glucuronate interconversions,16,3,0.657,0.18243,1,0.74718,0
-Pentose phosphate pathway,19,1,0.72613,0.13899,1,0.80062,0.11621
-Terpenoid backbone biosynthesis,30,1,0.7316,0.13573,1,0.80062,0
-Thiamine metabolism,22,2,0.7646,0.11657,1,0.82123,0
-Tryptophan metabolism,28,1,0.81944,0.086485,1,0.8487,0.12037
-Indole alkaloid biosynthesis,4,1,0.81944,0.086485,1,0.8487,0
-C5-Branched dibasic acid metabolism,6,2,0.93199,0.030589,1,0.94834,0
-Zeatin biosynthesis,21,2,0.97936,0.0090588,1,0.97936,0
diff --git a/runs/Met_whole/cana1_wl_pathway_results.csv b/runs/Met_whole/cana1_wl_pathway_results.csv
deleted file mode 100644
index 84111004e8bfcae37b3e4f3c4927437b44b05c84..0000000000000000000000000000000000000000
--- a/runs/Met_whole/cana1_wl_pathway_results.csv
+++ /dev/null
@@ -1,59 +0,0 @@
-,Total Cmpd,Hits,Raw p,#NAME?,Holm adjust,FDR,Impact
-Fructose and mannose metabolism,20,1,1.30E-08,7.8867,7.53E-07,7.53E-07,0
-Pantothenate and CoA biosynthesis,23,4,7.69E-08,7.1142,4.38E-06,1.69E-06,0.08423
-"Valine, leucine and isoleucine biosynthesis",22,6,1.18E-07,6.9289,6.60E-06,1.69E-06,0.10727
-Terpenoid backbone biosynthesis,30,1,1.63E-07,6.7886,8.95E-06,1.69E-06,0
-"Valine, leucine and isoleucine degradation",37,3,1.66E-07,6.7811,8.95E-06,1.69E-06,0
-Glucosinolate biosynthesis,65,6,1.75E-07,6.7567,9.28E-06,1.69E-06,0
-Tryptophan metabolism,28,1,2.89E-07,6.539,1.50E-05,2.10E-06,0.12037
-Indole alkaloid biosynthesis,4,1,2.89E-07,6.539,1.50E-05,2.10E-06,0
-Glutathione metabolism,26,7,5.86E-07,6.2319,2.93E-05,3.78E-06,0.13362
-Phosphatidylinositol signaling system,26,2,1.15E-06,5.9387,5.64E-05,6.68E-06,0.03285
-Inositol phosphate metabolism,28,3,2.03E-06,5.6935,9.72E-05,1.07E-05,0.12552
-Sulfur metabolism,15,3,1.08E-05,4.9681,0.00050581,5.20E-05,0.10774
-C5-Branched dibasic acid metabolism,6,2,2.11E-05,4.6765,0.00096885,9.40E-05,0
-Arginine and proline metabolism,34,8,3.60E-05,4.4434,0.0016212,0.00014926,0.5247
-Pyrimidine metabolism,38,3,4.58E-05,4.3393,0.0020144,0.00016923,0.05766
-Glyoxylate and dicarboxylate metabolism,29,11,4.67E-05,4.3308,0.0020144,0.00016923,0.50324
-Nitrogen metabolism,12,2,5.26E-05,4.2794,0.0022072,0.00017929,0
-Purine metabolism,63,4,5.74E-05,4.2409,0.0023544,0.00018504,0.07925
-Tyrosine metabolism,16,4,9.40E-05,4.027,0.0037586,0.00026937,0.27703
-Isoquinoline alkaloid biosynthesis,6,2,9.56E-05,4.0196,0.0037586,0.00026937,0.5
-Ubiquinone and other terpenoid-quinone biosynthesis,38,2,9.75E-05,4.0109,0.0037586,0.00026937,0.00097
-"Phenylalanine, tyrosine and tryptophan biosynthesis",22,4,0.0001319,3.8798,0.0048803,0.00034773,0.1016
-Starch and sucrose metabolism,22,7,0.00033327,3.4772,0.011998,0.00084042,0.72431
-Glycolysis / Gluconeogenesis,26,3,0.00047536,3.323,0.016638,0.0011052,0.12036
-Citrate cycle (TCA cycle),20,7,0.00047637,3.3221,0.016638,0.0011052,0.32413
-Phenylpropanoid biosynthesis,46,3,0.00088182,3.0546,0.0291,0.0018302,0.05295
-Phenylalanine metabolism,11,1,0.00088356,3.0538,0.0291,0.0018302,0.47059
-"Tropane, piperidine and pyridine alkaloid biosynthesis",8,1,0.00088356,3.0538,0.0291,0.0018302,0
-Glycerolipid metabolism,21,4,0.0018387,2.7355,0.05516,0.0036773,0.02235
-Sphingolipid metabolism,17,1,0.0019075,2.7195,0.055316,0.0036878,0
-Propanoate metabolism,20,1,0.0029375,2.532,0.082249,0.0054959,0
-Arginine biosynthesis,18,8,0.003661,2.4364,0.098847,0.0066355,0.34079
-Aminoacyl-tRNA biosynthesis,46,19,0.0047869,2.3199,0.12446,0.0084134,0.11111
-Galactose metabolism,27,7,0.0091016,2.0409,0.22754,0.015526,0.29394
-Butanoate metabolism,17,5,0.0098481,2.0066,0.23635,0.01632,0.13636
-Porphyrin and chlorophyll metabolism,48,1,0.011279,1.9477,0.25942,0.018172,0
-"Alanine, aspartate and glutamate metabolism",22,10,0.017776,1.7502,0.39107,0.027865,0.84892
-Cyanoamino acid metabolism,29,6,0.023478,1.6293,0.49304,0.035835,0.35593
-Lysine degradation,18,3,0.026983,1.5689,0.53967,0.039744,0
-Histidine metabolism,15,1,0.02741,1.5621,0.53967,0.039744,0.04264
-Pentose phosphate pathway,19,1,0.032153,1.4928,0.57875,0.045484,0.11621
-Thiamine metabolism,22,2,0.047944,1.3193,0.81504,0.066208,0
-Selenocompound metabolism,13,1,0.05842,1.2334,0.93472,0.078799,0
-Glycerophospholipid metabolism,37,2,0.06874,1.1628,1,0.090203,0.09832
-Lysine biosynthesis,9,3,0.069985,1.155,1,0.090203,0
-Pyruvate metabolism,22,4,0.078343,1.106,1,0.09878,0.32193
-Amino sugar and nucleotide sugar metabolism,50,5,0.080745,1.0929,1,0.099642,0.10791
-Ascorbate and aldarate metabolism,18,5,0.088924,1.051,1,0.10745,0.40299
-Pentose and glucuronate interconversions,16,3,0.12346,0.90847,1,0.14614,0
-Carbon fixation in photosynthetic organisms,21,5,0.16395,0.7853,1,0.19018,0.10075
-Cysteine and methionine metabolism,46,6,0.21104,0.67563,1,0.23597,0.16135
-beta-Alanine metabolism,18,4,0.21156,0.67456,1,0.23597,0.32937
-"Glycine, serine and threonine metabolism",33,8,0.22696,0.64406,1,0.24837,0.60216
-Nicotinate and nicotinamide metabolism,13,2,0.25348,0.59605,1,0.26781,0
-Monobactam biosynthesis,8,2,0.25396,0.59524,1,0.26781,0
-Biosynthesis of secondary metabolites - unclassified,5,1,0.44773,0.34898,1,0.46372,1
-Fatty acid biosynthesis,56,2,0.50236,0.29899,1,0.51117,0
-Zeatin biosynthesis,21,2,0.81428,0.089227,1,0.81428,0
diff --git a/runs/Met_whole/complete_norm.qs b/runs/Met_whole/complete_norm.qs
deleted file mode 100644
index cfbff313d9aa44244057a5960528e7f3fda9a2a1..0000000000000000000000000000000000000000
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deleted file mode 100644
index 21b880d7bd04e101d01a9527d18079a5550b8220..0000000000000000000000000000000000000000
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deleted file mode 100644
index eac70b4d67751c5f54deca0e8768817f3fb548f1..0000000000000000000000000000000000000000
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diff --git a/runs/Met_whole/data_proc.qs b/runs/Met_whole/data_proc.qs
deleted file mode 100644
index cfbff313d9aa44244057a5960528e7f3fda9a2a1..0000000000000000000000000000000000000000
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diff --git a/runs/Met_whole/name_map.csv b/runs/Met_whole/name_map.csv
deleted file mode 100644
index 8d8ea26d1ed97308347a6fb9bcf6663b14b76585..0000000000000000000000000000000000000000
--- a/runs/Met_whole/name_map.csv
+++ /dev/null
@@ -1,101 +0,0 @@
-"Query","Match","HMDB","PubChem","KEGG","SMILES","Comment"
-"group","NA","NA","NA","NA","NA","0"
-"C00147","Adenine","HMDB0000034","190","C00147","NC1=C2NC=NC2=NC=N1","1"
-"C00020","Adenosine monophosphate","HMDB0000045","6083","C00020","NC1=C2N=CN([C@@H]3O[C@H](COP(O)(O)=O)[C@@H](O)[C@H]3O)C2=NC=N1","1"
-"C00956","Aminoadipic acid","HMDB0000510","92136","C00956","N[C@@H](CCCC(O)=O)C(O)=O","1"
-"C06104","Adipic acid","HMDB0000448","196","C06104","OC(=O)CCCCC(O)=O","1"
-"C00041","L-Alanine","HMDB0000161","5950","C00041","C[C@H](N)C(O)=O","1"
-"C02512","3-Cyano-L-alanine","METPA0300",NA,"C02512",NA,"1"
-"C00099","beta-Alanine","HMDB0000056","239","C00099","NCCC(O)=O","1"
-"C00062","L-Arginine","HMDB0000517","6322","C00062","N[C@@H](CCCNC(N)=N)C(O)=O","1"
-"C00072","Ascorbic acid","HMDB0000044","54670067","C00072","[H][C@@]1(OC(=O)C(O)=C1O)[C@@H](O)CO","1"
-"C00152","L-Asparagine","HMDB0000168","6267","C00152","N[C@@H](CC(N)=O)C(O)=O","1"
-"C00049","L-Aspartic acid","HMDB0000191","5960","C00049","N[C@@H](CC(O)=O)C(O)=O","1"
-"C00156","4-Hydroxybenzoic acid","HMDB0000500","135","C00156","OC(=O)C1=CC=C(O)C=C1","1"
-"C00180","Benzoic acid","HMDB0001870","243","C00180","OC(=O)C1=CC=CC=C1","1"
-"C00556","Benzyl alcohol","HMDB0003119","244","C00556","OCC1=CC=CC=C1","1"
-"C00334","gamma-Aminobutyric acid","HMDB0000112","119","C00334","NCCCC(O)=O","1"
-"C10850","NA","NA","NA","NA","NA","0"
-"C10851","Calystegine B2","HMDB0038594","3693124","C10851","OC1C2CCC(O)(N2)C(O)C1O","1"
-"C00811","4-Hydroxycinnamic acid","HMDB0002035","637542","C00811","OC(=O)\C=C\C1=CC=C(O)C=C1","1"
-"C00158","Citric acid","HMDB0000094","311","C00158","OC(=O)CC(O)(CC(O)=O)C(O)=O","1"
-"C01571","Capric acid","HMDB0000511","2969","C01571","CCCCCCCCCC(O)=O","1"
-"C00503","Erythritol","HMDB0002994","222285","C00503","OC[C@H](O)[C@H](O)CO","1"
-"C21593","NA","NA","NA","NA","NA","0"
-"C00189","Ethanolamine","HMDB0000149","700","C00189","NCCO","1"
-"C00085","Fructose 6-phosphate","HMDB0000124","69507","C00085","OCC(=O)[C@@H](O)[C@H](O)[C@H](O)COP(O)(O)=O","1"
-"C00095","D-Fructose","HMDB0000660","439709","C00095","OC[C@H]1O[C@](O)(CO)[C@@H](O)[C@@H]1O","1"
-"C01019","L-Fucose","HMDB0000174","17106","C01019","C[C@@H]1O[C@@H](O)[C@@H](O)[C@H](O)[C@@H]1O","1"
-"C00122","Fumaric acid","HMDB0000134","444972","C00122","OC(=O)\C=C\C(O)=O","1"
-"C01235","beta-Cortol","HMDB0005821","439451","C01235","[H][C@@]12CC[C@](O)([C@H](O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])CC[C@]2([H])C[C@H](O)CC[C@]12C","1"
-"C01115","NA","NA","NA","NA","NA","0"
-"C00333","Galacturonic acid","HMDB0002545","84740","C00333","O[C@@H](C=O)[C@@H](O)[C@@H](O)[C@H](O)C(O)=O","1"
-"C00345","6-Phosphogluconic acid","HMDB0001316","91493","C00345","O[C@H](COP(O)(O)=O)[C@@H](O)[C@H](O)[C@@H](O)C(O)=O","1"
-"C00103","Glucose 1-phosphate","HMDB0001586","65533","C00103","OC[C@H]1O[C@H](OP(O)(O)=O)[C@H](O)[C@@H](O)[C@@H]1O","1"
