Supernumerary tentacles and fitness parameters of the host and the tumors

Budding and supernumerary tentacles

Transmissible tumors dataset

donor_transN <- subset(donor_trans, donor_trans$abnormalities == "Normal")

models <- glmulti(buds_10 ~ donor * donor_status * receiver * diff_maxR * Tumors,
    data = donor_transN, level = 2, method = "h", crit = "aicc", fitfunction = "lm",
    pl = FALSE)

tmp <- weightable(models)
tmp2 <- tmp[tmp$aicc <= min(tmp$aicc) + 2, ]
tmp2

best_rt1 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    receiver:donor_status + Tumors:receiver + Tumors:diff_maxR, data = donor_transN)

best_rt2 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    receiver:donor_status + Tumors:donor_status + Tumors:receiver, data = donor_transN)

best_rt3 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    receiver:donor_status + Tumors:donor_status + Tumors:receiver + Tumors:diff_maxR,
    data = donor_transN)

best_rt4 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    donor_status:donor + receiver:donor_status + Tumors:receiver + Tumors:diff_maxR,
    data = donor_transN)

best_rt5 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    receiver:donor_status + Tumors:receiver, data = donor_transN)

best_rt6 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    receiver:donor_status + Tumors:donor_status + Tumors:receiver + donor:diff_maxR,
    data = donor_transN)

tab_model(best_rt1, best_rt2, best_rt3, best_rt4, best_rt5, best_rt6, show.intercept = F)


It appears that the appearance of tumors in certain receivers and the interaction of recipient and donor status are the factors influencing the most the number of buds produced. However, the number of tentacles also seems to play a role that cannot be ruled out. To focus the analysis on our parameters of interest, we will create groups of donors and recipients to analyze the general relationship between tentacle number and budding at the intra-group level.

Intra-group analysis

donor_transN$groupDR <- paste0(donor_transN$donor, donor_transN$donor_status, donor_transN$receiver)

best_rt10.4 <- lmer(buds_10 ~ 1 + diff_maxR * Tumors + (1 | groupDR), data = donor_transN)
best_rt10.3 <- lmer(buds_10 ~ 1 + diff_maxR + Tumors + (1 | groupDR), data = donor_transN)
best_rt10.2 <- lmer(buds_10 ~ 1 + diff_maxR + (1 | groupDR), data = donor_transN)
best_rt10.1 <- lmer(buds_10 ~ 1 + Tumors + (1 | groupDR), data = donor_transN)
best_rt10.0 <- lmer(buds_10 ~ 1 + (1 | groupDR), data = donor_transN)

AICc(best_rt10.4, best_rt10.3, best_rt10.2, best_rt10.1, best_rt10.0)
##             df     AICc
## best_rt10.4  6 648.2747
## best_rt10.3  5 653.0875
## best_rt10.2  4 660.7359
## best_rt10.1  4 673.5175
## best_rt10.0  3 678.8187


Table of the results of the best fitted models (lower AICc+2)

  buds_10 buds_10 buds_10 buds_10 buds_10
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
diff maxR 2.99 0.71 – 5.27 0.011 0.71 -0.33 – 1.76 0.179 0.36 -0.68 – 1.40 0.498
Tumors [1] -3.08 -7.40 – 1.24 0.160 -5.29 -9.19 – -1.38 0.009 -4.02 -7.72 – -0.32 0.033
diff maxR × Tumors [1] -2.75 -5.21 – -0.29 0.029
Random Effects
σ2 58.63 60.95 63.37 60.53 61.64
τ00 19.45 groupDR 21.64 groupDR 33.02 groupDR 24.18 groupDR 32.80 groupDR
ICC 0.25 0.26 0.34 0.29 0.35
N 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR
Observations 92 92 92 95 95
Marginal R2 / Conditional R2 0.115 / 0.335 0.072 / 0.315 0.004 / 0.345 0.044 / 0.317 0.000 / 0.347 
simulateResiduals(best_rt10.4, plot = T)


