Load datasets
rm(list = ls())
library(RColorBrewer)
library(lmodel2)
library(viridisLite)
library(seriation)
library(reshape2)
pathtofigures <- "./"
dataGR <- read.table(file=paste(pathtofigures, "AveragelogGRperstrain241het16hom20181128.csv", sep="/"), header=TRUE, sep=",")
dataGR$GR <- exp(dataGR$logGR)
dataWL <- read.table(file=paste(pathtofigures, "AveragelogWLperstrain198het16hom20180717.csv", sep="/"), header=TRUE, sep=";")
dataWL$weightloss <- exp(dataWL$logweightloss)-1
head(dataWL)
## Acceptor Donor geno_DonorSG logweightloss ID DonorSG AcceptorName
## 1 11 7 11_7_1 0.1750899 Het46 1 FSE7
## 2 11 30 11_30_0 0.2043858 Het205 0 FSE7
## 3 11 9 11_9_0 0.2307522 Het54 0 FSE7
## 4 11 32 11_32_0 0.2866868 Het234 0 FSE7
## 5 11 11 11_0 0.1324016 Hom11 0 FSE7
## 6 11 25 11_25_1 0.1792176 Het172 1 FSE7
## DonorName AcceptorPop DonorPop geno geneticdistance
## 1 95156 Helsinki_Finland Yekaterinburg_Russia 11_7 0.3761182
## 2 HR32 Helsinki_Finland Smolyan_Bulgaria 11_30 0.4094155
## 3 95191 Helsinki_Finland Yekaterinburg_Russia 11_9 0.4146880
## 4 Sa948 Helsinki_Finland Sätuna_Sweden 11_32 0.3880394
## 5 FSE7 Helsinki_Finland Helsinki_Finland 11 0.0000000
## 6 93134 Helsinki_Finland Aalborg_Denmark 11_25 0.4086827
## Acceptormito geographicdistance initweight weightloss plate_rep_time
## 1 5 2078 1.1409 0.1913534 X2_2_i
## 2 5 1858 1.0707 0.2267713 X3_2_i
## 3 5 2078 1.1425 0.2595471 X1_1_i
## 4 5 778 1.1308 0.3320070 X2_2_i
## 5 5 0 0.8004 0.1415667 X3_2_i
## 6 5 934 1.2094 0.1962811 X1_1_i
## plate rep Type ID_plate geographicdistancestd
## 1 X2 2 Heterokaryon Het46_X2 0.6313005
## 2 X3 2 Heterokaryon Het205_X3 0.4212340
## 3 X1 1 Heterokaryon Het54_X1 0.6313005
## 4 X2 2 Heterokaryon Het234_X2 -0.6100014
## 5 X3 2 Homokaryon Hom11_X3 -1.3528728
## 6 X1 1 Heterokaryon Het172_X1 -0.4610452
## geneticdistancestd geneticdistancestdSq geneticdistancefac initweightstd
## 1 0.06125775 0.003752512 0.37 0.7106281
## 2 0.37385710 0.139769132 0.40 0.1910697
## 3 0.42335619 0.179230466 0.41 0.7224698
## 4 0.17317604 0.029989939 0.38 0.6358768
## 5 -3.46978937 12.039438250 0.00 -1.8094522
## 6 0.36697757 0.134672534 0.40 1.2176046
## initweightstdSq AcceptorMM Midparent Mito AccDonCov HetDonorbyHom
## 1 0.50499224 11 7 5 7 7
## 2 0.03650762 11 30 5 30 30
## 3 0.52196268 11 9 5 9 9
## 4 0.40433928 11 32 5 32 32
## 5 3.27411733 11 11 5 11 11
## 6 1.48256088 11 25 5 25 25
## HetAccbyHom DonorhomWL AcchomWL midhompar midhomparcent
## 1 11 NA 0.1324016 NA NA
## 2 11 0.07476087 0.1324016 0.1035812 -0.06348117
## 3 11 0.13500723 0.1324016 0.1337044 -0.03335799
## 4 11 0.23740947 0.1324016 0.1849055 0.01784313
## 5 11 0.13240160 0.1324016 0.1324016 -0.03466080
## 6 11 NA 0.1324016 NA NA
## midparentheterosis hetvigor besthompar Acceptor15 DonorhomWL.1
## 1 NA NA NA 11 NA
## 2 0.10080454 0.10080454 0.1324016 11 0.07476087
## 3 0.09704782 0.09704782 0.1350072 11 0.13500723
## 4 0.10178127 0.10178127 0.2374095 11 0.23740947
## 5 0.00000000 0.00000000 0.1324016 11 0.13240160
## 6 NA NA NA 11 NA
## AcchomWL.1 logWLcent besthomparcent strain18
## 1 0.1324016 -0.037622462 NA 3
## 2 0.1324016 -0.008326635 -0.08182514 1
## 3 0.1324016 0.018039832 -0.07921951 1
## 4 0.1324016 0.073974403 0.02318273 1
## 5 0.1324016 -0.080310802 -0.08182514 1
## 6 0.1324016 -0.033494787 NA 3
data <- rbind(dataGR[, c("geno", "Type", "DonorSG", "Acceptor", "Donor")], dataWL[, c("geno", "Type", "DonorSG", "Acceptor", "Donor")])
data$Trait <- c(rep("GR", nrow(dataGR)), rep("WL", nrow(dataWL)))
data$geno_trait <- paste(data$geno, data$Trait, sep="_")
data <- data[!duplicated(data$geno_trait)&data$Type=="Heterokaryon",]
data$geno <- factor(data$geno)
data$Acceptor <- factor(data$Acceptor, levels=sort(unique(data$Donor)))
table(data$geno, data$Trait)
##
## GR WL
## 11_15 1 1
## 11_18 1 1
## 11_19 1 1
## 11_2 1 1
## 11_20 1 0
## 11_21 1 1
## 11_22 1 1
## 11_23 1 1
## 11_24 1 1
## 11_25 1 1
## 11_26 1 1
## 11_30 1 1
## 11_31 1 1
## 11_32 1 1
## 11_34 1 1
## 11_5 1 1
## 11_6 1 1
## 11_7 1 1
## 11_9 1 1
## 14_30 1 0
## 14_5 1 1
## 18_11 1 1
## 18_19 1 1
## 18_2 1 1
## 18_20 1 1
## 18_24 1 1
## 18_25 1 1
## 18_26 1 1
## 18_30 1 1
## 18_31 1 1
## 18_32 1 1
## 18_33 1 1
## 18_5 1 1
## 18_9 1 1
## 19_1 1 1
## 19_11 1 1
## 19_15 1 1
## 19_18 1 1
## 19_2 1 1
## 19_20 1 1
## 19_24 1 1
## 19_26 1 1
## 19_27 1 1
## 19_30 1 1
## 19_31 1 1
## 19_32 1 1
## 19_5 1 1
## 19_9 1 1
## 2_11 1 1
## 2_14 1 1
## 2_15 1 1
## 2_18 1 1
## 2_19 1 1
## 2_20 1 0
## 2_24 1 1
## 2_26 1 1
## 2_27 1 1
## 2_3 1 1
## 2_30 1 1
## 2_31 1 1
## 2_32 1 1
## 2_33 1 1
## 2_34 1 1
## 2_5 1 1
## 2_9 1 1
## 20_11 1 1
## 20_12 1 0
## 20_14 1 1
## 20_15 1 1
## 20_18 1 0
## 20_19 1 0
## 20_2 1 0
## 20_24 1 0
## 20_26 1 0
## 20_27 1 1
## 20_3 1 1
## 20_30 1 0
## 20_31 1 0
## 20_32 1 1
## 20_5 1 0
## 20_7 1 0
## 20_9 1 0
## 23_11 1 1
## 24_1 1 0
## 24_10 1 1
## 24_11 1 1
## 24_13 1 1
## 24_14 1 1
## 24_18 1 1
## 24_19 1 1
## 24_2 1 0
## 24_20 1 1
## 24_22 1 0
## 24_25 1 0
## 24_26 1 0
## 24_27 1 0
## 24_3 1 0
## 24_30 1 1
## 24_31 1 0
## 24_32 1 0
## 24_33 1 0
## 24_34 1 1
## 24_35 1 0
## 24_4 1 0
## 24_5 1 0
## 24_7 1 1
## 24_9 1 1
## 26_1 1 1
## 26_11 1 1
## 26_18 1 1
## 26_19 1 1
## 26_2 1 1
## 26_20 1 1
## 26_24 1 1
## 26_27 1 1
## 26_30 1 1
## 26_31 1 1
## 26_32 1 1
## 26_35 1 1
## 26_5 1 1
## 26_7 1 1
## 26_9 1 1
## 27_18 1 1
## 27_19 1 1
## 27_2 1 1
## 27_20 1 0
## 27_24 1 1
## 27_26 1 1
## 27_30 1 1
## 27_31 1 1
## 27_32 1 1
## 27_5 1 1
## 30_11 1 1
## 30_12 1 1
## 30_13 1 1
## 30_14 1 1
## 30_15 1 1
## 30_18 1 1
## 30_19 1 1
## 30_2 1 1
## 30_20 1 1
## 30_21 1 1
## 30_22 1 1
## 30_23 1 1
## 30_24 1 1
## 30_26 1 1
## 30_27 1 1
## 30_31 1 1
## 30_32 1 1
## 30_33 1 1
## 30_4 1 1
## 30_5 1 1
## 30_6 1 1
## 30_9 1 1
## 31_11 1 1
## 31_13 1 1
## 31_18 1 1
## 31_19 1 1
## 31_2 1 1
## 31_20 1 1
## 31_21 1 1
## 31_24 1 1
## 31_26 1 1
## 31_27 1 1
## 31_30 1 1
## 31_32 1 1
## 31_33 1 1
## 31_34 1 1
## 31_5 1 1
## 31_7 1 1
## 31_9 1 1
## 32_1 1 1
## 32_10 1 1
## 32_11 1 1
## 32_13 1 1
## 32_14 1 1
## 32_15 1 1
## 32_18 1 1
## 32_19 1 1
## 32_2 1 1
## 32_20 1 0
## 32_23 1 1
## 32_24 1 1
## 32_26 1 1
## 32_27 1 1
## 32_3 1 1
## 32_30 1 1
## 32_31 1 1
## 32_33 1 1
## 32_35 1 1
## 32_4 1 1
## 32_5 1 1
## 32_6 1 1
## 32_9 1 1
## 5_1 1 1
## 5_11 1 1
## 5_12 1 1
## 5_13 1 1
## 5_14 1 1
## 5_15 1 1
## 5_19 1 1
## 5_2 1 1
## 5_20 1 1
## 5_22 1 1
## 5_24 1 1
## 5_26 1 1
## 5_27 1 1
## 5_3 1 1
## 5_30 1 1
## 5_31 1 1
## 5_32 1 1
## 5_33 1 1
## 5_4 1 1
## 5_9 1 1
## 9_11 1 1
## 9_18 1 1
## 9_19 1 1
## 9_2 1 1
## 9_20 1 1
## 9_26 1 1
## 9_27 1 1
## 9_30 1 1
## 9_31 1 1
## 9_32 1 1
## 9_5 1 1
## 32_21 0 1
## 5_23 0 1
table(data$Acceptor, data$Trait)
##
## GR WL
## 1 0 0
## 2 17 16
## 3 0 0
## 4 0 0
## 5 20 21
## 6 0 0
## 7 0 0
## 9 11 11
## 10 0 0
## 11 19 18
## 12 0 0
## 13 0 0
## 14 2 1
## 15 0 0
## 18 13 13
## 19 14 14
## 20 17 6
## 21 0 0
## 22 0 0
## 23 1 1
## 24 24 11
## 25 0 0
## 26 15 15
## 27 10 9
## 30 22 22
## 31 17 17
## 32 23 23
## 33 0 0
## 34 0 0
## 35 0 0
table(data$Donor, data$Trait)
##
## GR WL
## 1 5 4
## 2 12 10
## 3 5 4
## 4 4 3
## 5 13 11
## 6 3 3
## 7 5 4
## 9 11 10
## 10 2 2
## 11 12 12
## 12 3 2
## 13 5 5
## 14 6 6
## 15 7 7
## 18 11 10
## 19 12 11
## 20 12 8
## 21 3 4
## 22 4 3
## 23 3 4
## 24 11 10
## 25 3 2
## 26 12 10
## 27 10 9
## 30 13 11
## 31 12 10
## 32 12 11
## 33 7 6
## 34 4 4
## 35 3 2
table(data$Acceptor[!duplicated(data$geno)])
##
## 1 2 3 4 5 6 7 9 10 11 12 13 14 15 18 19 20 21 22 23 24 25 26 27 30
## 0 17 0 0 21 0 0 11 0 19 0 0 2 0 13 14 17 0 0 1 24 0 15 10 22
## 31 32 33 34 35
## 17 24 0 0 0
table(data$Donor[!duplicated(data$geno)])
##
## 1 2 3 4 5 6 7 9 10 11 12 13 14 15 18 19 20 21 22 23 24 25 26 27 30
## 5 12 5 4 13 3 5 11 2 12 3 5 6 7 11 12 12 4 4 4 11 3 12 10 13
## 31 32 33 34 35
## 12 12 7 4 3
data <- merge(x=dataGR, y=dataWL, by="geno_DonorSG")
head(data)
## geno_DonorSG Acceptor.x Donor.x logGR logintGR unit intGR
## 1 11_0 11 11 1.942217 2.761546 11_X6_1_1 12.75000
## 2 11_15_0 11 15 1.621652 2.725354 11_15_X4_1_3 13.91667
## 3 11_18_0 11 18 1.936294 2.830828 11_18_X1_1_4 18.75000
## 4 11_19_0 11 19 1.941956 2.930961 11_19_X1_2_1 19.83333
## 5 11_2_0 11 2 1.765159 2.663283 11_2_X1_1_1 11.66667
## 6 11_21_1 11 21 1.647588 2.443278 11_21_X1_2_2 15.41667
## slopeGR ID.x DonorSG.x AcceptorName.x DonorName.x AcceptorPop.x
## 1 6.25 Hom11 0 FSE7 FSE7 Helsinki_Finland
## 2 4.25 Het96 0 FSE7 RB9411 Helsinki_Finland
## 3 7.25 Het103 0 FSE7 Sa1595(Ref) Helsinki_Finland
## 4 7.50 Het117 0 FSE7 87074 Helsinki_Finland
## 5 6.00 Het9 0 FSE7 87179 Helsinki_Finland
## 6 5.75 Het144 1 FSE7 90137 Helsinki_Finland
## DonorPop.x geno.x geneticdistance.x Acceptormito.x
## 1 Helsinki_Finland 11 0.0000000 5
## 2 Ramsåsa_Sweden 11_15 0.3798179 5
## 3 Sätuna_Sweden 11_18 0.4538297 5
## 4 Vicenza_Italy 11_19 0.4065469 5
## 5 Oslo_Norway 11_2 0.3914800 5
## 6 Kaunas_Lithuania 11_21 0.3873960 5
## Geographicdistance assay plate.x rep.x HoStockplatesAcc HoStockplatesDon
## 1 0 6 1 1 11_X3 11_X3
## 2 827 4 1 3 1 1
## 3 778 1 1 4 1 1
## 4 1823 1 2 1 1 1
## 5 812 1 1 1 1 1
## 6 590 1 2 2 1 1
## HetSynthesis HetStockplates HetPrecultures Date Type.x
## 1 NA NA 11_4 Mar2016 Homokaryon
## 2 1 3 4 3 Heterokaryon
## 3 1 1 1 4 Heterokaryon
## 4 1 1 1 4 Heterokaryon
## 5 1 1 1 4 Heterokaryon
## 6 1 1 1 4 Heterokaryon
## geographicdistancestd.x geneticdistancestd.x geneticdistancestdSq.x
## 1 -1.3016693 -2.9616024 8.77108856
## 2 -0.5120013 0.1742739 0.03037141
## 3 -0.5587894 0.7853352 0.61675145
## 4 0.4390378 0.3949555 0.15598982
## 5 -0.5263242 0.2705593 0.07320231
## 6 -0.7383028 0.2368410 0.05609364
## geneticdistancestdCub geneticdistancegrp geneticdistancefac.x
## 1 -25.976476623 0.00 0.00
## 2 0.005292945 0.37 0.37
## 3 0.484356650 0.45 0.45
## 4 0.061609031 0.40 0.40
## 5 0.019805564 0.39 0.39
## 6 0.013285270 0.38 0.38
## geno_synthesis geno_synthesis_HetStockplates
## 1 11_NA 11_NA_NA
## 2 11_15_1 11_15_1_3
## 3 11_18_1 11_18_1_1
## 4 11_19_1 11_19_1_1
## 5 11_2_1 11_2_1_1
## 6 11_21_1 11_21_1_1
## geno_synthesis_HetStockplates_HetPreculture
## 1 11_NA_NA_11_4
## 2 11_15_1_3_4
## 3 11_18_1_1_1
## 4 11_19_1_1_1
## 5 11_2_1_1_1
## 6 11_21_1_1_1
## geno_synthesis_HetStockplates_HetPreculture_assay
## 1 11_NA_NA_11_4_6
## 2 11_15_1_3_4_4