-"C00092","Glucose 6-phosphate","HMDB0001401","5958","C00092","OC1O[C@H](COP(O)(O)=O)[C@@H](O)[C@H](O)[C@H]1O","1"
-"C22350","NA","NA","NA","NA","NA","0"
-"C00031","D-Glucose","HMDB0000122","5793","C00031","OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O","1"
-"C00025","Glutamic acid","HMDB0000148","33032","C00025","N[C@@H](CCC(O)=O)C(O)=O","1"
-"C00064","Glutamine","HMDB0000641","5961","C00064","N[C@@H](CCC(N)=O)C(O)=O","1"
-"C00026","Oxoglutaric acid","HMDB0000208","51","C00026","OC(=O)CCC(=O)C(O)=O","1"
-"C00489","Glutaric acid","HMDB0000661","743","C00489","OC(=O)CCCC(O)=O","1"
-"C00258","Glyceric acid","HMDB0000139","439194","C00258","OC[C@@H](O)C(O)=O","1"
-"C00093","Glycerol 3-phosphate","HMDB0000126","439162","C00093","OC[C@@H](O)COP(O)(O)=O","1"
-"C00116","Glycerol","HMDB0000131","753","C00116","OCC(O)CO","1"
-"C00037","Glycine","HMDB0000123","750","C00037","NCC(O)=O","1"
-"C00160","Glycolate",NA,"3460","C00160",NA,"1"
-"C17349","Guanidine","HMDB0001842","3520","C17349","NC(N)=N","1"
-"C01040","L-Gulonolactone","HMDB0003466","439373","C01040","[H][C@@]1(OC(=O)[C@@H](O)[C@H]1O)[C@@H](O)CO","1"
-"C00135","Histidine","HMDB0000177","6274","C00135","N[C@@H](CC1=CN=CN1)C(O)=O","1"
-"C00263","L-Homoserine","HMDB0000719","12647","C00263","N[C@@H](CCO)C(O)=O","1"
-"C04006","myo-Inositol 1-phosphate","HMDB0000213",NA,"C04006","O[C@H]1[C@H](O)[C@@H](O)[C@H](OP(O)(O)=O)[C@H](O)[C@@H]1O","1"
-"C00137","myo-Inositol","HMDB0000211",NA,"C00137","O[C@H]1[C@H](O)[C@@H](O)[C@H](O)[C@H](O)[C@@H]1O","1"
-"C00311","Isocitric acid","HMDB0000193","1198","C00311","OC(C(CC(O)=O)C(O)=O)C(O)=O","1"
-"C00407","Isoleucine","HMDB0000172","6306","C00407","CC[C@H](C)[C@H](N)C(O)=O","1"
-"C00490","Itaconic acid","HMDB0002092","811","C00490","OC(=O)CC(=C)C(O)=O","1"
-"C00186","Lactic acid","HMDB0000190","107689","C00186","C[C@H](O)C(O)=O","1"
-"C00123","Leucine","HMDB0000687","6106","C00123","CC(C)C[C@H](N)C(O)=O","1"
-"C00047","Lysine","HMDB0000182","5962","C00047","NCCCC[C@H](N)C(O)=O","1"
-"C00476","Isocarboxazid","HMDB0015377","3759","C00476","CC1=CC(=NO1)C(=O)NNCC1=CC=CC=C1","1"
-"C02612","(R)-2-Methylmalate","METPA0308",NA,"C02612",NA,"1"
-"C00149","Malic acid","HMDB0000156","222656","C00149","O[C@@H](CC(O)=O)C(O)=O","1"
-"C00185","Cellobiose","HMDB0000055","10712","C00185","OC[C@H]1O[C@@H](O[C@H]2[C@H](O)[C@@H](O)[C@H](O)O[C@@H]2CO)[C@H](O)[C@@H](O)[C@@H]1O","1"
-"C00208","D-Maltose","HMDB0000163","10991489","C00208","OC[C@H]1O[C@H](O[C@H]2[C@H](O)[C@H](O)[C@@H](O)O[C@@H]2CO)[C@H](O)[C@@H](O)[C@@H]1O","1"
-"C00073","Methionine","HMDB0000696","6137","C00073","CSCC[C@H](N)C(O)=O","1"
-"C00253","Nicotinic acid","HMDB0001488","938","C00253","OC(=O)C1=CN=CC=C1","1"
-"C01020","6-Hydroxynicotinic acid","HMDB0002658","72924","C01020","OC(=O)C1=CN=C(O)C=C1","1"
-"C01601","Pelargonic acid","HMDB0000847","8158","C01601","CCCCCCCCC(O)=O","1"
-"C00077","Ornithine","HMDB0000214","6262","C00077","NCCC[C@H](N)C(O)=O","1"
-"C01742","NA","NA","NA","NA","NA","0"
-"C00079","Phenylalanine","HMDB0000159","6140","C00079","N[C@@H](CC1=CC=CC=C1)C(O)=O","1"
-"C00009","Phosphate","HMDB0001429","1004","C00009","OP(O)(O)=O","1"
-"C00408","Pipecolic acid","HMDB0000070","849","C00408","OC(=O)C1CCCCN1","1"
-"C00148","Proline","HMDB0000162","145742","C00148","OC(=O)[C@@H]1CCCN1","1"
-"C01157","4-Hydroxyproline","HMDB0000725","5810","C01157","O[C@H]1CN[C@@H](C1)C(O)=O","1"
-"C06468","NA","NA","NA","NA","NA","0"
-"C00134","Putrescine","HMDB0001414","1045","C00134","NCCCCN","1"
-"C02502","2-Hydroxypyridine","HMDB0013751","8871","C02502","OC1=CC=CC=N1","1"
-"C01879","Pyroglutamic acid","HMDB0000267","7405","C01879","OC(=O)[C@@H]1CCC(=O)N1","1"
-"C00022","Pyruvic acid","HMDB0000243","1060","C00022","CC(=O)C(O)=O","1"
-"C00296","Quinic acid","HMDB0003072","6508","C00296","O[C@@H]1C[C@@](O)(C[C@@H](O)[C@H]1O)C(O)=O","1"
-"C00492","Raffinose","HMDB0003213","439242","C00492","OC[C@H]1O[C@@](CO)(O[C@H]2O[C@H](CO[C@H]3O[C@H](CO)[C@H](O)[C@H](O)[C@H]3O)[C@@H](O)[C@H](O)[C@H]2O)[C@@H](O)[C@@H]1O","1"
-"C00507","Rhamnose","HMDB0000849","25310","C00507","C[C@@H]1OC(O)[C@H](O)[C@H](O)[C@H]1O","1"
-"C00979","O-Acetylserine","HMDB0003011","99478","C00979","CC(=O)OC[C@H](N)C(O)=O","1"
-"C00065","Serine","HMDB0000187","5951","C00065","N[C@@H](CO)C(O)=O","1"
-"C00493","Shikimic acid","HMDB0003070","8742","C00493","O[C@@H]1CC(=C[C@@H](O)[C@H]1O)C(O)=O","1"
-"C00315","Spermidine","HMDB0001257","1102","C00315","NCCCCNCCCN","1"
-"C00042","Succinic acid","HMDB0000254","1110","C00042","OC(=O)CCC(O)=O","1"
-"C00089","Sucrose","HMDB0000258","5988","C00089","OC[C@H]1O[C@@](CO)(O[C@H]2O[C@H](CO)[C@@H](O)[C@H](O)[C@H]2O)[C@@H](O)[C@@H]1O","1"
-"C06424","Myristic acid","HMDB0000806","11005","C06424","CCCCCCCCCCCCCC(O)=O","1"
-"C01620","Threonic acid","HMDB0000943","5460407","C01620","OC[C@H](O)[C@@H](O)C(O)=O","1"
-"C00188","L-Threonine","HMDB0000167","6288","C00188","C[C@@H](O)[C@H](N)C(O)=O","1"
-"C01083","Trehalose","HMDB0000975","7427","C01083","OC[C@H]1O[C@H](O[C@H]2O[C@H](CO)[C@@H](O)[C@H](O)[C@H]2O)[C@H](O)[C@@H](O)[C@@H]1O","1"
-"C00078","L-Tryptophan","HMDB0000929","6305","C00078","N[C@@H](CC1=CNC2=C1C=CC=C2)C(O)=O","1"
-"C00483","Tyramine","HMDB0000306","5610","C00483","NCCC1=CC=C(O)C=C1","1"
-"C00082","L-Tyrosine","HMDB0000158","6057","C00082","N[C@@H](CC1=CC=C(O)C=C1)C(O)=O","1"
-"C05422","Dehydroascorbic acid","HMDB0001264","440667","C05422","[H][C@@]1(OC(=O)C(=O)C1=O)[C@@H](O)CO","1"
-"C00482","Sinapic acid","HMDB0032616","637775","C00482","COC1=CC(\C=C\C(O)=O)=CC(OC)=C1O","1"
-"C00106","Uracil","HMDB0000300","1174","C00106","O=C1NC=CC(=O)N1","1"
-"C00086","Urea","HMDB0000294","1176","C00086","NC(N)=O","1"
-"C00183","L-Valine","HMDB0000883","6287","C00183","CC(C)[C@H](N)C(O)=O","1"
-"C00181","D-Xylose","HMDB0000098","135191","C00181","O[C@@H]1COC(O)[C@H](O)[C@H]1O","1"
diff --git a/runs/Met_whole/prenorm.qs b/runs/Met_whole/prenorm.qs
deleted file mode 100644
index cfbff313d9aa44244057a5960528e7f3fda9a2a1..0000000000000000000000000000000000000000
Binary files a/runs/Met_whole/prenorm.qs and /dev/null differ
diff --git a/runs/Met_whole/preproc.qs b/runs/Met_whole/preproc.qs
deleted file mode 100644
index cfbff313d9aa44244057a5960528e7f3fda9a2a1..0000000000000000000000000000000000000000
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diff --git a/runs/Met_whole/raw_dataview.csv b/runs/Met_whole/raw_dataview.csv
deleted file mode 100644
index d0b87c9ef0a4ec5fbebe3b27cb1d8dfacb5fe932..0000000000000000000000000000000000000000
--- a/runs/Met_whole/raw_dataview.csv
+++ /dev/null
@@ -1,51 +0,0 @@
-"","aliquot_ID","genotype","group","C00147","C00020","C00956","C06104","C00041","C02512","C00099"
-"1","B17c","M82","green_leaf_M82",0.517550328266783,0.116297314385273,0.521565529098574,0.753867050446336,0.912389041443362,0.498391615702464,0.587732847033946
-"2","I05b","M82","green_leaf_M82",0.873197488141036,0.673488910735222,0.65133049828935,0.91454982019302,1.09606248301126,2.02933958337809,0.67000572421169
-"3","B05b","M82","green_leaf_M82",0.893863818037196,1.26603937004449,0.881668671004367,0.871399471536548,0.501880672476331,0.356120883168139,1.00876931959325
-"4","B17a","M82","green_leaf_M82",0.772333326603507,1.33401993755097,0.882331492905826,1.0129234705862,1.1120760489314,0.222983955208823,0.40236443457727
-"5","I06c","M82","green_leaf_M82",0.895739751006669,0.482208152992893,1.78263474524841,1.1734645281022,1.34804718755421,0.339719755337666,1.01419731819396
-"6","B06c","M82","green_leaf_M82",0.971279446981424,1.01449164952299,1.03253289474338,0.97692408074514,1.52166119301564,1.99717591134928,1.10362364221421
-"7","B06b","M82","green_leaf_M82",1.22139206948076,1.23024382217404,0.958739470886406,0.91222580911007,1.60465544060946,0.611845666274938,1.58021319970939
-"8","B11d","CANA1","green_leaf_CANA1",0.69605674587893,0.499725225926165,1.9392905628835,1.31336198336717,4.43005543951608,0.542494456871184,2.10304399387154
-"9","B14d","CANA1","green_leaf_CANA1",1.28296341670762,1.28447407502476,2.01194828743908,0.732881032419114,2.3918031379216,0.615742450303569,1.37280044279856
-"10","B12d","CANA1","green_leaf_CANA1",1.27000451234034,1.062606338981,2.37949215854601,1.03092785678314,3.37909302787288,1.04125619572873,1.56514262657594
-"11","B14a","CANA1","green_leaf_CANA1",0.707724435475351,0.834295885197712,0.935511810800953,1.45020749741446,2.91525725632371,1.22873072521149,1.90914374971176
-"12","B11c","CANA1","green_leaf_CANA1",0.885808267268499,0.632382949783313,0.958181099496651,1.56461542956252,4.04787074457592,0.721210335251307,1.61250812810363
-"13","B13d","CANA1","green_leaf_CANA1",1.10797805549254,0.73313059148844,2.05892840765958,0.897724636559396,2.19203314759952,1.29258322434074,1.53890222647633
-"14","I01c","CANA1","green_leaf_CANA1",1.19801089748378,1.95408109445323,1.60908994717651,0.923904120926657,3.38442487050826,0.547369766234931,1.59852928268639
-"15","2131598","M82","green_leaf_M82",0.285637304125361,0.198976739417061,0.680199804224252,0.726288802830615,0.357249250884837,0.441014925745586,0.414670336248597
-"16","2131600","M82","green_leaf_M82",0.130548405094935,0.62439029004948,0.489224559664597,1.40388809622427,0.317752149705409,0.730727539481527,2.62531432381745
