├── CIBERSORT_data ├── .DS_Store ├── CIBERSORT papaer_data │ ├── .DS_Store │ ├── GSE11103_mentionedFig4_Cibersort.txt │ ├── GSE19380_GeneExpressionBrainMixture.csv │ ├── GSE19380_mentioendFig4CIBERSORT.xls │ ├── dataFig1_CIBERSORT'.xlsx │ ├── journal.pone.0006098.s001 (2).PDF │ └── validationOnExternalDataSource_CIBERSORT.xls ├── CIBERSORT-ExampleDATA │ ├── .DS_Store │ ├── ExampleMixtures-GEPs.csv │ ├── ExampleMixtures-GroundTruth (2).csv │ ├── ExampleMixtures-GroundTruth .csv │ ├── LM22.csv │ └── merge.R ├── ExampleMixtures-GEPs.csv ├── ExampleMixtures-GroundTruth.csv ├── Final │ ├── 102 │ │ ├── .DS_Store │ │ ├── mixDaraShort.csv │ │ ├── resultCoefs.csv │ │ ├── resultSamples.csv │ │ ├── result_Mix1.csv │ │ ├── result_Mix2.csv │ │ ├── result_Mix3.csv │ │ ├── result_Mix4.csv │ │ ├── result_Mix5.csv │ │ └── tuned.csv │ ├── .DS_Store │ ├── 102_normalized │ │ ├── .DS_Store │ │ ├── LM22-mostVar102.csv │ │ ├── mixDaraShort.csv │ │ ├── mixDaraShortValues.csv │ │ ├── mixDataNormalized.csv │ │ ├── mixData_dataNormalized.csv │ │ ├── sigMatNormalized.csv │ │ ├── sigMat_dataNormalized.csv │ │ └── svr │ │ │ ├── .DS_Store │ │ │ ├── resultCoefs.csv │ │ │ ├── resultSamples.csv │ │ │ ├── result_Mix1.csv │ │ │ ├── result_Mix2.csv │ │ │ ├── result_Mix3.csv │ │ │ ├── result_Mix4.csv │ │ │ ├── result_Mix5.csv │ │ │ └── tuned.csv │ ├── CIBERSORT-ExampleDATA │ │ ├── .DS_Store │ │ ├── ExampleMixtures-GEPs.csv │ │ ├── ExampleMixtures-GroundTruth (2).csv │ │ ├── ExampleMixtures-GroundTruth .csv │ │ ├── LM22-mostVar102.csv │ │ ├── LM22.csv │ │ └── svrCIBERSORT.R │ ├── Normalize_merge_data.R │ ├── all547-svr │ │ ├── .DS_Store │ │ ├── mixDaraShort.csv │ │ ├── mixDaraShortValues.csv │ │ ├── mixDataNormalizedbyRow.csv │ │ ├── resultCoefs.csv │ │ ├── resultSamples.csv │ │ ├── result_Mix1.csv │ │ ├── result_Mix2.csv │ │ ├── result_Mix3.csv │ │ ├── result_Mix4.csv │ │ ├── result_Mix5.csv │ │ ├── sigMatFinal.csv │ │ ├── sigMatNormalizedbyRow.csv │ │ └── tuned.csv │ ├── all547_normalized │ │ ├── .DS_Store │ │ ├── 102_normalized │ │ │ └── .DS_Store │ │ ├── Normalized_data │ │ │ ├── .DS_Store │ │ │ ├── mixDaraShort.csv │ │ │ ├── mixDaraShortValues.csv │ │ │ ├── mixDataNormalized.csv │ │ │ ├── sigMatNormalized-varComparison.csv │ │ │ ├── sigMatNormalized-varComparison102.csv │ │ │ ├── sigMatNormalized.csv │ │ │ ├── sigMatNormalizedwithGeneSymbol.csv │ │ │ ├── sigMatNormalizedwithGeneSymbol_compVar.csv │ │ │ └── sigMat_dataNormalized.csv │ │ ├── resultCoefs.csv │ │ ├── resultSamples.csv │ │ ├── result_Mix1.csv │ │ ├── result_Mix2.csv │ │ ├── result_Mix3.csv │ │ ├── result_Mix4.csv │ │ ├── result_Mix5.csv │ │ └── tuned.csv │ ├── mixDataNormalizedByCol.pdf │ ├── mixDataNormilizedbyRow.pdf │ ├── mixData_notNormalized.pdf │ ├── sigMat-notNormalized.pdf │ ├── sigMatNormalized-byRow.pdf │ ├── sigMatNormalizedbyCol.pdf │ └── svrCIBERSORT_updated1.R ├── LM22.csv ├── margeCIBERSORTTables.R ├── mixDataNormalized ├── p_cibersortdata.pptx ├── sigMatNormalized └── svrCIBERSORT.R ├── README.md └── codes ├── 1.produceSignature Matrix ├── .DS_Store ├── SigMatFinall.R ├── calculateAdjPval.R ├── calculateEffectSize.R ├── produceSigMat.R ├── produceSigMat.docx └── produceType_avgFiles.R ├── 2.MergeSigMatwithMix ├── .DS_Store └── Merge_Mix_SigMat.R ├── 3.nnls └── nnlm.R ├── 4.svr ├── .DS_Store └── svr.R ├── 5.plots ├── .DS_Store ├── comPlotSampleR.R └── comPlotType.R ├── README.md └── readme.docx /CIBERSORT_data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/CIBERSORT papaer_data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/CIBERSORT papaer_data/.DS_Store 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https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/CIBERSORT-ExampleDATA/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/CIBERSORT-ExampleDATA/ExampleMixtures-GroundTruth (2).csv: -------------------------------------------------------------------------------- 1 | ,B cells naive,B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils Mix1,0.18,0.16,0.13,0.11,0.09,0.07,0.06,0.05,0.04,0.03,0.02,0.02,0.01,0.01,0.01,0.01,0,0,0,0,0,0 Mix2,0.2,0,0.19,0,0.18,0,0.1,0,0.09,0,0.08,0,0.05,0,0.04,0,0.04,0,0.02,0,0.02,0 Mix3,0,0,0.01,0.01,0.01,0.02,0.03,0.05,0.07,0.11,0.17,0.17,0.11,0.07,0.05,0.03,0.02,0.01,0.01,0.01,0,0 Mix4,0.17,0.12,0.08,0.06,0.03,0.02,0.01,0,0,0,0,0,0,0,0,0.01,0.02,0.03,0.06,0.08,0.12,0.17 Mix5,0,0,0,0,0,0,0,0.01,0.03,0.04,0.03,0.02,0.02,0.04,0.09,0.11,0.08,0.05,0.04,0.07,0.14,0.2 -------------------------------------------------------------------------------- /CIBERSORT_data/CIBERSORT-ExampleDATA/ExampleMixtures-GroundTruth .csv: -------------------------------------------------------------------------------- 1 | ,B cells naive,B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils Mix1,0.18,0.16,0.13,0.11,0.09,0.07,0.06,0.05,0.04,0.03,0.02,0.02,0.01,0.01,0.01,0.01,0,0,0,0,0,0 Mix2,0.2,0,0.19,0,0.18,0,0.1,0,0.09,0,0.08,0,0.05,0,0.04,0,0.04,0,0.02,0,0.02,0 Mix3,0,0,0.01,0.01,0.01,0.02,0.03,0.05,0.07,0.11,0.17,0.17,0.11,0.07,0.05,0.03,0.02,0.01,0.01,0.01,0,0 Mix4,0.17,0.12,0.08,0.06,0.03,0.02,0.01,0,0,0,0,0,0,0,0,0.01,0.02,0.03,0.06,0.08,0.12,0.17 Mix5,0,0,0,0,0,0,0,0.01,0.03,0.04,0.03,0.02,0.02,0.04,0.09,0.11,0.08,0.05,0.04,0.07,0.14,0.2 -------------------------------------------------------------------------------- /CIBERSORT_data/CIBERSORT-ExampleDATA/merge.R: -------------------------------------------------------------------------------- 1 | B <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/B cells naive.csv") 2 | C <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/B cells memory_2.csv") 3 | D <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Plasma cells_3.csv") 4 | E <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells CD8_4.csv") 5 | F1 <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells CD4 naive_5.csv") 6 | G <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells CD4 memory resting_6.csv") 7 | H <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells CD4 memory activated_7.csv") 8 | I <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells follicular helper_8.csv") 9 | J <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells regulatory (Tregs)_9.csv") 10 | K <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/T cells gamma delta_10.csv") 11 | L <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/NK cells resting_11.csv") 12 | M <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/NK cells activated_12.csv") 13 | N <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Monocytes_13.csv") 14 | O <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Macrophages M0_14.csv") 15 | P <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Macrophages M1_15.csv") 16 | Q <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Macrophages M2_16.csv") 17 | R <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Dendritic cells resting_17.csv") 18 | S <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Dendritic cells activated_18.csv") 19 | T1 <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Mast cells resting_19.csv") 20 | U <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Mast cells activated_20.csv") 21 | V <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Eosinophils_21.csv") 22 | W <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/Neutrophils_22.csv") 23 | 24 | 25 | 26 | mydata1 <- merge(B, C, by = "GeneSymbol", all=TRUE) 27 | write.csv(mydata1, "mydata1.csv") 28 | 29 | mydata2 <- merge(mydata1, D, by = "GeneSymbol", all=TRUE) 30 | write.csv(mydata2, "mydata2.csv") 31 | 32 | mydata3 <- merge(mydata2, E, by = "GeneSymbol", all=TRUE) 33 | write.csv(mydata3, "mydata3.csv") 34 | 35 | mydata4 <- merge(mydata3, F1, by = "GeneSymbol", all=TRUE) 36 | write.csv(mydata4, "mydata4.csv") 37 | 38 | mydata5 <- merge(mydata4, G, by = "GeneSymbol", all=TRUE) 39 | write.csv(mydata5, "mydata5.csv") 40 | 41 | mydata6 <- merge(mydata5, H, by = "GeneSymbol", all=TRUE) 42 | write.csv(mydata6, "mydata6.csv") 43 | 44 | mydata7 <- merge(mydata6, I, by = "GeneSymbol", all=TRUE) 45 | write.csv(mydata7, "mydata7.csv") 46 | 47 | mydata8 <- merge(mydata7, J, by = "GeneSymbol", all=TRUE) 48 | write.csv(mydata8, "mydata8.csv") 49 | 50 | mydata9 <- merge(mydata8, K, by = "GeneSymbol", all=TRUE) 51 | write.csv(mydata9, "mydata9.csv") 52 | 53 | mydata10 <- merge(mydata9, L, by = "GeneSymbol", all=TRUE) 54 | write.csv(mydata10, "mydata10.csv") 55 | 56 | mydata11 <- merge(mydata10, M, by = "GeneSymbol", all=TRUE) 57 | write.csv(mydata11, "mydata11.csv") 58 | 59 | mydata12 <- merge(mydata11, N, by = "GeneSymbol", all=TRUE) 60 | write.csv(mydata12, "mydata12.csv") 61 | 62 | mydata13 <- merge(mydata12, O, by = "GeneSymbol", all=TRUE) 63 | write.csv(mydata13, "mydata13.csv") 64 | 65 | mydata14 <- merge(mydata13, P, by = "GeneSymbol", all=TRUE) 66 | write.csv(mydata14, "mydata14.csv") 67 | 68 | mydata15 <- merge(mydata14, Q, by = "GeneSymbol", all=TRUE) 69 | write.csv(mydata15, "mydata15.csv") 70 | 71 | mydata16 <- merge(mydata15, R, by = "GeneSymbol", all=TRUE) 72 | write.csv(mydata16, "mydata16.csv") 73 | 74 | mydata17 <- merge(mydata16, S, by = "GeneSymbol", all=TRUE) 75 | write.csv(mydata17, "mydata17.csv") 76 | 77 | mydata18 <- merge(mydata17, T1, by = "GeneSymbol", all=TRUE) 78 | write.csv(mydata18, "mydata18.csv") 79 | 80 | mydata19 <- merge(mydata18, U, by = "GeneSymbol", all=TRUE) 81 | write.csv(mydata19, "mydata19.csv") 82 | 83 | mydata20 <- merge(mydata19, V, by = "GeneSymbol", all=TRUE) 84 | write.csv(mydata20, "mydata20.csv") 85 | 86 | mydata21 <- merge(mydata20, W, by = "GeneSymbol", all=TRUE) 87 | write.csv(mydata21, "mydataFinal.csv") 88 | 89 | LM22 <- read.csv("LM22.csv") 90 | mydataFinal_filled <- merge(LM22, mydata21, by = "GeneSymbol", all=TRUE) 91 | write.csv(mydataFinal_filled, "mydataFinal_filled.csv") 92 | -------------------------------------------------------------------------------- /CIBERSORT_data/ExampleMixtures-GroundTruth.csv: -------------------------------------------------------------------------------- 1 | ,B cells naive,B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils Mix1,0.18,0.16,0.13,0.11,0.09,0.07,0.06,0.05,0.04,0.03,0.02,0.02,0.01,0.01,0.01,0.01,0,0,0,0,0,0 Mix2,0.2,0,0.19,0,0.18,0,0.1,0,0.09,0,0.08,0,0.05,0,0.04,0,0.04,0,0.02,0,0.02,0 Mix3,0,0,0.01,0.01,0.01,0.02,0.03,0.05,0.07,0.11,0.17,0.17,0.11,0.07,0.05,0.03,0.02,0.01,0.01,0.01,0,0 Mix4,0.17,0.12,0.08,0.06,0.03,0.02,0.01,0,0,0,0,0,0,0,0,0.01,0.02,0.03,0.06,0.08,0.12,0.17 Mix5,0,0,0,0,0,0,0,0.01,0.03,0.04,0.03,0.02,0.02,0.04,0.09,0.11,0.08,0.05,0.04,0.07,0.14,0.2 -------------------------------------------------------------------------------- /CIBERSORT_data/Final/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/102/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102/resultCoefs.csv: -------------------------------------------------------------------------------- 1 | \,"\""B cells naive""",B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils,Pearson Correlation,P-value,RMSE Mix1,0.06,0.01,0.05,0,0.07,0,0.1,0,0,0.06,0,0.05,0.09,0.03,0,0.1,0.1,0.1,0.16,0.02,0,0,-0.169,0.453,0.076752258 Mix2,0.07,0,0,0,0.05,0,0.05,0,0,0.06,0.02,0.01,0.12,0.06,0,0,0.11,0.25,0.13,0.06,0,0,-0.033,0.886,0.090804485 Mix3,0.19,0.02,0,0.09,0.1,0.05,0.03,0,0,0.1,0.11,0.17,0.09,0,0,0,0.03,0,0,0,0,0,0.459,0.032,0.057287155 Mix4,0.03,0.02,0,0.09,0,0,0.02,0.03,0,0,0,0,0.16,0.09,0.1,0.08,0,0.1,0.09,0.04,0.02,0.12,0.073,0.748,0.070097335 Mix5,0,0,0,0.16,0,0.01,0,0.05,0,0,0.02,0,0.07,0.12,0.06,0,0,0.12,0,0.04,0.15,0.22,0.57,0.006,0.054979335 -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102/resultSamples.csv: -------------------------------------------------------------------------------- 1 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 2 | "Mix1",256,0.25,1720048.89839688,1.28260626638664,1,0 3 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 4 | "Mix2",64,0.75,1232591.49625771,614.645380261084,0.963,0 5 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 6 | "Mix3",32,0.25,1429860.6316194,638.57538007104,0.968,0 7 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 8 | "Mix4",256,0.25,996883.136377533,0.986273273156434,1,0 9 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 10 | "Mix5",256,0.25,767862.598665577,44.1832624562197,1,0 11 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102/result_Mix1.csv: -------------------------------------------------------------------------------- 1 | "\",\"Y","predictedY","Pearson Correlation","rounded Correlation" 2 | "1",1045.560791,1045.84965193825,0.99999989508294,1 3 | "2",437.1365868,436.744702324941,0.99999989508294,1 4 | "3",1739.537757,1739.86855197409,0.99999989508294,1 5 | "4",137.7601396,137.333632711678,0.99999989508294,1 6 | "5",1955.123937,1953.83309084317,0.99999989508294,1 7 | "6",536.6568716,537.159747659678,0.99999989508294,1 8 | "7",1067.363243,1066.80162155672,0.99999989508294,1 9 | "8",285.5756566,285.295506655164,0.99999989508294,1 10 | "9",184.8956726,184.926511536386,0.99999989508294,1 11 | "10",430.5917682,431.058756598952,0.99999989508294,1 12 | "11",91.89634803,91.3190877281213,0.99999989508294,1 13 | "12",2974.398205,2974.44971114497,0.99999989508294,1 14 | "13",2976.357645,2976.60829713456,0.99999989508294,1 15 | "14",3297.292647,3296.59030236946,0.99999989508294,1 16 | "15",4491.010824,4489.10494628211,0.99999989508294,1 17 | "16",3328.45929,3327.88685312698,0.99999989508294,1 18 | "17",3602.59341,3601.51979444187,0.99999989508294,1 19 | 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| "86",668.7493204,669.664694651863,0.999680740536475,1 88 | "87",2006.291209,2007.18077040271,0.999680740536475,1 89 | "88",2346.451465,2347.43389559045,0.999680740536475,1 90 | "89",4122.614306,4121.61611742762,0.999680740536475,1 91 | "90",4261.687963,4262.16453749187,0.999680740536475,1 92 | "91",625.6145996,626.095576121263,0.999680740536475,1 93 | "92",653.4107499,654.421598693591,0.999680740536475,1 94 | "93",2080.258763,2081.23572927042,0.999680740536475,1 95 | "94",537.7332778,538.78799993148,0.999680740536475,1 96 | "95",3159.091855,3158.01472170525,0.999680740536475,1 97 | "96",3687.181673,3686.15858459206,0.999680740536475,1 98 | "97",1327.31073,1328.3698866379,0.999680740536475,1 99 | "98",1354.847735,1356.06160314467,0.999680740536475,1 100 | "99",1376.555923,1377.4572699194,0.999680740536475,1 101 | "100",3880.392959,3436.42996531458,0.999680740536475,1 102 | "101",417.0751317,418.063132882264,0.999680740536475,1 103 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102/tuned.csv: -------------------------------------------------------------------------------- 1 | "\",\"nu","cost" 2 | "Mix1",0.25,256 3 | "\",\"nu","cost" 4 | "Mix1",0.25,256 5 | "\",\"nu","cost" 6 | "Mix1",0.25,256 7 | "\",\"nu","cost" 8 | "Mix2",0.25,128 9 | "\",\"nu","cost" 10 | "Mix2",0.25,64 11 | "\",\"nu","cost" 12 | "Mix2",0.75,64 13 | "\",\"nu","cost" 14 | "Mix3",0.25,32 15 | "\",\"nu","cost" 16 | "Mix3",0.25,32 17 | "\",\"nu","cost" 18 | "Mix3",0.25,32 19 | "\",\"nu","cost" 20 | "Mix4",0.25,128 21 | "\",\"nu","cost" 22 | "Mix4",0.25,256 23 | "\",\"nu","cost" 24 | "Mix4",0.25,256 25 | "\",\"nu","cost" 26 | "Mix5",0.25,128 27 | "\",\"nu","cost" 28 | "Mix5",0.25,256 29 | "\",\"nu","cost" 30 | "Mix5",0.25,256 31 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/102_normalized/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/LM22-mostVar102.csv: -------------------------------------------------------------------------------- 1 | GeneSymbol,B cells naive,B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils, IGKC,27164.52012,30254.0679,36524.44074,3097.474216,489.1798399,40.36292074,67.88787751,329.5978842,351.6837529,2059.744494,324.3571791,121.6124805,318.0695055,78.07530888,235.9541063,170.8361038,64.18688874,223.333702,100.706419,162.9166765,1108.467068,119.9968023,119864997.9 CCL4,1661.464071,817.4816647,307.8066133,2564.294556,270.8722361,442.4757485,11191.41447,356.4726389,481.2791164,8310.145807,11202.80591,38944.01868,3000.888772,1968.542066,11885.1113,3139.008389,2072.163482,4282.933369,1133.500818,35339.68536,6568.915331,2680.048326,110966556.4 IGLL3P,6633.535586,8962.420222,42851.28444,217.4652812,18.00926412,41.51758679,96.40860336,50.27476208,551.6984117,36.19278033,33.45486904,62.78986393,70.0394246,45.93797103,157.7551766,102.1260256,159.0326323,142.2968228,37.36510829,56.06416798,80.27971882,83.43489613,85462974.07 MMP9,338.94428,388.1056494,357.9745698,286.9137339,359.6153524,395.4202673,258.4783862,424.4791722,396.8938938,338.8946519,688.2490954,284.1304512,335.4295326,38944.01868,2727.981464,16386.05747,3188.623948,2899.726139,1191.482432,12655.47888,635.0862911,6429.776491,78522426.74 TPSAB1,1102.29779,794.7203184,151.7313169,376.0368021,43.36581215,284.8186666,147.2741095,481.0725006,28.78893808,262.6432093,465.2204213,311.7403372,543.6290931,358.1005731,368.9130231,365.4798555,397.8866549,396.8599394,27467.08759,29437.28907,918.5119625,1033.132677,68105858.22 IGHM,21960.79819,14250.20741,31273.67963,361.7379219,46.93569691,156.9391502,260.5780432,2116.005563,593.8569709,226.0772361,162.309451,134.8648186,127.0436672,32.10709446,32.67724104,26.51832445,44.41793335,41.25586808,70.02577331,41.64994169,834.002996,98.13122154,67988764.08 CXCL9,597.5841917,438.2088306,268.3992061,125.8362376,77.45884479,151.5042347,259.4612394,143.5770588,159.2182742,220.9114526,186.9242102,590.3615841,265.0825127,168.4971354,38944.01868,199.4359175,179.9813131,2918.819703,123.5555835,99.76021313,193.9209434,403.0543959,67979344.49 PRF1,201.9573668,236.1351207,213.4124783,8182.518825,847.8104385,2130.073632,1288.368436,953.5011061,1643.00791,15440.12701,26164.60132,27164.52012,142.7487889,88.71604457,55.13574502,23.15489547,67.86490889,103.9210768,346.3646077,285.2205902,268.3717602,422.6697907,66700803.28 CPA3,786.5967435,653.570348,431.8870773,147.6958112,25.20265438,243.2991012,226.7134742,148.1833551,145.5265348,271.5067499,458.1363071,308.1273629,135.1465191,139.386735,247.6828366,206.9088618,132.4987072,149.4195584,28290.30984,22328.97153,1091.417527,522.5988941,54962149.76 GZMB,102.255299,68.32230989,62.71521001,2080.697741,85.36127082,58.9177582,21960.79819,64.03542312,53.64876826,6018.578754,15276.85279,23235.35012,61.71915165,136.87295,159.2974346,111.0281937,30.67820522,82.17563279,947.0769465,5600.547397,226.9706864,154.330993,50570496.49 TRAC,572.5113556,1006.350088,215.8269579,13721.45552,14057.43026,15532.16188,8731.942274,20117.66469,19538.4969,3189.238319,815.6846136,506.0537577,208.9784165,252.2397003,151.3173183,142.6133419,215.6237237,255.9384554,159.567675,187.1669874,880.0902068,790.0055576,49433062.13 CCL19,78.52160611,139.7443318,258.1016824,77.62297831,36.87140683,60.92923205,89.77834495,48.98562369,34.99339888,21.17848138,109.9231796,146.4600699,115.5525527,114.3667988,32553.88074,137.0742038,170.8471166,3048.871233,38.04867174,19.15243809,152.8012995,117.2636391,47864311.51 CXCL10,761.3944185,395.6381026,246.2438258,157.22658,120.4987696,152.9758784,529.512254,127.9224652,256.6183684,488.5453368,201.1109308,1510.914551,500.9300164,163.1981892,31273.67963,278.3434035,163.7176364,11302.3948,112.8085953,313.9187307,332.722032,486.9671286,47492047.48 IDO1,241.0650179,312.4524995,345.5621343,241.3331484,192.0869901,248.9097098,201.7392421,241.4925332,91.27779079,183.5919996,667.1972433,671.6285143,269.4662793,338.8974249,26473.42255,91.36792646,131.5732395,20536.85787,207.8915127,164.8463693,1389.417624,527.4082419,47388098.77 GUSBP11,8048.07448,10388.03123,30254.0679,521.3361484,587.5664324,151.2654723,9.073509293,447.7619209,268.2718711,15.07275333,311.6532884,98.43888337,43.51505491,6.401562035,14.61435957,286.719961,9.045254731,9.611149339,51.74630466,22.89358188,371.5811685,277.7647569,45975403.57 GNLY,9.016559093,14.52025513,19.25302109,3732.536058,134.9186388,1198.37505,233.4211369,12.65987353,24.36657201,8201.567151,21760.60028,23235.35012,19.94743339,16.57744727,20.40693277,15.74284151,22.37056888,21.45038814,26.17106781,18.37211337,24.52606794,118.2602821,44692589.49 NCF2,153.4955828,345.1359814,194.190923,62.34284219,225.0278308,88.17850315,60.47608557,68.81156687,425.6902381,1620.910475,483.3850653,168.835817,12280.85608,15650.12064,762.0351121,11487.88902,11149.65069,3147.592954,956.1678627,1619.076321,20110.62767,17226.82018,44355678.9 CCL22,46.07125476,31.32507211,19.84751256,24.03529684,39.42959137,27.87853733,28.17031886,24.33306321,560.1844434,23.02227816,26.9167755,76.9655571,33.117333,2604.46642,464.9729614,880.2022045,4123.086134,30950.32751,24.52312784,113.920916,94.52151623,59.92678374,43300660.19 CCL5,19.56849893,88.23956549,30.32542187,11434.94887,189.4932768,3143.971755,1850.010235,69.75841522,171.0009164,19080.91475,17238.65692,11744.53738,102.8425968,100.2336827,16862.16904,250.750217,59.29347738,6507.212063,46.19150568,660.0913807,300.4379114,133.3630669,42941475.44 CD3D,103.2248459,174.4025802,88.37677893,14396.85632,14916.803,12009.36242,7456.754789,18921.47848,12721.06851,11894.34724,250.8695535,243.8532814,79.18206065,74.30949816,26.01377304,12.22050099,106.1967153,132.5505719,40.77673955,13.20972496,795.027696,250.9974436,42101582.09 FCN1,160.5487443,92.91123613,109.9311142,130.0754644,144.5883358,98.71765801,63.37271678,116.5979064,122.2714734,5094.70768,465.2336051,121.8089713,29800.42108,1401.079575,306.8296547,593.0958939,121.7194027,120.9633626,483.4804269,562.6779332,168.3568238,5370.355855,40548293.39 FPR1,361.3212533,387.2639146,343.9217634,210.2034137,161.9808797,240.3083941,161.2444569,248.213136,222.4633603,911.9483813,397.2639649,303.3669097,9800.634834,880.9211063,801.8866109,408.5415267,281.0707287,222.4014754,660.8588777,716.9951177,3809.534017,27644.58995,36632057.52 LTB,10351.64143,12117.94539,106.6290931,9829.724672,13247.62405,12009.36242,5257.453469,9725.14216,17485.2086,3372.608476,1009.906661,16862.16904,321.3564756,127.317149,116.2863022,161.002021,184.5481051,112.0607213,222.2586327,400.9057785,2136.992363,5458.249488,36596524.69 GZMA,204.958358,217.1903236,91.85142742,6301.839315,440.9534741,4034.52042,1173.96483,130.7405303,318.1893547,14188.10221,13660.47953,22380.29444,132.0433731,95.37711154,109.7080589,68.0390322,93.29389844,128.6984558,50.07477091,68.27005989,150.5888289,334.8696127,36114935.2 IL7R,277.7164299,454.5504216,126.0983758,15195.16858,19513.31337,18085.9446,1965.113683,3066.253514,507.1604755,2118.827609,1159.340489,341.6294609,163.3050166,5078.293455,2163.365124,85.59144108,2156.179359,7799.647099,36.56114051,325.7294175,358.0842541,742.6718185,35797710.35 LAMP3,47.28278554,46.66817965,128.4317353,134.7841361,120.9265077,160.3048593,368.6319956,165.5835013,264.0396917,132.2010638,210.8261404,418.7096295,38.76231871,316.7767691,17116.19562,113.1413857,1014.136741,23356.30643,81.94628982,130.0600242,540.5707204,320.9232259,35600739.69 IL2RB,2118.459057,1945.518804,848.6873784,4000.216538,818.7927424,2988.605281,7562.491571,7279.249666,12662.47545,10958.76994,18026.39249,19190.61594,321.9612928,294.5506048,221.9788101,172.9682935,246.8795571,366.1434094,341.9687806,442.6048403,381.1943018,1044.851269,34940735.17 TRBC1,575.8478308,930.328823,525.0492335,8923.922332,12460.73946,10179.78364,3287.836318,16623.7078,19876.20631,5338.319292,4520.50562,2812.593654,78.94320693,56.8335872,223.4955364,131.0995558,189.1209091,145.715446,114.6022938,47.87130436,551.5102039,181.0122765,34731186.23 PRG2,98.84281244,125.3109437,157.5298559,126.6467707,107.0290453,139.1010495,165.4537351,35.31282708,68.52536924,16.54700608,70.81916153,82.7123624,117.086355,103.3752118,295.678865,263.6529152,85.47893602,89.83062765,19061.38442,20880.60329,189.2703998,59.65270386,34201427.9 FCGR3B,434.6883898,191.9423831,121.1090782,178.1993614,61.46749025,133.7448215,106.4982845,64.11513732,203.850527,446.2450155,494.7560439,369.6606901,402.4458452,256.3156774,154.0822062,155.6717837,158.6201963,125.0983352,290.4715044,123.6149676,321.0470841,27546.9664,33940209.09 HPGDS,203.7910498,224.547027,125.9523542,83.72110944,88.13751751,182.147041,92.78922471,56.70706623,219.6942345,118.9847774,177.6752993,148.3391263,36.75265198,239.7846459,64.8769987,138.6741034,195.4447153,125.1233721,21760.60028,17743.45795,256.5193159,219.1322864,33655918.43 CLC,317.0133169,263.8099578,158.2726851,91.38509289,21.54663623,110.2519917,184.5831263,91.98053835,38.52410842,337.9147534,509.9159527,237.9275689,86.56595204,69.27115976,226.7134742,172.4089256,98.81299309,102.2341069,3917.54615,3098.629915,26617.23891,4605.247829,32270973.9 MS4A1,18085.9446,17478.14564,396.3913895,343.9223112,52.22897356,220.8581711,125.466199,112.2871276,272.6999955,505.1556973,193.3316222,177.3861387,128.6338935,31.97803261,94.49057817,64.19016147,64.56613288,122.1899811,80.29122433,52.09389632,704.4005013,274.6589928,26798018.17 MNDA,29.5032353,276.0791648,5.02270779,50.81863936,9.217386051,8.750532337,4.525700455,13.50066477,38.28622427,2157.226727,790.3716005,301.3206314,11447.56145,2609.874357,4016.523813,1867.617958,4559.644609,2133.559627,2154.019115,1279.186504,3154.581987,22598.07416,26391578.82 CD247,23.66779296,53.69027623,65.55259868,5508.526368,7038.536622,5318.708728,2156.371127,5302.661793,9743.994501,9129.432322,16099.47966,15108.26954,35.27406041,23.92868112,26.9337182,81.83870096,43.67897681,32.48553108,128.4661867,105.4058403,459.1326292,59.83784952,25743721.45 IGHD,10811.41236,2526.481303,21173.52695,62.3476822,125.466199,26.14159933,21.20380232,135.5693196,28.36731347,27.10540257,32.58210537,21.68739568,50.4303708,44.70050298,97.72702921,106.3962528,78.42668997,56.63839916,32.60266038,30.03853946,257.5071029,79.25564732,24447608.13 TRDC,88.17850315,81.15497901,101.0670278,3014.575466,282.0101621,781.3316356,486.8794819,370.2752613,124.6527289,17980.35398,10797.21174,11382.00872,61.01801703,30.12404697,22.31868643,24.26650252,46.6420448,30.42258616,247.3533379,209.0182876,165.8509884,162.1231973,22930740.45 CCR7,3811.326733,1939.045683,202.9261759,5074.201816,8788.597127,2693.556404,2885.120974,1858.531026,6339.949771,490.8051753,672.4665585,482.4429323,165.2353496,328.1949289,17366.91051,123.7463123,290.530233,14223.15516,66.65895255,81.24584846,647.9636327,376.1557339,22397430.37 NKG7,6.994769751,10.33850341,7.726182177,8640.630717,106.5387015,679.6910465,947.3726372,7.582193721,18.51226305,10086.41757,13911.17937,14574.73469,34.65463899,4.93466715,217.2245766,6.547727883,4.961302934,6.557717872,429.3526592,573.0034371,24.2643021,13.31565465,22343434.7 EBI3,10.50267123,16.93649086,8.897758981,12.21399038,15.05714204,11.53252065,119.0986128,11.63480358,164.024998,8.368172831,10.01431702,16.85549775,12.4947299,13.18836854,20437.69231,311.2058679,16.81680477,6496.243408,8.959876311,134.9737807,9.497299095,11.62603766,20227794.55 CD2,419.4350192,409.5822139,377.8722518,8182.951228,8002.722844,9578.936337,7866.426688,14057.43026,11226.69889,5897.393241,6247.906848,3097.47128,222.357789,149.914948,112.6050172,188.1947985,187.6124847,146.9688776,332.6238386,138.5888054,614.9120433,576.8272959,19828961.65 KLRB1,119.5727193,241.3252476,39.37347577,8818.269216,317.5656356,10026.23944,500.6894521,4477.729529,1259.996581,12634.61161,12957.39238,5576.724303,90.24623781,26.68511703,33.3990692,13.5124231,94.79879985,37.00213293,37.48705237,72.13784357,276.4397193,322.6159656,19353070.35 TNFAIP6,267.9179363,259.8916347,141.1803804,81.29435266,36.71555117,94.24800971,79.12333165,63.61546587,131.8853818,147.6840939,123.7740746,145.8185128,233.4391136,347.1638911,19190.61594,325.3643379,245.9512171,8826.486407,253.2624558,2285.704444,203.2806125,3460.952883,19145457.83 SELL,11102.0298,8002.61479,376.9049927,7418.862396,9805.46284,7104.510327,2531.49842,4188.175594,5165.445456,10585.56882,5741.494585,1590.659482,7242.740165,103.9204555,234.1716363,287.2191393,121.953751,137.3301011,353.9899758,288.7165574,3001.389383,13724.93946,18620151.97 MZB1,46.81934,122.2593141,20117.66469,42.83903749,70.2363913,9.914966708,21.78305128,18.16093232,65.46672008,62.51579389,21.76048739,26.15989089,24.85648896,22.27069193,30.4274162,33.81911592,23.21322482,22.41174764,30.79487517,29.49339673,52.52655798,29.79626914,18326733.04 ACP5,605.8973843,1935.201479,1120.104684,306.3125193,744.6565995,557.8198203,248.5469318,711.9497439,958.9160181,493.96912,340.8201368,239.2770679,512.9313823,13644.1723,1062.475909,7633.960182,13825.05363,3989.865169,263.4979304,323.3812772,860.5633739,307.1427978,16445062.51 VNN2,64.9845649,79.68108191,51.04583679,445.4839483,574.7325605,87.43886769,7.680556652,183.1808863,81.25132533,1027.324135,600.7634958,133.9832531,3300.115435,462.8123954,6.198523194,8.255798098,6.33332463,7.339096499,242.3176356,17.98841772,9.753158043,18515.3558,15499161.29 GZMH,21.35853068,33.95743123,41.24636078,8555.821981,198.9335618,630.7749174,896.7120375,26.30390688,79.31202778,10428.44501,11889.00904,7419.827913,36.90093477,24.82219085,15.89364427,14.88173325,17.75887232,26.34630303,17.9815414,19.61378309,52.48247611,41.85793547,14528407.35 CST7,29.71165699,21.33475082,14.02145998,5181.597893,197.8545424,1470.064999,746.2695012,322.0620463,1323.231227,12102.63842,11514.68497,10179.78364,63.6732263,254.880064,186.8061087,292.5617552,1490.444911,4059.440795,2285.360966,2487.597945,616.2101884,2347.728878,14366061.52 