Final model results:

tab_model(best_rt10.4, show.intercept = F)
  buds_10
Predictors Estimates CI p
diff maxR 2.99 0.71 – 5.27 0.011
Tumors [1] -3.08 -7.40 – 1.24 0.160
diff maxR × Tumors [1] -2.75 -5.21 – -0.29 0.029
Random Effects
σ2 58.63
τ00 groupDR 19.45
ICC 0.25
N groupDR 8
Observations 92
Marginal R2 / Conditional R2 0.115 / 0.335


If we look at the intra-group level, the number of supernumerary tentacles and the presence of tumors interact significantly to explain the budding rate.

When does this relationship establish ?

best_rt10 <- lmer(buds_10 ~ 1 + tenta_10 * Tumors + (1 | groupDR), data = donor_transN)
best_rt9 <- lmer(buds_9 ~ 1 + tenta_9 * Tumors + (1 | groupDR), data = donor_transN)
best_rt8 <- lmer(buds_8 ~ 1 + tenta_8 * Tumors + (1 | groupDR), data = donor_transN)
best_rt7 <- lmer(buds_7 ~ 1 + tenta_7 * Tumors + (1 | groupDR), data = donor_transN)
best_rt6 <- lmer(buds_6 ~ 1 + tenta_6 * Tumors + (1 | groupDR), data = donor_transN)
best_rt5 <- lmer(buds_5 ~ 1 + tenta_5 * Tumors + (1 | groupDR), data = donor_transN)
best_rt4 <- lmer(buds_4 ~ 1 + tenta_4 * Tumors + (1 | groupDR), data = donor_transN)
best_rt3 <- lmer(buds_3 ~ 1 + tenta_3 * Tumors + (1 | groupDR), data = donor_transN)
best_rt2 <- lmer(buds_2 ~ 1 + tenta_2 * Tumors + (1 | groupDR), data = donor_transN)
best_rt1 <- lmer(buds_1 ~ 1 + tenta_1 * Tumors + (1 | groupDR), data = donor_transN)
tab_model(best_rt10, best_rt9, best_rt8, best_rt7, best_rt6, best_rt5, best_rt4,
    best_rt3, best_rt2, best_rt1, show.intercept = F)
  buds_10 buds_9 buds_8 buds_7 buds_6 buds_5 buds_4 buds_3 buds_2 buds_1
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
tenta 10 4.87 1.98 – 7.75 0.001
Tumors [1] 20.45 3.48 – 37.42 0.019 11.30 1.36 – 21.24 0.026 10.62 0.07 – 21.18 0.049 15.78 6.99 – 24.58 0.001 5.23 -3.13 – 13.58 0.218 3.78 -1.89 – 9.46 0.189 3.65 -0.86 – 8.15 0.112 0.53 -3.37 – 4.42 0.789 -0.12 -2.25 – 2.00 0.908 -0.66 -1.88 – 0.56 0.289
tenta 10 × Tumors [1] -4.57 -7.58 – -1.56 0.003
tenta 9 2.93 1.38 – 4.49 <0.001
tenta 9 × Tumors [1] -2.68 -4.43 – -0.94 0.003
tenta 8 2.62 0.96 – 4.28 0.002
tenta 8 × Tumors [1] -2.23 -4.04 – -0.42 0.016
tenta 7 3.30 1.93 – 4.68 <0.001
tenta 7 × Tumors [1] -3.16 -4.69 – -1.62 <0.001
tenta 6 1.45 0.16 – 2.75 0.028
tenta 6 × Tumors [1] -1.12 -2.61 – 0.36 0.136
tenta 5 0.91 0.05 – 1.77 0.038
tenta 5 × Tumors [1] -0.77 -1.77 – 0.22 0.128
tenta 4 0.90 0.27 – 1.53 0.006
tenta 4 × Tumors [1] -0.72 -1.48 – 0.05 0.066
tenta 3 0.37 -0.21 – 0.95 0.210
tenta 3 × Tumors [1] -0.14 -0.82 – 0.55 0.699
tenta 2 -0.03 -0.33 – 0.28 0.861
tenta 2 × Tumors [1] 0.04 -0.34 – 0.42 0.846
tenta 1 -0.05 -0.22 – 0.12 0.575
tenta 1 × Tumors [1] 0.14 -0.07 – 0.36 0.189
Random Effects
σ2 56.37 46.86 41.98 29.99 24.69 15.82 9.96 4.84 1.62 0.37
τ00 15.35 groupDR 19.27 groupDR 14.62 groupDR 6.05 groupDR 6.05 groupDR 3.66 groupDR 1.37 groupDR 0.94 groupDR 0.40 groupDR 0.08 groupDR
ICC 0.21 0.29 0.26 0.17 0.20 0.19 0.12 0.16 0.20 0.18
N 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR
Observations 95 91 111 116 125 138 146 147 152 154
Marginal R2 / Conditional R2 0.149 / 0.331 0.126 / 0.381 0.079 / 0.317 0.164 / 0.304 0.040 / 0.229 0.031 / 0.213 0.053 / 0.168 0.018 / 0.177 0.001 / 0.201 0.024 / 0.204