-"17","2131596","M82","green_leaf_M82",0.285647485279867,0.290496753697845,0.73623828931482,1.03700941560192,1.34171635242358,1.4851255463651,1.06821983145755
-"18","2131599","M82","green_leaf_M82",0.611379867760303,2.67912911011042,3.5391936696865,2.76068612318482,1.27003553082329,3.99001036002593,3.21339813019689
-"19","2131597","M82","green_leaf_M82",2.95496142899016,0.618283091911722,1.36648501101274,1.12802001065491,1.31168014093896,1.5403867522058,0.916333574610609
-"20","2131609","CANA1","green_leaf_CANA1",1.23971093429929,0.962340463192019,0.270940678046416,4.55384429180132,0.149917378814419,13.637467964092,3.00960613589722
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-"37","2131622","CANA1","green_leaf_CANA1",0.0968303306794077,0.143726006204678,2.89475075180171,0.518999108606748,8.25328572236853,8.06846260105493,1.9740110123636
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-"48","2227174","CANA1","green_leaf_CANA1",0.474732332334937,0.595592179962751,2.40424548845806,0.209176946775961,5.39796343410305,0.0399933376787281,0.827465649307349
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diff --git a/runs/Met_whole/row_norm.qs b/runs/Met_whole/row_norm.qs
deleted file mode 100644
index cfbff313d9aa44244057a5960528e7f3fda9a2a1..0000000000000000000000000000000000000000
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diff --git a/runs/Met_whole/tosend.rds b/runs/Met_whole/tosend.rds
deleted file mode 100644
index 504fabeb64efe845a4f5502e51691eb5bad7ab83..0000000000000000000000000000000000000000
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diff --git a/studies/canal-1_2017_GH/resources/Labels.xlsx b/studies/canal-1_2017_GH/resources/Labels.xlsx
deleted file mode 100644
index 1c0731f37dbd77f73703aef46766b8cb37c3fe6a..0000000000000000000000000000000000000000
Binary files a/studies/canal-1_2017_GH/resources/Labels.xlsx and /dev/null differ
diff --git a/workflows/Met_whole/Met_whole.R b/workflows/Met_whole/Met_whole.R
index be4ff2132d616baba4388260955ca334a7c47b99..8d451cffa6853a33f6a98dee92f386cb23ebb6b5 100644
--- a/workflows/Met_whole/Met_whole.R
+++ b/workflows/Met_whole/Met_whole.R
@@ -11,8 +11,6 @@ library(viridis)
library(car)
library(broom)
library(here)
-library(ggrepel)
-library(openxlsx)
# Directory setting -------------------------------------------------------
@@ -51,7 +49,7 @@ metdat_GC_class <- readxl::read_xlsx(here(dat_in,
isa_tidy <- read_csv(here(prim_source, "isa_tidy.csv")) %>%
distinct(source_name_study, tissue_group, year, leaf_col, genotype, treatment,
- genotype_alt, tissue, group, sample_name_study)
+ genotype_alt, tissue, group)
fc_1_prim <- read_csv(here(prim_source, "mean_values_se_n.csv")) %>%
select(-Compound_Class) %>%
@@ -97,8 +95,7 @@ fc_1_ind_sec <- read_csv(here(sec_source, "individual_values.csv")) %>%
"tissue_group",
"sample_name_study")) %>%
left_join(plot_label, by = c("genotype_alt" = "geno",
- "tissue" = "tissue_label")) %>%
- rename(aliquot_ID = source_name_study)
+ "tissue" = "tissue_label"))
sig_sec <- read_csv(here(sec_source, "p_values.csv"))
@@ -112,15 +109,7 @@ lip_met <- read_csv(here(lip_source, "metabolitedata_lipids.csv")) %>%
fc_1_lip <- read_csv(here(lip_source, "mean_values_se_n.csv")) %>%
left_join(lip_met)
-fc_1_ind_lip <- read_csv(here(lip_source, "individual_values.csv")) %>%
- select(-group) %>%
- left_join(isa_tidy_lc, by = c("tissue",
- "treatment",
- "genotype",
- "tissue_group",
- "aliquot_ID" = "source_name_study")) %>%
- left_join(plot_label, by = c("genotype_alt" = "geno",
- "tissue" = "tissue_label"))
+fc_1_ind_lip <- read_csv(here(lip_source, "individual_values.csv"))
sig_lip <- read_csv(here(lip_source, "p_values.csv"))
@@ -298,7 +287,7 @@ ggsave(plot = heat_all,
units = "cm",
dpi = 300)
-if(!dev.cur() == 1){
+if(!is.null(dev.cur())){
dev.off()
}
@@ -420,176 +409,165 @@ ggsave(plot = p1, here(out, "asparagine.png"),
saveRDS(p1, here(out, "asparagine_leaves.RDS"))
-# # MetaboanalystR ----------------------------------------------------------
-# #currently defunct/browser utility used
-# #not recommended for further usage until stable
-# library(MetaboAnalystR)
-#
-# # fc_1 <- read_csv(here(analysis, "mean_values_se_n.csv")) %>%
-# # mutate(genotype = fct_relevel(genotype, wt))
-# #
-# # genotypes <- fc_1 %>%
-# # distinct(alias, genotype)
-# #
-# # fc_1_ind <- read_csv(here(analysis,"individual_values.csv")) %>%
-# # mutate(genotype = fct_relevel(genotype, wt))
-# #
-# # sig_GC <- read_csv(here(analysis,"p_values.csv")) #%>%
-# #
-# # maf <- readxl::read_xlsx("H:/3. cmQTL mapping/Shared_source_files/Primary_metabolites_classification_MWA.xlsx")
-# #
-# # met_dat <- fc_1 %>%
-# # distinct(met, Compound_Name, Compound_Class, component)
-# #
-# # genotypes <- fc_1 %>%
-# # distinct(alias, genotype)
-#
-# #think of way to select best features for analysis
-# skip = T
-# if(skip == T) {
-# } else{
-#
-# }
-#
-# #for test
-# geno_path <- "CANA1"
-# col_path <- c("green", "white")
-# tiss_path <- "leaf"
-# group_path <- c("green_leaf_CANA1", "green_leaf_M82")
-#
-# pathqea <- function(wt = "M82",
-# geno_path,
-# col_path,
-# tiss_path,
-# group_path){
-#
-# comp <- str_c(geno_path, col_path, tiss_path, sep = "_")
-#
-# sig_kegg <- fc_1 %>%
-# separate(tissue, into = c("leaf_col", "tissue"), sep = 5) %>%
-# mutate(group = as_factor(str_c(leaf_col, tissue, genotype, sep = "_"))) %>%
-# ungroup() %>%
-# filter(group %in% group_path, !is.na(component), treatment == "control") %>%
-# #left_join(metdat_GC_class, by = c("component" = "Xcal_name_xreport")) #%>%
-# pivot_wider(id_cols = c(KEGG_ID_ChEBI_mapped, met),
-# names_from = group,
-# values_from = mean_fc) %>%
-# filter(!is.na(KEGG_ID_ChEBI_mapped)) %>%
-# rowwise() %>%
-# mutate(total_change = sum(abs(c_across(where(is.numeric))))) %>%
-# group_by(KEGG_ID_ChEBI_mapped) %>%
-# mutate(rank = rank(-total_change, ties.method = "first")) %>%
-# filter(rank == 1)
-#
-# conc_tab <- fc_1_ind %>%
-# separate(tissue, into = c("leaf_col", "tissue"), sep = 5) %>%
-# select(-Compound_Name) %>%
-# left_join(met_dat) %>%
-# mutate(group = as_factor(str_c(leaf_col, tissue, genotype, sep = "_"))) %>%
-# ungroup() %>%
-# filter(group %in% group_path, !is.na(component), treatment == "control") %>%
-# left_join(metdat_GC_class, by = c("component" = "Xcal_name_xreport")) %>%
-# filter(!is.na(KEGG_ID_ChEBI_mapped), met %in% sig_kegg$met) %>%
-# #group_by(KEGG_ID_ChEBI_mapped) %>%
-# #mutate(rank_fc = -rank(abs(fc)),
-# # rank_p = rank(p.value),
-# # rank_c = rank_fc + rank_p,
-# # rank_rank = rank(rank_c, ties.method = "average"),
-# # rank_rank = if_else(rank_rank%%1 != 0, rank_p, rank_rank)) %>%
-# #ungroup() %>%
-# #filter(rank_rank == 1) %>%
-# #distinct(KEGG_ID_ChEBI_mapped, LIMS_ID, tissue, treatment, genotype, .keep_all = T) %>%
-# pivot_wider(id_cols = c(aliquot_ID, genotype, group),
-# names_from = KEGG_ID_ChEBI_mapped,
-# values_from = fc) %>%
-# as.data.frame()
-#
-# write_csv(conc_tab, here(out, "ma_concentration_table.csv"))
-#
-# # Enrichment --------------------------------------------------------------
-#
-# #setwd(here(out))
-#
-# mSet <- NULL
-# # Create vector consisting of compounds for enrichment analysis
-# #tmp.vec <- c("Acetoacetic acid", "Beta-Alanine", "Creatine", "Dimethylglycine", "Fumaric acid", "Glycine", "Homocysteine", "L-Cysteine", "L-Isolucine", "L-Phenylalanine", "L-Serine", "L-Threonine", "L-Tyrosine", "L-Valine", "Phenylpyruvic acid", "Propionic acid", "Pyruvic acid", "Sarcosine")
-# # Create mSetObj for storing objects created during your analysis
-# anal.type <- "pathqea"
-# mSet<-InitDataObjects(data.type = "conc", anal.type = "pathqea", paired = FALSE)
-# mSet <- Read.TextData(mSetObj = mSet, filePath = here(out, "ma_concentration_table.csv"), format = "rowu", lbl.type = "disc")
-# # Set up mSetObj with the list of compounds
-# #mSet<-Setup.MapData(mSet, conc_tab);
-# # Cross reference list of compounds against libraries (hmdb, pubchem, chebi, kegg, metlin)
-# mSet<-CrossReferencing(mSetObj = mSet, q.type = "kegg", lipid = F);
-# # Creates a mapping result table; shows HMDB, KEGG, PubChem, etc. IDs
-# # Saved as "name_map.csv" or can be found in mSet$dataSet$map.table
-# # Compounds with no hits will contain NAs across the columns
-# mSet<-CreateMappingResultTable(mSet);
-# # From the mapping result table, L-Isolucine has no matches
-# # Now, perform potential matching with our database against this compound
-# #mSet<-PerformDetailMatch(mSet, "L-Isolucine");
-# # Get list of candidates for matching
-# # Results are found in mSet$name.map$hits.candidate.list
-# #mSet<-GetCandidateList(mSet);
-# # Replace L-Isolucine with selected compound (L-Isoleucine)
-# #mSet<-SetCandidate(mSet, "L-Isolucine", "L-Isoleucine");
-# # Select the pathway library, ranging from mammals to prokaryotes
-# # Note the third parameter, where users need to input the KEGG pathway version.
-#
-# 1
-# mSet<-SanityCheckData(mSet)
-# mSet<-ReplaceMin(mSet)
-# mSet<-PreparePrenormData(mSet)
-# mSet<-Normalization(mSet, "NULL", "NULL", "NULL")
-#
-# # Use "current" for the latest KEGG pathway library or "v2018" for the KEGG pathway library version prior to November 2019.