## 3 11_18_1_1_1_1
## 4 11_19_1_1_1_1
## 5 11_2_1_1_1_1
## 6 11_21_1_1_1_1
## geno_synthesis_HetStockplates_HetPreculture_assay_plate
## 1 11_NA_NA_11_4_6_1
## 2 11_15_1_3_4_4_1
## 3 11_18_1_1_1_1_1
## 4 11_19_1_1_1_1_2
## 5 11_2_1_1_1_1_1
## 6 11_21_1_1_1_1_2
## geno_synthesis_HetStockplates_HetPreculture_assay_plate_rep
## 1 11_NA_NA_11_4_6_1_1
## 2 11_15_1_3_4_4_1_3
## 3 11_18_1_1_1_1_1_4
## 4 11_19_1_1_1_1_2_1
## 5 11_2_1_1_1_1_1_1
## 6 11_21_1_1_1_1_2_2
## Acceptor_HoStockplatesAcc Donor_HoStockplatesDon AcceptorMM.x
## 1 11_11_X3 11_11_X3 11
## 2 11_1 15_1 11
## 3 11_1 18_1 11
## 4 11_1 19_1 11
## 5 11_1 2_1 11
## 6 11_1 21_1 11
## Midparent.x AccDonCov.x HetDonorbyHom.x HetAccbyHom.x Mito.x
## 1 11 11 11 11 5
## 2 15 15 15 11 5
## 3 18 18 18 11 5
## 4 19 19 19 11 5
## 5 2 2 2 11 5
## 6 21 21 21 11 5
## Acceptor15.x DonorhomGR AcchomGR midhompar.x midhomparcent.x logGRcent
## 1 11 1.9422174 1.942217 1.942217 0.29473623 0.14798276
## 2 11 1.3637976 1.942217 1.653007 0.00552631 -0.17258289
## 3 11 0.7038478 1.942217 1.323033 -0.32444859 0.14205902
## 4 11 1.6269875 1.942217 1.784602 0.13712130 0.14772128
## 5 11 1.3268665 1.942217 1.634542 -0.01293924 -0.02907527
## 6 11 NA 1.942217 NA NA -0.14664696
## midparentheterosis.x hetvigor.x besthompar.x besthomparcent.x
## 1 0.00000000 0.00000000 1.942217 0.07045624
## 2 -0.03135574 -0.03135574 1.942217 0.07045624
## 3 0.61326107 0.61326107 1.942217 0.07045624
## 4 0.15735345 0.15735345 1.942217 0.07045624
## 5 0.13061743 0.13061743 1.942217 0.07045624
## 6 NA NA NA NA
## strain18.x GR Acceptor.y Donor.y logweightloss ID.y DonorSG.y
## 1 1 6.974198 11 11 0.1324016 Hom11 0
## 2 1 5.061444 11 15 0.1178965 Het96 0
## 3 2 6.933007 11 18 0.2118536 Het103 0
## 4 1 6.972375 11 19 0.1823559 Het117 0
## 5 1 5.842503 11 2 0.2292160 Het9 0
## 6 3 5.194434 11 21 0.2371694 Het144 1
## AcceptorName.y DonorName.y AcceptorPop.y DonorPop.y geno.y
## 1 FSE7 FSE7 Helsinki_Finland Helsinki_Finland 11
## 2 FSE7 RB9411 Helsinki_Finland Ramsåsa_Sweden 11_15
## 3 FSE7 Sa1595(Ref) Helsinki_Finland Sätuna_Sweden 11_18
## 4 FSE7 87074 Helsinki_Finland Vicenza_Italy 11_19
## 5 FSE7 87179 Helsinki_Finland Oslo_Norway 11_2
## 6 FSE7 90137 Helsinki_Finland Kaunas_Lithuania 11_21
## geneticdistance.y Acceptormito.y geographicdistance initweight
## 1 0.0000000 5 0 0.8004
## 2 0.3798179 5 827 1.1448
## 3 0.4538297 5 778 1.0529
## 4 0.4065469 5 1823 1.2000
## 5 0.3914800 5 812 1.1969
## 6 0.3873960 5 590 1.0883
## weightloss plate_rep_time plate.y rep.y Type.y ID_plate
## 1 0.1415667 X3_2_i X3 2 Homokaryon Hom11_X3
## 2 0.1251276 X1_1_i X1 1 Heterokaryon Het96_X1
## 3 0.2359669 X3_2_i X3 2 Heterokaryon Het103_X3
## 4 0.2000412 X3_2_i X3 2 Heterokaryon Het117_X3
## 5 0.2576137 X1_1_i X1 1 Heterokaryon Het9_X1
## 6 0.2676558 X2_2_i X2 2 Heterokaryon Het144_X2
## geographicdistancestd.y geneticdistancestd.y geneticdistancestdSq.y
## 1 -1.3528728 -3.46978937 12.039438250
## 2 -0.5632139 0.09599101 0.009214274
## 3 -0.6100014 0.79082403 0.625402648
## 4 0.3878143 0.34692624 0.120357815
## 5 -0.5775366 0.20547629 0.042220506
## 6 -0.7895127 0.16713547 0.027934265
## geneticdistancefac.y initweightstd initweightstdSq AcceptorMM.y
## 1 0.00 -1.80945222 3.274117329 11
## 2 0.37 0.73949242 0.546849035 11
## 3 0.45 0.05932978 0.003520023 11
## 4 0.40 1.14803407 1.317982217 11
## 5 0.39 1.12509060 1.265828868 11
## 6 0.38 0.32132932 0.103252530 11
## Midparent.y Mito.y AccDonCov.y HetDonorbyHom.y HetAccbyHom.y DonorhomWL
## 1 11 5 11 11 11 0.13240160
## 2 15 5 15 15 11 0.07285694
## 3 18 5 18 18 11 0.02297559
## 4 19 5 19 19 11 0.26477279
## 5 2 5 2 2 11 0.10118138
## 6 21 5 21 21 11 NA
## AcchomWL midhompar.y midhomparcent.y midparentheterosis.y hetvigor.y
## 1 0.1324016 0.1324016 -0.03466080 0.00000000 0.00000000
## 2 0.1324016 0.1026293 -0.06443313 0.01526719 0.01526719
## 3 0.1324016 0.0776886 -0.08937381 0.13416502 0.13416502
## 4 0.1324016 0.1985872 0.03152479 -0.01623128 -0.01623128
## 5 0.1324016 0.1167915 -0.05027091 0.11242452 0.11242452
## 6 0.1324016 NA NA NA NA
## besthompar.y Acceptor15.y DonorhomWL.1 AcchomWL.1 logWLcent
## 1 0.1324016 11 0.13240160 0.1324016 -0.080310802
## 2 0.1324016 11 0.07285694 0.1324016 -0.094815941
## 3 0.1324016 11 0.02297559 0.1324016 -0.000858789
## 4 0.2647728 11 0.26477279 0.1324016 -0.030356487
## 5 0.1324016 11 0.10118138 0.1324016 0.016503612
## 6 NA 11 NA 0.1324016 0.024456950
## besthomparcent.y strain18.y
## 1 -0.08182514 1
## 2 -0.08182514 1
## 3 -0.08182514 2
## 4 0.05054605 1
## 5 -0.08182514 1
## 6 NA 3
Summary and distribution of the two traits
Summary statistics
## For MGR
## Heterokaryon with synthesised with the same homokaryon (once as a slow grower, once as a normal grower)
SG0_1 <- levels(dataGR$genoHeterokaryon)[table(dataGR$genoHeterokaryon)>1]
tapply(dataGR$GR[dataGR$genoHeterokaryon%in%SG0_1], list(dataGR$DonorSG[dataGR$genoHeterokaryon%in%SG0_1],dataGR$genoHeterokaryon[dataGR$genoHeterokaryon%in%SG0_1]), mean)
## <0 x 0 matrix>
## Get rid of the replicate where the donor homokaryon is senescent
dataGR <- dataGR[!dataGR$geno_DonorSG%in%paste0(SG0_1, "_1"),]
length(dataGR$GR[dataGR$Type=="Heterokaryon"])
## [1] 230
length(dataGR$GR[dataGR$Type=="Heterokaryon"&dataGR$DonorSG==0])
## [1] 169
length(dataGR$GR[dataGR$Type=="Heterokaryon"&dataGR$DonorSG==1])
## [1] 61
## For WWL
## Heterokaryon with synthesised with the same homokaryon (once as a slow grower, once as a normal grower)
SG0_1 <- levels(dataWL$geno)[table(dataWL$geno)>1]
tapply(dataWL$logweightloss[dataWL$geno%in%SG0_1], list(dataWL$DonorSG[dataWL$geno%in%SG0_1],dataWL$geno[dataWL$geno%in%SG0_1]), mean)
## 11 11_15 11_18 11_19 11_2 11_21 11_22 11_23 11_24 11_25 11_26 11_30
## 0 NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 11_31 11_32 11_34 11_5 11_6 11_7 11_9 13 14 14_5 15 18 18_11 18_19 18_2
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 18_20 18_24 18_25 18_26 18_30 18_31 18_32 18_33 18_5 18_9 19 19_1 19_11
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19_15 19_18 19_2 19_20 19_24 19_26 19_27 19_30 19_31 19_32 19_5 19_9 2
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2_11 2_14 2_15 2_18 2_19 2_24 2_26 2_27 2_3 2_30 2_31 2_32 2_33 2_34 2_5
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2_9 20 20_11 20_14 20_15 20_27 20_3 20_32 23_11 24 24_10 24_11 24_13
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24_14 24_18 24_19 24_20 24_30 24_34 24_7 24_9 26 26_1 26_11 26_18 26_19
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26_2 26_20 26_24 26_27 26_30 26_31 26_32 26_35 26_5 26_7 26_9 27 27_18
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27_19 27_2 27_24 27_26 27_30 27_31 27_32 27_5 30 30_11 30_12 30_13 30_14
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 30_15 30_18 30_19 30_2 30_20 30_21 30_22 30_23 30_24 30_26 30_27 30_31
## 0 NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 30_32 30_33 30_4 30_5 30_6 30_9 31 31_11 31_13 31_18 31_19 31_2 31_20
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 31_21 31_24 31_26 31_27 31_30 31_32 31_33 31_34 31_5 31_7 31_9 32 32_1
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 32_10 32_11 32_13 32_14 32_15 32_18 32_19 32_2 32_21 32_23 32_24 32_26
## 0 NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 32_27 32_3 32_30 32_31 32_33 32_35 32_4 32_5 32_6 32_9 5 5_1 5_11 5_12
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5_13 5_14 5_15 5_19 5_2 5_20 5_22 5_23 5_24 5_26 5_27 5_3 5_30
## 0 NA NA NA NA NA NA NA NA 0.23026306 NA NA NA NA
## 1 NA NA NA NA NA NA NA NA 0.03741417 NA NA NA NA
## 5_31 5_32 5_33 5_4 5_9 9 9_11 9_18 9_19 9_2 9_20 9_26 9_27 9_30 9_31
## 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9_32 9_5
## 0 NA NA
## 1 NA NA
## Get rid of the replicate where the donor homokaryon is senescent
dataWL <- dataWL[!dataWL$geno_DonorSG%in%paste0(SG0_1, "_1"),]
head(dataWL)
## Acceptor Donor geno_DonorSG logweightloss ID DonorSG AcceptorName
## 1 11 7 11_7_1 0.1750899 Het46 1 FSE7
## 2 11 30 11_30_0 0.2043858 Het205 0 FSE7
## 3 11 9 11_9_0 0.2307522 Het54 0 FSE7
## 4 11 32 11_32_0 0.2866868 Het234 0 FSE7
## 5 11 11 11_0 0.1324016 Hom11 0 FSE7
## 6 11 25 11_25_1 0.1792176 Het172 1 FSE7
## DonorName AcceptorPop DonorPop geno geneticdistance
## 1 95156 Helsinki_Finland Yekaterinburg_Russia 11_7 0.3761182
## 2 HR32 Helsinki_Finland Smolyan_Bulgaria 11_30 0.4094155
## 3 95191 Helsinki_Finland Yekaterinburg_Russia 11_9 0.4146880
## 4 Sa948 Helsinki_Finland Sätuna_Sweden 11_32 0.3880394
## 5 FSE7 Helsinki_Finland Helsinki_Finland 11 0.0000000
## 6 93134 Helsinki_Finland Aalborg_Denmark 11_25 0.4086827
## Acceptormito geographicdistance initweight weightloss plate_rep_time
## 1 5 2078 1.1409 0.1913534 X2_2_i
## 2 5 1858 1.0707 0.2267713 X3_2_i
## 3 5 2078 1.1425 0.2595471 X1_1_i
## 4 5 778 1.1308 0.3320070 X2_2_i
## 5 5 0 0.8004 0.1415667 X3_2_i
## 6 5 934 1.2094 0.1962811 X1_1_i
## plate rep Type ID_plate geographicdistancestd
## 1 X2 2 Heterokaryon Het46_X2 0.6313005
## 2 X3 2 Heterokaryon Het205_X3 0.4212340
## 3 X1 1 Heterokaryon Het54_X1 0.6313005
## 4 X2 2 Heterokaryon Het234_X2 -0.6100014
## 5 X3 2 Homokaryon Hom11_X3 -1.3528728
## 6 X1 1 Heterokaryon Het172_X1 -0.4610452
## geneticdistancestd geneticdistancestdSq geneticdistancefac initweightstd
## 1 0.06125775 0.003752512 0.37 0.7106281
## 2 0.37385710 0.139769132 0.40 0.1910697
## 3 0.42335619 0.179230466 0.41 0.7224698
## 4 0.17317604 0.029989939 0.38 0.6358768
## 5 -3.46978937 12.039438250 0.00 -1.8094522
## 6 0.36697757 0.134672534 0.40 1.2176046
## initweightstdSq AcceptorMM Midparent Mito AccDonCov HetDonorbyHom
## 1 0.50499224 11 7 5 7 7
## 2 0.03650762 11 30 5 30 30
## 3 0.52196268 11 9 5 9 9
## 4 0.40433928 11 32 5 32 32
## 5 3.27411733 11 11 5 11 11
## 6 1.48256088 11 25 5 25 25
## HetAccbyHom DonorhomWL AcchomWL midhompar midhomparcent
## 1 11 NA 0.1324016 NA NA
## 2 11 0.07476087 0.1324016 0.1035812 -0.06348117
## 3 11 0.13500723 0.1324016 0.1337044 -0.03335799
## 4 11 0.23740947 0.1324016 0.1849055 0.01784313
## 5 11 0.13240160 0.1324016 0.1324016 -0.03466080
## 6 11 NA 0.1324016 NA NA
## midparentheterosis hetvigor besthompar Acceptor15 DonorhomWL.1
## 1 NA NA NA 11 NA
## 2 0.10080454 0.10080454 0.1324016 11 0.07476087
## 3 0.09704782 0.09704782 0.1350072 11 0.13500723
## 4 0.10178127 0.10178127 0.2374095 11 0.23740947
## 5 0.00000000 0.00000000 0.1324016 11 0.13240160
## 6 NA NA NA 11 NA
## AcchomWL.1 logWLcent besthomparcent strain18
## 1 0.1324016 -0.037622462 NA 3
## 2 0.1324016 -0.008326635 -0.08182514 1
## 3 0.1324016 0.018039832 -0.07921951 1
## 4 0.1324016 0.073974403 0.02318273 1
## 5 0.1324016 -0.080310802 -0.08182514 1
## 6 0.1324016 -0.033494787 NA 3
length(dataWL$logweightloss[dataWL$Type=="Heterokaryon"])
