Supplementary text for GBE ms GBE-210319

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Last updated: 2021-06-25

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---

brief overview of analysis and updates :

• We read in the data made available in the lastest version of the poyDFE outputs summary by Jun Chen.

• We define a covariate \(\delta\) as the change in discretized SIFT scores

• Rationale for conditioning DFE on \(\delta\) is to illustrate that change in SIFT scores are a powerful way to capture the expected effect of mutations and the fact that DFEs are quite different. There is a sharp divide between \(\delta \leq 0\) and \(\delta >0\)

• we explore the amount of variation explained by \(\delta\) for

  • pin/piS
  • other DFE property: probability of sizable amount of beneficial mutations ( eg \(Prob[ p_b >0.1 ]\))

Reading the data

dfe_sift <- read.table("data/summary_table_v3.txt",header=T)
dim(dfe_sift)

[1] 322  28

names(dfe_sift)

 [1] "species"     "group"       "category"    "fold"        "from"       
 [6] "to"          "delta"       "PiS"         "PiN"         "PiNPiS"     
[11] "syn_counts"  "nsyn_counts" "Lsyn"        "Lnsyn"       "TD4"        
[16] "TD0"         "GC3"         "gradient"    "eps"         "Sd"         
[21] "beta"        "pb"          "Sb"          "alpha"       "D1"         
[26] "D2"          "D3"          "D4"         

dfe_sift$delta <- dfe_sift$to - dfe_sift$from
head( dfe_sift) %>% kable(digits = 2)

species	group	category	fold	from	to	delta	PiS	PiN	PiNPiS	syn_counts	nsyn_counts	Lsyn	Lnsyn	TD4	TD0	GC3	gradient	eps	Sd	beta	pb	Sb	alpha	D1	D2	D3	D4
Qa	tree	0:DEL-DEL	0	-2	-2	0	0.01	0.01	0.51	13643	1468	251324	54855	-0.70	-0.58	0.46	0	0.00	-1117.94	0.65	0.38	2.74	0.99	0.01	0.02	0.11	0.48
Qa	tree	0:DEL-EXDEL	0	-2	-3	-1	0.01	0.00	0.18	12147	1331	217252	116333	-0.72	-1.18	0.46	0	0.00	-3290.26	0.24	0.00	0.15	0.00	0.16	0.12	0.20	0.52
Qa	tree	0:DEL-STABLE	0	-2	0	2	0.01	0.05	4.31	33381	4811	649224	28635	-0.69	0.44	0.46	0	0.00	0.00	0.01	0.99	50.00	1.00	0.01	0.00	0.00	0.00
Qa	tree	0:DEL-TOL	0	-2	-1	1	0.01	0.02	1.47	16856	1878	308853	28779	-0.67	0.13	0.46	0	0.02	-4909.01	4.56	0.79	49.99	1.00	0.00	0.00	0.00	0.21
Qa	tree	0:EXDEL-DEL	0	-3	-2	1	0.01	0.06	5.57	1075	131	25633	760	-0.48	0.83	0.46	0	0.00	-2309.41	4.74	0.99	49.97	1.00	0.00	0.00	0.00	0.01
Qa	tree	0:EXDEL-EXDEL	0	-3	-3	0	0.02	0.00	0.09	333	31	4890	4596	-0.50	-0.65	0.47	0	0.03	-4916.59	0.59	0.05	9.37	0.99	0.01	0.02	0.08	0.84

Filtering data :

Removing obs with too high \(\epsilon_{anc}\)

I n what follows I use the subset obtained with the condition filter(cat04==0) to generate df_tofit used throughout for fitting . NB Filtering on eps means 15% data obs removed but I think it is needed in order to avoid weird outliers

dim(dfe_sift)

[1] 322  28

species_low_nchr <- c("Qmango", "Shua", "Bnana")
dfe_sift <- dfe_sift %>%  
  filter(!(species %in% species_low_nchr)) %>%
  filter(eps <0.1)  # 
dim(dfe_sift)