-# mSet<-SetKEGG.PathLib(mSet, "ath", "current")
-# # Set the metabolite filter
-# # Default set to false
-# mSet<-SetMetabolomeFilter(mSet, T);
-# # Calculate the over representation analysis score, here we selected to use the hypergeometric test (alternative is Fisher's exact test)
-# # A results table "pathway_results.csv" will be created and found within your working directory
-# api.base <- "http://api.xialab.ca"
-# mSet<-CalculateQeaScore(mSetObj = mSet, nodeImp = "rbc", "gt")
-# # Plot of the Pathway Analysis Overview
-# #mSet<-PlotPathSummary(mSet, show.grid = T, "path_view_0_", "png", dpi = 72, width=NA)
-# # Plot a specific metabolic pathway, in this case "Glycine, serine and threonine metabolism"
-# #mSet<-PlotKEGGPath(mSet, "Glycine, serine and threonine metabolism",528, 480, "png")
-#
-# paths <- tibble("pathway ID" = mSet$analSet$path.ids,
-# pathway = mSet$analSet$path.nms)
-#
-# mapped_cpds <- tibble("pathway ID" = names(mSet$analSet$qea.hits),
-# hits = map(mSet$analSet$qea.hits,
-# .f = ~tibble(Compound = names(.x),
-# KEGG = .x))) %>%
-# unnest(cols = c("pathway ID", hits)) %>%
-# left_join(paths)
-#
-# path_res <- read_csv("pathway_results.csv",
-# col_names = c("pathway",
-# "total compounds",
-# "hits", "p-values",
-# "-log10(p)",
-# "holm",
-# "FDR",
-# "impact"),
-# skip = 1) %>%
-# left_join(paths) %>%
-# mutate(ratio = hits/`total compounds`,
-# `-log10(p)` = -log10(`p-values`)) %>%
-# select("pathway ID", "pathway", ratio, "total compounds", "hits", "p-values", "-log10(p)", "holm", "FDR", "impact")
-#
-# wb <- createWorkbook()
-# addWorksheet(wb, "Pathway results")
-# addWorksheet(wb, "Mapped Compounds")
-# writeDataTable(wb, sheet = 1, x = path_res)
-# writeDataTable(wb, sheet = 2, x = mapped_cpds)
-# write.xlsx(list(path_res, mapped_cpds),
-# here(out, str_c(comp, "pathway_results.xlsx", sep = "_")))
-# }
-#
-#
+# MetaboanalystR ----------------------------------------------------------
+
+fc_1 <- read_csv(here(analysis, "mean_values_se_n.csv")) %>%
+ mutate(genotype = fct_relevel(genotype, wt))
+
+genotypes <- fc_1 %>%
+ distinct(alias, genotype)
+
+fc_1_ind <- read_csv(here(analysis,"individual_values.csv")) %>%
+ mutate(genotype = fct_relevel(genotype, wt))
+
+sig_GC <- read_csv(here(analysis,"p_values.csv")) #%>%
+
+maf <- readxl::read_xlsx("H:/3. cmQTL mapping/Shared_source_files/Primary_metabolites_classification_MWA.xlsx")
+
+met_dat <- fc_1 %>%
+ distinct(met, Compound_Name, Compound_Class, component)
+
+genotypes <- fc_1 %>%
+ distinct(alias, genotype)
+
+#think of way to select best features for analysis
+skip = T
+if(skip == T) {
+} else{
+
+}
+
+pathqea <- function(wt = "M82",
+ geno_path,
+ col_path,
+ tiss_path,
+ group_path){
+
+ comp <- str_c(geno_path, col_path, tiss_path, sep = "_")
+
+ sig_kegg <- fc_1 %>%
+ separate(tissue, into = c("leaf_col", "tissue"), sep = 5) %>%
+ mutate(group = as_factor(str_c(leaf_col, tissue, genotype, sep = "_"))) %>%
+ ungroup() %>%
+ filter(group %in% group_path, !is.na(component), treatment == "control") %>%
+ #left_join(metdat_GC_class, by = c("component" = "Xcal_name_xreport")) #%>%
+ pivot_wider(id_cols = c(KEGG_ID_ChEBI_mapped, met),
+ names_from = group,
+ values_from = mean_fc) %>%
+ filter(!is.na(KEGG_ID_ChEBI_mapped)) %>%
+ rowwise() %>%
+ mutate(total_change = sum(abs(c_across(where(is.numeric))))) %>%
+ group_by(KEGG_ID_ChEBI_mapped) %>%
+ mutate(rank = rank(-total_change, ties.method = "first")) %>%
+ filter(rank == 1)
+
+ conc_tab <- fc_1_ind %>%
+ separate(tissue, into = c("leaf_col", "tissue"), sep = 5) %>%
+ select(-Compound_Name) %>%
+ left_join(met_dat) %>%
+ mutate(group = as_factor(str_c(leaf_col, tissue, genotype, sep = "_"))) %>%
+ ungroup() %>%
+ filter(group %in% group_path, !is.na(component), treatment == "control") %>%
+ left_join(metdat_GC_class, by = c("component" = "Xcal_name_xreport")) %>%
+ filter(!is.na(KEGG_ID_ChEBI_mapped), met %in% sig_kegg$met) %>%
+ #group_by(KEGG_ID_ChEBI_mapped) %>%
+ #mutate(rank_fc = -rank(abs(fc)),
+ # rank_p = rank(p.value),
+ # rank_c = rank_fc + rank_p,
+ # rank_rank = rank(rank_c, ties.method = "average"),
+ # rank_rank = if_else(rank_rank%%1 != 0, rank_p, rank_rank)) %>%
+ #ungroup() %>%
+ #filter(rank_rank == 1) %>%
+ #distinct(KEGG_ID_ChEBI_mapped, LIMS_ID, tissue, treatment, genotype, .keep_all = T) %>%
+ pivot_wider(id_cols = c(aliquot_ID, genotype, group),
+ names_from = KEGG_ID_ChEBI_mapped,
+ values_from = fc) %>%
+ as.data.frame()
+
+ write_csv(conc_tab, here(out, "ma_concentration_table.csv"))
+
+ # Enrichment --------------------------------------------------------------
+
+ mSet <- NULL
+ # Create vector consisting of compounds for enrichment analysis
+ #tmp.vec <- c("Acetoacetic acid", "Beta-Alanine", "Creatine", "Dimethylglycine", "Fumaric acid", "Glycine", "Homocysteine", "L-Cysteine", "L-Isolucine", "L-Phenylalanine", "L-Serine", "L-Threonine", "L-Tyrosine", "L-Valine", "Phenylpyruvic acid", "Propionic acid", "Pyruvic acid", "Sarcosine")
+ # Create mSetObj for storing objects created during your analysis
+ anal.type <- "pathqea"
+ mSet<-InitDataObjects(data.type = "conc", anal.type = "pathqea", paired = FALSE)
+ mSet <- Read.TextData(mSetObj = mSet, filePath = here(out, "ma_concentration_table.csv"), format = "rowu", lbl.type = "disc")
+ # Set up mSetObj with the list of compounds
+ #mSet<-Setup.MapData(mSet, conc_tab);
+ # Cross reference list of compounds against libraries (hmdb, pubchem, chebi, kegg, metlin)
+ mSet<-CrossReferencing(mSetObj = mSet, q.type = "kegg", lipid = F);
+ # Creates a mapping result table; shows HMDB, KEGG, PubChem, etc. IDs
+ # Saved as "name_map.csv" or can be found in mSet$dataSet$map.table
+ # Compounds with no hits will contain NAs across the columns
+ mSet<-CreateMappingResultTable(mSet);
+ # From the mapping result table, L-Isolucine has no matches
+ # Now, perform potential matching with our database against this compound
+ #mSet<-PerformDetailMatch(mSet, "L-Isolucine");
+ # Get list of candidates for matching
+ # Results are found in mSet$name.map$hits.candidate.list
+ #mSet<-GetCandidateList(mSet);
+ # Replace L-Isolucine with selected compound (L-Isoleucine)
+ #mSet<-SetCandidate(mSet, "L-Isolucine", "L-Isoleucine");
+ # Select the pathway library, ranging from mammals to prokaryotes
+ # Note the third parameter, where users need to input the KEGG pathway version.
+
+ 1
+ mSet<-SanityCheckData(mSet)
+ mSet<-ReplaceMin(mSet)
+ mSet<-PreparePrenormData(mSet)
+ mSet<-Normalization(mSet, "NULL", "NULL", "NULL")
+
+ # Use "current" for the latest KEGG pathway library or "v2018" for the KEGG pathway library version prior to November 2019.
+ mSet<-SetKEGG.PathLib(mSet, "ath", "current")
+ # Set the metabolite filter
+ # Default set to false
+ mSet<-SetMetabolomeFilter(mSet, T);
+ # Calculate the over representation analysis score, here we selected to use the hypergeometric test (alternative is Fisher's exact test)
+ # A results table "pathway_results.csv" will be created and found within your working directory
+ api.base <- "http://api.xialab.ca"
+ mSet<-CalculateQeaScore(mSetObj = mSet, nodeImp = "rbc", "gt")
+ # Plot of the Pathway Analysis Overview
+ #mSet<-PlotPathSummary(mSet, show.grid = T, "path_view_0_", "png", dpi = 72, width=NA)
+ # Plot a specific metabolic pathway, in this case "Glycine, serine and threonine metabolism"
+ #mSet<-PlotKEGGPath(mSet, "Glycine, serine and threonine metabolism",528, 480, "png")
+
+ paths <- tibble("pathway ID" = mSet$analSet$path.ids,
+ pathway = mSet$analSet$path.nms)
+
+ mapped_cpds <- tibble("pathway ID" = names(mSet$analSet$qea.hits),
+ hits = map(mSet$analSet$qea.hits,
+ .f = ~tibble(Compound = names(.x),
+ KEGG = .x))) %>%
+ unnest(cols = c("pathway ID", hits)) %>%
+ left_join(paths)
+
+ path_res <- read_csv("pathway_results.csv",
+ col_names = c("pathway",
+ "total compounds",
+ "hits", "p-values",
+ "-log10(p)",
+ "holm",
+ "FDR",
+ "impact"),
+ skip = 1) %>%
+ left_join(paths) %>%
+ mutate(ratio = hits/`total compounds`,
+ `-log10(p)` = -log10(`p-values`)) %>%
+ select("pathway ID", "pathway", ratio, "total compounds", "hits", "p-values", "-log10(p)", "holm", "FDR", "impact")
+
+ wb <- createWorkbook()
+ addWorksheet(wb, "Pathway results")
+ addWorksheet(wb, "Mapped Compounds")
+ writeDataTable(wb, sheet = 1, x = path_res)
+ writeDataTable(wb, sheet = 2, x = mapped_cpds)
+ write.xlsx(list(path_res, mapped_cpds),
+ here(out, str_c(comp, "pathway_results.xlsx", sep = "_")))
+}
+
+
# Metaboanalyst bubble plots ---------------------------------------------------
path_tidy <- readxl::read_xlsx(here(dat_in, "ma_pathway_abbreviations.xlsx"))
@@ -623,10 +601,12 @@ bub_theme <- theme(axis.text.x = element_markdown(vjust = 2, hjust = 0.5, size =
# WTglB_vs_MUwl -----------------------------------------------------------
cont <- "WTglB_vs_MUwl"
-bub <- read_csv(here(out, "cana1_wl_pathway_results.csv"),
+bub <- read_csv("H:/2. CANA1, SCO2/CANA1_GC_analysis/230130_analysis/cana1_wl_pathway_results.csv",
col_names = c("pathway", "total compounds", "hits", "p-values", "-log10(p)", "holm", "FDR", "impact"),
skip = 1)
+setwd(out_dir)
+
bub_tidy <- bub %>%
left_join(path_tidy) %>%
rename(path_old = pathway,
@@ -650,18 +630,23 @@ bub_tidy %>%
#theme(legend.position = "")
scale_fill_continuous(type = "viridis")
-ggsave(here(out, str_c("Metaboanalyst_Bubble_", cont, ".png")),
+ggsave(str_c("Metaboanalyst_Bubble_", cont, ".png"),
width = 5.5, height = 10, units = "cm")
-write.xlsx(bub_tidy, here(out, str_c("Metaboanalyst_bubble_", cont, ".xlsx")))
+write.xlsx(bub_tidy, str_c("H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Metaboanalyst_bubble_", cont, ".xlsx"))
# WTglB_vs_MUgl -----------------------------------------------------------
+setwd(current)
+
+readxl::read_xlsx("221026_MUglB_vs_WTgl_ChemRICH_results.xlsx")
cont <- "WTglB_vs_MUgl"
-bub <- read_csv(here(out,"cana1_gl_pathway_results.csv"),
+bub <- read_csv("H:/2. CANA1, SCO2/CANA1_GC_analysis/230130_analysis/cana1_gl_pathway_results.csv",
col_names = c("pathway", "total compounds", "hits", "p-values", "-log10(p)", "holm", "FDR", "impact"),
skip = 1)
+setwd(out_dir)
+
bub_tidy <- bub %>%
left_join(path_tidy) %>%
rename(path_old = pathway,
@@ -685,10 +670,10 @@ bub_tidy %>%
#theme(legend.position = "")
scale_fill_continuous(type = "viridis")
-ggsave(here(out, str_c("Metaboanalyst_Bubble_", cont, ".png")),
+ggsave(str_c("Metaboanalyst_Bubble_", cont, ".png"),
width = 5.5, height = 10, units = "cm")
-write.xlsx(bub_tidy, here(out, str_c("Metaboanalyst_bubble_", cont, ".xlsx")))
+write.xlsx(bub_tidy, str_c("H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Metaboanalyst_bubble_", cont, ".xlsx"))
## Combined ----------------------------------------------------------------
@@ -715,246 +700,383 @@ bub_comb %>%