## [1] 198
length(dataWL$logweightloss[dataWL$Type=="Heterokaryon"&dataWL$DonorSG==0])
## [1] 146
length(dataWL$logweightloss[dataWL$Type=="Heterokaryon"&dataWL$DonorSG==1])
## [1] 52
tapply(dataGR$GR, dataGR$Type, mean)
## Heterokaryon Homokaryon
## 6.259496 5.347162
tapply(dataGR$GR, dataGR$Type, sd)
## Heterokaryon Homokaryon
## 1.256798 2.013554
tapply(dataGR$GR, dataGR$Type, var)
## Heterokaryon Homokaryon
## 1.579542 4.054400
tapply(dataWL$weightloss, dataWL$Type, mean)
## Heterokaryon Homokaryon
## 0.2464382 0.1704119
tapply(dataWL$weightloss, dataWL$Type, sd)
## Heterokaryon Homokaryon
## 0.07576439 0.10237175
tapply(dataWL$weightloss, dataWL$Type, var)
## Heterokaryon Homokaryon
## 0.005740243 0.010479974
Fig3: Plot distributions
# Function to make color transparent
makeTransparent = function(color, alpha = 0.5) {
if (alpha < 0 | alpha > 1)
stop("alpha must be between 0 and 1")
newcol <- col2rgb(col = color, alpha = FALSE)
return(rgb(red = newcol[1], green = newcol[2], blue = newcol[3], alpha = 255 *
alpha, maxColorValue = 255))
}
colpal <- brewer.pal(3, "Set2")
bordercol <- makeTransparent(colpal[2], 0)
wcorr <- 7
hcorr <- 4
pdf(file="Figure3.pdf", width = wcorr, height = hcorr)
head(dataGR)
## Acceptor Donor geno_DonorSG logGR logintGR unit intGR
## 1 11 26 11_26_0 1.922916 2.802864 11_26_X1_2_2 17.91667
## 2 11 20 11_20_0 1.958506 2.839034 11_20_X6_1_3 16.83333
## 3 11 23 11_23_1 2.058940 3.065760 11_23_X1_2_3 21.83333
## 4 11 11 11_0 1.942217 2.761546 11_X6_1_1 12.75000
## 5 11 2 11_2_0 1.765159 2.663283 11_2_X1_1_1 11.66667
## 6 11 9 11_9_0 1.887094 2.740700 11_9_X4_2_4 11.83333
## slopeGR ID DonorSG AcceptorName DonorName AcceptorPop
## 1 6.25 Het178 0 FSE7 Fas161 Helsinki_Finland
## 2 7.00 Het132 0 FSE7 87075 Helsinki_Finland
## 3 7.50 Het155 1 FSE7 91132 Helsinki_Finland
## 4 6.25 Hom11 0 FSE7 FSE7 Helsinki_Finland
## 5 6.00 Het9 0 FSE7 87179 Helsinki_Finland
## 6 5.50 Het54 0 FSE7 95191 Helsinki_Finland
## DonorPop geno geneticdistance Acceptormito
## 1 Munich_Germany 11_26 0.3902914 5
## 2 Vicenza_Italy 11_20 0.4085754 3
## 3 Saarema_Estonia 11_23 0.3996568 5
## 4 Helsinki_Finland 11 0.0000000 5
## 5 Oslo_Norway 11_2 0.3914800 5
## 6 Yekaterinburg_Russia 11_9 0.4146880 5
## Geographicdistance assay plate rep HoStockplatesAcc HoStockplatesDon
## 1 1589 1 2 2 1 1
## 2 1823 6 1 3 3 2
## 3 292 1 2 3 1 1
## 4 0 6 1 1 11_X3 11_X3
## 5 812 1 1 1 1 1
## 6 2078 4 2 4 1 1
## HetSynthesis HetStockplates HetPrecultures Date Type
## 1 1 1 1 4 Heterokaryon
## 2 2 4 6 9 Heterokaryon
## 3 1 1 1 4 Heterokaryon
## 4 NA NA 11_4 Mar2016 Homokaryon
## 5 1 1 1 4 Heterokaryon
## 6 1 3 4 3 Heterokaryon
## geographicdistancestd geneticdistancestd geneticdistancestdSq
## 1 0.2156009 0.2607463 0.06798862
## 2 0.4390378 0.4117039 0.16950014
## 3 -1.0228506 0.3380697 0.11429109
## 4 -1.3016693 -2.9616024 8.77108856
## 5 -0.5263242 0.2705593 0.07320231
## 6 0.6825268 0.4621707 0.21360178
## geneticdistancestdCub geneticdistancegrp geneticdistancefac
## 1 0.01772778 0.39 0.39
## 2 0.06978387 0.40 0.40
## 3 0.03863835 0.39 0.39
## 4 -25.97647662 0.00 0.00
## 5 0.01980556 0.39 0.39
## 6 0.09872049 0.41 0.41
## geno_synthesis geno_synthesis_HetStockplates
## 1 11_26_1 11_26_1_1
## 2 11_20_2 11_20_2_4
## 3 11_23_1 11_23_1_1
## 4 11_NA 11_NA_NA
## 5 11_2_1 11_2_1_1
## 6 11_9_1 11_9_1_3
## geno_synthesis_HetStockplates_HetPreculture
## 1 11_26_1_1_1
## 2 11_20_2_4_6
## 3 11_23_1_1_1
## 4 11_NA_NA_11_4
## 5 11_2_1_1_1
## 6 11_9_1_3_4
## geno_synthesis_HetStockplates_HetPreculture_assay
## 1 11_26_1_1_1_1
## 2 11_20_2_4_6_6
## 3 11_23_1_1_1_1
## 4 11_NA_NA_11_4_6
## 5 11_2_1_1_1_1
## 6 11_9_1_3_4_4
## geno_synthesis_HetStockplates_HetPreculture_assay_plate
## 1 11_26_1_1_1_1_2
## 2 11_20_2_4_6_6_1
## 3 11_23_1_1_1_1_2
## 4 11_NA_NA_11_4_6_1
## 5 11_2_1_1_1_1_1
## 6 11_9_1_3_4_4_2
## geno_synthesis_HetStockplates_HetPreculture_assay_plate_rep
## 1 11_26_1_1_1_1_2_2
## 2 11_20_2_4_6_6_1_3
## 3 11_23_1_1_1_1_2_3
## 4 11_NA_NA_11_4_6_1_1
## 5 11_2_1_1_1_1_1_1
## 6 11_9_1_3_4_4_2_4
## Acceptor_HoStockplatesAcc Donor_HoStockplatesDon AcceptorMM Midparent
## 1 11_1 26_1 11 26
## 2 11_3 20_2 11 20
## 3 11_1 23_1 11 23
## 4 11_11_X3 11_11_X3 11 11
## 5 11_1 2_1 11 2
## 6 11_1 9_1 11 9
## AccDonCov HetDonorbyHom HetAccbyHom Mito Acceptor15 DonorhomGR AcchomGR
## 1 26 26 11 5 11 1.930952 1.942217
## 2 20 20 11 3 11 1.654298 1.942217
## 3 23 23 11 5 11 NA 1.942217
## 4 11 11 11 5 11 1.942217 1.942217
## 5 2 2 11 5 11 1.326866 1.942217
## 6 9 9 11 5 11 1.137679 1.942217
## midhompar midhomparcent logGRcent midparentheterosis hetvigor
## 1 1.936585 0.28910352 0.12868091 -0.01366915 -0.01366915
## 2 1.798258 0.15077637 0.16427157 0.16024867 0.16024867
## 3 NA NA 0.26470569 NA NA
## 4 1.942217 0.29473623 0.14798276 0.00000000 0.00000000
## 5 1.634542 -0.01293924 -0.02907527 0.13061743 0.13061743
## 6 1.539948 -0.10753281 0.09285899 0.34714527 0.34714527
## besthompar besthomparcent strain18 GR
## 1 1.942217 0.07045624 1 6.840874
## 2 1.942217 0.07045624 1 7.088730
## 3 NA NA 3 7.837660
## 4 1.942217 0.07045624 1 6.974198
## 5 1.942217 0.07045624 1 5.842503
## 6 1.942217 0.07045624 1 6.600158
rang <- range(dataGR$GR)
brks <- seq(0, 10, by = 0.5)
par(mar=c(5, 5, 2, 1), mfrow=c(1, 1), xpd=TRUE)
histFull <- hist(dataGR$GR[dataGR$Type=="Heterokaryon"], breaks = brks, plot = FALSE)
histFull$counts <- histFull$counts/sum(histFull$counts)
plot(histFull, col = colpal[1], xlab = "Mycelium growth rate (mm/day)", ylab = "Frequency distribution", axes = TRUE, main = "", border = NA, freq=TRUE, xlim=c(0, 10), ylim=c(0, 0.25), las=1)
histAns <- hist(dataGR$GR[dataGR$Type=="Homokaryon"], breaks = brks, plot = FALSE)
histAns$counts <- histAns$counts/sum(histAns$counts)
lines(histAns, col=colpal[2], border=bordercol)
lines(histFull, col = NA, border = "white")
legend(-0.25, 0.275, c("homokaryons (n=16)", "heterokaryons (n=225)"), fill=colpal[2:1], border="white", bty="n")
dev.off()
## quartz_off_screen
## 2
FigS6: Plot distributions
colpal <- brewer.pal(3, "Set2")
bordercol <- makeTransparent(colpal[2], 0)
wcorr <- 7
hcorr <- 4
pdf(file="FigureS6_Distributions.pdf", width = wcorr, height = hcorr)
## Graph for MGR
head(dataGR)
## Acceptor Donor geno_DonorSG logGR logintGR unit intGR
## 1 11 26 11_26_0 1.922916 2.802864 11_26_X1_2_2 17.91667
## 2 11 20 11_20_0 1.958506 2.839034 11_20_X6_1_3 16.83333
## 3 11 23 11_23_1 2.058940 3.065760 11_23_X1_2_3 21.83333
## 4 11 11 11_0 1.942217 2.761546 11_X6_1_1 12.75000
## 5 11 2 11_2_0 1.765159 2.663283 11_2_X1_1_1 11.66667
## 6 11 9 11_9_0 1.887094 2.740700 11_9_X4_2_4 11.83333
## slopeGR ID DonorSG AcceptorName DonorName AcceptorPop
## 1 6.25 Het178 0 FSE7 Fas161 Helsinki_Finland
## 2 7.00 Het132 0 FSE7 87075 Helsinki_Finland
## 3 7.50 Het155 1 FSE7 91132 Helsinki_Finland
## 4 6.25 Hom11 0 FSE7 FSE7 Helsinki_Finland
## 5 6.00 Het9 0 FSE7 87179 Helsinki_Finland
## 6 5.50 Het54 0 FSE7 95191 Helsinki_Finland
## DonorPop geno geneticdistance Acceptormito
## 1 Munich_Germany 11_26 0.3902914 5
## 2 Vicenza_Italy 11_20 0.4085754 3
## 3 Saarema_Estonia 11_23 0.3996568 5
## 4 Helsinki_Finland 11 0.0000000 5
## 5 Oslo_Norway 11_2 0.3914800 5
## 6 Yekaterinburg_Russia 11_9 0.4146880 5
## Geographicdistance assay plate rep HoStockplatesAcc HoStockplatesDon
## 1 1589 1 2 2 1 1
## 2 1823 6 1 3 3 2
## 3 292 1 2 3 1 1
## 4 0 6 1 1 11_X3 11_X3
## 5 812 1 1 1 1 1
## 6 2078 4 2 4 1 1
## HetSynthesis HetStockplates HetPrecultures Date Type
## 1 1 1 1 4 Heterokaryon
## 2 2 4 6 9 Heterokaryon
## 3 1 1 1 4 Heterokaryon
## 4 NA NA 11_4 Mar2016 Homokaryon
## 5 1 1 1 4 Heterokaryon
## 6 1 3 4 3 Heterokaryon
## geographicdistancestd geneticdistancestd geneticdistancestdSq
## 1 0.2156009 0.2607463 0.06798862
## 2 0.4390378 0.4117039 0.16950014
## 3 -1.0228506 0.3380697 0.11429109
## 4 -1.3016693 -2.9616024 8.77108856
## 5 -0.5263242 0.2705593 0.07320231
## 6 0.6825268 0.4621707 0.21360178
## geneticdistancestdCub geneticdistancegrp geneticdistancefac
## 1 0.01772778 0.39 0.39
## 2 0.06978387 0.40 0.40
## 3 0.03863835 0.39 0.39
## 4 -25.97647662 0.00 0.00
## 5 0.01980556 0.39 0.39
## 6 0.09872049 0.41 0.41
## geno_synthesis geno_synthesis_HetStockplates
## 1 11_26_1 11_26_1_1
## 2 11_20_2 11_20_2_4
## 3 11_23_1 11_23_1_1
## 4 11_NA 11_NA_NA
## 5 11_2_1 11_2_1_1
## 6 11_9_1 11_9_1_3
## geno_synthesis_HetStockplates_HetPreculture
## 1 11_26_1_1_1
## 2 11_20_2_4_6
## 3 11_23_1_1_1
## 4 11_NA_NA_11_4
## 5 11_2_1_1_1
## 6 11_9_1_3_4
## geno_synthesis_HetStockplates_HetPreculture_assay
## 1 11_26_1_1_1_1
## 2 11_20_2_4_6_6
## 3 11_23_1_1_1_1
## 4 11_NA_NA_11_4_6
## 5 11_2_1_1_1_1
## 6 11_9_1_3_4_4
## geno_synthesis_HetStockplates_HetPreculture_assay_plate
## 1 11_26_1_1_1_1_2
## 2 11_20_2_4_6_6_1
## 3 11_23_1_1_1_1_2
## 4 11_NA_NA_11_4_6_1
## 5 11_2_1_1_1_1_1
## 6 11_9_1_3_4_4_2
## geno_synthesis_HetStockplates_HetPreculture_assay_plate_rep
## 1 11_26_1_1_1_1_2_2
## 2 11_20_2_4_6_6_1_3
## 3 11_23_1_1_1_1_2_3
## 4 11_NA_NA_11_4_6_1_1
## 5 11_2_1_1_1_1_1_1
## 6 11_9_1_3_4_4_2_4
## Acceptor_HoStockplatesAcc Donor_HoStockplatesDon AcceptorMM Midparent
## 1 11_1 26_1 11 26
## 2 11_3 20_2 11 20
## 3 11_1 23_1 11 23
## 4 11_11_X3 11_11_X3 11 11
## 5 11_1 2_1 11 2
## 6 11_1 9_1 11 9
## AccDonCov HetDonorbyHom HetAccbyHom Mito Acceptor15 DonorhomGR AcchomGR
## 1 26 26 11 5 11 1.930952 1.942217
## 2 20 20 11 3 11 1.654298 1.942217
## 3 23 23 11 5 11 NA 1.942217
## 4 11 11 11 5 11 1.942217 1.942217
## 5 2 2 11 5 11 1.326866 1.942217
## 6 9 9 11 5 11 1.137679 1.942217
## midhompar midhomparcent logGRcent midparentheterosis hetvigor
## 1 1.936585 0.28910352 0.12868091 -0.01366915 -0.01366915
## 2 1.798258 0.15077637 0.16427157 0.16024867 0.16024867
## 3 NA NA 0.26470569 NA NA
## 4 1.942217 0.29473623 0.14798276 0.00000000 0.00000000
## 5 1.634542 -0.01293924 -0.02907527 0.13061743 0.13061743
## 6 1.539948 -0.10753281 0.09285899 0.34714527 0.34714527
## besthompar besthomparcent strain18 GR
## 1 1.942217 0.07045624 1 6.840874
## 2 1.942217 0.07045624 1 7.088730
## 3 NA NA 3 7.837660
## 4 1.942217 0.07045624 1 6.974198
## 5 1.942217 0.07045624 1 5.842503
## 6 1.942217 0.07045624 1 6.600158
rang <- range(dataGR$GR)