GZMK,184.0395826,278.1808222,100.8148697,8814.387272,1423.65694,3724.329794,187.7181578,119.8476496,51.85294841,15903.70006,1967.19369,233.6749633,67.13748264,69.54165218,107.0724606,24.66700128,79.45607344,81.98912286,38.19756342,76.14236186,151.2474716,421.2121304,14189757.64 MS4A6A,1102.971199,616.0957235,326.3520803,221.1962643,167.8644908,216.3694922,177.7168106,149.2510114,453.0762101,2149.718655,222.7095319,191.822788,8814.800982,268.5081463,402.5623243,16167.182,3743.66762,167.4361623,704.4355853,501.7726181,408.7375549,1136.619238,14078162.46 IL1B,508.1423339,364.9197528,198.7896427,144.2186891,136.961868,251.1017935,200.4208119,204.2875485,284.0853434,352.1592413,250.0432905,241.5910262,1257.182131,1273.463059,675.1218227,291.824541,437.0700597,2781.903216,3282.430504,17770.60586,1137.887994,3099.346146,13997342.21 CXCR2,26.76355546,12.64597482,20.8510013,73.61765642,5.107669662,20.88194891,7.644655016,11.15831089,62.58074509,341.3628029,963.1701029,280.0023863,408.3528365,115.5505074,174.1206652,64.07909887,176.8360878,48.42702155,126.093962,56.93465721,442.7589038,17610.41012,13884952.57 FAIM3,11191.41447,8362.288551,254.0902643,5723.984822,8841.004838,5134.615329,1145.360549,7380.970003,4680.731681,1866.749257,2548.903709,478.4201622,117.871065,315.0175529,53.69761011,157.4129632,147.2831703,49.08029322,58.68422735,45.15325264,488.4465999,735.0353714,12332068.28 BCL2A1,112.6417383,314.019133,76.39326014,575.0352025,452.9144897,755.8584033,1206.5058,698.6513948,171.062582,431.9749142,539.9592883,526.5774707,2261.391134,5560.455558,7038.536622,195.959802,135.5924775,5551.253657,869.1471248,3291.227822,13257.93204,8790.33201,12297397.37 LCK,337.4400897,327.1096376,40.80982734,8763.864592,8948.349603,7786.120969,4042.377012,9578.936337,6736.744097,4299.610023,3604.565205,2808.159439,80.75884433,64.45522652,25.99553349,22.85969301,43.79168499,57.6156067,33.89599017,20.84968098,245.2328838,186.8691266,12260829.73 EMR1,499.0111023,383.0612119,194.3869003,195.8964573,428.0646447,312.812165,400.2184096,182.9389251,335.8350828,447.9667425,348.0789413,188.5213167,1815.513906,203.312067,3528.214438,404.3935503,178.6781861,237.420847,287.3791829,248.1729828,16330.70798,1253.613443,11866163.84 MMP12,746.9258791,488.893271,163.9010445,79.27159307,52.70903607,114.8747346,105.1489388,81.69270078,265.1392895,233.2582309,98.16379,84.9536497,95.85109514,243.058639,399.1895721,221.5740634,15992.46965,3929.353984,207.7939338,2823.785573,241.2406344,296.4122687,11790086.55 CD38,588.2648306,659.9477137,12117.94539,318.883456,296.2994306,205.305051,492.6276527,1145.360549,696.5074435,224.0112245,1418.265485,867.2194289,237.7018615,90.8542014,12009.36242,642.6104871,65.77761797,3371.628895,814.6736615,1366.953817,298.7045221,400.4888321,11656598.5 CD1B,1351.157456,338.3307439,343.6898904,55.67758898,22.28653167,192.5532686,45.50400457,21.53089091,52.94106332,252.4165956,152.9052072,109.5758377,198.2817057,165.9539997,76.0826143,132.2193618,15969.53236,2349.595156,124.0396525,158.8890881,118.1030265,356.8836932,11420473.29 CD37,13247.62405,11610.29035,802.2248179,4472.430176,2997.964479,4287.536344,865.4159386,3602.317619,4831.809557,1823.236151,2090.502205,1604.568691,4508.112105,1453.500603,340.0169777,2337.797784,408.0617171,99.31070429,783.7092702,1104.720671,1908.23192,3204.567177,11287266.71 ITK,468.9985735,409.7558423,192.2179775,6471.889098,9578.936337,7704.627853,3954.854034,9006.839826,7176.363772,5787.063934,3056.935046,3981.741268,124.1014103,115.0222464,90.2244571,71.26574474,102.079759,166.1778128,1344.476541,1083.589895,654.6984165,380.4695205,10939343.48 IRF8,9506.386392,7886.092193,218.3135972,350.3493603,279.6879535,45.41225697,487.5663213,610.5494813,196.6158219,992.3568261,1625.377439,2594.872072,2458.705658,1929.656915,12117.94539,3495.416283,1652.355711,2798.258545,363.7262259,798.9447795,519.9851623,281.068165,10720584.7 CD79A,12117.94539,9835.923,3860.541888,425.4456431,183.3771612,161.6033691,97.84605147,437.0541054,424.1217962,196.7666923,149.4947867,139.2030869,174.974517,124.5658582,108.8170218,63.57950923,138.9136781,189.639752,61.91581828,118.775806,511.4149111,261.960019,10444715.03 AIF1,24.0742984,20.32185897,21.96957308,742.8158187,718.6147868,35.1722477,39.50876033,19.07768435,29.36703511,1086.959659,105.7345527,23.17042572,8655.680002,1841.269508,1611.228571,11610.29035,3285.640815,26.9238589,1851.124125,1672.630327,51.17646344,6260.640469,9639551.6 CD1A,1294.174017,818.7834717,504.0040168,208.3949257,135.3722907,191.4053509,139.7165139,156.4816454,511.8243369,265.2998497,152.5357864,145.9248694,312.0225877,224.9858071,160.945247,156.9391502,14772.22773,1315.229715,110.8168315,174.7119173,260.6061886,419.7944138,9556177.829 AQP9,21.34280658,15.11459289,15.66433549,13.67548268,14.1921014,14.8941249,13.11596785,18.66670647,64.26406026,188.8727485,93.46925864,12.57775597,2044.034835,4583.500675,4309.341662,399.4746192,1132.650087,1454.899429,576.553304,1876.266127,128.8196425,13705.68672,9345232.502 S100A12,190.663133,205.6096122,206.614297,103.0892269,122.0464109,78.96882194,77.60907212,114.3269206,93.856191,2375.556565,1088.208915,220.6263312,11149.65069,143.0161359,258.3004281,123.701236,132.8315959,138.8702312,1054.174516,976.5083429,374.4516108,8781.768452,8341561.669 C5AR1,92.39233333,64.88366132,25.374709,71.29814039,39.1758312,24.57422621,23.7998802,27.36083709,42.31424562,422.4027943,335.1748215,170.9594157,4237.561451,4989.998907,262.8037637,1920.668117,1195.953955,889.9578222,536.888673,756.9007984,2647.728393,12587.17469,8132008.071 CTSW,16.65790643,14.10507421,16.31812595,4894.215252,487.0683652,963.2767736,23.08754551,14.6382422,12.83317361,4144.004572,6980.778276,10811.41236,16.93731154,15.94992913,13.08202394,26.76355546,16.23954329,15.8711173,119.4688824,272.4656742,41.78979254,20.49140221,8092422.566 CD40,1493.347557,1328.267122,838.3890443,203.0571538,119.8051169,168.9573287,169.3131278,129.5124355,338.6601465,269.4014427,164.1619074,204.8063535,298.5733076,1146.591678,12966.60573,643.639203,1598.408075,4943.303284,111.7010328,416.0597275,204.0560124,275.5927662,7933804.564 SAMSN1,197.0884279,441.9005895,574.9216874,794.816235,609.2761383,1660.128114,2859.409338,577.4007393,1973.849465,1571.696585,1266.566015,2876.602126,1412.29406,645.6886884,5090.528151,3133.89778,2070.552906,7507.176097,6327.078194,4360.895256,11409.47833,1654.100485,7691510.826 KLRK1,670.2708554,280.4713279,77.84292347,8228.460511,353.8455751,276.2455792,69.14462123,105.1489388,44.32349787,5199.517616,8790.503534,5454.73273,78.91616578,44.14759366,38.56086836,62.47198947,71.21203215,47.2591819,88.01964485,77.98871131,441.4201255,246.4439838,7611505.666 HCK,273.5706061,489.3443473,130.5834263,106.4569423,107.2851313,80.31784648,81.79892574,43.76861448,75.08844559,737.406205,391.7033029,184.1658664,7368.002228,3576.179245,8146.586422,4811.755806,4370.59343,3772.369126,616.1411859,834.9287387,3730.856433,7443.161769,7591752.982 HDC,147.9736788,177.8451002,310.1156544,123.8596636,18.24228689,118.9595662,82.7123624,23.22265447,91.91206074,90.33473739,200.3498771,150.8083027,129.5522336,114.2614669,200.9810494,172.2889652,149.3724413,109.8186109,9299.383668,9389.503009,1119.899701,150.6140872,7312787.295 P2RX5,9725.14216,9116.674392,2524.75311,784.9884834,670.8311858,770.0178375,524.8550881,2876.602126,725.3438736,1108.125761,507.7460936,492.8199974,175.5409499,33.05027296,21.68739568,20.0115018,37.21278271,35.5352843,174.3664719,298.9924978,355.9021695,35.46460502,7289900.095 CD69,8246.962733,5037.059721,131.1507128,3798.407191,4397.041606,5489.202469,1071.887678,6081.160743,539.3938343,4572.324665,1781.265844,5471.704926,66.10288756,31.65121441,59.02137051,28.81247015,39.31238934,69.45698718,440.3983738,1484.634024,6363.149838,436.4221698,7244145.237 CCL18,19.93877192,21.68816978,56.60919591,35.31761702,24.35123147,12.82579482,81.202364,31.27670734,20.56828687,109.4946381,39.1384328,79.51319871,40.68134871,2820.01871,1252.527933,12334.37123,1697.959294,1749.786143,423.1104795,930.7824996,47.81945962,49.52876721,6996707.632 KLRF1,153.1504496,198.6098814,112.8639054,851.1875307,21.90774996,127.2371142,37.05749544,13.02444905,8.539704435,4962.361286,10475.7852,5911.213148,100.1145382,41.2903796,76.15970242,182.5359056,75.15348707,65.02829076,69.58226846,59.59794077,107.0253016,121.9079756,6882768.456 TCL1A,12117.94539,763.1449829,493.5179123,106.739179,133.6927662,105.523008,62.11998297,138.6741034,108.7230911,106.6455477,104.9994128,78.2920509,161.4604075,108.830925,109.218595,72.95993424,90.24888922,122.3968151,50.03226763,93.0399196,259.7561099,261.7385971,6517495.622 CD27,388.8197482,4138.587476,7350.586942,5506.313911,4042.377012,4188.175594,1125.960048,5710.976341,6979.725316,2288.252945,196.4705551,127.3316351,136.4091916,90.85495783,137.4401366,136.436149,125.1576644,111.9872056,123.9116687,89.47064836,468.4975465,300.1045211,6512829.201 PLA2G7,100.7324553,79.06118962,69.94065389,24.31875779,12.76669965,36.51263796,39.26542516,37.60063067,108.8451808,178.7417662,73.09936856,50.30565877,1290.202807,10066.60469,1679.064856,1569.463396,5706.822398,5086.100024,1067.947829,980.107544,43.5153618,59.09237491,6302165.145 BANK1,7633.960182,9177.359918,108.7748971,27.08870144,39.56449682,35.51276327,4.929861418,18.99250539,33.46526866,55.75942073,33.00482498,37.1948578,86.58724759,7.014183016,16.02027146,29.19263501,49.22937803,19.41137259,79.8154876,92.53939613,993.0314299,39.18510516,6084881.543 CD4,138.4573773,118.3512664,351.0163026,164.9509326,2495.269425,3710.379943,1725.414846,4363.016183,5524.681644,399.5375472,81.07568775,75.96955929,3343.906153,552.3052303,1574.336844,10105.34674,1340.751384,701.0986434,213.8663803,287.7837723,163.6390935,364.5571609,6029147.987 LY86,6947.982466,7763.972248,815.0088739,382.951912,398.8717851,198.5520941,176.7444504,208.4980914,379.3366843,1584.314572,243.1158973,224.0756082,5218.063407,1832.781159,1246.621776,6395.572444,1997.214217,601.1020453,297.4214468,221.121219,654.4709644,691.7851206,5882699.802 LST1,29.98420127,20.94422774,39.94821809,520.3780954,264.6015763,332.3730932,131.0995558,36.87140683,71.2981542,1394.203591,672.9594285,291.3221307,5975.189254,2191.478522,2243.545374,2286.079952,3691.087351,946.6004764,1224.997584,1641.972335,4186.037105,9953.015759,5851674.238 C3AR1,131.0995558,97.68302989,235.8730787,218.067024,100.57262,198.3433582,109.0856459,110.3968405,521.2960334,667.738611,753.7180287,473.1714371,1337.368233,799.6447121,4247.947251,2754.457112,879.0878468,259.2776033,4587.545998,4865.330497,9437.290602,746.04456,5421108.606 HLA-DQA1,942.7749076,1379.121703,161.7246112,173.4117383,122.6942485,106.9349988,167.0688665,89.17794598,621.2518682,356.7324528,219.2102706,214.9325691,275.3534268,874.2342868,4169.716387,5225.867112,7712.247914,6417.316621,190.2949934,390.0099509,68.2343922,218.2099668,5180082.249 IL4R,11009.29392,1883.66674,291.1304361,884.659355,2125.328314,1148.814515,864.1960215,414.2300341,3102.322518,913.5881164,851.30019,1609.901147,1192.215763,1054.288345,3007.592556,846.6140012,1093.752458,2410.484546,3106.475575,4652.787414,1117.612583,2809.100785,5146414.072 APOBEC3G,1283.451966,1135.293933,2034.562362,2090.85254,336.6456997,2340.674986,1540.772502,386.0535874,979.7203756,5415.054458,6314.770723,8895.302228,865.7405246,278.7681406,1036.993881,258.3004281,203.4477818,1393.358139,627.1314036,327.1943796,306.3062145,464.4412542,5056324.949 APOL3,222.6275107,530.3981335,754.8680347,1209.122376,950.7902718,1615.032254,1119.486881,351.1290483,993.4624966,1173.270474,1172.693097,1002.837652,1000.826958,238.5980956,11009.29392,437.5272083,219.6004846,861.4587391,264.675663,98.21606371,107.8051428,106.4245095,5044062.322 BIRC3,1762.323129,1072.239215,363.8164437,370.9307246,677.9807091,1121.424012,2888.707681,402.5623243,2449.370773,238.9246256,283.6871924,1173.96483,159.952492,230.7650134,1971.112539,237.5017836,442.7298273,10553.57544,71.25617244,1951.560424,1582.230325,132.8542597,4900164.674 FAM65B,4719.699579,5267.741473,237.5875518,3376.093302,4454.485846,2808.159439,256.5379971,665.2076215,910.8875823,2376.662113,1586.716996,696.5310083,2428.861545,92.82935445,193.0268711,86.87890188,136.2081634,126.2864186,187.4701407,170.8249765,2755.053484,8329.197426,4883561.326 SIK1,5471.704926,5620.453994,7279.249666,1219.200313,494.4265452,410.006561,162.0409056,4922.268476,942.2614617,393.1870068,457.6523281,248.2954165,544.8091055,146.1531348,292.9918215,111.2439257,104.8119808,228.1768122,59.70681574,326.9960366,2371.944991,401.5465651,4819312.539 LAT,157.4129632,133.1507052,111.4231016,3224.978584,5669.605245,3282.260737,1447.742457,8480.619457,3200.697142,2738.316414,1722.578313,1262.245177,239.5833162,603.6863585,162.813157,181.9526719,205.2650782,218.7467931,3738.758621,2477.465096,405.7028156,209.6546017,4681773.633 ADAMDEC1,371.0335929,318.478799,127.9674477,44.61628687,80.9645004,47.43351387,30.55297649,68.73789331,156.7076551,231.8272543,68.27758592,48.39924584,106.301336,4464.262133,8524.022801,5004.969419,164.8412494,1784.613941,730.1921708,1569.761722,175.1699108,185.5059228,4639552.337 CHI3L1,9.040054131,10.11185342,10.0823446,13.16060514,12.37533181,7.285693652,12.94364358,9.949791059,19.88900634,16.32499594,344.721706,59.67623931,12.16796964,8959.873277,3395.663462,2050.816822,89.88071419,473.9150298,477.0548529,636.7916524,89.33007873,3791.328311,4394335.214 RNASE6,1799.683549,1157.232625,2281.114952,17.05134667,28.13135229,64.4438491,8.863552717,22.17275389,11.37316837,777.2554078,84.58792101,14.36766267,4038.448716,2527.65046,306.1693936,5075.103934,8418.335804,853.6151668,681.3654348,506.4674568,144.0092188,2002.128163,4386652.486 CD8A,564.4780916,348.1087905,44.21285974,9990.737125,41.07506248,147.1712529,161.1817391,172.4731305,80.70305906,1127.953469,959.5951565,1190.400365,203.576442,135.9847653,45.89341104,63.47614462,176.6671569,163.4447696,87.086495,102.8346227,176.3456177,623.2298556,4379930.8 P2RY14,1638.857158,196.0477382,30.69853755,154.8611784,63.19960659,75.4038863,76.77594259,33.74435445,102.9533339,512.624542,214.6448472,215.2283477,51.7107481,7.681017154,21.35853068,852.5563739,214.7529288,24.7275648,2202.531209,1476.439279,9767.658851,1604.23718,4360356.071 CD1C,1349.225244,2899.133023,31.29737364,44.14189025,77.23468675,204.5100438,33.58018677,40.92391452,16.8510481,50.88172305,36.32319474,30.67802465,526.834127,29.85115497,22.85969301,167.5032595,9523.260691,1002.723527,32.85124884,34.92829111,24.63751709,258.2129965,4289321.52 CXCL11,90.26229276,71.79483122,44.79050636,22.77124293,6.633637977,14.33061258,52.00544678,23.48774462,42.51314662,42.34729139,28.53859717,183.3079432,38.41873667,16.90510228,9059.841342,29.35940998,21.12034731,3956.496269,27.84781811,33.7735536,33.86659948,74.22060498,4238228.818 -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/mixDaraShortValues.csv: -------------------------------------------------------------------------------- 1 | "","GeneSymbol","Mix1","Mix2","Mix3","Mix4","Mix5" 2 | "1","ACP5",1045.560791,1250.323739,1919.91837,1148.432534,3095.048627 3 | "2","ADAMDEC1",351.7709226,507.6668018,1011.75353,446.630648,1824.456385 4 | "3","AIF1",437.1365868,822.083209,1778.205196,1577.991266,3444.281038 5 | "4","APOBEC3G",1739.537757,1557.442405,3618.807544,939.5533036,1039.109981 6 | "5","APOL3",884.0795293,1161.822176,1390.973673,394.9373033,1323.166073 7 | "6","AQP9",137.7601396,358.8970485,881.803115,2622.415449,3763.160679 8 | "7","BANK1",2869.121643,1579.737722,38.59314444,2546.997381,173.3878292 9 | "8","BCL2A1",536.6568716,982.4683692,1436.843816,3722.277034,5164.021313 10 | "9","BIRC3",1067.363243,1211.281268,864.1732623,1251.031181,1288.861885 11 | "10","C3AR1",285.5756566,761.1002142,950.7378799,2058.587192,2907.443338 12 | "11","C5AR1",184.8956726,397.7411839,1073.428439,2667.495421,3673.442368 13 | "12","CCL18",199.5844181,161.7359153,741.4999988,339.1686473,1922.812838 14 | "13","CCL19",430.5917682,1412.785208,1748.353522,196.9080838,3179.596326 15 | "14","CCL22",91.89634803,263.3010587,692.7487906,1069.981592,2178.511981 16 | "15","CCL4",2974.398205,3372.934154,11504.36736,5034.534056,7392.630077 17 | "16","CCL5",2976.357645,2320.506676,8257.169142,1106.873895,3533.12846 18 | "17","CCR7",3297.292647,4029.248206,2136.519147,2132.000875,2769.159732 19 | "18","CD1A",535.6130974,1072.320301,515.411225,839.988698,1470.873735 20 | "19","CD1B",386.3737146,1018.732928,475.0169479,792.3539707,1555.700574 21 | "20","CD1C",749.6475138,707.1507806,294.9850223,861.7915354,910.5817174 22 | "21","CD2",4491.010824,3948.573535,4390.837772,1367.761758,1254.388781 23 | "22","CD247",3328.45929,3694.14186,7598.359202,771.4485043,1608.870819 24 | "23","CD27",3602.59341,2900.820219,1404.246745,1787.074063,553.7361201 25 | "24","CD37",6075.778917,4357.798294,2258.01783,5129.068446,1841.773543 26 | "25","CD38",2058.771164,3070.425498,1370.698309,1494.295122,1692.033666 27 | "26","CD3D",6123.141026,4679.324756,4022.950813,1827.359204,1250.928966 28 | "27","CD4",1307.604965,1513.315737,1662.78028,508.8447111,1844.074235 29 | "28","CD40",829.716493,1144.937743,1014.636614,797.6893247,1822.036307 30 | "29","CD69",4169.868928,2894.707397,2343.121654,3474.360732,1544.596561 31 | "30","CD79A",4393.288488,3267.003814,213.5131726,3718.601542,212.9665358 32 | "31","CD8A",1382.794537,252.7608113,656.3364216,898.0764307,304.9436662 33 | "32","CHI3L1",162.8406805,188.1950465,953.4865745,777.8945363,1771.924174 34 | "33","CLC",186.9276847,789.3263113,286.3565868,4586.063481,5126.618648 35 | "34","CPA3",394.1205809,926.1532376,750.9258866,3988.233574,3070.625912 36 | "35","CST7",1624.936464,1292.241405,5420.807862,1327.737823,2287.371585 37 | "36","CTSW",1143.566314,663.2163203,3577.425157,372.8736092,634.9995688 38 | "37","CXCL10",690.2686735,1605.492289,2155.580797,717.9277328,3681.523105 39 | "38","CXCL11",140.7553254,406.5512454,548.9367468,170.5659502,1053.603566 40 | "39","CXCL9",690.8953126,1826.557382,2223.509731,390.9729694,3858.828979 41 | "40","CXCR2",65.77048345,139.5368707,325.1531904,3087.949081,3707.773465 42 | "41","EBI3",232.845385,856.2659917,1126.11092,220.5310958,2228.147891 43 | "42","EMR1",388.0876638,884.6573089,613.9936483,2414.64371,3054.122567 44 | "43","FAIM3",5939.068467,4659.837224,1752.517878,3860.205139,648.049611 45 | "44","FAM65B",2919.618803,2217.698832,1185.922839,3629.396036,2364.491279 46 | "45","FCGR3B",216.7693251,234.0065663,306.2967974,4890.132853,5701.963368 47 | "46","FCN1",593.1959139,1655.658237,4112.670368,1083.661232,2146.981448 48 | "47","FPR1",417.1686908,858.9379203,1482.519025,5442.04966,6600.954396 49 | "48","GNLY",1681.330421,1807.532627,8667.134233,286.9932995,1489.527715 50 | "49","GUSBP11",7100.331199,7355.652878,436.3674454,5119.266732,170.9318363 51 | "50","GZMA",2346.604073,1401.496856,7968.466844,643.0780178,1574.657215 52 | "51","GZMB",2566.498589,3520.08042,8028.325622,943.8103,1704.779812 53 | "52","GZMH",1782.720162,1105.517462,4591.722624,571.2141853,954.2752736 54 | "53","GZMK",1998.226231,507.7935289,2347.055581,824.9577237,844.6672965 55 | "54","HCK",454.4943769,1103.64529,1951.060594,2198.798256,4244.600149 56 | "55","HDC",144.1657308,353.5446961,315.894784,1564.781075,1297.881897 57 | "56","HLA-DQA1",612.4058572,826.7356201,855.4301928,844.0504798,2066.805608 58 | "57","HPGDS",150.528005,578.0167477,526.195245,2896.545116,2274.004673 59 | "58","IDO1",531.6521244,1342.931522,1882.194687,1037.176992,3827.468731 60 | "59","IGHD",4934.922932,5935.254344,239.3391369,3774.165992,92.45912252 61 | "60","IGHM",10412.62838,10297.11001,562.1560335,8045.200517,213.1486204 62 | "61","IGKC",15007.15084,12599.43186,811.840831,11577.86801,369.1549548 63 | "62","IGLL3P",8264.555431,9546.209545,523.4823733,5684.863396,102.6522586 64 | "63","IL1B",309.7492553,424.9655176,686.4803813,2550.757818,2542.146607 65 | "64","IL2RB",3938.407325,4129.5192,9247.784478,1355.201796,2225.492136 66 | "65","IL4R",3033.202909,3392.825104,1344.437507,3552.527911,2000.509166 67 | "66","IL7R",5310.770862,4127.303595,2059.751364,2473.653761,1370.735098 68 | "67","IRF8",3462.454312,2892.082656,2089.404304,2973.185006,2233.776416 69 | "68","ITK",3591.16909,3204.123882,3285.941627,1334.033649,1047.330362 70 | "69","KLRB1",2831.92644,1310.936004,5201.882743,898.4771128,1219.97515 71 | "70","KLRF1",663.8829716,916.5229636,3385.471823,161.1711607,719.9689684 72 | "71","KLRK1",1590.266872,947.4890685,3125.495768,775.4204318,726.9104018 73 | "72","LAMP3",303.4476676,875.0596681,1312.351238,902.5808079,3008.310983 74 | "73","LAT",1956.259484,1757.694652,1809.996659,1016.274909,881.2685234 75 | "74","LCK",3695.346097,3006.147829,2993.658726,1161.5195,725.9136191 76 | "75","LST1",319.1331041,780.3544323,1448.977301,2591.68114,3840.595846 77 | "76","LTB",8941.888454,6764.266104,5837.266594,5766.912791,2620.357194 78 | "77","LY86",2926.711779,2088.113039,1326.296932,2567.923014,1532.613959 79 | "78","MMP12",296.1102411,903.9055881,532.9502012,967.6442164,1875.723482 80 | "79","MMP9",928.252259,606.9026239,3907.842971,2746.408005,6395.340581 81 | "80","MNDA",346.2319451,1101.058084,2280.703862,4685.962105,6650.45783 82 | "81","MS4A1",6232.029124,3795.155875,186.3633137,5395.230202,230.0865249 83 | "82","MS4A6A",759.0464809,1018.311224,1951.012832,877.7383843,2778.015853 84 | "83","MZB1",2516.354673,3643.858849,220.489098,1563.436694,33.28143194 85 | "84","NCF2",630.6920973,1710.029406,3456.840615,6077.241547,9799.009651 86 | "85","NKG7",1951.995483,1257.246393,6138.064234,626.8222774,1203.372291 87 | "86","P2RX5",4003.002509,2722.962771,586.6361499,3128.03059,218.639527 88 | "87","P2RY14",399.9815643,629.1130246,219.0024597,2029.545203,2035.464374 89 | "88","PLA2G7",209.0054697,438.0382184,1219.91812,476.0040752,1610.290382 90 | "89","PRF1",2959.715733,2634.185442,11171.48501,805.5706508,2192.136127 91 | "90","PRG2",117.1775333,504.481541,492.4372035,2923.587005,2350.853207 92 | "91","RNASE6",953.5546959,1365.73759,1108.285981,1310.013912,2006.291209 93 | "92","S100A12",350.488775,806.51058,1788.000049,1778.510878,2346.451465 94 | "93","SAMSN1",951.7462531,1537.375494,1958.667169,2947.771722,4122.614306 95 | "94","SELL",6657.594065,5699.49939,4224.123424,6559.582454,4261.687963 96 | "95","SIK1",3301.26614,2698.588688,670.9623891,2633.582703,625.6145996 97 | "96","TCL1A",2438.119029,2602.948741,106.285601,2309.174203,149.7291181 98 | "97","TNFAIP6",351.2758412,917.1692775,1222.692308,1182.480524,3159.091855 99 | "98","TPSAB1",483.6777667,938.4740416,899.4416512,4645.541518,3687.181673 100 | "99","TRAC",6621.304915,5450.21166,3878.048809,2163.541863,1327.31073 101 | "100","TRBC1",5292.929838,4996.389508,4628.921069,1518.854401,1354.847735 102 | "101","TRDC",1495.45384,1028.706702,5882.623928,323.7992965,1376.555923 103 | "102","VNN2",234.0583635,353.1118104,665.0680469,3245.701159,3880.392959 104 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/mixDataNormalized.csv: -------------------------------------------------------------------------------- 1 | "","Mix1","Mix2","Mix3","Mix4","Mix5" 2 | "1",-0.456151363586832,-0.413601352600512,-0.172718704316792,-0.567952468785709,0.49442109216837 3 | "2",-0.726397221291192,-0.75193607963122,-0.540385556523772,-0.929787531506813,-0.236854035345869 4 | "3",-0.693145485872464,-0.608696335981804,-0.230090708061002,-0.346480549379072,0.695417893065042 5 | "4",-0.185832627401985,-0.273686265536345,0.515069687549485,-0.675646436186817,-0.688851358125011 6 | "5",-0.519051737401658,-0.453920308311461,-0.386859819642909,-0.956439589580347,-0.5253660573085 7 | 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"1",2.6843995436096e-17,0.996062976839839 3 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/sigMat_dataNormalized.csv: -------------------------------------------------------------------------------- 1 | "","meanSigMatNorm","sdSigMatNorm" 2 | "1",-1.10252586561898e-17,0.995307760951483 3 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/svr/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/102_normalized/svr/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/102_normalized/svr/resultCoefs.csv: -------------------------------------------------------------------------------- 1 | "\",\"B cells naive","B cells memory","Plasma cells","T cells CD8","T cells CD4 naive","T cells CD4 memory resting","T cells CD4 memory activated","T cells follicular helper","T cells regulatory (Tregs)","T cells gamma delta","NK cells resting","NK cells activated","Monocytes","Macrophages M0","Macrophages M1","Macrophages M2","Dendritic cells resting","Dendritic cells activated","Mast cells resting","Mast cells activated","Eosinophils","Neutrophils","Pearson Correlation","P-value","RMSE" 2 | "Mix1",0,0,0.05,0.01,0,0.1,0.03,0,0.07,0.06,0,0.06,0.07,0.01,0.01,0.1,0.07,0,0.06,0.01,0.01,0.29,-0.291,0.188,0.0941710243024988 3 | "data...2.","data...3.","data...4.","data...5.","data...6.","data...7.","data...8.","data...9.","data...10.","data...11.","data...12.","data...13.","data...14.","data...15.","data...16.","data...17.","data...18.","data...19.","data...20.","data...21.","data...22.","data...23.","corrCoef","pvalCoef","RMSEcoef" 4 | "Mix2",0,0,0.02,0,0,0.1,0.05,0,0,0.03,0,0.05,0.08,0,0,0.1,0.26,0,0.12,0,0,0.19,-0.205,0.36,0.104250572267886 5 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25 | "\",\"nu","cost" 26 | "Mix5",0.25,64 27 | "\",\"nu","cost" 28 | "Mix5",0.25,64 29 | "\",\"nu","cost" 30 | "Mix5",0.25,64 31 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/CIBERSORT-ExampleDATA/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/CIBERSORT-ExampleDATA/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/CIBERSORT-ExampleDATA/ExampleMixtures-GroundTruth (2).csv: -------------------------------------------------------------------------------- 1 | ,B cells naive,B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils Mix1,0.18,0.16,0.13,0.11,0.09,0.07,0.06,0.05,0.04,0.03,0.02,0.02,0.01,0.01,0.01,0.01,0,0,0,0,0,0 Mix2,0.2,0,0.19,0,0.18,0,0.1,0,0.09,0,0.08,0,0.05,0,0.04,0,0.04,0,0.02,0,0.02,0 Mix3,0,0,0.01,0.01,0.01,0.02,0.03,0.05,0.07,0.11,0.17,0.17,0.11,0.07,0.05,0.03,0.02,0.01,0.01,0.01,0,0 Mix4,0.17,0.12,0.08,0.06,0.03,0.02,0.01,0,0,0,0,0,0,0,0,0.01,0.02,0.03,0.06,0.08,0.12,0.17 Mix5,0,0,0,0,0,0,0,0.01,0.03,0.04,0.03,0.02,0.02,0.04,0.09,0.11,0.08,0.05,0.04,0.07,0.14,0.2 -------------------------------------------------------------------------------- /CIBERSORT_data/Final/CIBERSORT-ExampleDATA/ExampleMixtures-GroundTruth .csv: -------------------------------------------------------------------------------- 1 | ,B cells naive,B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils Mix1,0.18,0.16,0.13,0.11,0.09,0.07,0.06,0.05,0.04,0.03,0.02,0.02,0.01,0.01,0.01,0.01,0,0,0,0,0,0 Mix2,0.2,0,0.19,0,0.18,0,0.1,0,0.09,0,0.08,0,0.05,0,0.04,0,0.04,0,0.02,0,0.02,0 Mix3,0,0,0.01,0.01,0.01,0.02,0.03,0.05,0.07,0.11,0.17,0.17,0.11,0.07,0.05,0.03,0.02,0.01,0.01,0.01,0,0 Mix4,0.17,0.12,0.08,0.06,0.03,0.02,0.01,0,0,0,0,0,0,0,0,0.01,0.02,0.03,0.06,0.08,0.12,0.17 Mix5,0,0,0,0,0,0,0,0.01,0.03,0.04,0.03,0.02,0.02,0.04,0.09,0.11,0.08,0.05,0.04,0.07,0.14,0.2 -------------------------------------------------------------------------------- /CIBERSORT_data/Final/CIBERSORT-ExampleDATA/svrCIBERSORT.R: -------------------------------------------------------------------------------- 1 | #svr <- function() { 2 | library(e1071) 3 | library(som) 4 | 5 | sigMat <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/CIBERSORT_ExampleDATA/LM22.csv") 6 | mixDataMain <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/CIBERSORT_ExampleDATA/ExampleMixtures-GEPs.csv") 7 | 8 | mixDataShort <- merge(mixDataMain, sigMat, by = "GeneSymbol") 9 | write.csv(mixDataShort, "mixDaraShort.csv") 10 | mixData <- mixDataShort[1:6] 11 | 12 | # mean, sd, normalize kust accept numeric variables. To convert a data frame to numeric matrix , data.matrix() function is needed 13 | #sigMat <- as.data.frame(normalize(data.matrix(sigMat), byrow = F)) 14 | #mixData <- as.data.frame(normalize(data.matrix(mixData), byrow = F)) 15 | 16 | #write.csv(sigMat,"sigMatNormalized.csv") 17 | #write.csv(mixData,"mixDataNormalized.csv") 18 | 19 | #print(head(sigMat)) 20 | #print(head(mixData)) 21 | 22 | Ref <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/ExampleMixtures-GroundTruth.csv") 23 | 24 | 25 | 26 | for (j in 1:5) { 27 | data = cbind(mixData[,j+1], sigMat[2:23]) 28 | 29 | 30 | sampleName = sprintf( "Mix%01d", j) 31 | fileName = paste("result_", sampleName, ".csv", sep = "") 32 | #print(class(data)) 33 | #print(head(data)) 34 | 35 | #svr_model3 <- svm( data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 36 | # +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 37 | # +data[,22] +data[,23] ,data ,scale = TRUE, type = "nu-regression") 38 | #print(svr_model3) 39 | nu = 0.5 40 | cost = 1 41 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 42 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 43 | +data[,22] +data[,23], data = data, type = "nu-regression", 44 | ranges = list(nu = seq(0.25, 0.75, 0.25), cost = 2^(2:9))) 45 | print(tuneResult) 46 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 47 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 48 | 49 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 50 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 51 | +data[,22] +data[,23], data = data, type = "nu-regression", 52 | ranges = list(nu = tuneResult$best.model$nu, cost = 2^(2:9))) 53 | 54 | print(tuneResult) 55 | 56 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 57 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 58 | 59 | 60 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 61 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 62 | +data[,22] +data[,23], data = data, type = "nu-regression", 63 | ranges = list(nu = seq(0.25, 0.75, 0.25), cost = tuneResult$best.model$cost)) 64 | 65 | print(tuneResult) 66 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 67 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 68 | 69 | 70 | tunedModel <- tuneResult$best.model 71 | #print(tunedModel) 72 | 73 | predicted_tunedModel <- predict(tunedModel, data) 74 | #print(head(predicted_tunedModel)) 75 | 76 | errorT <- data[,1] - predicted_tunedModel 77 | #print(errorT) 78 | 79 | tunedModelRMSE <- sqrt(mean(errorT^2)) 80 | #print(tunedModelRMSE) 81 | 82 | tst <- cor.test(data[,1], predicted_tunedModel) 83 | corr1 <- round(tst$estimate, 3) 84 | pval1 <- round(tst$p.value,3) 85 | 86 | resultT <- cbind(tunedModel$cost, tunedModel$nu, tuneResult$best.performance, tunedModelRMSE, corr1 , pval1 ) 87 | write.table( as.data.frame(resultT), file = "resultSamples.