scale_fill_continuous(type = "viridis") +
ylim(c(0,7))
-saveRDS(last_plot(), here(out,
- "Combined_bubble_metabolites.RDS"))
+saveRDS(last_plot(),"H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/Combined_bubble_metabolites.RDS")
-ggsave(here(out, str_c("Bubble_", "comb", ".png")),
+ggsave(str_c("Bubble_", "comb", ".png"),
width = 16.5, height = 10, units = "cm")
+# Pathview ----------------------------------------------------------------
+
+
+######### --------------- try double pathview ####
+library(pathview)
+setwd("H:/2. CANA1, SCO2/CANA1_RNAseq_experiment/CANA1_RNAseq_analysis")
+allfiles_allgenes <- list.files(pattern = "allgenes.csv")
+dates_allfiles <- as.numeric(str_extract(allfiles_allgenes, pattern = "^\\d{6}"))
+cur_date <- as.numeric(str_replace_all(Sys.Date(),"^.{2}|-",""))
+latest_allgenes <- allfiles_allgenes[[which.min(cur_date - dates_allfiles)]]
+
+all_genes <- read_csv(latest_allgenes)
+sampleTable <- read.csv("CANA1_2017_RNAseq_samplelist.csv")
+
+all_genes_long <- all_genes %>%
+ dplyr::select(-contains("log2"),
+ -contains("padj"),) %>%
+ pivot_longer(cols = c(contains("Mu"),
+ contains ("Wt")),
+ names_to = "geno",
+ values_to = "exp") %>%
+ filter(is.na(exp)==F) %>%
+ mutate(geno = as_factor(geno)) %>%
+ separate(col = geno,
+ into = c("geno", "rep"),
+ sep = -1) %>%
+ mutate(geno_tissue = as_factor(geno)) %>%
+ separate(col = geno,
+ into = c("geno","tissue"),
+ sep=2) %>%
+ mutate(tissue = as_factor(tissue),
+ geno = as_factor(geno)) %>%
+ dplyr::rename(gene = Row.names)
+
+entrez_solyc_ID_paper <-read_csv("solyc_vs_entrez_unique_original.csv")
+
+degs <- all_genes %>%
+ dplyr::select(Row.names, contains("padj"), contains("Log2FC")) %>%
+ mutate(DEG_WTglB_vs_MUwl = if_else(padj_WTglB_vs_MUwl <= 0.1 & abs(log2FC_WTglB_vs_MUwl) >= 1, T,F),
+ DEG_WTglB_vs_MUgl = if_else(padj_WTglB_vs_MUgl <= 0.1 & abs(log2FC_WTglB_vs_MUgl) >= 1, T,F),
+ DEG_MUglB_vs_MUwl = if_else(padj_MUglB_vs_MUwl <= 0.1 & abs(log2FC_MUglB_vs_MUwl) >= 1, T,F),
+ DEG_WTsB_vs_MUs = if_else(padj_WTsB_vs_MUs <= 0.1 & abs(log2FC_WTsB_vs_MUs) >= 1, T,F),
+ Row.names = str_replace_all(Row.names, pattern = "(\\.\\d){2,3}", replacement = "")) %>%
+ rename(SolycID = Row.names) %>%
+ left_join(entrez_solyc_ID_paper)
+
+
+pv_leaf_genes <- degs %>%
+ select(log2FC_WTglB_vs_MUwl, DEG_WTglB_vs_MUwl,log2FC_WTglB_vs_MUgl, DEG_WTglB_vs_MUgl,
+ SolycID, EntrezID) %>%
+ filter(!is.na(EntrezID)) %>%
+ #mutate(log2FC_WTglB_vs_MUwl = if_else(DEG_WTglB_vs_MUwl == T, log2FC_WTglB_vs_MUwl, 0),
+ # log2FC_WTglB_vs_MUgl = if_else(DEG_WTglB_vs_MUgl == T, log2FC_WTglB_vs_MUgl, 0)) %>%
+ select(EntrezID, log2FC_WTglB_vs_MUgl, log2FC_WTglB_vs_MUwl) %>%
+ as.data.frame()
-# Heatmap SAD----------------------------------------------------------------
-
-library(pheatmap)
+row.names(pv_leaf_genes) <- pv_leaf_genes$EntrezID
+pv_leaf_genes_plot <- pv_leaf_genes[,-1]
-plot_label <- read.xlsx("studies/canal-1_2017_GH/resources/Labels.xlsx")
-red_met_class <- read_csv(here(dat_in, "reduced_met_classes.csv")) %>%
- select(-n)
-heat_base <- fc_1_ind %>%
- filter(!str_detect(genotype, "on"),
- !str_detect(treatment, "drought")) %>%
- select(- Compound_Name,
- - geno_tissue,
- - geno_label,
- - geno_tissue_label,
- - genotype_alt) %>%
- left_join(met_dat) %>%
- left_join(plot_label, by = c("genotype" = "geno_old",
- "tissue" = "tissue_label")) %>%
- group_by(met,
- Compound_Name,
- Compound_Class,
- tissue,
- treatment,
- genotype,
- geno_label,
- tissue_group,
- geno_tissue_label) %>%
- mutate(sad = (abs(fc - median(fc)))/median(fc)) %>%
- summarise(sad = mean(sad)) %>%
- ungroup()
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+getwd()
+#test pathview, need distinct occurences of KEGG_IDs
+setwd(out_dir)
+#metdat_GC_class <- readxl::read_xlsx("H:/3. cmQTL mapping/Shared_source_files/210118_primary_metabolites_classification.xlsx")
-sad_M82 <- heat_base %>%
- filter(genotype == "M82") %>%
- group_by(met,
- Compound_Name,
- Compound_Class,
- tissue,
- treatment,
- genotype,
- geno_label,
- tissue_group,
- geno_tissue_label) %>%
- summarise(sad_M82 = mean(sad)) %>%
+sig_kegg <- fc_1 %>%
+ filter(tissue_group == "leaves", genotype %in% c("M82", "CANA1")) %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ lfc = log2(mean_fc)) %>%
ungroup() %>%
- select(-genotype, -geno_label, -geno_tissue_label, -tissue)
-
-heat_nom <- heat_base %>%
- left_join(sad_M82) %>%
- mutate(mean_fc = sad/sad_M82) %>%
- filter(!is.na(geno_tissue_label)) %>%
- group_by(Compound_Name, met) %>%
- mutate(log_norm = log2(mean_fc),
- log_norm = if_else(is.infinite(log_norm), 0, log_norm),
- log_norm_level = (log_norm - mean(log_norm))/(max(log_norm)-min(log_norm)),
- z_score = (mean_fc - mean(mean_fc))/(max(mean_fc)-min(mean_fc))) %>%
+ filter(treatment == "control") %>%
+ #left_join(metdat_GC_class, by = c("component" = "Xcal_name_xreport")) %>%
+ pivot_wider(id_cols = c(KEGG_ID_ChEBI_mapped, met),
+ names_from = group,
+ values_from = lfc) %>%
+ filter(!is.na(KEGG_ID_ChEBI_mapped)) %>%
+ rowwise() %>%
+ mutate(total_change = sum(abs(c_across(where(is.numeric))))) %>%
+ group_by(KEGG_ID_ChEBI_mapped) %>%
+ mutate(rank = rank(-total_change, ties.method = "first")) %>%
+ filter(rank == 1)
+
+
+pv_leaf_compound <- fc_1 %>%
+ mutate(group = as_factor(str_c(tissue, treatment, genotype, sep = "_")),
+ lfc = log2(mean_fc)) %>%
+ #mutate(lfc = if_else(signif == "*", lfc, 0)) %>%
+ filter(tissue_group == "leaves", genotype == "CANA1") %>%
+ #group_by(tissue, met,stress) %>%
ungroup() %>%
- mutate(group = as_factor(str_c(tissue, genotype, treatment, sep = "_"))) %>%
- pivot_wider(id_cols = c(Compound_Name, Compound_Class, met),
- names_from = geno_tissue_label,
- values_from = log_norm) %>%
- left_join(red_met_class) %>%
- select(-Compound_Class, Compound_Class = Compound_Class_dense) %>%
- arrange(Compound_Class, Compound_Name) %>%
+ filter(treatment == "control"
+ , met %in% sig_kegg$met
+ ) %>%
+ #left_join(metdat_GC_class, by = c("component" = "Xcal_name_xreport")) %>%
+ pivot_wider(id_cols = c(KEGG_ID_ChEBI_mapped, met, genotype),
+ names_from = group,
+ values_from = lfc) %>%
+ filter(!is.na(KEGG_ID_ChEBI_mapped)) %>%
+ #rowwise() %>%
+ #mutate(total_change = sum(abs(c_across(where(is.numeric))))) %>%
+ #group_by(KEGG_ID_ChEBI_mapped) %>%
+ #mutate(rank = rank(-total_change, ties.method = "first")) %>%
+ #filter(rank == 1) %>%
+ #select(-met, - genotype, -total_change, -rank) %>%
as.data.frame()
-rownames(heat_nom) <- heat_nom$met
-
-mat.heat_nom <- heat_nom %>%
- select(#contains("flowers"), contains("roots"),
- #contains("*canal-1*"),
- #contains("M82")
- contains("fr")
- ) %>% as.matrix()
-
-annotation_row <- heat_nom %>%
- select(Compound_Class)
-
-rownames(annotation_row) <- heat_nom$met
-
-annotation_col <- heat_base %>%
- distinct(tissue, treatment, geno_label, geno_tissue_label) %>%
- #mutate(group = as_factor(str_c(tissue, genotype, treatment, sep = "_"))) %>%
- filter(geno_tissue_label %in% colnames(mat.heat_nom)) %>%
- select(tissue, genotype = geno_label, geno_tissue_label) %>%
+row.names(pv_leaf_compound) <- pv_leaf_compound$KEGG_ID_ChEBI_mapped
+pv_leaf_compound_plot <- pv_leaf_compound[,-(1:3)]
+
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00290",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "val_leu_ile_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00250",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "ala_asp_asn_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00195",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "photosynthesis_leaves")
+
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00196",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "photosynthesis_antenna_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00500",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "starch_suc_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00710",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "met_path_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00592",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "alpha_lin_acid_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00620",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "pyruvate_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00020",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "tca_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00010",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "glycolysis_leaves")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00280",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "val_ile_leu_deg")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00630",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "glyoxylate")
+
+pathview(gene.data = pv_leaf_genes_plot[,1:2],
+ cpd.data = pv_leaf_compound_plot[,1:2],
+ species = "sly",
+ pathway.id = "00030",
+ kegg.native = T,
+ low = list(gene = "magenta", cpd = "blue"),
+ mid = list(gene = "gray", cpd ="gray"),
+ high = list(gene = "green", cpd = "yellow"),
+ limit = list(gene = 2, cpd = 4),
+ bins = list(gene = 10, cpd = 10),
+ out.suffix = "PPP")
+
+
+# SBGN View ---------------------------------------------------------------
+library(SBGNview)
+data("sbgn.xmls")
+data("pathways.info", "pathways.stats")
+data("mapped.ids")
+
+gene.data <- as.matrix(pv_leaf_genes_plot)
+cpd.data <- as.matrix(pv_leaf_compound_plot)
+
+SBGN_gene <- changeDataId(data.input.id = gene.data,
+ input.type = "entrez",
+ output.type = "KO",
+ mol.type = "gene",
+ sum.method = "sum",
+ org = "sly")
+
+SBGNview(gene.data = SBGN_gene[,1:2],
+ cpd.data = cpd.data[,1:2],
+ org = "sly",
+ input.sbgn = "PWY-1042",
+ output.file = "Glyc_test_MetaCyc",
+ gene.id.type = "KO",
+ cpd.id.type = "kegg",
+ output.formats = "svg")
+
+# Test own layout ---------------------------------------------------------
+
+setwd(current)
+
+#invertase <- load("Invertase_test_file.sbgn")
+
+gene <- tribble(~gene, ~mugl, ~muwl,
+ "Inv", -1, 1,
+ "GAPDH", 3,4) %>%
+ mutate(mugl = as.numeric(mugl),
+ muwl = as.numeric(muwl)) %>%
as.data.frame()
-rownames(annotation_col) <- annotation_col$geno_tissue_label
-
-annotation_col <- annotation_col %>%
- select(-geno_tissue_label, tissue, genotype)
-
-heat_cols <- colnames(mat.heat_nom)
-
-ann_colors = list(
- tissue = c('fruit' = "red",
- '#greenleaf' = "darkgreen", 'whiteleaf' = "beige",
- #'roots' = "grey",
- 'stem' = "lightgreen"
- #,flowers = "yellow"
- ),
- genotype = c(M82 = cb_scale[1], `*canal-1*` = cb_scale[2]),
- #treatment = c("control" = vir_scale[1], "drought" = vir_scale[3]),
- Compound_Class = c("Amino Acid or derivative" = "#0072B2",
- "Carboxylic Acid" = "#D55E00",
- "Carbohydrate or derivative" = "#CC79A7",
- "Cinnamic Acid" = "#484E37",
- "Dipeptide" = "#4AEE2F",