brks <- seq(0, 10, by = 0.5)
par(mar=c(5, 5, 2, 1), mfrow=c(1, 2), xpd=TRUE)
histFull <- hist(dataGR$GR[dataGR$Type=="Heterokaryon"], breaks = brks, plot = FALSE)
histFullSG <- hist(dataGR$GR[dataGR$Type=="Heterokaryon"&dataGR$DonorSG==1], breaks = brks, plot = FALSE)
histFullSG$counts <- histFullSG$counts/sum(histFull$counts)
histFull$counts <- histFull$counts/sum(histFull$counts)
plot(histFull, col = colpal[1], xlab = "Mycelium growth rate (mm/day)", ylab = "Frequency distribution", axes = TRUE, main = "", border = NA, freq=TRUE, xlim=c(0, 10), ylim=c(0, 0.35), las=1)
histAns <- hist(dataGR$GR[dataGR$Type=="Homokaryon"], breaks = brks, plot = FALSE)
histAns$counts <- histAns$counts/sum(histAns$counts)
lines(histAns, col=colpal[2], border=bordercol)
lines(histFull, col = NA, border = "white")
lines(histFullSG, col = NA, border = colpal[3])
legend(-0.1, 0.35, c("homokaryons (n=16)", "all heterokaryons (n=225)", "senescent heterokaryons (n=56)"), fill=c(colpal[2:1], NA), border=c("white","white",colpal[3]), bty="n", cex=0.75, x.intersp=0.5)
mtext("A", side=3, at=-2, line=0, cex=1.5)
mtext("B", side=3, at=14.25, line=0, cex=1.5)
## Graph for WWL
rang <- range(dataWL$weightloss)
brks <- seq(-0.1, 0.45, by = 0.025)
histFull <- hist(dataWL$weightloss[dataWL$Type=="Heterokaryon"], breaks = brks, plot = FALSE)
histFullSG <- hist(dataWL$weightloss[dataWL$Type=="Heterokaryon"&dataWL$DonorSG==1], breaks = brks, plot = FALSE)
histFullSG$counts <- histFullSG$counts/sum(histFull$counts)
histFull$counts <- histFull$counts/sum(histFull$counts)
plot(histFull, col = colpal[1], xlab = "Wood weight loss (mg)", ylab = NA, axes = TRUE, main = "", border = NA, freq=TRUE, xlim=c(-0.1, 0.5), ylim=c(0, 0.35), las=1)
histAns <- hist(dataWL$weightloss[dataGR$Type=="Homokaryon"], breaks = brks, plot = FALSE)
histAns$counts <- histAns$counts/sum(histAns$counts)
lines(histAns, col=colpal[2], border=bordercol)
lines(histFull, col = NA, border = "white")
lines(histFullSG, col = NA, border = colpal[3])
legend(-0.1, 0.35, c("homokaryons (n=16)", "all heterokaryons (n=198)", "senescent heterokaryons (n=52)"),
fill=c(colpal[2:1], NA), border=c("white", "white", colpal[3]), bty="n", cex=0.75, x.intersp=0.5)
dev.off()
## quartz_off_screen
## 2
Figure 4: MGR csnucl and csmit
pathtofigures <- "."
dataGR2 <- read.table(file=paste(pathtofigures, "AveragelogGRperstrain241het16hom20181128.csv", sep="/"), header=TRUE, sep=",")
head(dataGR2)
## Acceptor Donor geno_DonorSG logGR logintGR unit intGR
## 1 11 26 11_26_0 1.922916 2.802864 11_26_X1_2_2 17.91667
## 2 11 20 11_20_0 1.958506 2.839034 11_20_X6_1_3 16.83333
## 3 11 23 11_23_1 2.058940 3.065760 11_23_X1_2_3 21.83333
## 4 11 11 11_0 1.942217 2.761546 11_X6_1_1 12.75000
## 5 11 2 11_2_0 1.765159 2.663283 11_2_X1_1_1 11.66667
## 6 11 9 11_9_0 1.887094 2.740700 11_9_X4_2_4 11.83333
## slopeGR ID DonorSG AcceptorName DonorName AcceptorPop
## 1 6.25 Het178 0 FSE7 Fas161 Helsinki_Finland
## 2 7.00 Het132 0 FSE7 87075 Helsinki_Finland
## 3 7.50 Het155 1 FSE7 91132 Helsinki_Finland
## 4 6.25 Hom11 0 FSE7 FSE7 Helsinki_Finland
## 5 6.00 Het9 0 FSE7 87179 Helsinki_Finland
## 6 5.50 Het54 0 FSE7 95191 Helsinki_Finland
## DonorPop geno geneticdistance Acceptormito
## 1 Munich_Germany 11_26 0.3902914 5
## 2 Vicenza_Italy 11_20 0.4085754 3
## 3 Saarema_Estonia 11_23 0.3996568 5
## 4 Helsinki_Finland 11 0.0000000 5
## 5 Oslo_Norway 11_2 0.3914800 5
## 6 Yekaterinburg_Russia 11_9 0.4146880 5
## Geographicdistance assay plate rep HoStockplatesAcc HoStockplatesDon
## 1 1589 1 2 2 1 1
## 2 1823 6 1 3 3 2
## 3 292 1 2 3 1 1
## 4 0 6 1 1 11_X3 11_X3
## 5 812 1 1 1 1 1
## 6 2078 4 2 4 1 1
## HetSynthesis HetStockplates HetPrecultures Date Type
## 1 1 1 1 4 Heterokaryon
## 2 2 4 6 9 Heterokaryon
## 3 1 1 1 4 Heterokaryon
## 4 NA NA 11_4 Mar2016 Homokaryon
## 5 1 1 1 4 Heterokaryon
## 6 1 3 4 3 Heterokaryon
## geographicdistancestd geneticdistancestd geneticdistancestdSq
## 1 0.2156009 0.2607463 0.06798862
## 2 0.4390378 0.4117039 0.16950014
## 3 -1.0228506 0.3380697 0.11429109
## 4 -1.3016693 -2.9616024 8.77108856
## 5 -0.5263242 0.2705593 0.07320231
## 6 0.6825268 0.4621707 0.21360178
## geneticdistancestdCub geneticdistancegrp geneticdistancefac
## 1 0.01772778 0.39 0.39
## 2 0.06978387 0.40 0.40
## 3 0.03863835 0.39 0.39
## 4 -25.97647662 0.00 0.00
## 5 0.01980556 0.39 0.39
## 6 0.09872049 0.41 0.41
## geno_synthesis geno_synthesis_HetStockplates
## 1 11_26_1 11_26_1_1
## 2 11_20_2 11_20_2_4
## 3 11_23_1 11_23_1_1
## 4 11_NA 11_NA_NA
## 5 11_2_1 11_2_1_1
## 6 11_9_1 11_9_1_3
## geno_synthesis_HetStockplates_HetPreculture
## 1 11_26_1_1_1
## 2 11_20_2_4_6
## 3 11_23_1_1_1
## 4 11_NA_NA_11_4
## 5 11_2_1_1_1
## 6 11_9_1_3_4
## geno_synthesis_HetStockplates_HetPreculture_assay
## 1 11_26_1_1_1_1
## 2 11_20_2_4_6_6
## 3 11_23_1_1_1_1
## 4 11_NA_NA_11_4_6
## 5 11_2_1_1_1_1
## 6 11_9_1_3_4_4
## geno_synthesis_HetStockplates_HetPreculture_assay_plate
## 1 11_26_1_1_1_1_2
## 2 11_20_2_4_6_6_1
## 3 11_23_1_1_1_1_2
## 4 11_NA_NA_11_4_6_1
## 5 11_2_1_1_1_1_1
## 6 11_9_1_3_4_4_2
## geno_synthesis_HetStockplates_HetPreculture_assay_plate_rep
## 1 11_26_1_1_1_1_2_2
## 2 11_20_2_4_6_6_1_3
## 3 11_23_1_1_1_1_2_3
## 4 11_NA_NA_11_4_6_1_1
## 5 11_2_1_1_1_1_1_1
## 6 11_9_1_3_4_4_2_4
## Acceptor_HoStockplatesAcc Donor_HoStockplatesDon AcceptorMM Midparent
## 1 11_1 26_1 11 26
## 2 11_3 20_2 11 20
## 3 11_1 23_1 11 23
## 4 11_11_X3 11_11_X3 11 11
## 5 11_1 2_1 11 2
## 6 11_1 9_1 11 9
## AccDonCov HetDonorbyHom HetAccbyHom Mito Acceptor15 DonorhomGR AcchomGR
## 1 26 26 11 5 11 1.930952 1.942217
## 2 20 20 11 3 11 1.654298 1.942217
## 3 23 23 11 5 11 NA 1.942217
## 4 11 11 11 5 11 1.942217 1.942217
## 5 2 2 11 5 11 1.326866 1.942217
## 6 9 9 11 5 11 1.137679 1.942217
## midhompar midhomparcent logGRcent midparentheterosis hetvigor
## 1 1.936585 0.28910352 0.12868091 -0.01366915 -0.01366915
## 2 1.798258 0.15077637 0.16427157 0.16024867 0.16024867
## 3 NA NA 0.26470569 NA NA
## 4 1.942217 0.29473623 0.14798276 0.00000000 0.00000000
## 5 1.634542 -0.01293924 -0.02907527 0.13061743 0.13061743
## 6 1.539948 -0.10753281 0.09285899 0.34714527 0.34714527
## besthompar besthomparcent strain18
## 1 1.942217 0.07045624 1
## 2 1.942217 0.07045624 1
## 3 NA NA 3
## 4 1.942217 0.07045624 1
## 5 1.942217 0.07045624 1
## 6 1.942217 0.07045624 1
## For MGR
## Heterokaryon with synthesised with the same homokaryon (once as a slow grower, once as a normal grower)
SG0_1 <- levels(dataGR2$genoHeterokaryon)[table(dataGR2$genoHeterokaryon)>1]
## Get rid of the replicate where the donor homokaryon is senescent
dataGR2 <- dataGR2[!dataGR2$geno_DonorSG%in%paste0(SG0_1, "_1"),]
length(dataGR2$logGR[dataGR2$Type=="Heterokaryon"])
## [1] 230
length(dataGR2$logGR[dataGR2$Type=="Heterokaryon"&dataGR2$DonorSG==0])
## [1] 169
length(dataGR2$logGR[dataGR2$Type=="Heterokaryon"&dataGR2$DonorSG==1])
## [1] 61
tapply(dataGR2$ID, dataGR2$Type, length)
## Heterokaryon Homokaryon
## 230 16
dataGR2$logGRcentered <- NA
dataGR2$DonorhomlogGRcentered <- NA
dataGR2$AcchomGRlogGRcentered <- NA
dataGR2$logGRcentered[dataGR2$Type=="Heterokaryon"] <- dataGR2$logGR[dataGR2$Type=="Heterokaryon"] -mean(dataGR2$logGR[dataGR2$Type=="Heterokaryon"])
dataGR2$DonorhomlogGRcentered[dataGR2$Type=="Heterokaryon"] <- log(dataGR2$DonorhomGR[dataGR2$Type=="Heterokaryon"]) - mean(log(dataGR2$DonorhomGR[dataGR2$Type=="Heterokaryon"]), na.rm=TRUE)
dataGR2$AcchomlogGRcentered[dataGR2$Type=="Heterokaryon"] <- log(dataGR2$AcchomGR[dataGR2$Type=="Heterokaryon"]) - mean(log(dataGR2$AcchomGR[dataGR2$Type=="Heterokaryon"]), na.rm=TRUE)
## Sample size for figure
sum(!is.na(dataGR2$logGRcentered[dataGR2$Type=="Heterokaryon"])&!is.na(dataGR2$AcchomlogGRcentered[dataGR2$Type=="Heterokaryon"]))
## [1] 229
sum(!is.na(dataGR2$logGRcentered[dataGR2$Type=="Heterokaryon"])&!is.na(dataGR2$DonorhomlogGRcentered[dataGR2$Type=="Heterokaryon"]))
## [1] 176
color <- rgb(red = 1, green = 1, blue = 1, alpha = 255 * 0.25, maxColorValue = 255)
pdf(file=paste(pathtofigures, "Figure4.pdf", sep="/"), height=6, width=10)
## Effect of midparent
#rang <- range(c(dataGR2$logGRcentered[dataGR2$Type=="Heterokaryon"], dataGR2$DonorhomlogGRcentered[dataGR2$Type=="Heterokaryon"], dataGR2$AcchomGRlogGRcentered[dataGR2$Type=="Heterokaryon"]), na.rm=TRUE)
rang <- c(-1, 0.5)
cexlab <- 1.5
par(mar=c(6.1, 6.1, 4.1, 2.1), mfrow=c(1, 2), xpd=FALSE)
plot(dataGR2$logGRcentered[dataGR2$Type=="Heterokaryon"] ~ dataGR2$DonorhomlogGRcentered[dataGR2$Type=="Heterokaryon"], las=1, pch=16, axes=FALSE, xlim=rang, ylim=rang, xlab=NA, ylab=NA, col=color)
axis(1, at=seq(-1, 0.5, by=0.5))
axis(2, at=seq(-1, 0.5, by=0.5), las=1)
mtext("log(donor homokaryon MGR)", side = 1, line=3, cex=cexlab)
mtext("log(heterokaryon MGR)", side = 2, line=4, cex=cexlab)
abline(a=0, b=0.1693730, lty=1, lwd=2)
abline(a=0, b=0.06291601, lty=2, lwd=2)
abline(a=0, b=0.3310000, lty=2, lwd=2)
#legend(-1, 1, legend=c("Non slow grower donor parent", "Slow grower donor parent"), pch=16, col=1:2, bty="n")
mtext("A", side=3, line = 1, at=-1.2, cex=1.5)
## Effect of acceptor homokaryon parent
par(mar=c(6.1, 3.1, 4.1, 3.1))
plot(dataGR2$logGRcentered[dataGR2$Type=="Heterokaryon"] ~ dataGR2$AcchomlogGRcentered[dataGR2$Type=="Heterokaryon"], las=1, pch=16, axes=FALSE, xlim=rang, ylim=rang, xlab=NA, ylab=NA, col=color)
axis(1, at=seq(-1, 0.5, by=0.5))
axis(2, at=seq(-1, 0.5, by=0.5), las=1)
mtext("log(acceptor homokaryon MGR)", side = 1, line=3, cex=cexlab)
abline(a=0, b=0.1693730, lty=1, lwd=2)
abline(a=0, b=0.06291601, lty=2, lwd=2)
abline(a=0, b=0.3310000, lty=2, lwd=2)
mtext("B", side=3, line = 1, at=-1.2, cex=1.5)
#legend(-1, 1, legend=c("Non slow grower donor parent", "Slow grower donor parent"), pch=16, col=1:2, bty="n")
dev.off()
## quartz_off_screen
## 2
Figure S9: WWL csnucl and csmit
pathtofigures <- "."