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"cost", "nu", "best.performance", "RMSE", "Pearson Correlation", "P-Value")) 88 | 89 | values <- cbind(data[,1], predicted_tunedModel, tst$estimate, corr1) 90 | write.table(as.data.frame(values), file = fileName, append=T, sep="," , col.names=c("\",\"Y","predictedY", "Pearson Correlation", "rounded Correlation")) 91 | 92 | coefTuned = matrix(0, ncol= 22, nrow = 1) 93 | coefTuned <- t(tunedModel$coefs) %*% tunedModel$SV 94 | # Set negative svr regression coefficients to zero 95 | coefTuned[coefTuned <0 ] <- 0 96 | #normalize the coefs 97 | coefTunedNormed <- round(coefTuned /sum(coefTuned), 2) 98 | 99 | 100 | 101 | 102 | tst_coef <- cor.test(as.numeric(Ref[j,2:23]), as.numeric(coefTunedNormed[1,])) 103 | corrCoef <- round(tst_coef$estimate, 3) 104 | pvalCoef <- round(tst_coef$p.value,3) 105 | 106 | errorCoef <- as.numeric(Ref[j,2:23])- as.numeric(coefTunedNormed[1,]) 107 | RMSEcoef <- sqrt(mean(errorCoef^2)) 108 | 109 | 110 | resultCoef <- cbind(coefTunedNormed, corrCoef, pvalCoef, RMSEcoef ) 111 | 112 | if (j ==1){ 113 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName 114 | , col.names=c("\",\"B cells naive","B cells memory","Plasma cells", "T cells CD8", "T cells CD4 naive" , "T cells CD4 memory resting" , "T cells CD4 memory activated", 115 | "T cells follicular helper", "T cells regulatory (Tregs)", "T cells gamma delta", "NK cells resting", "NK cells activated", "Monocytes", "Macrophages M0", 116 | "Macrophages M1", "Macrophages M2", "Dendritic cells resting", "Dendritic cells activated", "Mast cells resting", "Mast cells activated", "Eosinophils", "Neutrophils" 117 | ,"Pearson Correlation", "P-value", "RMSE")) 118 | }else{ 119 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName) 120 | 121 | } 122 | 123 | 124 | } 125 | 126 | 127 | 128 | #} -------------------------------------------------------------------------------- /CIBERSORT_data/Final/Normalize_merge_data.R: -------------------------------------------------------------------------------- 1 | # extract the LM22 genes from mixtureMatrix(merge ExampleMixtures-GEPs and LM22) and then normalize the LM22(sigMat) and the extracted mixData 2 | library(som) 3 | 4 | sigMat <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/Final/102_normalized/LM22-mostVar102.csv") 5 | mixDataMain <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/CIBERSORT-ExampleDATA/ExampleMixtures-GEPs.csv") 6 | 7 | mixDataShort <- merge(mixDataMain, sigMat, by = "GeneSymbol") 8 | write.csv(mixDataShort, "mixDaraShort.csv") 9 | mixData <- mixDataShort[1:6] 10 | write.csv(mixData, "mixDaraShortValues.csv") 11 | #write.csv(sigMat, "sigMatFinal.csv") 12 | 13 | 14 | sigMatNorm <- as.data.frame(normalize(data.matrix(sigMat[,2:23]), byrow = F)) 15 | write.csv(sigMatNorm, "sigMatNormalized.csv") 16 | 17 | meanSigMatNorm <-mean(data.matrix(sigMatNorm)) 18 | sdSigMatNorm <- sd(data.matrix(sigMatNorm)) 19 | dataSigMat <- cbind(meanSigMatNorm, sdSigMatNorm) 20 | write.csv(dataSigMat, "sigMat_dataNormalized.csv") 21 | 22 | mixDataNorm <- as.data.frame(normalize(data.matrix(mixData[, 2:6]), byrow = F)) 23 | write.csv(mixDataNorm, "mixDataNormalized.csv") 24 | 25 | meanmixDataNorm <-mean(data.matrix(mixDataNorm)) 26 | sdmixDataNorm <- sd(data.matrix(mixDataNorm)) 27 | datamixDataNorm <- cbind(meanmixDataNorm, sdmixDataNorm) 28 | write.csv(datamixDataNorm, "mixData_dataNormalized.csv") -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547-svr/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/all547-svr/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547-svr/mixDaraShort.csv: -------------------------------------------------------------------------------- 1 | "","GeneSymbol","Mix1","Mix2","Mix3","Mix4","Mix5","B.cells.naive","B.cells.memory","Plasma.cells","T.cells.CD8","T.cells.CD4.naive","T.cells.CD4.memory.resting","T.cells.CD4.memory.activated","T.cells.follicular.helper","T.cells.regulatory..Tregs.","T.cells.gamma.delta","NK.cells.resting","NK.cells.activated","Monocytes","Macrophages.M0","Macrophages.M1","Macrophages.M2","Dendritic.cells.resting","Dendritic.cells.activated","Mast.cells.resting","Mast.cells.activated","Eosinophils","Neutrophils" 2 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547-svr/mixDaraShortValues.csv: -------------------------------------------------------------------------------- 1 | "","GeneSymbol","Mix1","Mix2","Mix3","Mix4","Mix5" 2 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547-svr/resultCoefs.csv: -------------------------------------------------------------------------------- 1 | \,"\""B cells naive""",B cells memory,Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting,T cells CD4 memory activated,T cells follicular helper,T cells regulatory (Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells resting,Mast cells activated,Eosinophils,Neutrophils,Pearson Correlation,P-value,RMSE Mix1,0.1,0.08,0,0.08,0.04,0.08,0.18,0.09,0.09,0.07,0.01,0.05,0,0,0,0,0.06,0,0,0.02,0.04,0,0.468,0.028,0.052353692 Mix2,0.1,0.1,0.02,0.05,0.06,0.05,0.18,0.05,0.05,0.05,0.04,0.06,0.02,0,0,0.01,0.02,0,0,0.05,0.05,0.02,0.31,0.161,0.065261711 Mix3,0.04,0,0,0.12,0.01,0.04,0.11,0.2,0.2,0.13,0,0,0,0,0,0,0.01,0,0.05,0.07,0,0,0.032,0.887,0.080903983 Mix4,0.17,0.16,0.04,0.07,0,0.04,0.09,0.03,0.03,0.08,0.05,0.06,0.01,0,0,0,0.04,0,0.02,0.12,0,0,0.395,0.068,0.057721904 Mix5,0.13,0.13,0.01,0.06,0.02,0.02,0.04,0.08,0,0.04,0.06,0.05,0,0,0,0,0.02,0,0.12,0.21,0,0,-0.275,0.216,0.085652575 -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547-svr/resultSamples.csv: -------------------------------------------------------------------------------- 1 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 2 | "Mix1",512,0.25,1226104.99674745,1009.14077940231,0.697,0 3 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 4 | "Mix2",256,0.25,940422.075029077,885.090057096345,0.701,0 5 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 6 | "Mix3",256,0.25,920866.549485834,885.757462082625,0.771,0 7 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 8 | "Mix4",512,0.25,1027762.4528091,947.991649007944,0.607,0 9 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 10 | "Mix5",512,0.25,930039.988626004,888.272553488795,0.618,0 11 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547-svr/tuned.csv: -------------------------------------------------------------------------------- 1 | "\",\"nu","cost" 2 | "Mix1",0.25,512 3 | "\",\"nu","cost" 4 | "Mix1",0.25,512 5 | "\",\"nu","cost" 6 | "Mix1",0.25,512 7 | "\",\"nu","cost" 8 | "Mix2",0.25,512 9 | "\",\"nu","cost" 10 | "Mix2",0.25,256 11 | "\",\"nu","cost" 12 | "Mix2",0.25,256 13 | "\",\"nu","cost" 14 | "Mix3",0.25,512 15 | "\",\"nu","cost" 16 | "Mix3",0.25,256 17 | "\",\"nu","cost" 18 | "Mix3",0.25,256 19 | "\",\"nu","cost" 20 | "Mix4",0.25,512 21 | "\",\"nu","cost" 22 | "Mix4",0.25,512 23 | "\",\"nu","cost" 24 | "Mix4",0.25,512 25 | "\",\"nu","cost" 26 | "Mix5",0.25,512 27 | "\",\"nu","cost" 28 | "Mix5",0.25,512 29 | "\",\"nu","cost" 30 | "Mix5",0.25,512 31 | -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/all547_normalized/Normalized_data/.DS_Store -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547_normalized/Normalized_data/sigMat_dataNormalized.csv: -------------------------------------------------------------------------------- 1 | "","meanSigMatNorm","sdSigMatNorm" 2 | "1",4.2561467151185e-18,0.9991270186027 3 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547_normalized/resultCoefs.csv: -------------------------------------------------------------------------------- 1 | "\",\"B cells naive","B cells memory","Plasma cells","T cells CD8","T cells CD4 naive","T cells CD4 memory resting","T cells CD4 memory activated","T cells follicular helper","T cells regulatory (Tregs)","T cells gamma delta","NK cells resting","NK cells activated","Monocytes","Macrophages M0","Macrophages M1","Macrophages M2","Dendritic cells resting","Dendritic cells activated","Mast cells resting","Mast cells activated","Eosinophils","Neutrophils","Pearson Correlation","P-value","RMSE" 2 | "Mix1",0.1,0.08,0,0.08,0.04,0.08,0.18,0.09,0.09,0.07,0.01,0.05,0,0,0,0,0.06,0,0,0.02,0.04,0,0.468,0.028,0.0523536922375976 3 | "data...2.","data...3.","data...4.","data...5.","data...6.","data...7.","data...8.","data...9.","data...10.","data...11.","data...12.","data...13.","data...14.","data...15.","data...16.","data...17.","data...18.","data...19.","data...20.","data...21.","data...22.","data...23.","corrCoef","pvalCoef","RMSEcoef" 4 | "Mix2",0.1,0.1,0.02,0.05,0.06,0.05,0.18,0.05,0.05,0.05,0.04,0.06,0.02,0,0,0.01,0.02,0,0,0.05,0.05,0.02,0.31,0.161,0.0652617108961366 5 | "data...2.","data...3.","data...4.","data...5.","data...6.","data...7.","data...8.","data...9.","data...10.","data...11.","data...12.","data...13.","data...14.","data...15.","data...16.","data...17.","data...18.","data...19.","data...20.","data...21.","data...22.","data...23.","corrCoef","pvalCoef","RMSEcoef" 6 | "Mix3",0.05,0,0,0.12,0.01,0.07,0.1,0.21,0.21,0.11,0,0,0,0,0,0,0.02,0,0.03,0.08,0,0,0.006,0.98,0.0828196287669228 7 | "data...2.","data...3.","data...4.","data...5.","data...6.","data...7.","data...8.","data...9.","data...10.","data...11.","data...12.","data...13.","data...14.","data...15.","data...16.","data...17.","data...18.","data...19.","data...20.","data...21.","data...22.","data...23.","corrCoef","pvalCoef","RMSEcoef" 8 | "Mix4",0.17,0.16,0.04,0.07,0,0.04,0.09,0.03,0.03,0.08,0.05,0.06,0.01,0,0,0,0.04,0,0.02,0.12,0,0,0.395,0.068,0.057721903830506 9 | "data...2.","data...3.","data...4.","data...5.","data...6.","data...7.","data...8.","data...9.","data...10.","data...11.","data...12.","data...13.","data...14.","data...15.","data...16.","data...17.","data...18.","data...19.","data...20.","data...21.","data...22.","data...23.","corrCoef","pvalCoef","RMSEcoef" 10 | "Mix5",0.13,0.12,0.02,0.05,0.04,0,0.07,0.08,0,0.04,0.06,0.05,0,0.01,0,0,0.01,0,0.13,0.2,0,0,-0.306,0.166,0.0857586465293469 11 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547_normalized/resultSamples.csv: -------------------------------------------------------------------------------- 1 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 2 | "Mix1",512,0.25,0.633917791651072,0.722117964307176,0.697,0 3 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 4 | "Mix2",256,0.25,0.59664914731219,0.715282409633659,0.701,0 5 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 6 | "Mix3",512,0.25,0.4681260434275,0.602730455527377,0.804,0 7 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 8 | "Mix4",512,0.25,0.749852228377398,0.80264342238834,0.607,0 9 | "\",\"cost","nu","best.performance","RMSE","Pearson Correlation","P-Value" 10 | "Mix5",256,0.25,0.735128600151644,0.811162901699678,0.594,0 11 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/all547_normalized/tuned.csv: -------------------------------------------------------------------------------- 1 | "\",\"nu","cost" 2 | "Mix1",0.25,512 3 | "\",\"nu","cost" 4 | "Mix1",0.25,512 5 | "\",\"nu","cost" 6 | "Mix1",0.25,512 7 | "\",\"nu","cost" 8 | "Mix2",0.25,256 9 | "\",\"nu","cost" 10 | "Mix2",0.25,256 11 | "\",\"nu","cost" 12 | "Mix2",0.25,256 13 | "\",\"nu","cost" 14 | "Mix3",0.25,512 15 | "\",\"nu","cost" 16 | "Mix3",0.25,512 17 | "\",\"nu","cost" 18 | "Mix3",0.25,512 19 | "\",\"nu","cost" 20 | "Mix4",0.25,512 21 | "\",\"nu","cost" 22 | "Mix4",0.25,512 23 | "\",\"nu","cost" 24 | "Mix4",0.25,512 25 | "\",\"nu","cost" 26 | "Mix5",0.25,512 27 | "\",\"nu","cost" 28 | "Mix5",0.25,256 29 | "\",\"nu","cost" 30 | "Mix5",0.25,256 31 | -------------------------------------------------------------------------------- /CIBERSORT_data/Final/mixDataNormalizedByCol.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/mixDataNormalizedByCol.pdf -------------------------------------------------------------------------------- /CIBERSORT_data/Final/mixDataNormilizedbyRow.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/Final/mixDataNormilizedbyRow.pdf 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read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/Final/102_normalized/sigMatNormalized.csv") 8 | mixData <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/Final/102_normalized/mixDataNormalized.csv") 9 | 10 | Ref <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/ExampleMixtures-GroundTruth.csv") 11 | 12 | 13 | for (j in 1:5) { 14 | data = cbind(mixData[,j+1], sigMat[2:23]) 15 | 16 | 17 | sampleName = sprintf( "Mix%01d", j) 18 | fileName = paste("result_", sampleName, ".csv", sep = "") 19 | #print(class(data)) 20 | #print(head(data)) 21 | 22 | #svr_model3 <- svm( data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 23 | # +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 24 | # +data[,22] +data[,23] ,data ,scale = TRUE, type = "nu-regression") 25 | #print(svr_model3) 26 | nu = 0.5 27 | cost = 1 28 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 29 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 30 | +data[,22] +data[,23], data = data, type = "nu-regression", 31 | ranges = list(nu = seq(0.25, 0.75, 0.25), cost = 2^(2:9))) 32 | print(tuneResult) 33 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 34 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 35 | 36 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 37 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 38 | +data[,22] +data[,23], data = data, type = "nu-regression", 39 | ranges = list(nu = tuneResult$best.model$nu, cost = 2^(2:9))) 40 | 41 | print(tuneResult) 42 | 43 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 44 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 45 | 46 | 47 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 48 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 49 | +data[,22] +data[,23], data = data, type = "nu-regression", 50 | ranges = list(nu = seq(0.25, 0.75, 0.25), cost = tuneResult$best.model$cost)) 51 | 52 | print(tuneResult) 53 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 54 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 55 | 56 | 57 | tunedModel <- tuneResult$best.model 58 | #print(tunedModel) 59 | 60 | predicted_tunedModel <- predict(tunedModel, data) 61 | #print(head(predicted_tunedModel)) 62 | 63 | errorT <- data[,1] - predicted_tunedModel 64 | #print(errorT) 65 | 66 | tunedModelRMSE <- sqrt(mean(errorT^2)) 67 | #print(tunedModelRMSE) 68 | 69 | tst <- cor.test(data[,1], predicted_tunedModel) 70 | corr1 <- round(tst$estimate, 3) 71 | pval1 <- round(tst$p.value,3) 72 | 73 | resultT <- cbind(tunedModel$cost, tunedModel$nu, tuneResult$best.performance, tunedModelRMSE, corr1 , pval1 ) 74 | write.table( as.data.frame(resultT), file = "resultSamples.