[1] 322  28

dim(dfe_sift)

[1] 322  28

table(dfe_sift$category)

      0:DEL-DEL     0:DEL-EXDEL    0:DEL-STABLE       0:DEL-TOL     0:EXDEL-DEL 
             22              19              22              22              16 
  0:EXDEL-EXDEL  0:EXDEL-STABLE     0:EXDEL-TOL    0:STABLE-DEL  0:STABLE-EXDEL 
             12              22              16              22              22 
0:STABLE-STABLE    0:STABLE-TOL       0:TOL-DEL     0:TOL-EXDEL    0:TOL-STABLE 
             18              22              22              21              22 
      0:TOL-TOL 
             22 

# dfe_sift %>% filter(cat04==0) -> df_tofit
dfe_sift  -> df_tofit

dim(df_tofit)

[1] 322  28

names(df_tofit)

 [1] "species"     "group"       "category"    "fold"        "from"       
 [6] "to"          "delta"       "PiS"         "PiN"         "PiNPiS"     
[11] "syn_counts"  "nsyn_counts" "Lsyn"        "Lnsyn"       "TD4"        
[16] "TD0"         "GC3"         "gradient"    "eps"         "Sd"         
[21] "beta"        "pb"          "Sb"          "alpha"       "D1"         
[26] "D2"          "D3"          "D4"         

length(unique(df_tofit$species))

[1] 22

Dependent variable : \(\frac{P_0}{P_4}\)

We use the corrected \(\frac{P_0}{P_4}\) analogous to \(\frac{\pi_N}{\pi_S}\)

We correct by adding +1 to every count: \(\pi_N =\frac{n_N + 1}{L_N + 1}\) and \(\pi_S =\frac{n_S + 1}{L_S + 1}\) we can then directly use a log scale

df_tofit$PiNPiScor <- (df_tofit$nsyn_counts+1)*(df_tofit$Lsyn+1)/((df_tofit$Lnsyn+1)*(df_tofit$syn_counts+1)) 

df_tofit %>%
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y=PiNPiScor, weight=(1+nsyn_counts)) )+ 
  # geom_point(aes(size = nsyn_counts)) + 
  geom_jitter(width = 0.2, aes(size = nsyn_counts)) + 
  scale_y_log10() +
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T, aes(color=NULL))+
  geom_smooth(method = "lm", formula ="y~ x", se =T, aes(color=NULL), color="orange")+
  theme_minimal(base_size = 20)+
  xlab(expression(paste("Change in SIFT score, " , delta)))+
  ylab("corrected P0/P4")+
  scale_color_colorblind()+
  theme(legend.position = "none")+
  NULL

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

# ggsave("manuscript_GBE/manuscript_GBE_Jun2/figures_update_Feb2020/corrected_p0p4_delta.pdf", device = "pdf")

A (linear) fit for each species separately

df_tofit %>%
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y=PiNPiScor, weight=sqrt((1+nsyn_counts))) )+ 
  # geom_point(aes(size = nsyn_counts)) + 
  geom_jitter(width = 0.2, aes(size = nsyn_counts)) + 
  scale_y_log10() +
  # facet_wrap(~ group) + 
  geom_smooth(method = "lm", formula ="y~ x", se =F)+
  theme_minimal(base_size = 20)+
  xlab(expression(paste("Change in SIFT score, " , delta)))+
  ylab("corrected P0/P4")+
  scale_color_viridis_d(direction = -1)+
  theme(legend.position = "none")+
  NULL# ggsave("manuscript_GBE/manuscript_GBE_Jun2/figures_update_Feb2020/corrected_p0p4_delta_manyslopes.pdf",device = "pdf")

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

Linear (mixed) models

dim(df_tofit)

[1] 322  29

df_tofit$weights <- sqrt(df_tofit$nsyn_counts)

qplot(log10(df_tofit$PiNPiScor), bins = 25)