The relationship between the number of tentacles, the tumor presence, and the budding seems to appear only after the third week, corresponding to the establishment of the tumorous phenotype and the increase in the number of tentacles.

Spontaneaous tumors dataset

donor_spontN <- subset(donor_spont, donor_spont$abnormalities == "Normal")

models <- glmulti(buds_10 ~ donor * donor_status * receiver * diff_maxR * Tumors,
    data = donor_spontN, level = 2, method = "h", crit = "aicc", fitfunction = "lm",
    pl = FALSE)

tmp <- weightable(models)
tmp2 <- tmp[tmp$aicc <= min(tmp$aicc) + 2, ]
tmp2

best_rt1 <- lm(buds_10 ~ 1 + donor + receiver + Tumors + diff_maxR + receiver:donor +
    Tumors:donor + donor_status:diff_maxR, data = donor_spontN)

best_rt2 <- lm(buds_10 ~ 1 + donor + receiver + Tumors + diff_maxR + receiver:donor +
    Tumors:donor + donor_status:diff_maxR + receiver:diff_maxR, data = donor_spontN)

best_rt3 <- lm(buds_10 ~ 1 + donor + receiver + diff_maxR + receiver:donor + donor:diff_maxR +
    donor_status:diff_maxR, data = donor_spontN)

best_rt4 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    receiver:donor + Tumors:donor + donor_status:diff_maxR, data = donor_spontN)

best_rt5 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + diff_maxR + receiver:donor +
    donor:diff_maxR + donor_status:diff_maxR, data = donor_spontN)

best_rt6 <- lm(buds_10 ~ 1 + donor + receiver + Tumors + diff_maxR + receiver:donor +
    Tumors:donor + donor_status:diff_maxR + Tumors:diff_maxR, data = donor_spontN)

best_rt7 <- lm(buds_10 ~ 1 + donor + receiver + Tumors + diff_maxR + receiver:donor +
    Tumors:donor + donor_status:diff_maxR + Tumors:diff_maxR, data = donor_spontN)

best_rt8 <- lm(buds_10 ~ 1 + donor + donor_status + receiver + Tumors + diff_maxR +
    donor_status:donor + receiver:donor + Tumors:donor + donor_status:diff_maxR,
    data = donor_spontN)
best_rt9 <- lm(buds_10 ~ 1 + donor + receiver + Tumors + diff_maxR + receiver:donor +
    Tumors:donor + donor:diff_maxR + donor_status:diff_maxR, data = donor_spontN)

tab_model(best_rt1, best_rt2, best_rt3, best_rt4, best_rt5, best_rt6, best_rt7, best_rt8,
    best_rt9, show.intercept = F)


It seems that the appearance of tumors in certain donors and the interaction of recipient and donor are the factors influencing the most the number of buds. The number of tentacles also seems to play a role when the donor was tumorous. To focus the analysis on the parameters of interest, we will create groups of donors and recipients.