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"cost", "nu", "best.performance", "RMSE", "Pearson Correlation", "P-Value")) 75 | 76 | values <- cbind(data[,1], predicted_tunedModel, tst$estimate, corr1) 77 | write.table(as.data.frame(values), file = fileName, append=T, sep="," , col.names=c("\",\"Y","predictedY", "Pearson Correlation", "rounded Correlation")) 78 | 79 | coefTuned = matrix(0, ncol= 22, nrow = 1) 80 | coefTuned <- t(tunedModel$coefs) %*% tunedModel$SV 81 | # Set negative svr regression coefficients to zero 82 | coefTuned[coefTuned <0 ] <- 0 83 | #normalize the coefs 84 | coefTunedNormed <- round(coefTuned /sum(coefTuned), 2) 85 | 86 | 87 | 88 | 89 | tst_coef <- cor.test(as.numeric(Ref[j,2:23]), as.numeric(coefTunedNormed[1,])) 90 | corrCoef <- round(tst_coef$estimate, 3) 91 | pvalCoef <- round(tst_coef$p.value,3) 92 | 93 | errorCoef <- as.numeric(Ref[j,2:23])- as.numeric(coefTunedNormed[1,]) 94 | RMSEcoef <- sqrt(mean(errorCoef^2)) 95 | 96 | 97 | resultCoef <- cbind(coefTunedNormed, corrCoef, pvalCoef, RMSEcoef ) 98 | 99 | if (j ==1){ 100 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName 101 | , col.names=c("\",\"B cells naive","B cells memory","Plasma cells", "T cells CD8", "T cells CD4 naive" , "T cells CD4 memory resting" , "T cells CD4 memory activated", 102 | "T cells follicular helper", "T cells regulatory (Tregs)", "T cells gamma delta", "NK cells resting", "NK cells activated", "Monocytes", "Macrophages M0", 103 | "Macrophages M1", "Macrophages M2", "Dendritic cells resting", "Dendritic cells activated", "Mast cells resting", "Mast cells activated", "Eosinophils", "Neutrophils" 104 | ,"Pearson Correlation", "P-value", "RMSE")) 105 | }else{ 106 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName) 107 | 108 | } 109 | 110 | 111 | } 112 | 113 | 114 | 115 | #} -------------------------------------------------------------------------------- /CIBERSORT_data/margeCIBERSORTTables.R: -------------------------------------------------------------------------------- 1 | sigMat <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/LM22.csv") 2 | mixData <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/ExampleMixtures-GEPs.csv") 3 | 4 | head(sigMat$GeneSymbol) #no idea why it needs a dot between Gene and symbol, Rstudio found it out 5 | head(mixData$GeneSymbol) -------------------------------------------------------------------------------- /CIBERSORT_data/mixDataNormalized: -------------------------------------------------------------------------------- 1 | "","GeneSymbol","Mix1","Mix2","Mix3","Mix4","Mix5" 2 | "1",-1.70053638023888,-0.511330901622295,-0.455339425438102,-0.235202264991033,-0.581602666558699,0.50106480183949 3 | "2",-1.66674911691037,-0.751911857048172,-0.654178490141105,-0.294225691177394,-0.358431158633769,0.701378427507155 4 | "3",-1.61268949558476,-0.236920974516652,-0.312739218259502,0.472383747868971,-0.690123083791347,-0.678185781321674 5 | "4",-1.59241713758766,-0.870290237135932,-0.869243381213407,-0.667576457149254,0.18418549066152,0.884282233191769 6 | "5",-1.51132770559925,-0.151674646702965,-0.212757598506891,-0.565457532397942,-0.809953906436541,-1.06598398273002 7 | "6",-1.50457025293354,-0.712559896629501,-0.57970904362139,-0.436402439087254,0.7556039025055,1.68779148322997 8 | 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"95",1.59034306795759,-0.785862610597148,-0.610028477234046,-0.52559637475576,-0.563913487631444,0.537798881484065 97 | "96",1.6444026892832,-0.733508707650603,-0.600136327681172,-0.660230020610925,1.23527364672994,0.840701999264153 98 | "97",1.6511601419489,1.69341019727256,1.49473705627435,0.580357532078943,-0.0542161585326116,-0.512878853516721 99 | "98",1.71197721594021,1.16814880401686,1.28402002711977,0.893095241372616,-0.3891549278249,-0.497084107273714 100 | "99",1.71873466860592,-0.333435777101079,-0.558239831336934,1.41526150206067,-1.01002986262811,-0.48463266889632 101 | "100",1.83361136392284,-0.83221233268744,-0.871929561647772,-0.757846443579705,0.508005265250018,0.95152462625845 102 | "101",1.86739862725134,-0.292772736562602,-0.382937857221852,-0.231761150532369,-0.95520094014413,-1.03497413208824 103 | -------------------------------------------------------------------------------- /CIBERSORT_data/p_cibersortdata.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/CIBERSORT_data/p_cibersortdata.pptx -------------------------------------------------------------------------------- /CIBERSORT_data/svrCIBERSORT.R: -------------------------------------------------------------------------------- 1 | #svr <- function() { 2 | library(e1071) 3 | library(som) 4 | 5 | sigMat <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/forConditionNumber/LM22_102.csv") 6 | mixDataMain <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/mixDataMerged.csv") 7 | 8 | mixDataShort <- merge(mixDataMain, sigMat, by = "GeneSymbol") 9 | write.csv(mixDataShort, "mixDaraShort.csv") 10 | mixData <- mixDataShort[1:6] 11 | 12 | # mean, sd, normalize kust accept numeric variables. To convert a data frame to numeric matrix , data.matrix() function is needed 13 | #sigMat <- as.data.frame(normalize(data.matrix(sigMat), byrow = F)) 14 | #mixData <- as.data.frame(normalize(data.matrix(mixData), byrow = F)) 15 | 16 | #write.csv(sigMat,"sigMatNormalized.csv") 17 | #write.csv(mixData,"mixDataNormalized.csv") 18 | 19 | #print(head(sigMat)) 20 | #print(head(mixData)) 21 | 22 | Ref <- read.csv("/Users/mitra/Desktop/cellType-R/CIBERSORT_data/ExampleMixtures-GroundTruth.csv") 23 | 24 | 25 | 26 | for (j in 1:5) { 27 | data = cbind(mixData[,j+1], sigMat[2:23]) 28 | 29 | 30 | sampleName = sprintf( "Mix%01d", j) 31 | fileName = paste("result_", sampleName, ".csv", sep = "") 32 | #print(class(data)) 33 | #print(head(data)) 34 | 35 | #svr_model3 <- svm( data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 36 | # +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 37 | # +data[,22] +data[,23] ,data ,scale = TRUE, type = "nu-regression") 38 | #print(svr_model3) 39 | nu = 0.5 40 | cost = 1 41 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 42 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 43 | +data[,22] +data[,23], data = data, type = "nu-regression", 44 | ranges = list(nu = seq(0.25, 0.75, 0.25), cost = 2^(2:9))) 45 | print(tuneResult) 46 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 47 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 48 | 49 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 50 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 51 | +data[,22] +data[,23], data = data, type = "nu-regression", 52 | ranges = list(nu = tuneResult$best.model$nu, cost = 2^(2:9))) 53 | 54 | print(tuneResult) 55 | 56 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 57 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 58 | 59 | 60 | tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 61 | +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 62 | +data[,22] +data[,23], data = data, type = "nu-regression", 63 | ranges = list(nu = seq(0.25, 0.75, 0.25), cost = tuneResult$best.model$cost)) 64 | 65 | print(tuneResult) 66 | res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 67 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","cost")) 68 | 69 | 70 | tunedModel <- tuneResult$best.model 71 | #print(tunedModel) 72 | 73 | predicted_tunedModel <- predict(tunedModel, data) 74 | #print(head(predicted_tunedModel)) 75 | 76 | errorT <- data[,1] - predicted_tunedModel 77 | #print(errorT) 78 | 79 | tunedModelRMSE <- sqrt(mean(errorT^2)) 80 | #print(tunedModelRMSE) 81 | 82 | tst <- cor.test(data[,1], predicted_tunedModel) 83 | corr1 <- round(tst$estimate, 3) 84 | pval1 <- round(tst$p.value,3) 85 | 86 | resultT <- cbind(tunedModel$cost, tunedModel$nu, tuneResult$best.performance, tunedModelRMSE, corr1 , pval1 ) 87 | write.table( as.data.frame(resultT), file = "resultSamples.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"cost", "nu", "best.performance", "RMSE", "Pearson Correlation", "P-Value")) 88 | 89 | values <- cbind(data[,1], predicted_tunedModel, tst$estimate, corr1) 90 | write.table(as.data.frame(values), file = fileName, append=T, sep="," , col.names=c("\",\"Y","predictedY", "Pearson Correlation", "rounded Correlation")) 91 | 92 | coefTuned = matrix(0, ncol= 22, nrow = 1) 93 | coefTuned <- t(tunedModel$coefs) %*% tunedModel$SV 94 | # Set negative svr regression coefficients to zero 95 | coefTuned[coefTuned <0 ] <- 0 96 | #normalize the coefs 97 | coefTunedNormed <- round(coefTuned /sum(coefTuned), 2) 98 | 99 | 100 | 101 | 102 | tst_coef <- cor.test(as.numeric(Ref[j,2:23]), as.numeric(coefTunedNormed[1,])) 103 | corrCoef <- round(tst_coef$estimate, 3) 104 | pvalCoef <- round(tst_coef$p.value,3) 105 | 106 | errorCoef <- as.numeric(Ref[j,2:23])- as.numeric(coefTunedNormed[1,]) 107 | RMSEcoef <- sqrt(mean(errorCoef^2)) 108 | 109 | 110 | resultCoef <- cbind(coefTunedNormed, corrCoef, pvalCoef, RMSEcoef ) 111 | 112 | if (j ==1){ 113 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName 114 | , col.names=c("\",\"B cells naive","B cells memory","Plasma cells", "T cells CD8", "T cells CD4 naive" , "T cells CD4 memory resting" , "T cells CD4 memory activated", 115 | "T cells follicular helper", "T cells regulatory (Tregs)", "T cells gamma delta", "NK cells resting", "NK cells activated", "Monocytes", "Macrophages M0", 116 | "Macrophages M1", "Macrophages M2", "Dendritic cells resting", "Dendritic cells activated", "Mast cells resting", "Mast cells activated", "Eosinophils", "Neutrophils" 117 | ,"Pearson Correlation", "P-value", "RMSE")) 118 | }else{ 119 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName) 120 | 121 | } 122 | 123 | 124 | } 125 | 126 | 127 | 128 | #} -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CIBERSORT 2 | -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/codes/1.produceSignature Matrix/.DS_Store -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/SigMatFinall.R: -------------------------------------------------------------------------------- 1 | # raed the cell-sorted-data files and produce the signature matrix (significant probes for each cell type) 2 | 3 | typesAvgFiles() 4 | pvalCalculation() 5 | effectSize() 6 | makeSigMat2() -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/calculateAdjPval.R: -------------------------------------------------------------------------------- 1 | 2 | #read the separated file for each cell type, number of probes x number of samples, and calculate the pairwise adjusted p-value between cell types and return the ones for each cell type in a separate file (PvalB, PvalG, ...) 3 | 4 | pvalCalculation <- function(directory = "/Users/mitra/Desktop/cellType-R/new-24July", sampleNo = 7 ) { 5 | #sourceFile = "/Users/mitra/Desktop/work/cell_sorted_data_sortedByName.csv") 6 | #LM7<- read.csv("cell_sorted_data_sortedByName.csv") 7 | 8 | PvalB <- 0 9 | PvalCD4T <- 0 10 | PvalCD8T <- 0 11 | PvalG <- 0 12 | PvalMo <- 0 13 | PvalNK <- 0 14 | PvalRBC <- 0 15 | 16 | i <- 0 17 | 18 | B <- read.csv(paste(directory,"/B.csv", sep = "")) 19 | CD4T <- read.csv(paste(directory,"/CD4T.csv", sep = "")) 20 | CD8T <- read.csv(paste(directory,"/CD8T.csv", sep = "")) 21 | G <- read.csv(paste(directory,"/G.csv", sep = "")) 22 | Mo <- read.csv(paste(directory,"/Mo.csv", sep = "")) 23 | NK <- read.csv(paste(directory,"/NK.csv", sep = "")) 24 | RBC <- read.csv(paste(directory,"/RBC.csv", sep = "")) 25 | 26 | #print(head(B)) 27 | 28 | 29 | for (i in 1:nrow(B)){ 30 | # for (i in 1:nrow(B)){ 31 | 32 | b = 0 33 | 34 | b5 <- t.test(B[i, 2:8], CD4T[i, 2:8])$p.value 35 | b6 <- t.test(B[i, 2:8], CD8T[i,2:7])$p.value 36 | b2 <- t.test(B[i, 2:8], G[i, 2:8])$p.value 37 | b1 <- t.test(B[i, 2:8], Mo[i, 2:8])$p.value 38 | b4 <- t.test(B[i, 2:8], NK[i, 2:8])$p.value 39 | b3 <- t.test(B[i, 2:8], RBC[i, 2:8])$p.value 40 | 41 | b <- cbind(b1,b2,b3,b4,b5,b6) 42 | b <- p.adjust(b, method = "bonferroni") 43 | 44 | print(b) 45 | if (i==1) {PvalB<-b} else{ 46 | PvalB <- rbind(PvalB,b) 47 | } 48 | 49 | 50 | cd4t = 0 51 | 52 | cd4t5 <- t.test(CD4T[i, 2:8],B[i, 2:8])$p.value 53 | 54 | cd4t6 <- t.test(CD4T[i, 2:8], CD8T[i, 2:7])$p.value 55 | cd4t2 <- t.test(CD4T[i, 2:8], G[i, 2:8])$p.value 56 | cd4t1 <- t.test(CD4T[i, 2:8], Mo[i, 2:8])$p.value 57 | cd4t4 <- t.test(CD4T[i, 2:8], NK[i, 2:8])$p.value 58 | cd4t3 <- t.test(CD4T[i, 2:8], RBC[i, 2:8])$p.value 59 | 60 | cd4t <- cbind(cd4t1,cd4t2,cd4t3,cd4t4,cd4t5,cd4t6) 61 | cd4t <- p.adjust(cd4t, method = "bonferroni") 62 | print(cd4t) 63 | if (i==1) {PvalCD4T<-cd4t} else{ 64 | PvalCD4T <- rbind(PvalCD4T,cd4t) 65 | } 66 | 67 | 68 | cd8t <- 0 69 | cd8t5 <- t.test(CD8T[i, 2:7],B[i, 2:8])$p.value 70 | cd8t6 <- t.test(CD8T[i, 2:7], CD4T[i, 2:8])$p.value 71 | cd8t2 <- t.test(CD8T[i, 2:7], G[i, 2:8])$p.value 72 | cd8t1 <- t.test(CD8T[i, 2:7], Mo[i, 2:8])$p.value 73 | cd8t4 <- t.test(CD8T[i, 2:7], NK[i, 2:8])$p.value 74 | cd8t3 <- t.test(CD8T[i, 2:7], RBC[i, 2:8])$p.value 75 | 76 | cd8t <- cbind(cd8t1,cd8t2,cd8t3,cd8t4,cd8t5,cd8t6) 77 | cd8t <- p.adjust(cd8t, method = "bonferroni") 78 | print(cd8t) 79 | if (i==1) {PvalCD8T<-cd8t} else{ 80 | PvalCD8T <- rbind(PvalCD8T,cd8t) 81 | } 82 | 83 | 84 | g <- 0 85 | g4 <- t.test(G[i, 2:8], B[i, 2:8])$p.value 86 | 87 | g5 <- t.test(G[i, 2:8], CD4T[i, 2:8])$p.value 88 | g6 <- t.test(G[i, 2:8], CD8T[i, 2:7])$p.value 89 | g1 <- t.test(G[i, 2:8], Mo[i, 2:8])$p.value 90 | g3 <- t.test(G[i, 2:8], NK[i, 2:8])$p.value 91 | g2 <- t.test(G[i, 2:8], RBC[i, 2:8])$p.value 92 | 93 | g <- cbind(g1,g2,g3,g4,g5,g6) 94 | g <- p.adjust(g, method = "bonferroni") 95 | print(g) 96 | if (i==1) {PvalG<-g} else{ 97 | PvalG <- rbind(PvalG,g) 98 | } 99 | 100 | 101 | mo <- 0 102 | mo4 <- t.test(Mo[i, 2:8], B[i, 2:8])$p.value 103 | mo5 <- t.test(Mo[i, 2:8], CD4T[i, 2:8])$p.value 104 | mo6 <- t.test(Mo[i, 2:8], CD8T[i, 2:7])$p.value 105 | mo1 <- t.test(Mo[i, 2:8], G[i, 2:8])$p.value 106 | mo3 <- t.test(Mo[i, 2:8], NK[i, 2:8])$p.value 107 | mo2 <- t.test(Mo[i, 2:8], RBC[i, 2:8])$p.value 108 | 109 | mo <- cbind(mo1,mo2,mo3,mo4,mo5,mo6) 110 | mo <- p.adjust(mo, method = "bonferroni") 111 | print(mo) 112 | if (i==1) {PvalMo<-mo} else{ 113 | PvalMo <- rbind(PvalMo,mo) 114 | } 115 | 116 | 117 | nk <- 0 118 | nk4 <- t.test(NK[i, 2:8], B[i, 2:8])$p.value 119 | nk5 <- t.test(NK[i, 2:8], CD4T[i, 2:8])$p.value 120 | nk6 <- t.test(NK[i, 2:8], CD8T[i, 2:7])$p.value 121 | nk2 <- t.test(NK[i, 2:8], G[i, 2:8])$p.value 122 | nk1 <- t.test(NK[i, 2:8], Mo[i, 2:8])$p.value 123 | nk3 <- t.test(NK[i, 2:8], RBC[i, 2:8])$p.value 124 | 125 | nk <- cbind(nk1,nk2,nk3,nk4,nk5,nk6) 126 | nk <- p.adjust(nk, method = "bonferroni") 127 | print(nk) 128 | if (i==1) {PvalNK<-nk} else{ 129 | PvalNK <- rbind(PvalNK,nk) 130 | } 131 | 132 | 133 | rbc4 <- t.test(RBC[i, 2:8], B[i, 2:8])$p.value 134 | rbc5 <- t.test(RBC[i, 2:8], CD4T[i, 2:8])$p.value 135 | rbc6 <- t.test(RBC[i, 2:8], CD8T[i, 2:7])$p.value 136 | rbc2 <- t.test(RBC[i, 2:8], G[i, 2:8])$p.value 137 | rbc1 <- t.test(RBC[i, 2:8], Mo[i, 2:8])$p.value 138 | rbc3 <- t.test(RBC[i, 2:8], NK[i, 2:8])$p.value 139 | 140 | rbc <- cbind(rbc1,rbc2,rbc3,rbc4,rbc5,rbc6) 141 | rbc <- p.adjust(rbc, method = "bonferroni") 142 | print(b) 143 | if (i==1) {PvalRBC<-rbc} else{ 144 | PvalRBC <- rbind(PvalRBC,rbc) 145 | } 146 | 147 | } 148 | 149 | 150 | 151 | 152 | #print(PvalB) 153 | write.csv(PvalMo, file = "PvalMo.csv") 154 | write.csv(PvalG, file = "PvalG.csv") 155 | write.csv(PvalRBC, file = "PvalRBC.csv") 156 | write.csv(PvalNK, file = "PvalNK.csv") 157 | write.csv(PvalB, file = "PvalB.csv") 158 | write.csv(PvalCD4T, file = "PvalCD4T.csv") 159 | write.csv(PvalCD8T, file = "PvalCD8T.csv") 160 | #PvalMin <- cbind(min(PvalMo), min(PvalG), min(PvalRBC), min(PvalNK), min(PvalB), min(PvalCD4T), min(PvalCD8T)) 161 | # write.csv(PvalMin, "PvalMin.csv") 162 | 163 | 164 | } 165 | 166 | 167 | 168 | 169 | 170 | 171 | -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/calculateEffectSize.R: -------------------------------------------------------------------------------- 1 | #read the files with the average values for each cell types, and return the effect size matrixes for them 2 | 3 | effectSize <- function( sourceFile = "/Users/mitra/Desktop/work/cell_sorted_data_sortedByName.csv",directory = "/Users/mitra/Desktop/cellType-R/new-24July", cellTypeNo = 7) { 4 | #LM7<- read.csv(sourceFile) 5 | 6 | effectSizeB <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 7 | effectSizeCD4T <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 8 | effectSizeCD8T <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 9 | effectSizeG <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 10 | effectSizeMo <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 11 | effectSizeNK <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 12 | effectSizeRBC <- matrix(0, nrow= nrow(LM7), ncol = (cellTypeNo - 1)) 13 | 14 | #print(head(effectSizeB)) 15 | 16 | i <- 0 17 | 18 | 19 | 20 | #print(head(B)) 21 | 22 | 23 | avgMo <- read.csv(paste(directory,"/avgMo.csv", sep = "")) 24 | avgG <- read.csv(paste(directory,"/avgG.csv", sep = "")) 25 | avgRBC <- read.csv(paste(directory,"/avgRBC.csv", sep = "")) 26 | avgNK <- read.csv(paste(directory,"/avgNK.csv", sep = "")) 27 | avgB <- read.csv(paste(directory,"/avgB.csv", sep = "")) 28 | avgCD4T <- read.csv(paste(directory,"/avgCD4T.csv", sep = "")) 29 | avgCD8T <- read.csv(paste(directory,"/avgCD8T.csv", sep = "")) 30 | 31 | print(head(avgB)) 32 | 33 | 34 | 35 | for (i in 1:nrow(LM7)){ 36 | 37 | print(i) 38 | 39 | b = 0 40 | 41 | b5 <- avgB[i, 2 ]- avgCD4T[i, 2 ] 42 | b6 <- avgB[i, 2 ]- avgCD8T[i, 2 ] 43 | b2 <- avgB[i, 2 ]- avgG[i, 2 ] 44 | b1 <- avgB[i, 2 ]- avgMo[i, 2 ] 45 | b4 <- avgB[i, 2 ]- avgNK[i, 2 ] 46 | b3 <- avgB[i, 2 ]- avgRBC[i, 2 ] 47 | 48 | #print("b1") 49 | print(head(b1)) 50 | 51 | b <- cbind(b1,b2,b3,b4,b5,b6) 52 | 53 | #print(i) 54 | #print("hereB") 55 | #print(head(effectSizeB)) 56 | 57 | if (i==1) {effectSizeB<-b} else{ 58 | effectSizeB <- rbind(effectSizeB,b) 59 | } 60 | 61 | #print(class(effectSizeB)) 62 | #print(dim(effectSizeB)) 63 | print(head(effectSizeB)) 64 | 65 | cd4t = 0 66 | 67 | cd4t5 <- avgCD4T[i, 2 ]- avgB[i, 2 ] 68 | cd4t6 <- avgCD4T[i, 2 ]- avgCD8T[i, 2 ] 69 | cd4t2 <- avgCD4T[i, 2 ]- avgG[i, 2 ] 70 | cd4t1 <- avgCD4T[i, 2 ]- avgMo[i, 2 ] 71 | cd4t4 <- avgCD4T[i, 2 ]- avgNK[i, 2 ] 72 | cd4t3 <- avgCD4T[i, 2 ]- avgRBC[i, 2 ] 73 | 74 | cd4t <- cbind(cd4t1,cd4t2,cd4t3,cd4t4,cd4t5,cd4t6) 75 | 76 | if (i==1) {effectSizeCD4T<-cd4t} else{ 77 | effectSizeCD4T <- rbind(effectSizeCD4T,cd4t) 78 | } 79 | 80 | #print(i) 81 | #print("hereCD4T") 82 | #print(head(effectSizeCD4T)) 83 | 84 | 85 | cd8t = 0 86 | 87 | cd8t5 <- avgCD8T[i, 2 ]- avgCD4T[i, 2 ] 88 | cd8t6 <- avgCD8T[i, 2 ]- avgB[i, 2 ] 89 | cd8t2 <- avgCD8T[i, 2 ]- avgG[i, 2 ] 90 | cd8t1 <- avgCD8T[i, 2 ]- avgMo[i, 2 ] 91 | cd8t4 <- avgCD8T[i, 2 ]- avgNK[i, 2 ] 92 | cd8t3 <- avgCD8T[i, 2 ]- avgRBC[i, 2 ] 93 | 94 | cd8t <- cbind(cd8t1,cd8t2,cd8t3,cd8t4,cd8t5,cd8t6) 95 | 96 | if (i==1) {effectSizeCD8T<-cd8t} else{ 97 | effectSizeCD8T <- rbind(effectSizeCD8T,cd8t) 98 | } 99 | 100 | print(i) 101 | #print("hereCD8T") 102 | print(head(effectSizeCD8T)) 103 | 104 | 105 | g = 0 106 | 107 | g5 <- avgG[i, 2 ]- avgCD4T[i, 2 ] 108 | g6 <- avgG[i, 2 ]- avgCD8T[i, 2 ] 109 | g2 <- avgG[i, 2 ]- avgB[i, 2 ] 110 | g1 <- avgG[i, 2 ]- avgMo[i, 2 ] 111 | g4 <- avgG[i, 2 ]- avgNK[i, 2 ] 112 | g3 <- avgG[i, 2 ]- avgRBC[i, 2 ] 113 | 114 | g <- cbind(g1,g2,g3,g4,g5,g6) 115 | 116 | if (i==1) {effectSizeG<-g} else{ 117 | effectSizeG <- rbind(effectSizeG,g) 118 | } 119 | 120 | print(i) 121 | #print("hereG") 122 | print(head(effectSizeG)) 123 | 124 | 125 | nk = 0 126 | 127 | nk5 <- avgNK[i, 2 ]- avgCD4T[i, 2 ] 128 | nk6 <- avgNK[i, 2 ]- avgCD8T[i, 2 ] 129 | nk2 <- avgNK[i, 2 ]- avgG[i, 2 ] 130 | nk1 <- avgNK[i, 2 ]- avgMo[i, 2 ] 131 | nk4 <- avgNK[i, 2 ]- avgB[i, 2 ] 132 | nk3 <- avgNK[i, 2 ]- avgRBC[i, 2 ] 133 | 134 | nk <- cbind(nk1,nk2,nk3,nk4,nk5,nk6) 135 | 136 | if (i==1) {effectSizeNK<-nk} else{ 137 | effectSizeNK <- rbind(effectSizeNK,nk) 138 | } 139 | print(i) 140 | #print("hereNK") 141 | print(head(effectSizeNK)) 142 | 143 | 144 | 145 | rbc = 0 146 | 147 | rbc5 <- avgRBC[i, 2 ]- avgCD4T[i, 2 ] 148 | rbc6 <- avgRBC[i, 2 ]- avgCD8T[i, 2 ] 149 | rbc2 <- avgRBC[i, 2 ]- avgG[i, 2 ] 150 | rbc1 <- avgRBC[i, 2 ]- avgMo[i, 2 ] 151 | rbc4 <- avgRBC[i, 2 ]- avgNK[i, 2 ] 152 | rbc3 <- avgRBC[i, 2 ]- avgB[i, 2 ] 153 | 154 | rbc <- cbind(rbc1,rbc2,rbc3,rbc4,rbc5,rbc6) 155 | 156 | if (i==1) {effectSizeRBC<-rbc} else{ 157 | effectSizeRBC <- rbind(effectSizeRBC,rbc) 158 | } 159 | 160 | #print(i) 161 | #print("hereRBC") 162 | #print(head(effectSizeRBC)) 163 | 164 | 165 | mo = 0 166 | 167 | mo5 <- avgMo[i, 2 ]- avgCD4T[i, 2 ] 168 | mo6 <- avgMo[i, 2 ]- avgCD8T[i, 2 ] 169 | mo2 <- avgMo[i, 2 ]- avgG[i, 2 ] 170 | mo1 <- avgMo[i, 2 ]- avgB[i, 2 ] 171 | mo4 <- avgMo[i, 2 ]- avgNK[i, 2 ] 172 | mo3 <- avgMo[i, 2 ]- avgRBC[i, 2 ] 173 | 174 | mo <- cbind(mo1,mo2,mo3,mo4,mo5,mo6) 175 | 176 | if (i==1) {effectSizeMo<-mo} else{ 177 | effectSizeMo <- rbind(effectSizeMo,mo) 178 | } 179 | 180 | print(i) 181 | #print("hereMo") 182 | print(head(effectSizeMo)) 183 | 184 | 185 | } 186 | 187 | 188 | 189 | 190 | 191 | write.csv(effectSizeMo, file = "effectSizeMo.csv") 192 | write.csv(effectSizeG, file = "effectSizeG.csv") 193 | write.csv(effectSizeRBC, file = "effectSizeRBC.csv") 194 | write.csv(effectSizeNK, file = "effectSizeNK.csv") 195 | write.csv(effectSizeB, file = "effectSizeB.csv") 196 | write.csv(effectSizeCD4T, file = "effectSizeCD4T.csv") 197 | write.csv(effectSizeCD8T, file = "effectSizeCD8T.csv") 198 | 199 | 200 | } 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/produceSigMat.R: -------------------------------------------------------------------------------- 1 | 2 | #read the Pvalue, effectSize and average value files. 3 | #check the threshold for Pvalues and effect size apirwise between cell types for each probe 4 | #every probe which passes both threshold will be save in signature matrix with its average (between samples) values (between samples) 5 | 6 | makeSigMat <- function(sourceFile = "/Users/mitra/Desktop/work/cell_sorted_data_sortedByName.csv", directory = "/Users/mitra/Desktop/cellType-R/new-24July", cellTypeNo = 7) { 7 | 8 | 9 | # probe has to be significant in all pairwise comparisons. 10 | # P-value threshold < 10-4 11 | # Effect size threshold: average difference >0.15 12 | 13 | #LM7<- read.csv(sourceFile) 14 | 15 | PvalMo <- read.csv(paste(directory,"/PvalMo.csv", sep = "")) 16 | PvalG <- read.csv(paste(directory,"/PvalG.csv", sep = "")) 17 | PvalRBC <- read.csv(paste(directory,"/PvalRBC.csv", sep = "")) 18 | PvalNK <- read.csv(paste(directory,"/PvalNK.csv", sep = "")) 19 | PvalB <- read.csv(paste(directory,"/PvalB.csv", sep = "")) 20 | PvalCD4T <- read.csv(paste(directory,"/PvalCD4T.csv", sep = "")) 21 | PvalCD8T <- read.csv(paste(directory,"/PvalCD8T.csv", sep = "")) 22 | 23 | #print(head(PvalMo)) 24 | 25 | effectSizeMo <- read.csv(paste(directory,"/effectSizeMo.csv", sep = "")) 26 | effectSizeG <- read.csv(paste(directory,"/effectSizeG.csv", sep = "")) 27 | effectSizeRBC <- read.csv(paste(directory,"/effectSizeRBC.csv", sep = "")) 28 | effectSizeNK <- read.csv(paste(directory,"/effectSizeNK.csv", sep = "")) 29 | effectSizeB <- read.csv(paste(directory,"/effectSizeB.csv", sep = "")) 30 | effectSizeCD4T <- read.csv(paste(directory,"/effectSizeCD4T.csv", sep = "")) 31 | effectSizeCD8T <- read.csv(paste(directory,"/effectSizeCD8T.csv", sep = "")) 32 | 33 | #print(head(effectSizeCD8T)) 34 | 35 | avgMo <- read.csv(paste(directory,"/avgMo.csv", sep = "")) 36 | avgG <- read.csv(paste(directory,"/avgG.csv", sep = "")) 37 | avgRBC <- read.csv(paste(directory,"/avgRBC.csv", sep = "")) 38 | avgNK <- read.csv(paste(directory,"/avgNK.csv", sep = "")) 39 | avgB <- read.csv(paste(directory,"/avgB.csv", sep = "")) 40 | avgCD4T <- read.csv(paste(directory,"/avgCD4T.csv", sep = "")) 41 | avgCD8T <- read.csv(paste(directory,"/avgCD8T.csv", sep = "")) 42 | 43 | #print(head(avgMo)) 44 | #print(head(avgG)) 45 | m=0 46 | 47 | sigMat = matrix(0,nrow = nrow(LM7), ncol = (cellTypeNo +1 )) 48 | print(head(sigMat)) 49 | 50 | for (i in 1: nrow(LM7)){ 51 | 52 | 53 | sigMat[i,1] = as.character(LM7[i,1]) 54 | # print(sigMat[i,1] ) 55 | 56 | 57 | 58 | if (all(PvalMo[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeMo[i, 2: (cellTypeNo - 1) ] > 0.15)) { 59 | print(i) 60 | print("Mo") 61 | 62 | m= rbind(i) 63 | sigMat[i, 2] = avgMo[i, 2] 64 | sigMat[i, 3] = avgG[i, 2] 65 | sigMat[i, 4] = avgRBC[i, 2] 66 | sigMat[i, 5] = avgNK[i, 2] 67 | sigMat[i, 6] = avgB[i, 2] 68 | sigMat[i, 7] = avgCD4T[i, 2] 69 | sigMat[i, 8] = avgCD8T[i, 2] 70 | #test 71 | print(avgMo[i, 2]) 72 | print(sigMat[i, 2]) 73 | print(avgG[i, 2]) 74 | print( sigMat[i, 3]) 75 | print(as.character(LM7[i,1]) ) 76 | print( sigMat[i,1]) 77 | #test 78 | } 79 | 80 | 81 | 82 | if (all(PvalG[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeG[i, 2: (cellTypeNo - 1) ] > 0.15)) { 83 | print(i) 84 | print("G") 85 | m= rbind(i) 86 | #sigMat[i,1] = as.character(LM7[i,1]) 87 | sigMat[i, 2] = avgMo[i, 2] 88 | sigMat[i, 3] = avgG[i, 2] 89 | sigMat[i, 4] = avgRBC[i, 2] 90 | sigMat[i, 5] = avgNK[i, 2] 91 | sigMat[i, 6] = avgB[i, 2] 92 | sigMat[i, 7] = avgCD4T[i, 2] 93 | sigMat[i, 8] = avgCD8T[i, 2] 94 | 95 | print(avgMo[i, 2]) 96 | print(sigMat[i, 2]) 97 | 98 | 99 | } 100 | 101 | 102 | if (all(PvalRBC[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeRBC[i, 2: (cellTypeNo - 1) ] > 0.15)) { 103 | print(i) 104 | print("RBC") 105 | m= rbind(i) 106 | #sigMat[i,1] = as.character(LM7[i,1]) 107 | sigMat[i, 2] = avgMo[i, 2] 108 | sigMat[i, 3] = avgG[i, 2] 109 | sigMat[i, 4] = avgRBC[i, 2] 110 | sigMat[i, 5] = avgNK[i, 2] 111 | sigMat[i, 6] = avgB[i, 2] 112 | sigMat[i, 7] = avgCD4T[i, 2] 113 | sigMat[i, 8] = avgCD8T[i, 2] 114 | 115 | 116 | print(avgMo[i, 2]) 117 | print(sigMat[i, 2]) 118 | 119 | 120 | } 121 | 122 | 123 | if (all(PvalNK[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeNK[i, 2: (cellTypeNo - 1) ] > 0.15)) { 124 | print(i) 125 | print("NK") 126 | m= rbind(i) 127 | #sigMat[i,1] = as.character(LM7[i,1]) 128 | sigMat[i, 2] = avgMo[i, 2] 129 | sigMat[i, 3] = avgG[i, 2] 130 | sigMat[i, 4] = avgRBC[i, 2] 131 | sigMat[i, 5] = avgNK[i, 2] 132 | sigMat[i, 6] = avgB[i, 2] 133 | sigMat[i, 7] = avgCD4T[i, 2] 134 | sigMat[i, 8] = avgCD8T[i, 2] 135 | 136 | print(avgMo[i, 2]) 137 | print(sigMat[i, 2]) 138 | 139 | 140 | } 141 | 142 | 143 | if (all(PvalB[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeB[i, 2: (cellTypeNo - 1) ] > 0.15)) { 144 | print(i) 145 | print("B") 146 | m= rbind(i) 147 | #sigMat[i,1] = as.character(LM7[i,1]) 148 | sigMat[i, 2] = avgMo[i, 2] 149 | sigMat[i, 3] = avgG[i, 2] 150 | sigMat[i, 4] = avgRBC[i, 2] 151 | sigMat[i, 5] = avgNK[i, 2] 152 | sigMat[i, 6] = avgB[i, 2] 153 | sigMat[i, 7] = avgCD4T[i, 2] 154 | sigMat[i, 8] = avgCD8T[i, 2] 155 | 156 | print(avgMo[i, 2]) 157 | print(sigMat[i, 2]) 158 | 159 | 160 | } 161 | 162 | if (all(PvalCD4T[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeCD4T[i, 2: (cellTypeNo - 1) ] > 0.15)) { 163 | print(i) 164 | print("CD4T") 165 | m= rbind(i) 166 | #sigMat[i,1] = as.character(LM7[i,1]) 167 | sigMat[i, 2] = avgMo[i, 2] 168 | sigMat[i, 3] = avgG[i, 2] 169 | sigMat[i, 4] = avgRBC[i, 2] 170 | sigMat[i, 5] = avgNK[i, 2] 171 | sigMat[i, 6] = avgB[i, 2] 172 | sigMat[i, 7] = avgCD4T[i, 2] 173 | sigMat[i, 8] = avgCD8T[i, 2] 174 | 175 | print(avgMo[i, 2]) 176 | print(sigMat[i, 2]) 177 | 178 | 179 | } 180 | 181 | 182 | 183 | if (all(PvalCD8T[i, 2: (cellTypeNo - 1) ] < 0.0001) & all(effectSizeCD8T[i, 2: (cellTypeNo - 1) ] > 0.15)) { 184 | print(i) 185 | print("CD8T") 186 | m= rbind(i) 187 | #sigMat[i,1] = as.character(LM7[i,1]) 188 | sigMat[i, 2] = avgMo[i, 2] 189 | sigMat[i, 3] = avgG[i, 2] 190 | sigMat[i, 4] = avgRBC[i, 2] 191 | sigMat[i, 5] = avgNK[i, 2] 192 | sigMat[i, 6] = avgB[i, 2] 193 | sigMat[i, 7] = avgCD4T[i, 2] 194 | sigMat[i, 8] = avgCD8T[i, 2] 195 | 196 | print(avgMo[i, 2]) 197 | print(sigMat[i, 2]) 198 | 199 | 200 | } 201 | 202 | 203 | #print(sigMat[i,]) 204 | 205 | } 206 | write.csv(m, "chosen.csv") 207 | write.csv(sigMat, paste(directory,"/sigMat.csv", sep = "")) 208 | } -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/produceSigMat.