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

# ggplot(df_tofit, 
#        aes(x=delta, y = log10(PiNPiScor), 
#            size = )) + 
#   geom_jitter(width = 0.1)+ 
#   geom_smooth(method="lm", se =T)+ NULL
# 

naive0 <- lm(log(PiNPiScor) ~ 1 , 
            data = df_tofit)
naive <- lm(log(PiNPiScor) ~ delta , 
            data = df_tofit) 
summary(naive)

Call:
lm(formula = log(PiNPiScor) ~ delta, data = df_tofit)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0001 -0.6308 -0.0714  0.5888  4.1915 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.65193    0.05637  -11.56   <2e-16 ***
delta        1.00326    0.03484   28.80   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.011 on 320 degrees of freedom
Multiple R-squared:  0.7216,    Adjusted R-squared:  0.7207 
F-statistic: 829.3 on 1 and 320 DF,  p-value: < 2.2e-16

# plot(naive)

AIC(naive)

[1] 924.9761

mixed_1 <-lmer(formula = log(PiNPiScor) ~ delta +(1|species) + GC3, 
     data = df_tofit, REML =F )

merTools::modelInfo(mixed_1)

  n.obs n.lvls      AIC     sigma
1   322      1 917.0645 0.9594275

lmerTest::ranova(mixed_1) 

ANOVA-like table for random-effects: Single term deletions

Model:
log(PiNPiScor) ~ delta + GC3 + (1 | species)
              npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>           5 -453.53 917.06                         
(1 | species)    4 -458.99 925.98 10.916  1  0.0009534 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mixed_2 <-lmer(formula = log(PiNPiScor) ~  delta +(0 + delta |species) + GC3, 
     data = df_tofit,  REML =F )

AIC(mixed_2)

[1] 921.301

merTools::modelInfo(mixed_2) 

  n.obs n.lvls     AIC     sigma
1   322      1 921.301 0.9702108

plotREsim(REsim(mixed_1)) # estimated random effects (intercepts) for each sp

Past versions of unnamed-chunk-4-2.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

summary(mixed_2)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
  method [lmerModLmerTest]
Formula: log(PiNPiScor) ~ delta + (0 + delta | species) + GC3
   Data: df_tofit

     AIC      BIC   logLik deviance df.resid 
   921.3    940.2   -455.7    911.3      317 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.8074 -0.6370 -0.0664  0.5968  3.2289 

Random effects:
 Groups   Name  Variance Std.Dev.
 species  delta 0.02863  0.1692  
 Residual       0.94131  0.9702  
Number of obs: 322, groups:  species, 22

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)  -1.22692    0.57135 301.09865  -2.147   0.0326 *  
delta         1.01025    0.04923  20.21591  20.522 5.22e-15 ***
GC3           1.21302    1.19566 301.04356   1.015   0.3111    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr) delta 
delta -0.016       
GC3   -0.996  0.018

plotREsim(REsim(mixed_2)) # estimated random effects (slopes) for each sp relative to the common slope

Past versions of unnamed-chunk-4-3.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

Summary of fits

vec_aic = c(AIC(naive0), AIC(naive), AIC(mixed_1), AIC(mixed_2))
models_names = c("lm no slope", "lm slope", "random intercept", "random slope") 
df_table <- tibble(models_names, vec_aic)
knitr::kable(df_table, digits = 2, col.names = c("Models for P0/P4 ratio", "AIC"))

Models for P0/P4 ratio	AIC
lm no slope	1334.67
lm slope	924.98
random intercept	917.06
random slope	921.30

1 - (deviance(mixed_2) /(1334.67))

[1] 0.3172088

Tech check: influence of eps

naive0 <- lm(log(PiNPiScor) ~ eps , 
            data = df_tofit)
summary(naive0)

Call:
lm(formula = log(PiNPiScor) ~ eps, data = df_tofit)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.7010 -1.1764 -0.3760  0.5029  7.4718 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.9601     0.1200  -8.004 2.25e-14 ***
eps          14.3176     3.2290   4.434 1.27e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.86 on 320 degrees of freedom
Multiple R-squared:  0.05788,   Adjusted R-squared:  0.05494 
F-statistic: 19.66 on 1 and 320 DF,  p-value: 1.273e-05

naive <- lm(log(PiNPiScor) ~ eps + delta + GC3, 
            data = df_tofit) 
summary(naive)