Intra group analysis

donor_spontN$groupDR <- as.factor(paste0(donor_spontN$donor, donor_spontN$donor_status,
    donor_spontN$receiver))
summary(donor_spontN$groupDR)
##  MTNTSpB   MTNTTV   MTTSpB    MTTTV SpBNTSpB  SpBNTTV  SpBTSpB   SpBTTV 
##        6       10        7        9       19       28       10        6
best_rt10.4 <- lmer(buds_10 ~ 1 + diff_maxR * Tumors + (1 | groupDR), data = donor_spontN)
best_rt10.3 <- lmer(buds_10 ~ 1 + diff_maxR + Tumors + (1 | groupDR), data = donor_spontN)
best_rt10.2 <- lmer(buds_10 ~ 1 + diff_maxR + (1 | groupDR), data = donor_spontN)
best_rt10.1 <- lmer(buds_10 ~ 1 + Tumors + (1 | groupDR), data = donor_spontN)
best_rt10.0 <- lmer(buds_10 ~ 1 + (1 | groupDR), data = donor_spontN)

AICc(best_rt10.4, best_rt10.3, best_rt10.2, best_rt10.1, best_rt10.0)
##             df     AICc
## best_rt10.4  6 373.7325
## best_rt10.3  5 376.3829
## best_rt10.2  4 377.8042
## best_rt10.1  4 399.9832
## best_rt10.0  3 400.6056

Table of the results of the best fitted models (lower AICc+2)

  buds_10 buds_10 buds_10 buds_10 buds_10
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
diff maxR -1.59 -5.37 – 2.19 0.402 0.83 -0.60 – 2.25 0.248 0.64 -0.73 – 2.01 0.352
Tumors [1] -2.29 -5.84 – 1.25 0.200 -1.64 -5.09 – 1.81 0.346 -0.62 -3.80 – 2.56 0.697
diff maxR × Tumors [1] 2.79 -1.25 – 6.83 0.172
Random Effects
σ2 29.43 30.04 30.20 29.18 28.83
τ00 20.67 groupDR 20.23 groupDR 18.91 groupDR 20.26 groupDR 19.60 groupDR
ICC 0.41 0.40 0.39 0.41 0.40
N 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR
Observations 58 58 58 62 62
Marginal R2 / Conditional R2 0.040 / 0.436 0.021 / 0.415 0.010 / 0.391 0.002 / 0.411 0.000 / 0.405 

tab_model(best_rt10.4, show.intercept = F)
  buds_10
Predictors Estimates CI p
diff maxR -1.59 -5.37 – 2.19 0.402
Tumors [1] -2.29 -5.84 – 1.25 0.200
diff maxR × Tumors [1] 2.79 -1.25 – 6.83 0.172
Random Effects
σ2 29.43
τ00 groupDR 20.67
ICC 0.41
N groupDR 8
Observations 58
Marginal R2 / Conditional R2 0.040 / 0.436


If we look at the intra-group level, there is no clear relationship, which is quite expected given the absence of an increased number of tentacles when the donor was a spontaneous tumor.