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/codes/1.produceSignature Matrix/produceSigMat.docx -------------------------------------------------------------------------------- /codes/1.produceSignature Matrix/produceType_avgFiles.R: -------------------------------------------------------------------------------- 1 | #read the signature matrix file and put each cell type values in a separate file. 2 | #Then, it calculaes the acerage value (between samples) for each cell type and return each in a separate file. 3 | 4 | typesAvgFiles <- function(sourceFile = "/Users/mitra/Desktop/work/cell_sorted_data_sortedByName.csv") { 5 | #produce B, ... , avgB, ... files 6 | 7 | 8 | LM7<- read.csv(sourceFile) 9 | 10 | 11 | i <- 0 12 | 13 | B <- LM7[, c(grep("_B", colnames(LM7)))] 14 | print(head(B)) 15 | 16 | CD4T <- LM7[, c(grep("_CD4T", colnames(LM7)))] 17 | CD8T <- LM7[, c(grep("_CD8T", colnames(LM7)))] 18 | G <- LM7[, c(grep("_G", colnames(LM7)))] 19 | Mo <- LM7[, c(grep("_Mo", colnames(LM7)))] 20 | NK <- LM7[, c(grep("_NK", colnames(LM7)))] 21 | RBC <- LM7[, c(grep("_RBC", colnames(LM7)))] 22 | 23 | write.csv(Mo, file = "Mo.csv") 24 | write.csv(G, file = "G.csv") 25 | write.csv(RBC, file = "RBC.csv") 26 | write.csv(NK, file = "NK.csv") 27 | write.csv(B, file = "B.csv") 28 | write.csv(CD4T, file = "CD4T.csv") 29 | write.csv(CD8T, file = "CD8T.csv") 30 | 31 | 32 | avgMo = avgG = avgRBC = avgNK = avgB = avgCD4T = avgCD8T = rep(0, length = nrow(LM7)) 33 | i <- 0 34 | 35 | for (i in 1:nrow(LM7)){ 36 | avgMo[i] <- mean(as.numeric(Mo[i,])) 37 | avgG[i] <- mean(as.numeric(G[i,])) 38 | avgRBC[i] <- mean(as.numeric(RBC[i,])) 39 | avgNK[i] <- mean(as.numeric(NK[i,])) 40 | avgB[i] <- mean(as.numeric(B[i,])) 41 | avgCD4T[i] <- mean(as.numeric(CD4T[i,])) 42 | avgCD8T[i] <- mean(as.numeric(CD8T[i,])) 43 | 44 | print(head(avgB)) 45 | } 46 | 47 | write.csv(avgMo, file = "avgMo.csv") 48 | write.csv(avgG, file = "avgG.csv") 49 | write.csv(avgRBC, file = "avgRBC.csv") 50 | write.csv(avgNK, file = "avgNK.csv") 51 | write.csv(avgB, file = "avgB.csv") 52 | write.csv(avgCD4T, file = "avgCD4T.csv") 53 | write.csv(avgCD8T, file = "avgCD8T.csv") 54 | 55 | } -------------------------------------------------------------------------------- /codes/2.MergeSigMatwithMix/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/codes/2.MergeSigMatwithMix/.DS_Store -------------------------------------------------------------------------------- /codes/2.MergeSigMatwithMix/Merge_Mix_SigMat.R: -------------------------------------------------------------------------------- 1 | # extract the signature matrix probes and their values from mixture Matrix (example: LM22, merge ExampleMixtures-GEPs ), to be used for modelling 2 | library(som) 3 | 4 | sigMat <- read.csv("allcellSortedData.csv") 5 | mixDataMain <- read.csv("mixedDataSorted.csv") 6 | 7 | #check if the column name is the same in both tables. Here "GeneSymbol". It was originally different in LM22 and #Mixture table and has been corrected. 8 | mixDataShort <- merge(mixDataMain, sigMat, by = "GeneSymbol") 9 | write.csv(mixDataShort, "mixDataAlignedSigMat.csv") 10 | #mixData <- mixDataShort[1:6] 11 | mixData <- mixDataShort[1:25] 12 | write.csv(mixData, "mixDataAligned.csv") 13 | sigMat <- mixDataShort[26:32] 14 | write.csv(sigMat, "cellSortedAligned.csv") -------------------------------------------------------------------------------- /codes/3.nnls/nnlm.R: -------------------------------------------------------------------------------- 1 | # To calcualte non-negative linear regression coefficinets for the 24 samples- predicting cell-types. The format of the files and name of the samples are important. 2 | # need "nnls" package. 3 | # IMPORTANT, the orders of columns in sigMat file and in the predicted coefficients output file has to be the same. 4 | 5 | library(nnls) 6 | nnlm <- function( directory = "C:/Users/mitra/Desktop/cellType-R/nnlm", significants = "sigMatFormatted.csv", mixData = "Mix.csv", sampleNo = 24, typeNo = 7, outputFile = "nnlmTest.csv"){ 7 | sigMat <- read.csv(significants) 8 | mixData <- read.csv(mixData) 9 | #set.seed(20+i) 10 | #B = sigMat[,2] 11 | #CD4T = sigMat[,3] 12 | #CD8T = sigMat[,4] 13 | #G = sigMat[,5] 14 | #Mo =sigMat[,6] 15 | #NK = sigMat[,7] 16 | #RBC = sigMat[,8] 17 | 18 | #print(head(sigMat)) 19 | #print(head(mixData)) 20 | 21 | for (i in 1:sampleNo){ 22 | 23 | 24 | no = sprintf("%01d", i) 25 | no2 = sprintf("%01d", i+221) 26 | # the following two lines are for jumping TS229 and TS239 which are absent in our data. 27 | if (i+221 > 228){ no2 = sprintf("%01d", i+222)} 28 | if (i+221 >238){no2 = sprintf("%01d", i+223)} 29 | 30 | Mix = mixData[,i+1] 31 | print(head(Mix)) 32 | nnlmModel = nnls(as.matrix(sigMat[,2:8]), Mix) 33 | 34 | 35 | #print(model) 36 | coefs <- coef(model) 37 | print(coefs) 38 | 39 | coefsNorm <- coefs*100/ sum(coefs) 40 | #filename = paste("lmCo", no, ".csv") 41 | sampleName = paste("TS", no2, sep = "") 42 | 43 | if (i ==1){ 44 | write.table( t(as.data.frame(coefsNorm)), file = outputFile, append=T, sep="," , row.names = sampleName, col.names=c("\",\"Mo","G","RBC", "NK", "B" , "CD4T" , "CD8T")) 45 | }else{ 46 | write.table( t(as.data.frame(coefsNorm)), file = outputFile, append=T, sep="," , row.names = sampleName, col.names= F) 47 | } 48 | 49 | 50 | } 51 | } 52 | -------------------------------------------------------------------------------- /codes/4.svr/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/codes/4.svr/.DS_Store -------------------------------------------------------------------------------- /codes/4.svr/svr.R: -------------------------------------------------------------------------------- 1 | # Read the normalized data from sigMatNormalized(LM22) and mixDataNormalized (mixture Data), and calculate the model 2 | 3 | #svr <- function() { 4 | library(e1071) 5 | #library(som) 6 | 7 | sigMat <- read.csv("sigMatAligned.csv") 8 | mixData <- read.csv("mixDataAligned.csv") 9 | 10 | Ref <- read.csv("Ref-229hazf.csv") 11 | 12 | 13 | for (j in 1:5) { 14 | data = cbind(mixData[,j+1], sigMat[2:23]) 15 | 16 | i = 0 17 | k = 0 18 | t = 0 19 | 20 | sampleName = sprintf( "Mix%01d", j) 21 | fileName = paste("result_", sampleName, ".csv", sep = "") 22 | #print(class(data)) 23 | #print(head(data)) 24 | 25 | #svr_model3 <- svm( data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 26 | # +data[,11]+data[,12]+data[,13]+data[,14]+data[,15]+data[,16]+data[,17]+data[,18]+data[,19]+data[,20] +data[,21] 27 | # +data[,22] +data[,23] ,data ,scale = TRUE, type = "nu-regression") 28 | #print(svr_model3) 29 | 30 | #tuneResult <- tune(svm, data[,1] ~ data[,2]+ data[,3] +data[,4] + data[,5] +data[,6] + data[,7] + data[,8] +data[,9]+data[,10] 31 | # +data[,22] +data[,23], data = data, type = "nu-regression", 32 | # ranges = list(nu = seq(0.25, 0.75, 0.25)), scale = F) 33 | 34 | #svr_Model <- svm(data[,1]~., data=data, type = "nu-regression", scale = F) 35 | #print(tuneResult) 36 | # res <- cbind(tuneResult$best.model$nu, tuneResult$best.model$cost) 37 | svrPredictionRMSE = array(0, length(nrow(data))) 38 | for (k in seq(0.25, 0.75, 0.25)){ 39 | svr_model <- svm(data[,1] ~ . , data, scale = F, type = "nu-regression", nu = k) 40 | predictedm <- predict(svr_model, data) 41 | #print(head(predictedm)) 42 | error <- data[,1] - predictedm 43 | #print(error) 44 | i = i+1 45 | svrPredictionRMSE[i] <- sqrt(mean(error^2)) 46 | print(svrPredictionRMSE[i]) 47 | } 48 | 49 | 50 | if (which.min(svrPredictionRMSE) ==1 ) 51 | {k=0.25 52 | t = 1} 53 | if (which.min(svrPredictionRMSE) ==2 ) 54 | {k=0.5 55 | t = 2} 56 | if (which.min(svrPredictionRMSE) ==3 ) 57 | {k=0.75 58 | t=3} 59 | 60 | res <- cbind(k,t ) 61 | write.table( as.data.frame(res), file = "tuned.csv", append=T, sep="," , row.names = sampleName, col.names=c("\",\"nu","svrPredictionRMSE")) 62 | 63 | svr_model <- svm(data[,1] ~ ., data, scale = F, type = "nu-regression", nu = k) 64 | #tunedModel <- tuneResult$best.model 65 | #print(svr_model) 66 | 67 | #changed -1 68 | predicted_svr_model <- predict(svr_model, data) 69 | #print(head(predicted_tunedModel)) 70 | 71 | error <- data[,1] - predicted_svr_model 72 | #print(errorT) 73 | 74 | predicted_svr_model_RMSE <- sqrt(mean(error^2)) 75 | #print(tunedModelRMSE) 76 | 77 | tst_Sample <- cor.test(data[,1], predicted_svr_model) 78 | corrSample <- round(tst_Sample$estimate, 3) 79 | pvalSample <- round(tst_Sample$p.value,3) 80 | 81 | #new 82 | #svmt = table(pred = predicted_tunedModel, true = data[ ,1] ) 83 | #write.csv(svmt, ("svmtComparison.csv")) 84 | 85 | resultT <- cbind(data[,1], predicted_svr_model, tunedModelRMSE, corrSample , pvalSample ) 86 | write.table( as.data.frame(resultT), file = fileName, append=T, sep=",", col.names=c("\",\"Mix", "predicted_svr_model", "RMSE", "Pearson Correlation", "P-Value")) 87 | 88 | #values <- cbind(data[,1], predicted_tunedModel, tst_Sample$estimate, corrSample) 89 | #write.table(as.data.frame(values), file = fileName, append=T, sep="," , col.names=c("\",\"Y","predictedY", "Pearson Correlation", "rounded Correlation")) 90 | 91 | coefTuned = matrix(0, ncol= 22, nrow = 1) 92 | coefTuned <- t(tunedModel$coefs) %*% tunedModel$SV 93 | # Set negative svr regression coefficients to zero 94 | coefTuned[coefTuned <0 ] <- 0 95 | #normalize the coefs 96 | coefTunedNormed <- round(coefTuned /sum(coefTuned), 2) 97 | print(coefTunedNormed) 98 | print(j) 99 | print(data[1,]) 100 | 101 | tst_coef <- cor.test(as.numeric(Ref[j,2:23]), as.numeric(coefTunedNormed[1,2:23])) 102 | corrCoef <- round(tst_coef$estimate, 3) 103 | pvalCoef <- round(tst_coef$p.value,3) 104 | 105 | errorCoef <- as.numeric(Ref[j,2:23])- as.numeric(coefTunedNormed[1,2:23]) 106 | RMSEcoef <- sqrt(mean(errorCoef^2)) 107 | 108 | 109 | resultCoef <- cbind(t(coefTunedNormed[2:23]), corrCoef, pvalCoef, RMSEcoef ) 110 | 111 | if (j ==1){ 112 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName, 113 | col.names=c("\",\"B cells naive","B cells memory","Plasma cells", "T cells CD8", "T cells CD4 naive" , "T cells CD4 memory resting" , "T cells CD4 memory activated", 114 | "T cells follicular helper", "T cells regulatory (Tregs)", "T cells gamma delta", "NK cells resting", "NK cells activated", "Monocytes", "Macrophages M0", 115 | "Macrophages M1", "Macrophages M2", "Dendritic cells resting", "Dendritic cells activated", "Mast cells resting", "Mast cells activated", "Eosinophils", "Neutrophils" 116 | ,"Pearson Correlation", "P-value", "RMSE")) 117 | }else{ 118 | write.table( as.data.frame(resultCoef), file = "resultCoefs.csv", append=T, sep="," , row.names = sampleName) 119 | 120 | } 121 | 122 | 123 | } 124 | 125 | 126 | -------------------------------------------------------------------------------- /codes/5.plots/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/codes/5.plots/.DS_Store -------------------------------------------------------------------------------- /codes/5.plots/comPlotSampleR.R: -------------------------------------------------------------------------------- 1 | # This function, reads the observed data and the predicted data from a single file (format important) amd make the scatterplot of predicted versus observed for each sample. 2 | 3 | comPlotSample <- function(directory = "/Users/mitra/Desktop/cellType-R/new-24July/ConNo", fileName = "svrPlot797.csv") { 4 | #code to clear all plots in Rstudio: 5 | #dev.off(dev.list()["RStudioGD"]) 6 | 7 | n.col = 6 8 | n.row = 6 9 | par(mfrow = c(n.row, n.col)) 10 | par(mar = c(0,1,3,2)) 11 | 12 | lm <- read.csv(paste(directory, "/", fileName, sep = "")) 13 | 14 | for (i in 1:24){ 15 | x= lm[1:7, i+1] # x is the reference ratios from cell-count file 16 | y = lm[8:14,i+1] #y is the ratios from the model 17 | 18 | plot(x,y, col = "blue", frame = F, xlim = range(x), ylim = range(y)) 19 | text(x,y, as.character(lm[1:7, 1]),cex= 0.7, pos=1) 20 | 21 | fit <- lm(y~x, data = lm) 22 | abline(fit, col = "red", lty = 2) 23 | 24 | tst <- cor.test(x, y) 25 | 26 | m <- paste("corr=", as.character(round(tst$estimate, 2)), ",p=" ,as.character(round(tst$p.value,3))) 27 | mtext(m, side = 3, cex = 0.5) 28 | 29 | # problem : "cex" is not working in changing the size of the font in mtext. It remanins too big for the plots. 30 | # mtext(paste("corr = ", as.character(round(tst$estimate, 2)), ",p = " ,as.character(round(tst$p.value,3)), side = 3, cex = 0.1)) 31 | 32 | 33 | } 34 | } 35 | 36 | -------------------------------------------------------------------------------- /codes/5.plots/comPlotType.R: -------------------------------------------------------------------------------- 1 | # This function, reads the observed data and the predicted data from a single file (format important) amd make the scatterplot of predicted versus observed for each cell type. 2 | 3 | comPlotType <- function(directory = "C:/Users/user/Desktop/mit", fileName = "lmPlot.csv") { 4 | #code to clear all plots in Rstudio: 5 | #dev.off(dev.list()["RStudioGD"]) 6 | 7 | n.col = 3 8 | n.row = 3 9 | par(mfrow = c(n.row, n.col)) 10 | par(mar = c(2,1,5,2)) 11 | 12 | lm <- read.csv(paste(directory, "/", fileName, sep = "")) 13 | 14 | for (i in 1:7){ 15 | x= as.numeric(lm[i, 2:25]) 16 | y = as.numeric(lm[i+7,2:25]) 17 | 18 | plot(x,y, col = i+3, frame = F, xlim = range(x), ylim = range(y), main = as.character(lm[i,1]), cex.main = 0.9, col.main = i+3 19 | ) 20 | text(x,y, as.character(c(1:24)),cex= 0.7, pos=1) 21 | 22 | fit <- lm(y~x, data = lm) 23 | abline(fit, col = "green", lty = 2) 24 | 25 | tst <- cor.test(x, y) 26 | 27 | m <- paste("corr=", as.character(round(tst$estimate, 2)), ",p=" ,as.character(round(tst$p.value,3))) 28 | mtext(m, side = 3, cex = 0.5) 29 | 30 | # problem : "cex" is not working in changing the size of the font in mtext. It remanins too big for the plots. 31 | # mtext(paste("corr = ", as.character(round(tst$estimate, 2)), ",p = " ,as.character(round(tst$p.value,3)), side = 3, cex = 0.1)) 32 | 33 | 34 | } 35 | } 36 | 37 | -------------------------------------------------------------------------------- /codes/README.md: -------------------------------------------------------------------------------- 1 | Final version of Cell Type Deconvoluion project. -------------------------------------------------------------------------------- /codes/readme.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zomithex/CIBERSORT/d3f84cd9a5aa3676f814d90e1214ae5fc20e1747/codes/readme.docx --------------------------------------------------------------------------------