Call:
lm(formula = log(PiNPiScor) ~ eps + delta + GC3, data = df_tofit)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9375 -0.6546 -0.0559  0.5646  4.0778 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.17284    0.59173  -1.982   0.0483 *  
eps         -3.86523    1.86450  -2.073   0.0390 *  
delta        1.03108    0.03702  27.854   <2e-16 ***
GC3          1.24931    1.23655   1.010   0.3131    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.006 on 318 degrees of freedom
Multiple R-squared:  0.7261,    Adjusted R-squared:  0.7235 
F-statistic:   281 on 3 and 318 DF,  p-value: < 2.2e-16

anova(naive, test = "F")

Analysis of Variance Table

Response: log(PiNPiScor)
           Df Sum Sq Mean Sq  F value    Pr(>F)    
eps         1  68.02   68.02  67.2088 6.075e-15 ***
delta       1 784.29  784.29 774.8765 < 2.2e-16 ***
GC3         1   1.03    1.03   1.0207    0.3131    
Residuals 318 321.86    1.01                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

car::vif(naive)

Registered S3 methods overwritten by 'car':
  method                          from
  influence.merMod                lme4
  cooks.distance.influence.merMod lme4
  dfbeta.influence.merMod         lme4
  dfbetas.influence.merMod        lme4

     eps    delta      GC3 
1.139763 1.140597 1.000781 

# plot(naive)

AIC(naive)

[1] 923.6579

mixed_1 <-lmer(formula = log(PiNPiScor) ~ eps + delta +(1|species) + GC3, 
     data = df_tofit, REML =F )

merTools::modelInfo(mixed_1)

  n.obs n.lvls     AIC     sigma
1   322      1 914.672 0.9530258

lmerTest::ranova(mixed_1) 

ANOVA-like table for random-effects: Single term deletions

Model:
log(PiNPiScor) ~ eps + delta + GC3 + (1 | species)
              npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>           6 -451.34 914.67                         
(1 | species)    5 -456.83 923.66 10.986  1  0.0009181 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mixed_2 <-lmer(formula = log(PiNPiScor) ~  eps + delta +(0 + delta |species) +GC3, 
     data = df_tofit,  REML =F )

AIC(mixed_2)

[1] 916.6742

merTools::modelInfo(mixed_2) 

  n.obs n.lvls      AIC     sigma
1   322      1 916.6742 0.9566721

plotREsim(REsim(mixed_1)) # estimated random effects (intercepts) for each sp

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

summary(mixed_2)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
  method [lmerModLmerTest]
Formula: log(PiNPiScor) ~ eps + delta + (0 + delta | species) + GC3
   Data: df_tofit

     AIC      BIC   logLik deviance df.resid 
   916.7    939.3   -452.3    904.7      316 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7872 -0.6122 -0.0811  0.5955  3.3003 

Random effects:
 Groups   Name  Variance Std.Dev.
 species  delta 0.03387  0.1840  
 Residual       0.91522  0.9567  
Number of obs: 322, groups:  species, 22

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)  -1.12812    0.56476 301.63540  -1.998   0.0467 *  
eps          -4.82117    1.84767 321.68074  -2.609   0.0095 ** 
delta         1.04440    0.05288  22.81322  19.751 7.65e-16 ***
GC3           1.19834    1.17927 300.91162   1.016   0.3104    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr) eps    delta 
eps   -0.066              
delta  0.001 -0.243       
GC3   -0.994  0.004  0.015

plotREsim(REsim(mixed_2)) # estimated random effects (slopes) for each sp relative to the common slope