- "Flavonoid (glycosides)" = "#BAD23A",
- "Galactolipid" = "#EF000B",
- "Phospholipid" = "#19605B",
- "Steroidal Glycoalkaloids" = "#7A5DF0",
- "Triacylglyceride" = "#6E3455",
- "Other" = "#000000")
-)
-
-pheatmap.all <- pheatmap(mat.heat_nom,
- colorRampPalette(c("#440154FF", "white", "#FDE725FF"))(25),
- #cellwidth = 16,
- #cellheight = 4,
- breaks = seq(-6.25, 6.25, 0.5),
- #clustering_distance_rows = dist((mat.heat_nom), method = "euclidean"),
- cluster_rows = F,
- cluster_cols = F,
- annotation_names_row = F,
- show_rownames = F,
- annotation_row = annotation_row,
- annotation_col = annotation_col,
- #display_numbers = mat.heat_nom_signif,
- number_color = "black",
- fontsize_number = 6,
- fontsize = 6,
- annotation_colors = ann_colors,
- #filename = str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),
- # "cmQTL_val_1_heatmap_rel_tissue_wt.jpg",
- # sep = "_")
-)
+rownames(gene) <- gene$gene
+gene <- gene[,-1]
+gene <- as.matrix(gene)
-dev.off()
-
-heat_all <- ggplotify::as.ggplot(pheatmap.all)
-saveRDS(heat_all, here(out, "Heatmap_all_met.RDS"))
-
-ggsave(plot = heat_all,
- here(out, "Heatmap_fruit_sad.jpg"),
- width = 16,
- height = 10,
- units = "cm",
- dpi = 300)
-if(!dev.cur() == 1){
- dev.off()
-}
-
-
-# Correlation to yield data? ----------------------------------------------
-
-yield <- read.xlsx(here("assays/canal-1_Phenotyping_2019/dataset/canal-1_yield_data.xlsx"))
-
-isa_fruit <- isa_tidy %>%
- filter(!str_detect(genotype, "on"), ! year == 2017) %>%
- distinct(source_name_study, genotype, genotype_alt, treatment)
-
-yield_sad <- yield %>%
- filter(fruit_yield != 0) %>%
- mutate(aliquot_ID = as.character(aliquot_ID)) %>%
- left_join(isa_fruit, by = c("aliquot_ID" = "source_name_study",
- "treatment")) %>%
- group_by(treatment, genotype, genotype_alt) %>%
- mutate(yield_sad = (abs(fruit_yield - median(fruit_yield))/median(fruit_yield)))
-
-fruit_met_sad <- fc_1_ind %>%
- filter(!str_detect(genotype, "on"),
- treatment == "control", tissue == "fruit") %>%
- mutate(genotype = as_factor(genotype)) %>%
- group_by(tissue, treatment, genotype, met) %>%
- mutate(sad = (abs(fc - median(fc)))/median(fc)) %>%
- ungroup()
-
-fruit_met_sad_wide <- fruit_met_sad %>%
- pivot_wider(id_cols = c(tissue, treatment, genotype, aliquot_ID, sample_name_study),
- names_from = met,
- values_from = sad)
-
-# Test metabolite variation -----------------------------------------------
-
-library(rstatix)
-
-sad <- fc_1_ind %>%
- filter(!str_detect(genotype, "on"),
- treatment == "control", tissue == "fruit") %>%
- mutate(genotype = as_factor(genotype)) %>%
- group_by(tissue, treatment, genotype, met) %>%
- mutate(sad = (abs(fc - median(fc)))/median(fc)) %>%
- ungroup()
-
-signif <- sad %>%
- group_by(tissue, treatment, met) %>%
- wilcox_test(formula = sad ~ genotype,
- ref.group = "M82",
- p.adjust.method = "fdr")
-
-p1 <- sad %>%
- filter(met == "m_86", str_detect(tissue, "fruit"), treatment == "control", !str_detect(genotype, "on")) %>%
- #mutate(genotype = as_factor(geno_tissue_label),
- # genotype = fct_relevel(genotype, c("gl M82", "gl *canal-1*", "wl *canal-1*"))) %>%
- ggplot(aes(x = genotype, y = sad)) +
- geom_boxplot() +
- geom_dotplot()#+
- #geom_errorbar(aes(ymin = (mean_fc-se), ymax = (mean_fc + se)), position = position_dodge(0.9), width = 0.25, linewidth = 0.75)+
- stat_pvalue_manual(step.group.by = c("treatment", "tissue_group"), sig_comb_bar, label = "p.signif", y.position = "y.position",
- step.increase = 0.07, tip.length = 0.01,
- hide.ns = F) +
- # theme(axis.text.x = element_markdown(angle = 45, hjust = 1),
- # panel.background = element_rect(fill = "white"),
- # panel.border = element_rect(color = "black",fill = NA),
- # text = element_text(size = 14),
- # legend.title = element_blank(),
- # legend.text = element_markdown()) +
- com_theme +
- xlab("") +
- ylab("Mean fold-change") +
- #facet_grid(rows = vars(treatment), cols = vars(tissue_group)) +
- ggtitle(label = "asparagine") +
- scale_fill_manual(values = c("darkgreen", "beige"), aesthetics = "fill") +
- scale_y_log10()
-
-p1
-
-ggsave(plot = p1, here(out, "asparagine.png"),
- width = 16, height = 10, units = "cm")
+cpd <- tribble(~cpd, ~mugl, ~muwl,
+ "Sucrose", "2", "1",
+ "Glucose", "1", "3",
+ "Fructose", "4", "0") %>%
+ as.data.frame()
+rownames(cpd) <- cpd$cpd
+
+cpd <- as.matrix(cpd)
+
+#id.mapping.gene_tbl <- tribble(~SYMBOL, ~ENTREZID,
+# "Inv", "Invertase",
+# "prevent", "character_vector") %>%
+# as.data.frame()
+
+id.mapping.gene <- as.matrix(id.mapping.gene_tbl)
+
+SBGNview(gene.data = gene[,1:2],
+ #cpd.data = cpd[,2:3],
+ #org = "sly",
+ input.sbgn = "Invertase_test_file_manually_modified.sbgn",
+ sbgn.id.attr = "id",
+ sbgn.gene.id.type = "test",
+ #id.mapping.gene = id.mapping.gene,
+ output.file = "Invertase_test",
+ gene.id.type = "test",
+ #cpd.id.type = "kegg",
+ output.formats = "svg",)
+
+#load("230221_Figures/SBGNview.tmp.data/ath_ENTREZID_KO.RData")
+#load("230221_Figures/SBGNview.tmp.data/kegg_metacyc.SBGN.RData")
+
+id.mapping.gene.table <- entrez_solyc_ID_paper %>%
+ select(entrez_in = EntrezID) %>%
+ mutate(entrez_out = entrez_in,
+ #entrez_in = str_c(entrez_in, "...")
+ ) %>%
+ as.data.frame() %>%
+ as.matrix()
+
+#Vanted SBGN
+setwd(current)
+
+pv_leaf_genes_matrix <- pv_leaf_genes_plot %>% as.matrix()
+
+SBGNview(gene.data = pv_leaf_genes_matrix[,1:2],
+ #cpd.data = pv_leaf_compound_plot[,1:2],
+ org = "sly",
+ input.sbgn = "Carbon fixation in photosynthetic organisms.sbgn",
+ sbgn.id.attr = "label",
+ sbgn.gene.id.type = "entrez_in",
+ #id.mapping.gene = id.mapping.gene.table,
+ #sbgn.cpd.id.type = "KEGG",
+ output.file = "VANTED_transformed_KEGG",
+ gene.id.type = "entrez_in",
+ #cpd.id.type = "kegg",
+ output.formats = "svg")
+
+load("230221_Figures/SBGNview.tmp.data/ath_ENTREZID_KO.RData")
+load("230221_Figures/SBGNview.tmp.data/kegg_metacyc.SBGN.RData")
-saveRDS(p1, here(out, "asparagine_leaves.RDS"))
diff --git a/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_Yield_analysis.R b/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_Yield_analysis.R
deleted file mode 100644
index 04be5ce1993cd25909efee669a2e2c0eb1cfa7aa..0000000000000000000000000000000000000000
--- a/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_Yield_analysis.R
+++ /dev/null
@@ -1,731 +0,0 @@
-library(tidyverse)
-library(car)
-library(broom)
-library(ggtext)
-library(ggpubr)
-library(openxlsx)
-library(ggbeeswarm)
-
-sampledata <- readxl::read_excel("H:/2. CANA1, SCO2/CANA1_2018_2019_samplelist.xlsx",
- sheet = 2)
-extraction <- readxl::read_excel("H:/2. CANA1, SCO2/CANA1_2018_2019_samplelist.xlsx",
- sheet = 3)
-treatment <- readxl::read_excel("H:/2. CANA1, SCO2/CANA1_2018_2019_samplelist.xlsx",
- sheet = 4)
-
-av_yield <- treatment %>%
- filter(Name != "no_LIMS") %>%
- mutate(fruit_yield = as.numeric(fruit_yield)) %>%
- group_by(genotype,trtmnt) %>%
- summarise(av_yield = mean(fruit_yield, na.rm = T),
- sd_yield = sd(fruit_yield, na.rm = T),
- obs = n()) %>%
- mutate(cv_yield = sd_yield/av_yield)
-
-iCV <- av_yield %>%
- pivot_wider(id_cols = c(genotype),
- names_from = trtmnt,
- values_from = cv_yield) %>%
- mutate(iCV = drought/control)
-
-treatment <- treatment %>%
- filter(Name != "no_LIMS") %>%
- mutate(genotype = as_factor(genotype),
- trtmnt = as_factor(trtmnt))
-
-yield_control <- treatment %>%
- filter(trtmnt == "control")
-
-yield_drought <- treatment %>%
- filter(trtmnt == "drought")
-
-plot_label <- tribble(~genotype, ~group,
- "M82", "M82",
- "CANA1", "*canal-1*")
-
-y_for_p <- treatment %>%
- group_by(trtmnt) %>%
- summarise(y.pos = 1.1*max(fruit_yield))
-
-p_ctrl <- tibble(p_value = kruskal.test(fruit_yield ~ genotype , data = yield_control)$p.value) %>%
- mutate(trtmnt = "control")
-
-p_drought <- tibble(p_value = kruskal.test(fruit_yield ~ genotype , data = yield_drought)$p.value) %>%
- mutate(trtmnt = "drought")
-
-yield_p <- bind_rows(p_ctrl, p_drought)
-
-sig_yield_plot <- y_for_p %>%
- left_join(yield_p) %>%
- mutate(group1 = "M82",
- group2 = "*canal-1*",
- group = group1,
- p.signif = if_else(p_value <= 0.0005, "***",
- if_else(p_value <= 0.005, "**",
- if_else(p_value <= 0.05, "*", "ns")))) %>%
- mutate(y = "fruit_yield")
-
-
-# Theme -------------------------------------------------------------------
-
-
-com_theme <- theme(axis.text.x = element_markdown(angle = 45, hjust = 1, size = 6, family = "sans"),
- axis.text.y = element_text(size = 6, family = "sans"),
- axis.title.x = element_blank(),
- axis.title.y = element_text(size = 6, family = "sans"),
- panel.background = element_rect(fill = "white"),
- panel.border = element_rect(color = "black",fill = NA),
- strip.text = element_text(size = 8, family = "sans", margin = margin(t = 1, r = 1, b = 1, l = 1 , unit = "pt")),
- text = element_text(size = 6, family = "sans"),
- legend.title = element_blank(),
- legend.text = element_markdown(size = 6),
- plot.margin = unit(c(1,1,1,1), "mm"),
- legend.margin = margin(t = 0, r = 2, b = 0, l = 2 , unit = "mm"))
-
-
-# Yield plot --------------------------------------------------------------
-
-
-treatment <- treatment %>%
- left_join(plot_label)
-
-binwidth <- treatment %>%
- summarise(binwidth = (max(fruit_yield) - min(fruit_yield))/50)
-
-treatment %>%
- mutate(group = as_factor(group),
- trtmnt = as_factor(trtmnt)) %>%
- ggplot(aes(x = group, y = fruit_yield, fill = group)) +
- geom_boxplot(size = 0.2, outlier.size = 0.2) +
- geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) +
- #geom_dotplot(binaxis = "y", stackdir = "center", binwidth = binwidth$binwidth) +
- stat_pvalue_manual(data = sig_yield_plot, y.position = "y.pos", label = "p.signif", xmin = "group1", xmax = "group2") +
- facet_grid(cols = vars(trtmnt), scales = "free") +
- scale_fill_manual(values = c("darkgreen", "beige")) +
- labs(x = "Genotype", y = "Fruit yield/ g/plant") +
- com_theme
-
-ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_fruit_yield.jpg"),
- width = 89,
- height = 60,
- units = "mm",
- dpi = 300)
-
-saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_yield.RDS")
-
-sum_stat <- treatment %>%
- group_by(trtmnt, group) %>%
- summarise(mean = mean(fruit_yield),
- sd = sd(fruit_yield),
- cv = sd/mean,
- n = n()) %>%
- ungroup() %>%
- mutate(group = str_remove_all(group, "\\*"),
- mean = round(mean, digits = 4),
- sd = round(sd, digits = 4),
- cv = round(cv, digits = 4)) %>%
- select(Treatment = trtmnt, Genotype = group, Mean = mean, SD = sd, CV = cv, N = n) %>%
- mutate(Genotype = as_factor(Genotype),
- Genotype = fct_relevel(Genotype, c("M82", "canal-1"))) %>%
- arrange(Treatment, Genotype)
-
-write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Yield_sum_stat.xlsx")
-
-# Test MAD ------------------------------------------------------
-
-mad <- treatment %>%
