Chapter 11 Associate microbial community metrics with chicken body weight
11.2 Associate alpha diversity metrics with chicken BW
The resulting plots are not included in Supplementary.
11.3 Phylogenetic
ggplot(alpha_div, aes(x = phylogenetic, y = chicken_body_weight)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~ sampling_time * trial, scales = 'free')
11.4 Functional KEGG
ggplot(alpha_div, aes(x = functional_kegg, y = chicken_body_weight)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~ sampling_time * trial, scales = 'free')
11.5 Linear regressions
11.5.1 Neutral
div_all_day_21 <-
alpha_div %>%
filter(sampling_time == '21')
N21 <- lme(chicken_body_weight ~ age + trial + neutral,
random = ~1|pen,
data = div_all_day_21)
summary(N21)$tTable Value Std.Error DF t-value p-value
(Intercept) -708.2995569 525.3549611 76 -1.3482305 0.181588833
age 78.4120899 24.3751376 76 3.2168881 0.001904929
trialCB 19.3624050 24.4490263 46 0.7919499 0.432454991
neutral -0.5109553 0.3755338 76 -1.3606104 0.177658403 numDF denDF F-value p-value
(Intercept) 1 76 6027.187 <.0001
age 1 76 9.989 0.0023
trial 1 46 0.734 0.3959
neutral 1 76 1.851 0.1777
The resulting plot corresponds to Figure 3a.
div_all_day_35 <-
alpha_div %>%
filter(sampling_time == '35')
N <- lme(chicken_body_weight ~ age + trial + neutral,
random = ~1|pen,
data = div_all_day_35)
summary(N)$tTable Value Std.Error DF t-value p-value
(Intercept) -2377.528166 1033.8704109 90 -2.2996385 2.378195e-02
age 136.609314 28.5987177 90 4.7767636 6.881135e-06
trialCB -37.822793 63.4255713 46 -0.5963335 5.538759e-01
neutral -2.899578 0.8470732 90 -3.4230549 9.337652e-04 numDF denDF F-value p-value
(Intercept) 1 90 5105.910 <.0001
age 1 90 23.632 <.0001
trial 1 46 0.006 0.9369
neutral 1 90 11.717 0.0009
11.5.2 Phylogenetic
P <- lme(chicken_body_weight ~ age + trial + phylogenetic,
random = ~1|pen,
data = div_all_day_35)
summary(P)$tTable Value Std.Error DF t-value p-value
(Intercept) -2405.58507 1082.18209 90 -2.2229023 2.872722e-02
age 142.66775 29.98571 90 4.7578581 7.415862e-06
trialCB 10.88858 62.33034 46 0.1746915 8.620887e-01
phylogenetic -64.21942 33.33088 90 -1.9267245 5.716910e-02 numDF denDF F-value p-value
(Intercept) 1 90 5247.952 <.0001
age 1 90 21.150 <.0001
trial 1 46 0.004 0.9507
phylogenetic 1 90 3.712 0.057211.5.3 Functional KEGG
Q_kegg <- lme(chicken_body_weight ~ age + trial + functional_kegg,
random = ~1|pen,
data = div_all_day_35)
summary(Q_kegg)$tTable Value Std.Error DF t-value p-value
(Intercept) -3102.407715 1382.90001 90 -2.24340710 2.732461e-02
age 135.602504 30.31664 90 4.47287326 2.247265e-05
trialCB -4.776721 63.68872 46 -0.07500106 9.405391e-01
functional_kegg 322.161132 726.21619 90 0.44361602 6.583849e-01 numDF denDF F-value p-value
(Intercept) 1 90 4950.964 <.0001
age 1 90 21.087 <.0001
trial 1 46 0.005 0.9462
functional_kegg 1 90 0.197 0.658411.6 Associate community MCI with diversity
domains_com_2 <-
funcs_com %>%
as.data.frame(optional = TRUE) %>%
rownames_to_column(var = "animal_code") %>%
rowwise() %>%
mutate(overall_com_mci = mean(c_across(B01:D09))) %>%
select(animal_code, overall_com_mci) %>%
left_join(alpha_div) %>%
filter(sampling_time == '35')
# Neutral
N <- lme(neutral ~ age + trial + overall_com_mci,
random = ~1|pen,
data = domains_com_2)
summary(N)Linear mixed-effects model fit by REML
Data: domains_com_2
AIC BIC logLik
1309.637 1327.113 -648.8184
Random effects:
Formula: ~1 | pen
(Intercept) Residual
StdDev: 12.86971 25.11553
Fixed effects: neutral ~ age + trial + overall_com_mci
Value Std.Error DF t-value p-value
(Intercept) 442.6914 114.50849 90 3.866014 0.0002
age -1.3636 2.68187 90 -0.508452 0.6124
trialCB -12.9086 5.71716 46 -2.257870 0.0287
overall_com_mci -909.1033 180.81677 90 -5.027760 0.0000
Correlation:
(Intr) age trilCB
age -0.867
trialCB 0.027 -0.094
overall_com_mci -0.541 0.052 0.055
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.01837033 -0.68738375 -0.05166506 0.56790691 2.44376691
Number of Observations: 140