When does this relationship establish ?


best_rt10 <- lmer(buds_10 ~ 1 + tenta_10 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt9 <- lmer(buds_9 ~ 1 + tenta_9 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt8 <- lmer(buds_8 ~ 1 + tenta_8 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt7 <- lmer(buds_7 ~ 1 + tenta_7 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt6 <- lmer(buds_6 ~ 1 + tenta_6 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt5 <- lmer(buds_5 ~ 1 + tenta_5 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt4 <- lmer(buds_4 ~ 1 + tenta_4 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt3 <- lmer(buds_3 ~ 1 + tenta_3 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt2 <- lmer(buds_2 ~ 1 + tenta_2 * Tumors + (1 | groupDR), data = donor_spontN)
best_rt1 <- lmer(buds_1 ~ 1 + tenta_1 * Tumors + (1 | groupDR), data = donor_spontN)

tab_model(best_rt10, best_rt9, best_rt8, best_rt7, best_rt6, best_rt5, best_rt4,
    best_rt3, best_rt2, best_rt1, show.intercept = F)
  buds_10 buds_9 buds_8 buds_7 buds_6 buds_5 buds_4 buds_3 buds_2 buds_1
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
tenta 10 0.94 -2.33 – 4.21 0.568
Tumors [1] -6.84 -23.33 – 9.64 0.409 -6.17 -17.01 – 4.67 0.259 -3.51 -15.38 – 8.36 0.557 -1.71 -11.55 – 8.14 0.730 -6.97 -15.84 – 1.91 0.122 -2.05 -8.39 – 4.28 0.521 -1.84 -5.94 – 2.26 0.375 -1.02 -4.99 – 2.95 0.610 -1.22 -3.57 – 1.13 0.305 -0.23 -1.66 – 1.20 0.748
tenta 10 × Tumors [1] 0.90 -2.48 – 4.27 0.597
tenta 9 0.04 -1.86 – 1.94 0.967
tenta 9 × Tumors [1] 1.14 -1.02 – 3.30 0.294
tenta 8 0.33 -1.77 – 2.44 0.752
tenta 8 × Tumors [1] 0.70 -1.59 – 2.99 0.543
tenta 7 0.83 -0.72 – 2.37 0.289
tenta 7 × Tumors [1] 0.33 -1.60 – 2.27 0.730
tenta 6 -0.26 -1.91 – 1.39 0.756
tenta 6 × Tumors [1] 1.48 -0.31 – 3.26 0.103
tenta 5 0.50 -0.64 – 1.64 0.384
tenta 5 × Tumors [1] 0.39 -0.87 – 1.65 0.537
tenta 4 0.44 -0.02 – 0.90 0.060
tenta 4 × Tumors [1] 0.33 -0.45 – 1.10 0.403
tenta 3 0.28 -0.40 – 0.95 0.414
tenta 3 × Tumors [1] 0.18 -0.59 – 0.95 0.645
tenta 2 -0.05 -0.40 – 0.29 0.765
tenta 2 × Tumors [1] 0.26 -0.19 – 0.71 0.254
tenta 1 0.05 -0.14 – 0.24 0.592
tenta 1 × Tumors [1] 0.07 -0.19 – 0.34 0.578
Random Effects
σ2 24.80 29.06 22.96 17.56 12.25 8.53 5.15 3.59 1.28 0.33
τ00 18.39 groupDR 13.96 groupDR 11.59 groupDR 5.69 groupDR 4.76 groupDR 3.65 groupDR 1.81 groupDR 0.86 groupDR 0.23 groupDR 0.03 groupDR
ICC 0.43 0.32 0.34 0.24 0.28 0.30 0.26 0.19 0.15 0.08
N 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR 8 groupDR
Observations 62 58 66 67 72 83 91 92 94 95
Marginal R2 / Conditional R2 0.114 / 0.491 0.060 / 0.365 0.049 / 0.368 0.060 / 0.290 0.092 / 0.346 0.062 / 0.343 0.077 / 0.317 0.049 / 0.234 0.021 / 0.170 0.042 / 0.117


This is coherent; no relationship is observed due to not enough variations.