dataWL2 <- read.table(file=paste(pathtofigures, "AveragelogWLperstrain198het16hom20180717.csv", sep="/"), header=TRUE, sep=";")
head(dataWL2)
## Acceptor Donor geno_DonorSG logweightloss ID DonorSG AcceptorName
## 1 11 7 11_7_1 0.1750899 Het46 1 FSE7
## 2 11 30 11_30_0 0.2043858 Het205 0 FSE7
## 3 11 9 11_9_0 0.2307522 Het54 0 FSE7
## 4 11 32 11_32_0 0.2866868 Het234 0 FSE7
## 5 11 11 11_0 0.1324016 Hom11 0 FSE7
## 6 11 25 11_25_1 0.1792176 Het172 1 FSE7
## DonorName AcceptorPop DonorPop geno geneticdistance
## 1 95156 Helsinki_Finland Yekaterinburg_Russia 11_7 0.3761182
## 2 HR32 Helsinki_Finland Smolyan_Bulgaria 11_30 0.4094155
## 3 95191 Helsinki_Finland Yekaterinburg_Russia 11_9 0.4146880
## 4 Sa948 Helsinki_Finland Sätuna_Sweden 11_32 0.3880394
## 5 FSE7 Helsinki_Finland Helsinki_Finland 11 0.0000000
## 6 93134 Helsinki_Finland Aalborg_Denmark 11_25 0.4086827
## Acceptormito geographicdistance initweight weightloss plate_rep_time
## 1 5 2078 1.1409 0.2369 X2_2_i
## 2 5 1858 1.0707 0.3283 X3_2_i
## 3 5 2078 1.1425 0.2520 X1_1_i
## 4 5 778 1.1308 0.3086 X2_2_i
## 5 5 0 0.8004 0.1398 X3_2_i
## 6 5 934 1.2094 0.2908 X1_1_i
## plate rep Type ID_plate geographicdistancestd
## 1 X2 2 Heterokaryon Het46_X2 0.6313005
## 2 X3 2 Heterokaryon Het205_X3 0.4212340
## 3 X1 1 Heterokaryon Het54_X1 0.6313005
## 4 X2 2 Heterokaryon Het234_X2 -0.6100014
## 5 X3 2 Homokaryon Hom11_X3 -1.3528728
## 6 X1 1 Heterokaryon Het172_X1 -0.4610452
## geneticdistancestd geneticdistancestdSq geneticdistancefac initweightstd
## 1 0.06125775 0.003752512 0.37 0.7106281
## 2 0.37385710 0.139769132 0.40 0.1910697
## 3 0.42335619 0.179230466 0.41 0.7224698
## 4 0.17317604 0.029989939 0.38 0.6358768
## 5 -3.46978937 12.039438250 0.00 -1.8094522
## 6 0.36697757 0.134672534 0.40 1.2176046
## initweightstdSq AcceptorMM Midparent Mito AccDonCov HetDonorbyHom
## 1 0.50499224 11 7 5 7 7
## 2 0.03650762 11 30 5 30 30
## 3 0.52196268 11 9 5 9 9
## 4 0.40433928 11 32 5 32 32
## 5 3.27411733 11 11 5 11 11
## 6 1.48256088 11 25 5 25 25
## HetAccbyHom DonorhomWL AcchomWL midhompar midhomparcent
## 1 11 NA 0.1324016 NA NA
## 2 11 0.07476087 0.1324016 0.1035812 -0.06348117
## 3 11 0.13500723 0.1324016 0.1337044 -0.03335799
## 4 11 0.23740947 0.1324016 0.1849055 0.01784313
## 5 11 0.13240160 0.1324016 0.1324016 -0.03466080
## 6 11 NA 0.1324016 NA NA
## midparentheterosis hetvigor besthompar Acceptor15 DonorhomWL.1
## 1 NA NA NA 11 NA
## 2 0.10080454 0.10080454 0.1324016 11 0.07476087
## 3 0.09704782 0.09704782 0.1350072 11 0.13500723
## 4 0.10178127 0.10178127 0.2374095 11 0.23740947
## 5 0.00000000 0.00000000 0.1324016 11 0.13240160
## 6 NA NA NA 11 NA
## AcchomWL.1 logWLcent besthomparcent strain18
## 1 0.1324016 -0.037622462 NA 3
## 2 0.1324016 -0.008326635 -0.08182514 1
## 3 0.1324016 0.018039832 -0.07921951 1
## 4 0.1324016 0.073974403 0.02318273 1
## 5 0.1324016 -0.080310802 -0.08182514 1
## 6 0.1324016 -0.033494787 NA 3
## For MGR
## Heterokaryon with synthesised with the same homokaryon (once as a slow grower, once as a normal grower)
SG0_1 <- levels(dataWL2$geno)[table(dataWL2$geno)>1]
## Get rid of the replicate where the donor homokaryon is senescent
dataWL2 <- dataWL2[!dataWL2$geno_DonorSG%in%paste0(SG0_1, "_1"),]
head(dataWL2)
## Acceptor Donor geno_DonorSG logweightloss ID DonorSG AcceptorName
## 1 11 7 11_7_1 0.1750899 Het46 1 FSE7
## 2 11 30 11_30_0 0.2043858 Het205 0 FSE7
## 3 11 9 11_9_0 0.2307522 Het54 0 FSE7
## 4 11 32 11_32_0 0.2866868 Het234 0 FSE7
## 5 11 11 11_0 0.1324016 Hom11 0 FSE7
## 6 11 25 11_25_1 0.1792176 Het172 1 FSE7
## DonorName AcceptorPop DonorPop geno geneticdistance
## 1 95156 Helsinki_Finland Yekaterinburg_Russia 11_7 0.3761182
## 2 HR32 Helsinki_Finland Smolyan_Bulgaria 11_30 0.4094155
## 3 95191 Helsinki_Finland Yekaterinburg_Russia 11_9 0.4146880
## 4 Sa948 Helsinki_Finland Sätuna_Sweden 11_32 0.3880394
## 5 FSE7 Helsinki_Finland Helsinki_Finland 11 0.0000000
## 6 93134 Helsinki_Finland Aalborg_Denmark 11_25 0.4086827
## Acceptormito geographicdistance initweight weightloss plate_rep_time
## 1 5 2078 1.1409 0.2369 X2_2_i
## 2 5 1858 1.0707 0.3283 X3_2_i
## 3 5 2078 1.1425 0.2520 X1_1_i
## 4 5 778 1.1308 0.3086 X2_2_i
## 5 5 0 0.8004 0.1398 X3_2_i
## 6 5 934 1.2094 0.2908 X1_1_i
## plate rep Type ID_plate geographicdistancestd
## 1 X2 2 Heterokaryon Het46_X2 0.6313005
## 2 X3 2 Heterokaryon Het205_X3 0.4212340
## 3 X1 1 Heterokaryon Het54_X1 0.6313005
## 4 X2 2 Heterokaryon Het234_X2 -0.6100014
## 5 X3 2 Homokaryon Hom11_X3 -1.3528728
## 6 X1 1 Heterokaryon Het172_X1 -0.4610452
## geneticdistancestd geneticdistancestdSq geneticdistancefac initweightstd
## 1 0.06125775 0.003752512 0.37 0.7106281
## 2 0.37385710 0.139769132 0.40 0.1910697
## 3 0.42335619 0.179230466 0.41 0.7224698
## 4 0.17317604 0.029989939 0.38 0.6358768
## 5 -3.46978937 12.039438250 0.00 -1.8094522
## 6 0.36697757 0.134672534 0.40 1.2176046
## initweightstdSq AcceptorMM Midparent Mito AccDonCov HetDonorbyHom
## 1 0.50499224 11 7 5 7 7
## 2 0.03650762 11 30 5 30 30
## 3 0.52196268 11 9 5 9 9
## 4 0.40433928 11 32 5 32 32
## 5 3.27411733 11 11 5 11 11
## 6 1.48256088 11 25 5 25 25
## HetAccbyHom DonorhomWL AcchomWL midhompar midhomparcent
## 1 11 NA 0.1324016 NA NA
## 2 11 0.07476087 0.1324016 0.1035812 -0.06348117
## 3 11 0.13500723 0.1324016 0.1337044 -0.03335799
## 4 11 0.23740947 0.1324016 0.1849055 0.01784313
## 5 11 0.13240160 0.1324016 0.1324016 -0.03466080
## 6 11 NA 0.1324016 NA NA
## midparentheterosis hetvigor besthompar Acceptor15 DonorhomWL.1
## 1 NA NA NA 11 NA
## 2 0.10080454 0.10080454 0.1324016 11 0.07476087
## 3 0.09704782 0.09704782 0.1350072 11 0.13500723
## 4 0.10178127 0.10178127 0.2374095 11 0.23740947
## 5 0.00000000 0.00000000 0.1324016 11 0.13240160
## 6 NA NA NA 11 NA
## AcchomWL.1 logWLcent besthomparcent strain18
## 1 0.1324016 -0.037622462 NA 3
## 2 0.1324016 -0.008326635 -0.08182514 1
## 3 0.1324016 0.018039832 -0.07921951 1
## 4 0.1324016 0.073974403 0.02318273 1
## 5 0.1324016 -0.080310802 -0.08182514 1
## 6 0.1324016 -0.033494787 NA 3
length(dataWL2$logweightloss[dataWL2$Type=="Heterokaryon"])
## [1] 198
length(dataWL2$logweightloss[dataWL2$Type=="Heterokaryon"&dataWL2$DonorSG==0])
## [1] 146
length(dataWL2$logweightloss[dataWL2$Type=="Heterokaryon"&dataWL2$DonorSG==1])
## [1] 52
dataWL2$logGRcentered <- NA
dataWL2$DonorhomlogGRcentered <- NA
dataWL2$AcchomGRlogGRcentered <- NA
dataWL2$logweightlosscentered[dataWL2$Type=="Heterokaryon"] <- dataWL2$logweightloss[dataWL2$Type=="Heterokaryon"] -mean(dataWL2$logweightloss[dataWL2$Type=="Heterokaryon"])
dataWL2$DonorhomlogWLcentered[dataWL2$Type=="Heterokaryon"] <- log(dataWL2$DonorhomWL[dataWL2$Type=="Heterokaryon"]) - mean(log(dataWL2$DonorhomWL[dataWL2$Type=="Heterokaryon"]), na.rm=TRUE)
dataWL2$AcchomlogWLcentered[dataWL2$Type=="Heterokaryon"] <- log(dataWL2$AcchomWL[dataWL2$Type=="Heterokaryon"]) - mean(log(dataWL2$AcchomWL[dataWL2$Type=="Heterokaryon"]), na.rm=TRUE)
## Sample size for figure
sum(!is.na(dataWL2$logweightlosscentered[dataWL2$Type=="Heterokaryon"])&!is.na(dataWL2$AcchomlogWLcentered[dataWL2$Type=="Heterokaryon"]))
## [1] 197
sum(!is.na(dataWL2$logweightlosscentered[dataWL2$Type=="Heterokaryon"])&!is.na(dataWL2$DonorhomlogWLcentered[dataWL2$Type=="Heterokaryon"]))
## [1] 151
pdf(file=paste(pathtofigures, "FigureS9.pdf", sep="/"), height=6, width=10)
## Effect of midparent
#rang <- range(c(dataWL2$logweightlosscentered[dataWL2$Type=="Heterokaryon"], dataWL2$AcchomlogWLcentered[dataWL2$Type=="Heterokaryon"], dataWL2$DonorhomlogWLcentered[dataWL2$Type=="Heterokaryon"]), na.rm=TRUE)
rang <- c(-2, 1)
cexlab <- 1.5
par(mar=c(6.1, 6.1, 4.1, 2.1), mfrow=c(1, 2), xpd=FALSE)