Past versions of unnamed-chunk-7-2.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

vec_aic = c(AIC(naive0), AIC(naive), AIC(mixed_1), AIC(mixed_2))
models_names = c("lm no slope", "lm slope", "random intercept", "random slope") 
df_table <- tibble(models_names, vec_aic)
knitr::kable(df_table, digits = 2, col.names = c("Models for P0/P4 ratio", "AIC"))

Models for P0/P4 ratio	AIC
lm no slope	1317.47
lm slope	923.66
random intercept	914.67
random slope	916.67

Dependent variable : \(p_b\)

Here if we want to analyse variation in \(p_b\), we probably need to discretize the data and go a logistic regression on the two categories

Overview

qplot(df_tofit$pb, bins = 30, fill = (df_tofit$pb>0.1)) + 
  theme_minimal(base_size = 15) 

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

df_tofit <- mutate(df_tofit, benef_mut = ifelse(pb<0.1,  0, 1)) 
table(df_tofit$benef_mut) %>% kable(col.names = c("benef_mut", "n"))

benef_mut	n
0	152
1	170

  df_tofit %>%
  ggplot(aes(x=delta, y=benef_mut, weight=sqrt((1+nsyn_counts))) )+ 
  # geom_point(aes(size = nsyn_counts)) + 
  geom_jitter( width = 0.15, height = 0.05, aes(size = nsyn_counts)) + 
  geom_smooth(method = "glm",
              method.args=list(family="binomial"), se=T, aes(color="black"), size = 2) + 
  theme_minimal(base_size = 20)+
  xlab(expression(paste("Change in SIFT score, " , delta)))+
  ylab(expression(paste("Probability of " , p[b], " > 0.1")))+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

`geom_smooth()` using formula 'y ~ x'

Warning in eval(family$initialize): non-integer #successes in a binomial glm!

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

A loess fit for each species separately

Linear (mixed) models

# df_tofit<- df_tofit[-c(247),] # 1 mega outlier
dim(df_tofit)

[1] 322  31

df_tofit$weights <- sqrt(df_tofit$nsyn_counts)


naive0 <- glm(benef_mut  ~ 1 , family = "binomial",
            data = df_tofit) 
summary(naive0)

Call:
glm(formula = benef_mut ~ 1, family = "binomial", data = df_tofit)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.225  -1.225   1.130   1.130   1.130  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)   0.1119     0.1116   1.003    0.316

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 445.38  on 321  degrees of freedom
Residual deviance: 445.38  on 321  degrees of freedom
AIC: 447.38

Number of Fisher Scoring iterations: 3

naive0$deviance

[1] 445.38

naive <- glm(benef_mut  ~ delta + GC3 , family = "binomial",
            data = df_tofit) 
summary(naive)

Call:
glm(formula = benef_mut ~ delta + GC3, family = "binomial", data = df_tofit)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.60990  -0.37654   0.01403   0.24966   2.33819  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   2.7715     2.0184   1.373     0.17    
delta         2.8966     0.3647   7.943 1.98e-15 ***
GC3          -4.7360     4.2077  -1.126     0.26    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 445.38  on 321  degrees of freedom
Residual deviance: 154.73  on 319  degrees of freedom
AIC: 160.73

Number of Fisher Scoring iterations: 7

anova(naive0, naive, test="Chisq")

Analysis of Deviance Table

Model 1: benef_mut ~ 1
Model 2: benef_mut ~ delta + GC3
  Resid. Df Resid. Dev Df Deviance  Pr(>Chi)    
1       321     445.38                          
2       319     154.73  2   290.65 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

plot(naive)

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1 - (naive$deviance/naive0$deviance) # pseudo R^2 

[1] 0.6525807

AIC(naive)

[1] 160.7336

mixed_1 <-glmer(benef_mut ~ delta +(1|species) , family = "binomial",
     data = df_tofit )

AIC(mixed_1)

[1] 161.5645

summary(mixed_1)

Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 Family: binomial  ( logit )
Formula: benef_mut ~ delta + (1 | species)
   Data: df_tofit

     AIC      BIC   logLik deviance df.resid 
   161.6    172.9    -77.8    155.6      319 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5858 -0.2553  0.0084  0.1698  3.1883 