Global Dataset: Spontaneous and Transmissible Tumors Together

Intra group analysis

data_1$groupDR <- as.factor(paste0(data_1$donor, data_1$donor_status, data_1$recipient))
## Warning: Unknown or uninitialised column: `recipient`.
summary(data_1$groupDR)
##       MTNT        MTT      RobNT       RobT SpB_spontT      SpBNT       SpBT 
##         17         16         37         36         20         56         46
data_1N <- subset(data_1, data_1$abnormalities == "Normal")

best_rt10.4 <- lmer(buds_10 ~ 1 + diff_maxR * Tumors + (1 | groupDR), data = donor_transN)
best_rt10.3 <- lmer(buds_10 ~ 1 + diff_maxR + Tumors + (1 | groupDR), data = donor_transN)
best_rt10.2 <- lmer(buds_10 ~ 1 + diff_maxR + (1 | groupDR), data = donor_transN)
best_rt10.1 <- lmer(buds_10 ~ 1 + Tumors + (1 | groupDR), data = donor_transN)
best_rt10.0 <- lmer(buds_10 ~ 1 + Tumors + (1 | groupDR), data = donor_transN)

AICc(best_rt10.4, best_rt10.3, best_rt10.2, best_rt10.1, best_rt10.0)
##             df     AICc
## best_rt10.4  6 648.2747
## best_rt10.3  5 653.0875
## best_rt10.2  4 660.7359
## best_rt10.1  4 673.5175
## best_rt10.0  4 673.5175

Table of the results of the best fitted models (lower AICc+2)

  buds_10
Predictors Estimates CI p
diff maxR 2.99 0.71 – 5.27 0.011
Tumors [1] -3.08 -7.40 – 1.24 0.160
diff maxR × Tumors [1] -2.75 -5.21 – -0.29 0.029
Random Effects
σ2 58.63
τ00 groupDR 19.45
ICC 0.25
N groupDR 8
Observations 92
Marginal R2 / Conditional R2 0.115 / 0.335


The combination of increased number of tentacles and tumor presence explains a significant part of the budding rate experienced intra-group.

When does this relationship establish ?

  buds_10 buds_9 buds_8 buds_7 buds_6 buds_5 buds_4 buds_3 buds_2 buds_1
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
tenta 10 4.81 2.40 – 7.23 <0.001
Tumors [1] 17.13 3.43 – 30.82 0.015 7.40 -1.82 – 16.62 0.115 8.33 -0.81 – 17.47 0.074 12.69 4.99 – 20.39 0.001 1.33 -5.89 – 8.56 0.716 2.54 -2.55 – 7.63 0.326 1.97 -1.77 – 5.71 0.300 0.65 -2.75 – 4.05 0.706 0.16 -1.81 – 2.13 0.873 -0.46 -1.66 – 0.73 0.447
tenta 10 × Tumors [1] -4.03 -6.56 – -1.50 0.002
tenta 9 2.76 1.25 – 4.26 <0.001
tenta 9 × Tumors [1] -1.97 -3.64 – -0.30 0.021
tenta 8 2.63 1.11 – 4.15 0.001
tenta 8 × Tumors [1] -1.90 -3.53 – -0.27 0.023
tenta 7 3.32 2.08 – 4.55 <0.001
tenta 7 × Tumors [1] -2.71 -4.10 – -1.33 <0.001
tenta 6 1.46 0.31 – 2.61 0.013
tenta 6 × Tumors [1] -0.46 -1.78 – 0.85 0.488
tenta 5 1.27 0.49 – 2.05 0.002
tenta 5 × Tumors [1] -0.60 -1.52 – 0.31 0.196
tenta 4 0.93 0.41 – 1.45 0.001
tenta 4 × Tumors [1] -0.48 -1.13 – 0.18 0.152
tenta 3 0.60 0.08 – 1.12 0.025
tenta 3 × Tumors [1] -0.19 -0.80 – 0.43 0.549
tenta 2 0.16 -0.12 – 0.43 0.262
tenta 2 × Tumors [1] -0.02 -0.38 – 0.33 0.893
tenta 1 0.03 -0.13 – 0.20 0.688
tenta 1 × Tumors [1] 0.10 -0.11 – 0.32 0.343
Random Effects
σ2 52.28 48.67 40.33 28.46 23.60 15.43 9.58 5.04 1.71 0.43
τ00 11.56 groupDR 13.03 groupDR 9.59 groupDR 4.52 groupDR 3.50 groupDR 2.08 groupDR 0.63 groupDR 0.27 groupDR 0.12 groupDR 0.01 groupDR
ICC 0.18 0.21 0.19 0.14 0.13 0.12 0.06 0.05 0.07 0.03
N 7 groupDR 7 groupDR 7 groupDR 7 groupDR 7 groupDR 7 groupDR 7 groupDR 7 groupDR 7 groupDR 7 groupDR
Observations 132 122 148 153 165 181 192 194 199 202
Marginal R2 / Conditional R2 0.156 / 0.309 0.114 / 0.301 0.097 / 0.270 0.167 / 0.281 0.079 / 0.198 0.083 / 0.192 0.084 / 0.140 0.055 / 0.103 0.013 / 0.077 0.030 / 0.059