- group_by(trtmnt, genotype) %>%
- mutate(mad = abs(fruit_yield - median(fruit_yield))/median(fruit_yield)) %>%
- ungroup()
-
-mad_nest <- mad %>%
- group_by(trtmnt) %>%
- nest()
-
-mad_p <- mad_nest %>%
- mutate(krusk = map(data, .f = ~kruskal.test(.x$mad ~ .x$genotype)$p.value)) %>%
- select(trtmnt, krusk)
-
-y_for_p <- mad %>%
- group_by(trtmnt) %>%
- summarise(y.pos = 1.1*max(mad))
-
-sig_yield_plot <- mad_p %>%
- left_join(y_for_p) %>%
- mutate(group1 = "M82",
- group2 = "*canal-1*",
- group = group1,
- p.signif = if_else(krusk <= 0.0005, "***",
- if_else(krusk <= 0.005, "**",
- if_else(krusk <= 0.05, "*", "ns")))) %>%
- mutate(y = "mad")
-
-binwidth <- mad %>%
- summarise(binwidth = (max(mad) - min(mad))/50)
-
-mad %>%
- mutate(group = as_factor(group),
- trtmnt = as_factor(trtmnt)) %>%
- ggplot(aes(x = group, y = mad, fill = group)) +
- geom_boxplot(size = 0.2, outlier.size = 0.2) +
- geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) +
- #geom_dotplot(binaxis = "y", stackdir = "center", binwidth = binwidth$binwidth) +
- stat_pvalue_manual(data = sig_yield_plot, hide.ns = T, y.position = "y.pos", label = "p.signif") +
- facet_grid(cols = vars(trtmnt), scales = "free") +
- scale_fill_manual(values = c("darkgreen", "beige")) +
- labs(x = "Genotype", y = "Scaled MAD Fruit yield/ g/plant") +
- com_theme
-
-ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_fruit_yield_MAD.jpg"),
- width = 89,
- height = 60,
- units = "mm",
- dpi = 300)
-
-saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_yield_mad_scaled.RDS")
-
-sum_stat <- mad %>%
- group_by(trtmnt, group) %>%
- summarise(mean = mean(mad),
- sd = sd(mad),
- cv = sd/mean,
- n = n()) %>%
- ungroup() %>%
- mutate(group = str_remove_all(group, "\\*"),
- mean = round(mean, digits = 4),
- sd = round(sd, digits = 4),
- cv = round(cv, digits = 4)) %>%
- select(Treatment = trtmnt, Genotype = group, Mean = mean, SD = sd, N = n) %>%
- mutate(Genotype = as_factor(Genotype),
- Genotype = fct_relevel(Genotype, c("M82", "canal-1"))) %>%
- arrange(Treatment, Genotype)
-
-write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Yield_MAD_sum_stat.xlsx")
-
-
-# Test Levene ------------------------------------------------------
-skip = T
-if(skip == T){
- print("skipped")
-} else{
-lev <- treatment %>%
- group_by(trtmnt, genotype) %>%
- mutate(lev = abs(log10(fruit_yield) - log10(median(fruit_yield)))) %>%
- ungroup()
-
-lev_nest <- lev %>%
- group_by(trtmnt) %>%
- nest()
-
-lev_p <- lev_nest %>%
- mutate(krusk = map(data, .f = ~kruskal.test(.x$lev ~ .x$genotype)$p.value)) %>%
- select(trtmnt, krusk)
-
-y_for_p <- lev %>%
- group_by(trtmnt) %>%
- summarise(y.pos = 1.1*max(lev))
-
-sig_yield_plot <- lev_p %>%
- left_join(y_for_p) %>%
- mutate(group1 = "M82",
- group2 = "*canal-1*",
- group = group1,
- p.signif = if_else(krusk <= 0.0005, "***",
- if_else(krusk <= 0.005, "**",
- if_else(krusk <= 0.05, "*", "ns")))) %>%
- mutate(y = "lev")
-
-binwidth <- lev %>%
- summarise(binwidth = (max(lev) - min(lev))/50)
-
-lev %>%
- mutate(group = as_factor(group),
- trtmnt = as_factor(trtmnt)) %>%
- ggplot(aes(x = group, y = lev, fill = group)) +
- geom_boxplot(size = 0.2, outlier.size = 0.2) +
- geom_dotplot(binaxis = "y", stackdir = "center", binwidth = binwidth$binwidth) +
- stat_pvalue_manual(data = sig_yield_plot, hide.ns = T, y.position = "y.pos", label = "p.signif") +
- facet_grid(cols = vars(trtmnt), scales = "free") +
- scale_fill_manual(values = c("darkgreen", "beige")) +
- labs(x = "Genotype", y = "Scaled lev Fruit yield/ g/plant") +
- com_theme
-
-ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_fruit_yield_lev.jpg"),
- width = 89,
- height = 60,
- units = "mm",
- dpi = 300)
-
-saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_yield_lev_scaled.RDS")
-
-sum_stat <- lev %>%
- group_by(trtmnt, group) %>%
- summarise(mean = mean(lev),
- sd = sd(lev),
- cv = sd/mean,
- n = n()) %>%
- ungroup() %>%
- mutate(group = str_remove_all(group, "\\*"),
- mean = round(mean, digits = 4),
- sd = round(sd, digits = 4),
- cv = round(cv, digits = 4)) %>%
- select(Treatment = trtmnt, Genotype = group, Mean = mean, SD = sd, N = n) %>%
- mutate(Genotype = as_factor(Genotype),
- Genotype = fct_relevel(Genotype, c("M82", "canal-1"))) %>%
- arrange(Treatment, Genotype)
-
-write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Yield_lev_sum_stat.xlsx")
-}
-
-# Chlorophyll -------------------------------------------------------------
-
-chl1 <- readxl::read_excel("210128_Chl_CANA1_1_to_10.xlsx",
- sheet = 2)
-
-chl1_reformat <- chl1 %>%
- pivot_longer(cols = matches("^\\d{1,2}$"),
- names_to = "column",
- values_to = "value") %>%
- pivot_wider(id_cols = c(row,column),
- names_from = identity,
- values_from = value)
-
-chl2 <- readxl::read_excel("210130_Chl_CANA1_1_to_10.xlsx",
- sheet = 2)
-
-chl2_reformat <- chl2 %>%
- pivot_longer(cols = matches("^\\d{1,2}$"),
- names_to = "column",
- values_to = "value") %>%
- pivot_wider(id_cols = c(row,column),
- names_from = identity,
- values_from = value)
-
-chl3 <- readxl::read_excel("210131_Chl_CANA1_1_to_10.xlsx",
- sheet = 2)
-
-chl3_reformat <- chl3 %>%
- pivot_longer(cols = matches("^\\d{1,2}$"),
- names_to = "column",
- values_to = "value") %>%
- pivot_wider(id_cols = c(row,column),
- names_from = identity,
- values_from = value)
-
-chl_all <- chl1_reformat %>%
- bind_rows(chl2_reformat,chl3_reformat) %>%
- filter(!is.na(extraction_num))
-
-blank <- chl_all %>%
- filter(extraction_num == 0) %>%
- summarise(blank_652 = mean(`652`),
- blank_665 = mean(`665`),
- blank_470 = mean(`470`))
-
-sampledata_tidy <- sampledata %>%
- left_join(extraction) %>%
- left_join(treatment, by = "Aliquot ID") %>%
- mutate(Genotype = as_factor(Genotype))
-
-write_csv(sampledata_tidy, str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"sampledata_tidy.csv"))
-
-levels(sampledata_tidy$Genotype)
-sampledata_tidy$Genotype <- ordered(sampledata_tidy$Genotype,
- levels = c("M82", "CANA1", "CRISPR_8619", "CRISPR_7472"))
-
-chl_calc <- chl_all %>%
- left_join(sampledata_tidy) %>%
- filter(trtmnt == "control") %>%
- mutate(path_652 = (`652`- blank$blank_652)/0.51,
- path_665 = (`665`- blank$blank_665)/0.51,
- path_470 = (`470`- blank$blank_470)/0.51,
- chl_a = ((16.72 * path_665 - 9.16 * path_652)),
- chl_b = ((34.09 * path_652 - 15.28 * path_665)),
- car_tot = (((1000 * path_470 - 1.63 * chl_a - 104.96 * chl_b) / 221)),
- chla_ug_g = if_else(chl_a < 0 , 0, (chl_a*2.5/fw) *1000),
- chlb_ug_g = if_else(chl_b < 0 , 0, (chl_b*2.5/fw) *1000),
- car_tot_ug_g = if_else(car_tot < 0, 0, (car_tot*2.5/fw) *1000),
- ratio_chla_chlb = chla_ug_g/chlb_ug_g,
- #ratio_chla_chlb = if_else(is.finite(ratio_chla_chlb), ratio_chla_chlb, 1),
- Tissue = as_factor(Tissue),
- tissue_group = as_factor(str_extract(Tissue, "(?<!\\w)\\w+$")),
- tissue_short = abbreviate(Tissue, minlength = 2),
- group_anova = as_factor(str_c(tissue_short,group, sep =" "))) %>%
- filter(is.finite(ratio_chla_chlb))
-
-levels(chl_calc$group_anova)
-
-chl_calc$group_anova <- fct_relevel(chl_calc$group_anova,
- c("gl M82",
- "gl *canal-1*",
- "wl *canal-1*",
- "st M82",
- "st *canal-1*"))
-
-chl_calc %>%
- filter(tissue_group == "stems") %>%
- ggplot(aes(x = Tissue, y = chla_ug_g, fill = genotype)) +
- geom_dotplot(binaxis = "y", stackdir = "center", position = "dodge")
-
-chl_plot <- chl_calc %>%
- pivot_longer(cols = c(contains("ug_g"), ratio_chla_chlb),
- names_to = "pigment",
- values_to = "concentration") %>%
- mutate(pigment = as_factor(pigment))
-
-sum_stats <- chl_plot %>%
- group_by(pigment, Tissue, genotype) %>%
- summarise(norm_dist = shapiro.test(concentration)$p.value,
- mean = mean(concentration),
- sd = sd(concentration),
- n = n()) %>%
- mutate(se = sd/sqrt(n))
-
-data_leaves <- chl_calc %>%
- filter(tissue_group == "leaves") %>%
- select(group_anova, chla_ug_g, chlb_ug_g,car_tot_ug_g,ratio_chla_chlb)
-
-numeric_data_leaves <- data_leaves %>%
- select(where(is.numeric))
-
-map_dfc(.x = numeric_data_leaves, .f = ~leveneTest(.x ~ data_leaves$group_anova))
-map_dfc(.x = numeric_data_leaves, .f = ~kruskal.test(.x ~ data_leaves$group_anova)$p.value)
-leaves_wilcox <- map(.x = numeric_data_leaves, .f = ~pairwise.wilcox.test(.x, data_leaves$group_anova))
-
-sig_leaves <- leaves_wilcox %>%
- map(.f = tidy) %>%
- map2(.y = names(leaves_wilcox), .f = ~.x %>% mutate(var = .y)) %>%
- purrr::reduce(bind_rows)
-
-data_stems <- chl_calc %>%
- filter(tissue_group == "stems") %>%
- select(group_anova, chla_ug_g, chlb_ug_g,car_tot_ug_g, ratio_chla_chlb)
-
-numeric_data_stems <- data_stems %>%
- select(where(is.numeric))
-
-map_dfc(.x = numeric_data_stems, .f = ~leveneTest(.x ~ data_stems$group_anova))
-map_dfc(.x = numeric_data_stems, .f = ~kruskal.test(.x ~ data_stems$group_anova)$p.value)
-stems_t.test <- map(.x = numeric_data_stems, .f = ~pairwise.t.test(.x, data_stems$group_anova))
-
-sig_stems <- stems_t.test %>%
- map(.f = tidy) %>%
- map2(.y = names(stems_t.test), .f = ~.x %>% mutate(var = .y)) %>%
- purrr::reduce(bind_rows)
-
-#single tests
-#leveneTest(chla_ug_g ~ group_anova, data = data_chl_a_leaves)
-#kruskal.test(chla_ug_g ~ group_anova, data = data_chl_a_leaves)$p.value
-#pairwise.wilcox.test(data_chl_a_leaves$chla_ug_g,
- # data_chl_a_leaves$group_anova)
-
-pigments <- tibble(
- chla_ug_g = "chlorophyll a",
- chlb_ug_g = "chlorophyll b",
- car_tot_ug_g = "total carotenoids",
- ratio_chla_chlb = "chlorophyll a/b ratio") %>%
- pivot_longer(cols = everything(),
- names_to = "pigment",
- values_to = "pigment_label")
-
-
-# Leaves pigment plot -----------------------------------------------------
-
-chl_plot_leaves <- chl_plot %>%
- filter(pigment != "ratio_chla_chlb",
- tissue_group == "leaves") %>%
- mutate(group = group_anova) %>%
- left_join(pigments)
-
-tissues <- chl_plot_leaves %>%
- distinct(group, Tissue)
-
-y_for_p <- chl_plot_leaves %>%
- group_by(pigment) %>%
- summarise(y.position = 1.1*max(concentration))
-
-sig_leaves_plot <- sig_leaves %>%
- left_join(tissues, by = c("group2" = "group")) %>%
- mutate(p.signif = if_else(p.value <= 0.0005, "***",
- if_else(p.value <= 0.005, "**",
- if_else(p.value <= 0.05, "*", "ns")))) %>%
- left_join(y_for_p, by = c("var" = "pigment")) %>%
- left_join(pigments, by = c("var" = "pigment")) %>%
- filter(var != "ratio_chla_chlb")
-
-binwidth <- chl_plot_leaves %>%
- summarise(binwidth = (max(concentration) - min(concentration))/100) %>%
- ungroup()
-
-chl_plot_leaves %>%
- ggplot(aes(x = group, y = concentration, fill = Tissue)) +
- geom_boxplot(size = 0.2, outlier.size = 0.2) +
- #geom_beeswarm(aes(x = group, y = concentration, fill = Tissue),
- # shape = 21) +
- geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) +
- stat_pvalue_manual(sig_leaves_plot, y.position = "y.position",