plot(dataWL2$logweightlosscentered[dataWL2$Type=="Heterokaryon"] ~ dataWL2$DonorhomlogWLcentered[dataWL2$Type=="Heterokaryon"], las=1, pch=16, axes=FALSE, xlim=rang, ylim=c(-0.25, 0.25), xlab=NA, ylab=NA)
axis(1, at=seq(-2, 1, by=1))
axis(2, at=seq(-0.25, 0.25, by=0.125), las=1)
mtext("log(donor homokaryon WWL)", side = 1, line=3, cex=cexlab)
mtext("log(heterokaryon WWL)", side = 2, line=4, cex=cexlab)
abline(a=0, b=0, lty=1, lwd=2)
mtext("A", side=3, line = 1, at=-2.5, cex=1.5)
## Effect of acceptor homokaryon parent
par(mar=c(6.1, 3.1, 4.1, 3.1))
plot(dataWL2$logweightlosscentered[dataWL2$Type=="Heterokaryon"] ~ dataWL2$AcchomlogWLcentered[dataWL2$Type=="Heterokaryon"], las=1, pch=16, axes=FALSE, xlim=rang, ylim=c(-0.25, 0.25), xlab=NA, ylab=NA)
axis(1, at=seq(-2, 1, by=1))
axis(2, at=seq(-0.25, 0.25, by=0.125), las=1)
mtext("log(acceptor homokaryon WWL)", side = 1, line=3, cex=cexlab)
abline(a=0, b=0, lty=1, lwd=2)
mtext("B", side=3, line = 1, at=-2.5, cex=1.5)
dev.off()
## quartz_off_screen
## 2
Figure 5: MGR heterosis~Genetic distance
pathtofigures <- "./"
dataGR2 <- read.table(file=paste(pathtofigures, "AveragelogGRperstrain241het16hom20181128.csv", sep="/"), header=TRUE, sep=",")
head(dataGR2)
## Acceptor Donor geno_DonorSG logGR logintGR unit intGR
## 1 11 26 11_26_0 1.922916 2.802864 11_26_X1_2_2 17.91667
## 2 11 20 11_20_0 1.958506 2.839034 11_20_X6_1_3 16.83333
## 3 11 23 11_23_1 2.058940 3.065760 11_23_X1_2_3 21.83333
## 4 11 11 11_0 1.942217 2.761546 11_X6_1_1 12.75000
## 5 11 2 11_2_0 1.765159 2.663283 11_2_X1_1_1 11.66667
## 6 11 9 11_9_0 1.887094 2.740700 11_9_X4_2_4 11.83333
## slopeGR ID DonorSG AcceptorName DonorName AcceptorPop
## 1 6.25 Het178 0 FSE7 Fas161 Helsinki_Finland
## 2 7.00 Het132 0 FSE7 87075 Helsinki_Finland
## 3 7.50 Het155 1 FSE7 91132 Helsinki_Finland
## 4 6.25 Hom11 0 FSE7 FSE7 Helsinki_Finland
## 5 6.00 Het9 0 FSE7 87179 Helsinki_Finland
## 6 5.50 Het54 0 FSE7 95191 Helsinki_Finland
## DonorPop geno geneticdistance Acceptormito
## 1 Munich_Germany 11_26 0.3902914 5
## 2 Vicenza_Italy 11_20 0.4085754 3
## 3 Saarema_Estonia 11_23 0.3996568 5
## 4 Helsinki_Finland 11 0.0000000 5
## 5 Oslo_Norway 11_2 0.3914800 5
## 6 Yekaterinburg_Russia 11_9 0.4146880 5
## Geographicdistance assay plate rep HoStockplatesAcc HoStockplatesDon
## 1 1589 1 2 2 1 1
## 2 1823 6 1 3 3 2
## 3 292 1 2 3 1 1
## 4 0 6 1 1 11_X3 11_X3
## 5 812 1 1 1 1 1
## 6 2078 4 2 4 1 1
## HetSynthesis HetStockplates HetPrecultures Date Type
## 1 1 1 1 4 Heterokaryon
## 2 2 4 6 9 Heterokaryon
## 3 1 1 1 4 Heterokaryon
## 4 NA NA 11_4 Mar2016 Homokaryon
## 5 1 1 1 4 Heterokaryon
## 6 1 3 4 3 Heterokaryon
## geographicdistancestd geneticdistancestd geneticdistancestdSq
## 1 0.2156009 0.2607463 0.06798862
## 2 0.4390378 0.4117039 0.16950014
## 3 -1.0228506 0.3380697 0.11429109
## 4 -1.3016693 -2.9616024 8.77108856
## 5 -0.5263242 0.2705593 0.07320231
## 6 0.6825268 0.4621707 0.21360178
## geneticdistancestdCub geneticdistancegrp geneticdistancefac
## 1 0.01772778 0.39 0.39
## 2 0.06978387 0.40 0.40
## 3 0.03863835 0.39 0.39
## 4 -25.97647662 0.00 0.00
## 5 0.01980556 0.39 0.39
## 6 0.09872049 0.41 0.41
## geno_synthesis geno_synthesis_HetStockplates
## 1 11_26_1 11_26_1_1
## 2 11_20_2 11_20_2_4
## 3 11_23_1 11_23_1_1
## 4 11_NA 11_NA_NA
## 5 11_2_1 11_2_1_1
## 6 11_9_1 11_9_1_3
## geno_synthesis_HetStockplates_HetPreculture
## 1 11_26_1_1_1
## 2 11_20_2_4_6
## 3 11_23_1_1_1
## 4 11_NA_NA_11_4
## 5 11_2_1_1_1
## 6 11_9_1_3_4
## geno_synthesis_HetStockplates_HetPreculture_assay
## 1 11_26_1_1_1_1
## 2 11_20_2_4_6_6
## 3 11_23_1_1_1_1
## 4 11_NA_NA_11_4_6
## 5 11_2_1_1_1_1
## 6 11_9_1_3_4_4
## geno_synthesis_HetStockplates_HetPreculture_assay_plate
## 1 11_26_1_1_1_1_2
## 2 11_20_2_4_6_6_1
## 3 11_23_1_1_1_1_2
## 4 11_NA_NA_11_4_6_1
## 5 11_2_1_1_1_1_1
## 6 11_9_1_3_4_4_2
## geno_synthesis_HetStockplates_HetPreculture_assay_plate_rep
## 1 11_26_1_1_1_1_2_2
## 2 11_20_2_4_6_6_1_3
## 3 11_23_1_1_1_1_2_3
## 4 11_NA_NA_11_4_6_1_1
## 5 11_2_1_1_1_1_1_1
## 6 11_9_1_3_4_4_2_4
## Acceptor_HoStockplatesAcc Donor_HoStockplatesDon AcceptorMM Midparent
## 1 11_1 26_1 11 26
## 2 11_3 20_2 11 20
## 3 11_1 23_1 11 23
## 4 11_11_X3 11_11_X3 11 11
## 5 11_1 2_1 11 2
## 6 11_1 9_1 11 9
## AccDonCov HetDonorbyHom HetAccbyHom Mito Acceptor15 DonorhomGR AcchomGR
## 1 26 26 11 5 11 1.930952 1.942217
## 2 20 20 11 3 11 1.654298 1.942217
## 3 23 23 11 5 11 NA 1.942217
## 4 11 11 11 5 11 1.942217 1.942217
## 5 2 2 11 5 11 1.326866 1.942217
## 6 9 9 11 5 11 1.137679 1.942217
## midhompar midhomparcent logGRcent midparentheterosis hetvigor
## 1 1.936585 0.28910352 0.12868091 -0.01366915 -0.01366915
## 2 1.798258 0.15077637 0.16427157 0.16024867 0.16024867
## 3 NA NA 0.26470569 NA NA
## 4 1.942217 0.29473623 0.14798276 0.00000000 0.00000000
## 5 1.634542 -0.01293924 -0.02907527 0.13061743 0.13061743
## 6 1.539948 -0.10753281 0.09285899 0.34714527 0.34714527
## besthompar besthomparcent strain18
## 1 1.942217 0.07045624 1
## 2 1.942217 0.07045624 1
## 3 NA NA 3
## 4 1.942217 0.07045624 1
## 5 1.942217 0.07045624 1
## 6 1.942217 0.07045624 1
# Function to make color transparent
makeTransparent = function(color, alpha = 0.5) {
if (alpha < 0 | alpha > 1)
stop("alpha must be between 0 and 1")
newcol <- col2rgb(col = color, alpha = FALSE)
return(rgb(red = newcol[1], green = newcol[2], blue = newcol[3], alpha = 255 *
alpha, maxColorValue = 255))
}
cbbPalette <- c("#000000", "#009E73", "#e79f00", "#9ad0f3", "#0072B2", "#D55E00",
"#CC79A7", "#F0E442")
cbbPalette[1] <- makeTransparent(cbbPalette[1], 0.2)
cbbPalette[2] <- makeTransparent(cbbPalette[2], 0.5)
cbbPalette[3] <- makeTransparent(cbbPalette[3], 0.5)
plot(1:8, 1:8, col=cbbPalette, pch=16)
plot(1:3, 1:3, pch=c(15, 16, 17))
## Color
#legcol <- cbbPalette[c(1, 1, 1)]
legcol <- cbbPalette[c(2, 1, 3)]
## GR
dataGR2$strain18col <- factor(dataGR2$strain18)
#levels(dataGR2$strain18col) <- cbbPalette[c(1, 1, 1)]
levels(dataGR2$strain18col) <- cbbPalette[c(2, 3, 1)]
dataGR2$strain18col <- as.character(dataGR2$strain18col)
## Symbol
symb <- c("16", "15", "17")
##GR
legsymb <- as.numeric(symb)[c(1,3,2)]
dataGR2$strain18pch <- factor(dataGR2$strain18)
levels(dataGR2$strain18pch) <- symb
dataGR2$strain18pch <- as.numeric(as.character(dataGR2$strain18pch))
## Size symbol
symbsize <- c(1.5, 1.5, 1)
cexsymb <- as.numeric(symbsize)[c(1, 3, 2)]
##GR
dataGR2$strain18pchsize <- factor(dataGR2$strain18)
levels(dataGR2$strain18pchsize) <- symbsize
dataGR2$strain18pchsize <- as.numeric(as.character(dataGR2$strain18pchsize))
jpeg(file=paste(pathtofigures, "Figure5.jpeg", sep="/"))
par(mar=c(6, 6, 3, 2), oma=c(1,1,2,0), mfcol=c(1, 1), xpd=FALSE)
#rang <- range(dataGR2$logGRcent)
rang <- c(-1, 1)
rangx <- c(0.3, 0.5)
cexlab <- 1.5
par(mar=c(5, 5, 2, 3), xpd=FALSE)
#par(oma=c(1, 2, 1, 1), xpd=FALSE)
plot(dataGR2$hetvigor[dataGR2$Type=="Heterokaryon"] ~ dataGR2$geneticdistance[dataGR2$Type=="Heterokaryon"], las=1, pch=dataGR2$strain18pch[dataGR2$Type=="Heterokaryon"], axes=FALSE, xlim=rangx, ylim=rang, xlab=NA, ylab=NA, col=dataGR2$strain18col[dataGR2$Type=="Heterokaryon"], cex=dataGR2$strain18pchsize[dataGR2$Type=="Heterokaryon"])
axis(1)
axis(2, at=c(-1, -0.5, 0, 0.5, 1), las=1)
mtext("Genetic distance between homokaryon parents", side = 1, line=3, cex=cexlab)
mtext("MGR heterokaryon vigor index", side = 2, line=3.5, cex=cexlab, at=0)
abline(h=mean(na.omit(dataGR2$hetvigor[dataGR2$Type=="Heterokaryon"])), lty=1)
par( xpd=TRUE)
legend(0.3, 1.25, c("Heterokaryons with non-senescent donor strain", "Heterokaryons with senescent donor strain", "Heterokaryons with strain 18 as a donor or acceptor"), col=legcol, pch=legsymb, bty="n", pt.cex=1.5)
dev.off()
## quartz_off_screen
## 2
length(na.omit(dataGR2$hetvigor[dataGR2$Type=="Heterokaryon"]))
## [1] 175
Figure S10: WWL heterosis~Genetic distance
pathtofigures <- "."