Again, very coherent results; the relationship starts when the tumorous phenotype, including supernumerary tentacles, is expressed.

##             df     AICc
## best_rt10.1  6 909.2623
## best_rt10.2  5 918.7976
## best_rt10.3  4 926.3033
## best_rt10.4  4 924.1592
## best_rt10.5  2 943.7884
  buds_10 buds_10
Predictors Estimates CI p Estimates CI p
tenta 10 4.81 2.40 – 7.23 <0.001
Tumors [1] 17.13 3.43 – 30.82 0.015 -3.34 -6.35 – -0.32 0.030
tenta 10 × Tumors [1] -4.03 -6.56 – -1.50 0.002
Random Effects
σ2 52.28 57.58
τ00 11.56 groupDR 20.30 groupDR
ICC 0.18 0.26
N 7 groupDR 7 groupDR
Observations 132 132
Marginal R2 / Conditional R2 0.156 / 0.309 0.033 / 0.285


The relationship between the direct number of tentacles at week 10 and budding rate is even stronger; however, we prefer to keep a consistent indicator in the final analysis.

Data visualisation

##        ID       Manipulator        donor    donor_status donor_tentacle  
##  2      :  1   Justine: 62   MT       :30   NT:85        Min.   : 4.000  
##  4      :  1   Océane :109   Rob      :55   T :86        1st Qu.: 6.000  
##  5      :  1                 SpB      :71                Median : 6.000  
##  6      :  1                 SpB_spont:15                Mean   : 7.327  
##  13     :  1                                             3rd Qu.: 8.000  
##  14     :  1                                             Max.   :18.000  
##  (Other):165                                                             
##  receiver receiver_tentacle      lot        date_draft  abnormalities
##  SpB:80   Min.   :3.000     22     : 18   05/04  :21   Excluded:  0  
##  TV :91   1st Qu.:5.000     18     : 11   12/04  :18   Lost    :  0  
##           Median :6.000     11     : 10   29/03  :18   Normal  :171  
##           Mean   :5.637     12     : 10   08/04  :15   Sick    :  0  
##           3rd Qu.:6.000     16     : 10   04/02  :13                 
##           Max.   :8.000     19     : 10   01/02  :11                 
##                             (Other):102   (Other):75                 
##      dateT       Tumors     dateD       Death      tenta_1         tenta_2     
##  Min.   : 6.00   0:79   Min.   :11.00   0:108   Min.   :3.000   Min.   :2.000  
##  1st Qu.:20.00   1:92   1st Qu.:34.00   1: 63   1st Qu.:5.000   1st Qu.:5.000  
##  Median :28.00          Median :41.00           Median :6.000   Median :6.000  
##  Mean   :32.46          Mean   :42.94           Mean   :5.561   Mean   :5.476  
##  3rd Qu.:42.00          3rd Qu.:56.00           3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :70.00          Max.   :70.00           Max.   :8.000   Max.   :8.000  
##  NA's   :80             NA's   :108                             NA's   :3      
##     tenta_3         tenta_4         tenta_5         tenta_6     
##  Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :1.000  
##  1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000   1st Qu.:5.000  
##  Median :6.000   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :5.417   Mean   :5.385   Mean   :5.377   Mean   :5.331  
##  3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :8.000   Max.   :8.000   Max.   :8.000   Max.   :8.000  
##  NA's   :8       NA's   :10      NA's   :20      NA's   :35     
##     tenta_7         tenta_8         tenta_9         tenta_10    
##  Min.   :2.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
##  1st Qu.:5.000   1st Qu.:5.000   1st Qu.:4.000   1st Qu.:5.000  
##  Median :6.000   Median :5.000   Median :5.000   Median :5.000  
##  Mean   :5.464   Mean   :5.342   Mean   :5.296   Mean   :5.415  
##  3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:6.000  
##  Max.   :8.000   Max.   :8.000   Max.   :8.000   Max.   :9.000  
##  NA's   :46      NA's   :51      NA's   :73      NA's   :65     
##      buds_1           buds_2           buds_3           buds_4      
##  Min.   :0.0000   Min.   :0.0000   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.: 0.000   1st Qu.: 1.000  
##  Median :0.0000   Median :0.0000   Median : 1.000   Median : 3.000  
##  Mean   :0.3099   Mean   :0.7976   Mean   : 1.969   Mean   : 3.559  
##  3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.: 3.000   3rd Qu.: 6.000  
##  Max.   :4.0000   Max.   :6.0000   Max.   :11.000   Max.   :15.000  
##                   NA's   :3        NA's   :8        NA's   :10      
##      buds_5           buds_6           buds_7           buds_8     
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.00  
##  1st Qu.: 2.000   1st Qu.: 3.000   1st Qu.: 4.000   1st Qu.: 4.00  
##  Median : 5.000   Median : 6.000   Median : 8.000   Median : 9.00  
##  Mean   : 5.477   Mean   : 7.199   Mean   : 8.683   Mean   :10.04  
##  3rd Qu.: 9.000   3rd Qu.:10.250   3rd Qu.:13.000   3rd Qu.:14.25  
##  Max.   :18.000   Max.   :22.000   Max.   :27.000   Max.   :33.00  
##  NA's   :20       NA's   :35       NA's   :45       NA's   :51     
##      buds_9         buds_10      Taille tumeur avant congelation
##  Min.   : 0.00   Min.   : 0.00   Min.   :0.000                  
##  1st Qu.: 4.00   1st Qu.: 6.00   1st Qu.:0.000                  
##  Median :10.00   Median :11.00   Median :1.000                  
##  Mean   :10.77   Mean   :12.26   Mean   :1.819                  
##  3rd Qu.:16.00   3rd Qu.:16.75   3rd Qu.:3.000                  
##  Max.   :39.00   Max.   :43.00   Max.   :6.000                  
##  NA's   :73      NA's   :65      NA's   :66                     
##  Date congelation     tenta_max        diff_max        diff_maxR      
##  Length:171         Min.   :3.000   Min.   :0.0000   Min.   :-2.0000  
##  Class :character   1st Qu.:5.000   1st Qu.:0.0000   1st Qu.: 0.0000  
##  Mode  :character   Median :6.000   Median :0.0000   Median : 0.0000  
##                     Mean   :6.029   Mean   :0.4678   Mean   : 0.3918  
##                     3rd Qu.:7.000   3rd Qu.:1.0000   3rd Qu.: 1.0000  
##                     Max.   :9.000   Max.   :3.0000   Max.   : 2.0000  
##                                                                       
##        groupDR  
##  MTNT      :15  
##  MTT       :15  
##  RobNT     :29  
##  RobT      :26  
##  SpB_spontT:15  
##  SpBNT     :41  
##  SpBT      :30
## `geom_smooth()` using formula = 'y ~ x'