Number of Groups: 48 # Phylogenetic
P <- lme(phylogenetic ~ age + trial + overall_com_mci,
random = ~1|pen,
data = domains_com_2)
summary(P)Linear mixed-effects model fit by REML
Data: domains_com_2
AIC BIC logLik
272.8188 290.2948 -130.4094
Random effects:
Formula: ~1 | pen
(Intercept) Residual
StdDev: 4.217194e-05 0.6099878
Fixed effects: phylogenetic ~ age + trial + overall_com_mci
Value Std.Error DF t-value p-value
(Intercept) 16.74885 2.645051 90 6.332146 0.0000
age 0.06901 0.062878 90 1.097553 0.2753
trialCB 0.17043 0.104053 46 1.637874 0.1083
overall_com_mci -36.22173 3.925729 90 -9.226751 0.0000
Correlation:
(Intr) age trilCB
age -0.884
trialCB 0.052 -0.116
overall_com_mci -0.522 0.064 0.059
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.09402112 -0.54578514 0.05390878 0.60148158 3.11538727
Number of Observations: 140
Number of Groups: 48 # Functional
Q <- lme(functional_kegg ~ age + trial + overall_com_mci,
random = ~1|pen,
data = domains_com_2)
summary(Q)Linear mixed-effects model fit by REML
Data: domains_com_2
AIC BIC logLik
-500.516 -483.0401 256.258
Random effects:
Formula: ~1 | pen
(Intercept) Residual
StdDev: 0.002372041 0.03545057
Fixed effects: functional_kegg ~ age + trial + overall_com_mci
Value Std.Error DF t-value p-value
(Intercept) 1.3205515 0.15393265 90 8.578761 0.0000
age 0.0056871 0.00365793 90 1.554730 0.1235
trialCB 0.0004502 0.00608846 46 0.073938 0.9414
overall_com_mci -0.4092000 0.22888069 90 -1.787831 0.0772
Correlation:
(Intr) age trilCB
age -0.883
trialCB 0.051 -0.116
overall_com_mci -0.523 0.063 0.059
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.4047065 -0.5785702 -0.1198138 0.4216641 4.1954558
Number of Observations: 140
Number of Groups: 48 11.7 Associate community MCI with chicken BW
mci <- lme(chicken_body_weight ~ age + trial + overall_com_mci,
random = ~1|pen,
data = domains_com_2)
summary(mci)Linear mixed-effects model fit by REML
Data: domains_com_2
AIC BIC logLik
1966.759 1984.235 -977.3796
Random effects:
Formula: ~1 | pen
(Intercept) Residual
StdDev: 140.9066 282.3369
Fixed effects: chicken_body_weight ~ age + trial + overall_com_mci
Value Std.Error DF t-value p-value
(Intercept) -2992.2828 1285.1423 90 -2.328367 0.0221
age 137.9025 30.1147 90 4.579239 0.0000
trialCB -2.8132 63.5521 46 -0.044266 0.9649
overall_com_mci 814.5620 2025.5024 90 0.402153 0.6885
Correlation:
(Intr) age trilCB
age -0.868
trialCB 0.028 -0.095
overall_com_mci -0.541 0.052 0.055
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-1.99872087 -0.61588910 -0.08184466 0.54575510 2.59044972
Number of Observations: 140
Number of Groups: 48 11.8 Associate community weighted genome size with chicken BW
metadata_day_35 <-
chicken_metadata %>%
filter(sampling_time == "35")
mag_lengthed <-
round(sweep(total, MARGIN = 1, genome_stats$mag_length, `*`), 0) %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "animal_code") %>%
rowwise() %>%
mutate(comm_length = mean(c_across(2:389))) %>%
select(animal_code, comm_length) %>%
filter(animal_code %in% metadata_day_35$animal_code) %>%
left_join(metadata_day_35, by = 'animal_code')
L <- lme(chicken_body_weight ~ scale(age) + trial + scale(log(comm_length)),
random = ~ 1|pen,
data = mag_lengthed)
summary(L)Linear mixed-effects model fit by REML
Data: mag_lengthed
AIC BIC logLik
1975.692 1993.168 -981.8459
Random effects:
Formula: ~1 | pen
(Intercept) Residual
StdDev: 144.7197 281.2538
Fixed effects: chicken_body_weight ~ scale(age) + trial + scale(log(comm_length))
Value Std.Error DF t-value p-value
(Intercept) 2231.4630 46.49952 90 47.98894 0.0000
scale(age) 114.0616 24.97663 90 4.56673 0.0000
trialCB -1.2398 66.94795 46 -0.01852 0.9853
scale(log(comm_length)) -4.9378 29.89947 90 -0.16515 0.8692
Correlation:
(Intr) scl(g) trilCB
scale(age) 0.088
trialCB -0.728 -0.113
scale(log(comm_length)) 0.211 0.070 -0.291
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.0015452 -0.6185148 -0.1001931 0.5667678 2.5817652
Number of Observations: 140
Number of Groups: 48 