dataWL2 <- read.table(file=paste(pathtofigures, "AveragelogWLperstrain198het16hom20180717.csv", sep="/"), header=TRUE, sep=";")
## WWL
dataWL2$strain18col <- factor(dataWL2$strain18)
levels(dataWL2$strain18col) <- cbbPalette[c(2, 3, 1)]
dataWL2$strain18col <- as.character(dataWL2$strain18col)
##WWL
dataWL2$strain18pch <- factor(dataWL2$strain18)
levels(dataWL2$strain18pch) <- symb
dataWL2$strain18pch <- as.numeric(as.character(dataWL2$strain18pch))
##WWL
dataWL2$strain18pchsize <- factor(dataWL2$strain18)
levels(dataWL2$strain18pchsize) <- symbsize
dataWL2$strain18pchsize <- as.numeric(as.character(dataWL2$strain18pchsize))
#rang <- range(na.omit(dataWL2$hetvigor))
rang <- c(-0.25, 0.25)
rangx <- c(0.3, 0.5)
cexlab <- 1.5
jpeg(file=paste(pathtofigures, "FigureS10.jpeg", sep="/"))
par(mar=c(5.1, 6, 3, 5), oma=c(1,1,2,0), mfcol=c(1, 1), xpd=FALSE)
plot(dataWL2$hetvigor[dataWL2$Type=="Heterokaryon"] ~ dataWL2$geneticdistance[dataWL2$Type=="Heterokaryon"], las=1, pch=dataWL2$strain18pch[dataWL2$Type=="Heterokaryon"], axes=FALSE, xlim=rangx, ylim=rang, xlab=NA, ylab=NA, col=dataWL2$strain18col[dataWL2$Type=="Heterokaryon"], cex=dataWL2$strain18pchsize[dataWL2$Type=="Heterokaryon"])
axis(1)
axis(2, at=c(-0.25, 0, 0.25), las=1)
mtext("Genetic distance between homokaryon parents", side = 1, line=3.5, cex=cexlab, at=0.4)
mtext("WWL heterokaryon vigor index", side = 2, line=3.5, cex=cexlab, at=0)
abline(h=mean(na.omit(dataWL2$hetvigor[dataWL2$Type=="Heterokaryon"])), lty=1)
par( xpd=TRUE)
legend(0.3, 0.35, c("Heterokaryons with non-senescent donor strain", "Heterokaryons with senescent donor strain", "Heterokaryons with strain 18 as a donor or acceptor"), col=legcol, pch=legsymb, bty="n", pt.cex=1.5)
dev.off()
## quartz_off_screen
## 2
length(na.omit(dataWL2$hetvigor[dataWL2$Type=="Heterokaryon"]))
## [1] 151
FigS3 and S4: Genetic distance matrix
pathtogeneticdistfiles <- "../GeneticDistance/"
## Donor genetic distance
donnucldist <- read.table(paste(pathtogeneticdistfiles, "GeneticDistances_30_isolates.csv", sep="/"), header=T , sep=";")
## Change names
names(donnucldist) <- gsub("X", "", names(donnucldist))
#pdf("FigureS1.pdf")
#A.mark <- donnuclsim
A.mark <- as.matrix(donnucldist)
neworder <- as.factor(c(25, 1, 2, 3, 4, 14, 34, 13, 15, 33, 18, 32, 31, 10, 11, 27, 5, 6, 7, 9, 21, 23, 22, 24, 26, 12, 35, 30, 19, 20))
A.mark <- A.mark[neworder, neworder]
rownames(A.mark) <- neworder
pimage(x=A.mark, order = NULL, axes="both", main = "", key = TRUE, key.lab="Genetic distance", xlab = "Homokaryon isolate identity", ylab = "Homokaryon isolate indentity")
#dev.copy(width = 750, height = 750, png,file=paste(getwd(),"DistanceMatrix.png",sep="/")); dev.off()
#dev.copy2pdf(width = 10, height = 10, file=paste(getwd(),"DistanceMatrix.pdf",sep="/")); dev.off()
A.mark.reorder <- seriate(A.mark)
#png(file="FigureS1 NuclearDistanceMatrix.png", width = 150, height = 150, res=300)
par(mar=c(3, 3, 2, 3))
pimage(A.mark, A.mark.reorder, prop = FALSE, axes="both", main = "Nuclear genetic distance matrix", key = TRUE, key.lab="Nuclear genetic distance", xlab = "Nuclear haplotype", ylab = "Nuclear haplotype")
wcorr <- 700
hcorr <- 500
dev.copy(width = wcorr, height = hcorr, png, file=paste(getwd(),"FigureS3.png",sep="/")); dev.off()
## quartz_off_screen
## 3
## quartz_off_screen
## 2
#dev.off()
## Mitochondrial distance matrix
accmitdist <- read.table(paste(pathtogeneticdistfiles, "AcceptorMitDist20170228.csv", sep="/") , header=T , sep=";")
## Change names
names(accmitdist) <- gsub("X", "", names(accmitdist))
hist(accmitdist[upper.tri(accmitdist)])
A.markmit <- as.matrix(accmitdist)
rownames(A.markmit) <- colnames(A.markmit)
pimage(x=A.markmit, order = NULL, axes="both", main = "", key = TRUE, key.lab="Genetic distance", xlab = "Mitochondrial haplotype", ylab = "Mitochondrial haplotype")
A.markmit.reorder <- seriate(A.markmit)
#png(file="FigureS2 MitochondrialDistanceMatrix.png", width = 150, height = 150, res=300)
par(mar=c(3, 3, 2, 3))
pimage(A.markmit, A.markmit.reorder, prop = FALSE, axes="both", main = "Mitochondiral genetic distance matrix", key = TRUE, key.lab="Mitochondiral genetic distance", xlab = "Mitochondrial haplotype", ylab = "Mitochondrial haplotype")
wcorr <- 700
hcorr <- 500
dev.copy(width = wcorr, height = hcorr, png,file=paste(getwd(),"FigureS4.png",sep="/")); dev.off()
## quartz_off_screen
## 3
## quartz_off_screen
## 2
FigS5: Correlation between nuclear and mitochondrial genetic distance
pathtogeneticdistfiles <- "../GeneticDistance/"
## Mitochondrial genetic distance
mitdist <- read.table(paste(pathtogeneticdistfiles, "MitGeneticDistance_30_isolates.csv", sep="/"), header=T , sep=";")
colnames(mitdist) <- gsub("X", "", colnames(mitdist))
colnames(mitdist)[ncol(mitdist)] <- "Sa1595(Ref)"
rownames(mitdist) <- colnames(mitdist)
## Nuclear genetic distance
nucldist <- read.table(paste(pathtogeneticdistfiles, "NuclGeneticDistance_30_isolates.csv", sep="/"), header=T , sep=";")
colnames(nucldist) <- gsub("X", "", colnames(nucldist))
colnames(nucldist)[colnames(nucldist)=="Sa1595.Ref."] <- "Sa1595(Ref)"
rownames(nucldist) <- colnames(nucldist)
data.frame(names(nucldist)[order(names(nucldist))], names(mitdist)[order(names(mitdist))])
## names.nucldist..order.names.nucldist...
## 1 87074
## 2 87075
## 3 87124
## 4 87179
## 5 87183
## 6 87215
## 7 90137
## 8 90166
## 9 91132
## 10 93028
## 11 93134
## 12 95123
## 13 95126
## 14 95156
## 15 95191
## 16 Br2444
## 17 Br5182
## 18 Fas161
## 19 FSE3
## 20 FSE7
## 21 HR32
## 22 OH22
## 23 OH235
## 24 Rb313
## 25 RB482
## 26 RB489
## 27 RB896
## 28 RB9411
## 29 Sa1595(Ref)
## 30 Sa948
## names.mitdist..order.names.mitdist...
## 1 87074
## 2 87075
## 3 87124
## 4 87179
## 5 87183
## 6 87215
## 7 90137
## 8 90166
## 9 91132
## 10 93028
## 11 93134
## 12 95123
## 13 95126
## 14 95156
## 15 95191
## 16 Br2444
## 17 Br5182
## 18 Fas161
## 19 FSE3
## 20 FSE7
## 21 HR32
## 22 OH22
## 23 OH235
## 24 Rb313
## 25 RB482
## 26 RB489
## 27 RB896
## 28 RB9411
## 29 Sa1595(Ref)
## 30 Sa948
## Sort matrices
nucldist <- nucldist[, order(colnames(nucldist))]
nucldist <- nucldist[order(rownames(nucldist)), ]
mitdist <- mitdist[, order(colnames(mitdist))]
mitdist <- mitdist[order(rownames(mitdist)), ]
### Correlation for homokaryons
##################
homnucldist <- as.matrix(nucldist[colnames(nucldist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"], colnames(nucldist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"]])
homnucldist <- as.vector(homnucldist[upper.tri(homnucldist)])
hommitdist <- as.matrix(mitdist[colnames(mitdist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"], colnames(mitdist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"]])
hommitdist <- as.vector(hommitdist[upper.tri(hommitdist)])
library(ade4)
set.seed(123)
r1 <- mantel.rtest(as.dist(nucldist[colnames(nucldist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"], colnames(nucldist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"]]), as.dist(mitdist[colnames(mitdist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"], colnames(mitdist)%in%dataGR$AcceptorName[dataGR$Type=="Homokaryon"]]), nrep=100)
## Warning in is.euclid(m2): Zero distance(s)
r1
## Monte-Carlo test
## Call: mantelnoneuclid(m1 = m1, m2 = m2, nrepet = nrepet)
##
## Observation: 0.4332972
##
## Based on 100 replicates
## Simulated p-value: 0.00990099
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 3.926326825 -0.005720802 0.012502354
jpeg(file="FigureS5.jpeg")
## Correlation between nuclear and mitochodnrial genetic distance
par(mfrow=c(1,1))
plot(homnucldist, hommitdist, col=rgb(red=0.5, green=0.5, blue=0.5, alpha=0.5), pch=16, las=1, bty="n", xlab="Nuclear genetic distance", ylab="Mitochondrial genetic distance", xlim=c(0.15, 0.50))
mtext(side=3, paste0("R=", round(r1$obs, 2)), at=0.2, line=-1)
mtext(side=3, paste0("P=", round(r1$pvalue, 2)), at=0.2, line=-2, font=3)
dev.off()
## quartz_off_screen
## 2
## Melt mitochondiral distance
mitdistframe <- melt(as.matrix(mitdist))
colnames(mitdistframe) <- c("AcceptorName", "DonorName", "mitdist")
mitdistframe$AccDon <- paste(mitdistframe$AcceptorName, mitdistframe$DonorName, sep="_")
## Melt nuclear distance
nucldistframe <- melt(as.matrix(nucldist))
colnames(nucldistframe) <- c("AcceptorName", "DonorName", "nucldist")
nucldistframe$AccDon <- paste(nucldistframe$AcceptorName, nucldistframe$DonorName, sep="_")
dataGR$AccDon <- paste(dataGR$AcceptorName, dataGR$DonorName, sep="_")
dataWL$AccDon <- paste(dataWL$AcceptorName, dataWL$DonorName, sep="_")
dataGR2 <- merge(x=dataGR, y=mitdistframe[, 3:4], by="AccDon")
dataGR2 <- merge(x=dataGR2, y=nucldistframe[, 3:4], by="AccDon")
dataWL2 <- merge(x=dataWL, y=mitdistframe[, 3:4], by="AccDon")
dataWL2 <- merge(x=dataWL2, y=nucldistframe[, 3:4], by="AccDon")
head(dataWL2)
## AccDon Acceptor Donor geno_DonorSG logweightloss ID DonorSG
## 1 87074_87074 19 19 19_0 0.2647728 Hom19 0
## 2 87074_87075 19 20 19_20_0 0.1766203 Het135 0
## 3 87074_87124 19 1 19_1_1 0.2268697 Het2 1
## 4 87074_87179 19 2 19_2_0 0.2094721 Het11 0
## 5 87074_93028 19 24 19_24_0 0.1885601 Het163 0
## 6 87074_95123 19 5 19_5_0 0.2525622 Het35 0
## AcceptorName DonorName AcceptorPop DonorPop geno
## 1 87074 87074 Vicenza_Italy Vicenza_Italy 19
## 2 87074 87075 Vicenza_Italy Vicenza_Italy 19_20
## 3 87074 87124 Vicenza_Italy Oslo_Norway 19_1
## 4 87074 87179 Vicenza_Italy Oslo_Norway 19_2
## 5 87074 93028 Vicenza_Italy Happerg_ Germany 19_24
## 6 87074 95123 Vicenza_Italy Yekaterinburg_Russia 19_5
## geneticdistance Acceptormito geographicdistance initweight weightloss
## 1 0.0000000 1 0 1.2067 0.3031349
## 2 0.3579682 3 10 0.7174 0.1931780
## 3 0.4082805 1 1559 1.0389 0.2546663
## 4 0.3742951 1 1559 1.0101 0.2330270
## 5 0.3724721 1 237 1.1249 0.2075097
## 6 0.3919447 1 3525 1.1744 0.2873195
## plate_rep_time plate rep Type ID_plate geographicdistancestd
## 1 X3_1_i X3 1 Homokaryon Hom19_X3 -1.3528728
## 2 X3_2_i X3 2 Heterokaryon Het135_X3 -1.3433244
## 3 X3_1_i X3 1 Heterokaryon Het2_X3 0.1357346
## 4 X3_2_i X3 2 Heterokaryon Het11_X3 0.1357346
## 5 X3_2_i X3 2 Heterokaryon Het163_X3 -1.1265740
## 6 X3_2_i X3 2 Heterokaryon Het35_X3 2.0129649
## geneticdistancestd geneticdistancestdSq geneticdistancefac initweightstd
## 1 -3.46978937 12.039438250 0.00 1.19762155
## 2 -0.10913658 0.011910794 0.35 -2.42374491
## 3 0.36320221 0.131915847 0.40 -0.04428585
## 4 0.04414281 0.001948588 0.37 -0.25743802
## 5 0.02702787 0.000730506 0.37 0.59221019
## 6 0.20983892 0.044032373 0.39 0.95856548
## initweightstdSq AcceptorMM Midparent Mito AccDonCov HetDonorbyHom
## 1 1.434297375 19 19 1 19 19
## 2 5.874539412 19 20 3 20 20
## 3 0.001961237 19 1 1 1 1
## 4 0.066274334 19 2 1 2 2
## 5 0.350712912 19 24 1 24 24
## 6 0.918847771 19 5 1 5 5
## HetAccbyHom DonorhomWL AcchomWL midhompar midhomparcent
## 1 19 0.2647728 0.2647728 0.2647728 0.09771038
## 2 19 0.2422296 0.2647728 0.2535012 0.08643878
## 3 19 NA 0.2647728 NA NA
## 4 19 0.1011814 0.2647728 0.1829771 0.01591468
## 5 19 0.2106861 0.2647728 0.2377295 0.07066704
## 6 19 0.2614209 0.2647728 0.2630969 0.09603444
## midparentheterosis hetvigor besthompar Acceptor15 DonorhomWL.1
## 1 0.00000000 0.00000000 0.2647728 19 0.2647728
## 2 -0.07688086 -0.07688086 0.2647728 19 0.2422296
## 3 NA NA NA 19 NA
## 4 0.02649505 0.02649505 0.2647728 19 0.1011814
## 5 -0.04916932 -0.04916932 0.2647728 19 0.2106861
## 6 -0.01053470 -0.01053470 0.2647728 19 0.2614209
## AcchomWL.1 logWLcent besthomparcent strain18 mitdist nucldist
## 1 0.2647728 0.052060386 0.05054605 1 0.0000000 0.0000000
## 2 0.2647728 -0.036092074 0.05054605 1 0.0000000 0.3579682
## 3 0.2647728 0.014157267 NA 3 0.2275862 0.4082805
## 4 0.2647728 -0.003240268 0.05054605 1 0.1931034 0.3742951
## 5 0.2647728 -0.024152278 0.05054605 1 0.0000000 0.3724721
## 6 0.2647728 0.039849749 0.05054605 1 0.2620690 0.3919447
head(dataGR2)