Random effects:
 Groups  Name        Variance Std.Dev.
 species (Intercept) 0.2315   0.4811  
Number of obs: 322, groups:  species, 22

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   0.5604     0.2471   2.268   0.0233 *  
delta         2.9841     0.4084   7.307 2.73e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr)
delta 0.321 

merTools::modelInfo(mixed_1)

  n.obs n.lvls      AIC sigma
1   322      1 161.5645     1

mixed_2 <-glmer(formula = benef_mut ~  delta +(0 + delta |species), family = "binomial",
     data = df_tofit)

AIC(mixed_2)

[1] 161.3266

summary(mixed_2)

Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 Family: binomial  ( logit )
Formula: benef_mut ~ delta + (0 + delta | species)
   Data: df_tofit

     AIC      BIC   logLik deviance df.resid 
   161.3    172.7    -77.7    155.3      319 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.29986 -0.23234  0.00439  0.13751  3.10008 

Random effects:
 Groups  Name  Variance Std.Dev.
 species delta 0.826    0.9088  
Number of obs: 322, groups:  species, 22

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   0.5245     0.2100   2.498   0.0125 *  
delta         3.2629     0.6507   5.015 5.32e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr)
delta 0.162 

merTools::modelInfo(mixed_2) 

  n.obs n.lvls      AIC sigma
1   322      1 161.3266     1

plotREsim(REsim(mixed_1)) # estimated random effects (intercepts) for each sp

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plotREsim(REsim(mixed_2)) # estimated random effects (slopes) for each sp relative to the common slope

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Summary of fits

vec_aic = c(AIC(naive0), AIC(naive), AIC(mixed_1), AIC(mixed_2))
models_names = c("glm no slope", "glm with slope", "glm with random intercept", "glm with random slope") 
df_table <- tibble(models_names, vec_aic)
knitr::kable(df_table, digits = 2, col.names = c("Models for proportion beneficial ", "AIC"))

Models for proportion beneficial	AIC
glm no slope	447.38
glm with slope	160.73
glm with random intercept	161.56
glm with random slope	161.33

Some pseudo R^2 to compare with the linear models on the piNpiS ratio Using the deviance of mixed_2 and comparing it to the most naive model (glm no slope) aka naive0 yields a pseudo R^2:

 1 -(deviance(mixed_2)/naive0$deviance)

[1] 0.6723806

tech check with eps included in the model

naive00 <- glm(benef_mut  ~ 1 , family = "binomial",
            data = df_tofit) 

naive0 <- glm(benef_mut  ~ eps , family = "binomial",
            data = df_tofit) 

# updated pseudo R2
  1 -(deviance(naive0)/naive00$deviance)

[1] 0.1099998

summary(naive0)

Call:
glm(formula = benef_mut ~ eps, family = "binomial", data = df_tofit)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3566  -1.0345   0.3584   1.2800   1.3280  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.3473     0.1322  -2.628  0.00859 ** 
eps          30.6481     5.4826   5.590 2.27e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 445.38  on 321  degrees of freedom
Residual deviance: 396.39  on 320  degrees of freedom
AIC: 400.39

Number of Fisher Scoring iterations: 4

naive0$deviance

[1] 396.3883

naive <- glm(benef_mut  ~ eps + delta + GC3, family = "binomial",
            data = df_tofit) 
summary(naive)

Call:
glm(formula = benef_mut ~ eps + delta + GC3, family = "binomial", 
    data = df_tofit)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.58904  -0.36495   0.01508   0.24802   2.36236  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   2.4569     2.0422   1.203    0.229    
eps           9.1879     7.2332   1.270    0.204    
delta         2.7935     0.3677   7.597 3.02e-14 ***
GC3          -4.4566     4.2389  -1.051    0.293    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 445.38  on 321  degrees of freedom
Residual deviance: 153.02  on 318  degrees of freedom
AIC: 161.02