- label = "p.signif", step.increase = 0.07,
- hide.ns = T) +
- facet_grid(cols = vars(pigment_label), scales = "free") +
- scale_fill_manual(values = c("darkgreen", "beige")) +
- labs(x = "Tissue and genotype", y = "concentration/ ?g/g FW") +
- com_theme
-
-ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_leaf_pigments.jpg"),
- width = 183,
- height = 100,
- units = "mm",
- dpi = 300)
-
-saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_leaf_pigments.RDS")
-
-sum_stat <- chl_plot_leaves %>%
- group_by(group, pigment_label) %>%
- summarise(mean = mean(concentration),
- sd = sd(concentration),
- cv = sd/mean,
- n = n()) %>%
- ungroup() %>%
- mutate(group = str_remove_all(group, "\\*"),
- mean = round(mean, digits = 4),
- sd = round(sd, digits = 4),
- cv = round(cv, digits = 4)) %>%
- select(Pigment = pigment_label, `Genotype and Tissue` = group, Mean = mean, SD = sd, N = n) %>%
- mutate(`Genotype and Tissue` = as_factor(`Genotype and Tissue`),
- `Genotype and Tissue` = fct_relevel(`Genotype and Tissue`, c("gl M82", "gl canal-1", "wl canal-1"))) %>%
- arrange(Pigment, `Genotype and Tissue`)
-
-write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Leaf_pigments_sum_stat.xlsx")
-
-
-# stems pigment plot -----------------------------------------------------
-
-chl_plot_stems <- chl_plot %>%
- filter(pigment != "ratio_chla_chlb",
- tissue_group == "stems") %>%
- mutate(group = group_anova) %>%
- left_join(pigments)
-
-tissues <- chl_plot_stems %>%
- distinct(group, Tissue)
-
-y_for_p <- chl_plot_stems %>%
- group_by(pigment) %>%
- summarise(y.position = 1.1*max(concentration))
-
-sig_stems_plot <- sig_stems %>%
- left_join(tissues, by = c("group2" = "group")) %>%
- mutate(p.signif = if_else(p.value <= 0.0005, "***",
- if_else(p.value <= 0.005, "**",
- if_else(p.value <= 0.05, "*", "ns")))) %>%
- left_join(y_for_p, by = c("var" = "pigment")) %>%
- left_join(pigments, by = c("var" = "pigment")) %>%
- filter(var != "ratio_chla_chlb") %>%
- mutate(group = group1)
-
-binwidth <- chl_plot_stems %>%
- summarise(binwidth = (max(concentration) - min(concentration))/100) %>%
- ungroup()
-
-chl_plot_stems %>%
- ggplot(aes(x = group, y = concentration, fill = group)) +
- geom_boxplot(size = 0.2, outlier.size = NA, outlier.shape = NA) +
- #geom_beeswarm(aes(x = group, y = concentration, fill = group),
- # shape = 21) +
- geom_quasirandom(method = "quasirandom", shape = 21, size = 0.5) +
- stat_pvalue_manual(sig_stems_plot, y.position = "y.position",
- label = "p.signif", step.increase = 0.07,
- hide.ns = T) +
- facet_grid(cols = vars(pigment_label), scales = "free") +
- scale_fill_manual(values = c("darkgreen", "#a0c45a63")) +
- labs(x = "Tissue and genotype", y = "concentration/ ?g/g FW") +
- com_theme
-
-ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_stem_pigments.jpg"),
- width = 183,
- height = 100,
- units = "mm",
- dpi = 300)
-
-saveRDS(last_plot(), "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Figures/RDS_files/canal1_stem_pigments.RDS")
-
-sum_stat <- chl_plot_stems %>%
- group_by(group, pigment_label) %>%
- summarise(mean = mean(concentration),
- sd = sd(concentration),
- cv = sd/mean,
- n = n()) %>%
- ungroup() %>%
- mutate(group = str_remove_all(group, "\\*"),
- mean = round(mean, digits = 4),
- sd = round(sd, digits = 4),
- cv = round(cv, digits = 4)) %>%
- select(Pigment = pigment_label, `Genotype and Tissue` = group, Mean = mean, SD = sd, N = n) %>%
- mutate(`Genotype and Tissue` = as_factor(`Genotype and Tissue`),
- `Genotype and Tissue` = fct_relevel(`Genotype and Tissue`, c("st M82", "st canal-1"))) %>%
- arrange(Pigment, `Genotype and Tissue`)
-
-write.xlsx(sum_stat, "H:/2. CANA1, SCO2/Manuscript_CANA1_2022/Tables/Stems_pigments_sum_stat.xlsx")
-
-
-# Comparison pigments apolar LC -------------------------------------------
-
-pig_LC <- read_csv("pigments_apolar_LC.csv") %>%
- left_join(plot_label) %>%
- mutate(tissue_group = as_factor(str_extract(tissue, "(?<!\\w)\\w+$")),
- tissue_short = abbreviate(tissue, minlength = 2),
- group_anova = as_factor(str_c(tissue_short,group, sep =" ")))
-
-pigments_LC <- tibble(
- Chlorophyll_a = "chlorophyll a",
- Chlorophyll_b = "chlorophyll b",
- car_tot_ug_g = "total carotenoids",
- ratio_chla_chlb = "chlorophyll a/b ratio") %>%
- pivot_longer(cols = everything(),
- names_to = "Compound_Name",
- values_to = "pigment_label")
-
-chl_calc_LC <- pig_LC %>%
- filter(str_detect(Compound_Name, "Chlorophyll"),
- tissue_group == "leaves" | tissue_group == "stems",
- year == "2018" | year == "2019") %>%
- mutate(fw_norm = area/fw_in_mg) %>%
- select(Compound_Name, genotype, Tissue = tissue, aliquot_ID, tissue_group, fw_norm, group_anova) %>%
- left_join(pigments_LC) %>%
- pivot_wider(id_cols = c(genotype, Tissue, aliquot_ID, tissue_group, group_anova),
- names_from = pigment_label,
- values_from = fw_norm)
-
-levels(chl_calc_LC$group_anova)
-
-chl_calc_LC$group_anova <- fct_relevel(chl_calc_LC$group_anova,
- c("gl M82",
- "gl *canal-1*",
- "wl *canal-1*",
- "st M82",
- "st *canal-1*"))
-
-chl_calc_LC %>%
- filter(tissue_group == "stems") %>%
- ggplot(aes(x = Tissue, y = `chlorophyll a`, fill = genotype)) +
- geom_dotplot(binaxis = "y", stackdir = "center", position = "dodge")
-
-chl_plot_LC <- chl_calc_LC %>%
- pivot_longer(cols = c(contains("chloro")),
- names_to = "pigment",
- values_to = "concentration") %>%
- mutate(pigment = as_factor(pigment))
-
-sum_stats_LC <- chl_plot_LC %>%
- group_by(pigment, Tissue, genotype) %>%
- summarise(norm_dist = shapiro.test(concentration)$p.value,
- mean = mean(concentration),
- sd = sd(concentration),
- n = n()) %>%
- mutate(se = sd/sqrt(n))
-
-data_leaves_LC <- chl_calc_LC %>%
- filter(tissue_group == "leaves") %>%
- select(group_anova, `chlorophyll a`, `chlorophyll b`)
-
-numeric_data_leaves <- data_leaves %>%
- select(where(is.numeric))
-
-map_dfc(.x = numeric_data_leaves, .f = ~leveneTest(.x ~ data_leaves$group_anova))
-map_dfc(.x = numeric_data_leaves, .f = ~kruskal.test(.x ~ data_leaves$group_anova)$p.value)
-leaves_wilcox_LC <- map(.x = numeric_data_leaves, .f = ~pairwise.wilcox.test(.x, data_leaves_LC$group_anova))
-
-sig_leaves_LC <- leaves_wilcox_LC %>%
- map(.f = tidy) %>%
- map2(.y = names(leaves_wilcox), .f = ~.x %>% mutate(var = .y)) %>%
- purrr::reduce(bind_rows)
-
-data_stems_LC <- chl_calc_LC %>%
- filter(tissue_group == "stems") %>%
- select(group_anova, `chlorophyll a`, `chlorophyll b`)
-
-numeric_data_stems_LC <- data_stems_LC %>%
- select(where(is.numeric))
-
-map_dfc(.x = numeric_data_stems_LC, .f = ~leveneTest(.x ~ data_stems_LC$group_anova))
-map_dfc(.x = numeric_data_stems_LC, .f = ~kruskal.test(.x ~ data_stems_LC$group_anova)$p.value)
-stems_t.test <- map(.x = numeric_data_stems_LC, .f = ~pairwise.t.test(.x, data_stems_LC$group_anova))
-
-sig_stems <- stems_t.test %>%
- map(.f = tidy) %>%
- map2(.y = names(stems_t.test), .f = ~.x %>% mutate(var = .y)) %>%
- purrr::reduce(bind_rows)
-
-#single tests
-#leveneTest(chla_ug_g ~ group_anova, data = data_chl_a_leaves)
-#kruskal.test(chla_ug_g ~ group_anova, data = data_chl_a_leaves)$p.value
-#pairwise.wilcox.test(data_chl_a_leaves$chla_ug_g,
-# data_chl_a_leaves$group_anova)
-
-
-# Scale and Combine both datasets ---------------------------------------------------
-
-
-chl_plot_leaves_LC <- chl_plot_LC %>%
- filter(pigment != "ratio_chla_chlb",
- tissue_group == "leaves") %>%
- mutate(group = group_anova) %>%
- rename(pigment_label = pigment)
-
-chl_leaves_phot <- chl_plot_leaves %>%
- filter(!str_detect(pigment_label, "carotenoid")) %>%
- select(genotype, Tissue, aliquot_ID = `Aliquot ID`, tissue_group, group_anova, pigment_label, concentration) %>%
- mutate(method = "Photometer")
-
-chl_leaves_LC <- chl_plot_leaves_LC %>%
- mutate(method = "apolar LC")
-
-id_in_both <- chl_leaves_phot %>%
- full_join(chl_leaves_LC) %>%
- distinct(group_anova, aliquot_ID, method) %>%
- group_by(group_anova, aliquot_ID) %>%
- summarise(n = n()) %>%
- ungroup()
-
-chl_leaves_comb <- chl_leaves_phot %>%
- bind_rows(chl_leaves_LC) %>%
- left_join(id_in_both) %>%
- filter(n == 2)
-
-wt_leaves <- chl_leaves_comb %>%
- filter(group_anova == "gl M82") %>%
- group_by(pigment_label, method) %>%
- summarise(wt_mean = mean(concentration))
-
-chl_leaves_scaled <- chl_leaves_comb %>%
- left_join(wt_leaves) %>%
- mutate(level = concentration/wt_mean)
-
-chl_leaves_scaled %>%
- ggplot(aes(x = group_anova, y = level, fill = method)) +
- geom_boxplot() +
- geom_point(shape = 21, position = position_dodge(0.75)) +
- facet_wrap(facets = vars(pigment_label)) +
- com_theme +
- theme(legend.position = "bottom")
-
-ggsave("comparison_chlorophyll.jpg", width = 16, height = 20, units = "cm")
-
-chl_leaves_scaled %>%
- ggplot(aes(x = method, y = level, fill = method)) +
- geom_boxplot() +
- geom_point(shape = 21, position = position_dodge(0.75)) +
- geom_line(aes(x = method, y = level, group = aliquot_ID)) +
- facet_grid(cols = vars(pigment_label), rows = vars(group_anova), scales = "free")
-
-ggsave("comparison_chlorophyll_ids_outlier.jpg", width = 16, height = 20, units = "cm")
-
-chl_leaves_scaled %>%
- filter(aliquot_ID != "2131611") %>%
- ggplot(aes(x = method, y = level, fill = method)) +
- geom_boxplot() +
- geom_point(shape = 21, position = position_dodge(0.75)) +
- geom_line(aes(x = method, y = level, group = aliquot_ID)) +
- facet_grid(cols = vars(pigment_label), rows = vars(group_anova), scales = "free")
-
-ggsave("comparison_chlorophyll_ids_outlier_removed.jpg", width = 16, height = 20, units = "cm")
diff --git a/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R b/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R
index 04be5ce1993cd25909efee669a2e2c0eb1cfa7aa..05be75e479ac717e180e647a28e1ecf34359fdd4 100644
--- a/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R
+++ b/workflows/Pigment_SpecPhot_analysis/Pigment_SpecPhot_analysis.R
@@ -1,3 +1,6 @@
+rm(list = ls())
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+getwd()
library(tidyverse)
library(car)
library(broom)
@@ -468,7 +471,7 @@ chl_plot_leaves %>%
hide.ns = T) +
facet_grid(cols = vars(pigment_label), scales = "free") +
scale_fill_manual(values = c("darkgreen", "beige")) +
- labs(x = "Tissue and genotype", y = "concentration/ ?g/g FW") +
+ labs(x = "Tissue and genotype", y = "concentration/ µg/g FW") +
com_theme
ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_leaf_pigments.jpg"),
@@ -538,7 +541,7 @@ chl_plot_stems %>%
hide.ns = T) +
facet_grid(cols = vars(pigment_label), scales = "free") +
scale_fill_manual(values = c("darkgreen", "#a0c45a63")) +
- labs(x = "Tissue and genotype", y = "concentration/ ?g/g FW") +
+ labs(x = "Tissue and genotype", y = "concentration/ µg/g FW") +
com_theme
ggsave(str_c(str_replace_all(Sys.Date(),"^.{2}|-",""),"_stem_pigments.jpg"),