## AccDon Acceptor Donor geno_DonorSG logGR logintGR unit
## 1 87074_87074 19 19 19_0 1.6269875 2.719588 19_X1_2_1
## 2 87074_87075 19 20 19_20_1 0.7751937 1.781454 19_20_X1_3_3
## 3 87074_87075 19 20 19_20_0 1.9259957 2.803219 19_20_X4_2_1
## 4 87074_87124 19 1 19_1_1 0.9938704 2.017857 19_1_X1_2_1
## 5 87074_87179 19 2 19_2_0 1.7479567 2.564858 19_2_X1_2_3
## 6 87074_93028 19 24 19_24_0 1.8169945 2.473449 19_24_X3_3_2
## intGR slopeGR ID DonorSG AcceptorName DonorName AcceptorPop
## 1 15.000000 5.00 Hom19 0 87074 87074 Vicenza_Italy
## 2 6.000000 2.50 Het134 1 87074 87075 Vicenza_Italy
## 3 17.083333 6.75 Het135 0 87074 87075 Vicenza_Italy
## 4 6.416667 2.75 Het2 1 87074 87124 Vicenza_Italy
## 5 10.333333 6.00 Het11 0 87074 87179 Vicenza_Italy
## 6 11.333333 6.50 Het163 0 87074 93028 Vicenza_Italy
## DonorPop geno geneticdistance Acceptormito Geographicdistance
## 1 Vicenza_Italy 19 0.0000000 1 0
## 2 Vicenza_Italy 19_20 0.3579682 1 10
## 3 Vicenza_Italy 19_20 0.3579682 3 10
## 4 Oslo_Norway 19_1 0.4082805 1 1559
## 5 Oslo_Norway 19_2 0.3742951 1 1559
## 6 Happerg_ Germany 19_24 0.3724721 1 237
## assay plate rep HoStockplatesAcc HoStockplatesDon HetSynthesis
## 1 1 2 1 19_X1 19_X1 NA
## 2 1 3 3 1 1 1
## 3 4 2 1 2 2 2
## 4 1 2 1 1 1 1
## 5 1 2 3 1 1 1
## 6 3 3 2 2 2 2
## HetStockplates HetPrecultures Date Type
## 1 NA 19_1 June2015 Homokaryon
## 2 1 1 4 Heterokaryon
## 3 4 4 3 Heterokaryon
## 4 1 1 4 Heterokaryon
## 5 1 1 4 Heterokaryon
## 6 2 3 5 Heterokaryon
## geographicdistancestd geneticdistancestd geneticdistancestdSq
## 1 -1.3016693 -2.961602364 8.771089e+00
## 2 -1.2921207 -0.006122689 3.748732e-05
## 3 -1.2921207 -0.006122689 3.748732e-05
## 4 0.1869552 0.409269142 1.675012e-01
## 5 0.1869552 0.128676756 1.655771e-02
## 6 -1.0753678 0.113625258 1.291070e-02
## geneticdistancestdCub geneticdistancegrp geneticdistancefac
## 1 -2.597648e+01 0.00 0.00
## 2 -2.295232e-07 0.35 0.35
## 3 -2.295232e-07 0.35 0.35
## 4 6.855309e-02 0.40 0.40
## 5 2.130592e-03 0.37 0.37
## 6 1.466982e-03 0.37 0.37
## geno_synthesis geno_synthesis_HetStockplates
## 1 19_NA 19_NA_NA
## 2 19_20_1 19_20_1_1
## 3 19_20_2 19_20_2_4
## 4 19_1_1 19_1_1_1
## 5 19_2_1 19_2_1_1
## 6 19_24_2 19_24_2_2
## geno_synthesis_HetStockplates_HetPreculture
## 1 19_NA_NA_19_1
## 2 19_20_1_1_1
## 3 19_20_2_4_4
## 4 19_1_1_1_1
## 5 19_2_1_1_1
## 6 19_24_2_2_3
## geno_synthesis_HetStockplates_HetPreculture_assay
## 1 19_NA_NA_19_1_1
## 2 19_20_1_1_1_1
## 3 19_20_2_4_4_4
## 4 19_1_1_1_1_1
## 5 19_2_1_1_1_1
## 6 19_24_2_2_3_3
## geno_synthesis_HetStockplates_HetPreculture_assay_plate
## 1 19_NA_NA_19_1_1_2
## 2 19_20_1_1_1_1_3
## 3 19_20_2_4_4_4_2
## 4 19_1_1_1_1_1_2
## 5 19_2_1_1_1_1_2
## 6 19_24_2_2_3_3_3
## geno_synthesis_HetStockplates_HetPreculture_assay_plate_rep
## 1 19_NA_NA_19_1_1_2_1
## 2 19_20_1_1_1_1_3_3
## 3 19_20_2_4_4_4_2_1
## 4 19_1_1_1_1_1_2_1
## 5 19_2_1_1_1_1_2_3
## 6 19_24_2_2_3_3_3_2
## Acceptor_HoStockplatesAcc Donor_HoStockplatesDon AcceptorMM Midparent
## 1 19_19_X1 19_19_X1 19 19
## 2 19_1 20_1 19 20
## 3 19_2 20_2 19 20
## 4 19_1 1_1 19 1
## 5 19_1 2_1 19 2
## 6 19_2 24_2 19 24
## AccDonCov HetDonorbyHom HetAccbyHom Mito Acceptor15 DonorhomGR AcchomGR
## 1 19 19 19 1 19 1.626988 1.626988
## 2 20 20 19 1 19 1.654298 1.626988
## 3 20 20 19 3 19 1.654298 1.626988
## 4 1 1 19 1 19 NA 1.626988
## 5 2 2 19 1 19 1.326866 1.626988
## 6 24 24 19 1 19 1.794049 1.626988
## midhompar midhomparcent logGRcent midparentheterosis hetvigor
## 1 1.626988 -0.020493628 -0.16724709 0.0000000 0.0000000
## 2 1.640643 -0.006838559 -1.01904090 -0.8654489 -0.8654489
## 3 1.640643 -0.006838559 0.13176109 0.2853531 0.2853531
## 4 NA NA -0.80036419 NA NA
## 5 1.476927 -0.170554168 -0.04627797 0.2710297 0.2710297
## 6 1.710518 0.063036893 0.02275983 0.1064764 0.1064764
## besthompar besthomparcent strain18 GR mitdist nucldist
## 1 1.626988 -0.24477362 1 5.088523 0.0000000 0.0000000
## 2 1.654298 -0.21746348 3 2.171013 0.0000000 0.3579682
## 3 1.654298 -0.21746348 1 6.861978 0.0000000 0.3579682
## 4 NA NA 3 2.701671 0.2275862 0.4082805
## 5 1.626988 -0.24477362 1 5.742856 0.1931034 0.3742951
## 6 1.794049 -0.07771257 1 6.153337 0.0000000 0.3724721
sum(dataWL2$Type=="Heterokaryon")
## [1] 198
sum(dataGR2$Type=="Heterokaryon")
## [1] 230
cor.test(dataWL2$nucldist[dataWL2$Type=="Heterokaryon"], dataWL2$mitdist[dataWL2$Type=="Heterokaryon"])
##
## Pearson's product-moment correlation
##
## data: dataWL2$nucldist[dataWL2$Type == "Heterokaryon"] and dataWL2$mitdist[dataWL2$Type == "Heterokaryon"]
## t = 4.5338, df = 196, p-value = 1.007e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1762179 0.4290963
## sample estimates:
## cor
## 0.308089
cor.test(dataGR2$nucldist[dataGR2$Type=="Heterokaryon"], dataGR2$mitdist[dataGR2$Type=="Heterokaryon"])
##
## Pearson's product-moment correlation
##
## data: dataGR2$nucldist[dataGR2$Type == "Heterokaryon"] and dataGR2$mitdist[dataGR2$Type == "Heterokaryon"]
## t = 6.3619, df = 228, p-value = 1.079e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2726044 0.4928743
## sample estimates:
## cor
## 0.388271
plot(dataGR2$nucldist[dataGR2$Type=="Heterokaryon"], jitter(dataGR2$mitdist[dataGR2$Type=="Heterokaryon"]), pch=16, col=rgb(red=0.5, green=0.5, blue=0.5, alpha=0.5))
plot(dataWL2$nucldist[dataWL2$Type=="Heterokaryon"], dataWL2$mitdist[dataWL2$Type=="Heterokaryon"])
data.frame(colnames(nucldist), colnames(mitdist))
## colnames.nucldist. colnames.mitdist.
## 1 87074 87074
## 2 87075 87075
## 3 87124 87124
## 4 87179 87179
## 5 87183 87183
## 6 87215 87215
## 7 90137 90137
## 8 90166 90166
## 9 91132 91132
## 10 93028 93028
## 11 93134 93134
## 12 95123 95123
## 13 95126 95126
## 14 95156 95156
## 15 95191 95191
## 16 Br2444 Br2444
## 17 Br5182 Br5182
## 18 Fas161 Fas161
## 19 FSE3 FSE3
## 20 FSE7 FSE7
## 21 HR32 HR32
## 22 OH22 OH22
## 23 OH235 OH235
## 24 Rb313 Rb313
## 25 RB482 RB482
## 26 RB489 RB489
## 27 RB896 RB896
## 28 RB9411 RB9411
## 29 Sa1595(Ref) Sa1595(Ref)
## 30 Sa948 Sa948
data.frame(rownames(nucldist), rownames(mitdist))
## rownames.nucldist. rownames.mitdist.
## 1 87074 87074
## 2 87075 87075
## 3 87124 87124
## 4 87179 87179
## 5 87183 87183
## 6 87215 87215
## 7 90137 90137
## 8 90166 90166
## 9 91132 91132
## 10 93028 93028
## 11 93134 93134
## 12 95123 95123
## 13 95126 95126
## 14 95156 95156
## 15 95191 95191
## 16 Br2444 Br2444
## 17 Br5182 Br5182
## 18 Fas161 Fas161
## 19 FSE3 FSE3
## 20 FSE7 FSE7
## 21 HR32 HR32
## 22 OH22 OH22
## 23 OH235 OH235
## 24 Rb313 Rb313
## 25 RB482 RB482
## 26 RB489 RB489
## 27 RB896 RB896
## 28 RB9411 RB9411
## 29 Sa1595(Ref) Sa1595(Ref)
## 30 Sa948 Sa948
cnucldistvec <- nucldist[upper.tri(nucldist)]
mitdistvec <-mitdist[upper.tri(mitdist)]
cor.test(cnucldistvec, mitdistvec)
##
## Pearson's product-moment correlation
##
## data: cnucldistvec and mitdistvec
## t = 8.4842, df = 433, p-value = 3.475e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2939652 0.4554051
## sample estimates:
## cor
## 0.3775505
cor.test(cnucldistvec[cnucldistvec>0.35], mitdistvec[cnucldistvec>0.35])
##
## Pearson's product-moment correlation
##
## data: cnucldistvec[cnucldistvec > 0.35] and mitdistvec[cnucldistvec > 0.35]
## t = 6.3509, df = 426, p-value = 5.497e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2050228 0.3783346
## sample estimates:
## cor
## 0.2940944
library(reshape)
##
## Attaching package: 'reshape'
## The following objects are masked from 'package:reshape2':
##
## colsplit, melt, recast
nucldist2 <- melt(nucldist)
## Using as id variables
names(nucldist2) <- c("strain1", "nucldist")
nucldist2$strain2 <- rep(names(nucldist), length(nucldist))
head(nucldist2)
## strain1 nucldist strain2
## 1 87074 0.0000000 87074
## 2 87074 0.3579682 87075
## 3 87074 0.4082805 87124
## 4 87074 0.3742951 87179
## 5 87074 0.3682094 87183
## 6 87074 0.3907651 87215
nucldist2$strain1_strain2 <- paste(nucldist2$strain1, nucldist2$strain2, sep="_")
nucldist2[nucldist2$nucldist>0.29&nucldist2$nucldist<0.31,]
## strain1 nucldist strain2 strain1_strain2
## 808 RB896 0.2998365 RB9411 RB896_RB9411
## 837 RB9411 0.2998365 RB896 RB9411_RB896
mitdist2 <- melt(mitdist)
## Using as id variables
names(mitdist2) <- c("strain1", "mitdist")
mitdist2$strain2 <- rep(names(mitdist), length(mitdist))
head(mitdist2)
## strain1 mitdist strain2
## 1 87074 0.0000000 87074
## 2 87074 0.0000000 87075
## 3 87074 0.2275862 87124
## 4 87074 0.1931034 87179
## 5 87074 0.2758621 87183
## 6 87074 0.1724138 87215
mitdist2$strain1_strain2 <- paste(mitdist2$strain1, mitdist2$strain2, sep="_")
FigS7: Correlation between fitness traits
homcor <- lmodel2(weightloss ~ GR, data=data[data$Type.x=="Homokaryon",], nperm=99)
## RMA was not requested: it will not be computed.
hetcor <- lmodel2(weightloss ~ GR, data=data[data$Type.x=="Heterokaryon",], nperm=99)
## RMA was not requested: it will not be computed.
homcor$rsquare
## [1] 0.1608898
homcor$P.param
## [1] 0.1236098
homcorReg <- unlist(homcor$regression.results[homcor$regression.results$Method=="MA",][2:3])
homcorCI <- unlist(homcor$confidence.intervals[homcor$confidence.intervals$Method=="MA",][2:5])
hetcorReg <- unlist(hetcor$regression.results[hetcor$regression.results$Method=="MA",][2:3])
hetcorCI <- unlist(hetcor$confidence.intervals[hetcor$confidence.intervals$Method=="MA",][2:5])
rangy <-c(-0.1, 0.5)
rangx <-c(0, 10)
wcorr <- 7
hcorr <- 4
pdf(file="FigureS6.pdf", width = wcorr, height = hcorr)
par(mar=c(5, 5, 2, 1), mfrow=c(1, 2), xpd=FALSE)
plot(data$GR[data$Type.x=="Homokaryon"], data$weightloss[data$Type.x=="Homokaryon"], bty="n", pch=16, las=1, xlab="Growth rate (mm/day)", ylab="Wood weight loss (mg)", xlim=rangx, ylim=rangy)
Lines <- bquote(paste("R"^"2","=",.(round(homcor$rsquare, 2)), sep=""))
mtext(Lines, side=3, at=2)
mtext(paste0("P-value=", round(homcor$P.param, 2)), side=3, at=3, line=-1)
abline(a=homcorReg[1], b=homcorReg[2])
mtext("A", side=3, at=-3, line=0, cex=1.5)
plot(data$GR[data$Type.x=="Heterokaryon"], data$weightloss[data$Type.x=="Heterokaryon"], bty="n", pch=16, las=1, xlab="Growth rate (mm/day)", ylab=NA, xlim=rangx, ylim=rangy)
Lines <- bquote(paste("R"^"2","=",.(round(hetcor$rsquare, 2)), sep=""))
mtext(Lines, side=3, at=2)
mtext(paste0("P-value=", round(hetcor$P.param, 3)), side=3, at=3.1, line=-1)
abline(a=hetcorReg[1], b=hetcorReg[2])
mtext("B", side=3, at=-3, line=0, cex=1.5)
dev.off()
## quartz_off_screen
## 2