Number of Fisher Scoring iterations: 7

anova(naive0, naive, test="Chisq")

Analysis of Deviance Table

Model 1: benef_mut ~ eps
Model 2: benef_mut ~ eps + delta + GC3
  Resid. Df Resid. Dev Df Deviance  Pr(>Chi)    
1       320     396.39                          
2       318     153.02  2   243.36 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(naive, test="Chisq")

Analysis of Deviance Table

Model: binomial, link: logit

Response: benef_mut

Terms added sequentially (first to last)

      Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
NULL                    321     445.38              
eps    1   48.992       320     396.39  2.57e-12 ***
delta  1  242.246       319     154.14 < 2.2e-16 ***
GC3    1    1.118       318     153.02    0.2903    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

1 - (naive$deviance/naive0$deviance) # pseudo R^2 

[1] 0.6139544

AIC(naive)

[1] 161.024

mixed_1 <-glmer(benef_mut ~ eps + delta +(1|species) , family = "binomial",
     data = df_tofit )

AIC(mixed_1)

[1] 161.6371

summary(mixed_1)

Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 Family: binomial  ( logit )
Formula: benef_mut ~ eps + delta + (1 | species)
   Data: df_tofit

     AIC      BIC   logLik deviance df.resid 
   161.6    176.7    -76.8    153.6      318 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9716 -0.2447  0.0097  0.1679  3.2852 

Random effects:
 Groups  Name        Variance Std.Dev.
 species (Intercept) 0.2584   0.5083  
Number of obs: 322, groups:  species, 22

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   0.3652     0.2818   1.296    0.195    
eps          10.1930     7.6034   1.341    0.180    
delta         2.8818     0.4095   7.037 1.96e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr) eps   
eps   -0.451       
delta  0.324 -0.097

merTools::modelInfo(mixed_1)

  n.obs n.lvls      AIC sigma
1   322      1 161.6371     1

mixed_2 <-glmer(formula = benef_mut ~ eps +  delta +(0 + delta |species), family = "binomial",
     data = df_tofit)

AIC(mixed_2)

[1] 161.3523

summary(mixed_2)

Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 Family: binomial  ( logit )
Formula: benef_mut ~ eps + delta + (0 + delta | species)
   Data: df_tofit

     AIC      BIC   logLik deviance df.resid 
   161.4    176.5    -76.7    153.4      318 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9577 -0.2194  0.0055  0.1521  3.2427 

Random effects:
 Groups  Name  Variance Std.Dev.
 species delta 0.8684   0.9319  
Number of obs: 322, groups:  species, 22

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   0.3293     0.2483   1.326    0.185    
eps          10.1583     7.5161   1.352    0.177    
delta         3.1732     0.6475   4.901 9.53e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr) eps   
eps   -0.521       
delta  0.164 -0.051

merTools::modelInfo(mixed_2) 

  n.obs n.lvls      AIC sigma
1   322      1 161.3523     1

plotREsim(REsim(mixed_1)) # estimated random effects (intercepts) for each sp

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plotREsim(REsim(mixed_2)) # estimated random effects (slopes) for each sp relative to the common slope

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car::vif(naive)

     eps    delta      GC3 
1.028107 1.034791 1.010204 

 df_tofit %>%
  ggplot(aes(x=eps, y=benef_mut))+ 
  # geom_point(aes(size = nsyn_counts)) + 
  geom_jitter( width = 0.0025, height = 0.05, aes(size = nsyn_counts)) + 
  geom_smooth(method = "glm",
              method.args=list(family="binomial"), se=T, aes(color=NULL), color="black") + 
  theme_minimal(base_size = 15)+
  xlab("eps")+
  ylab("Probability of p_b >0.1 ")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

`geom_smooth()` using formula 'y ~ x'

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706a03c	Thomas Bataillon	2021-03-13

car::vif(naive)

     eps    delta      GC3 
1.028107 1.034791 1.010204 

 df_tofit %>%
  ggplot(aes(x=delta, y=benef_mut))+ 
  # geom_point(aes(size = nsyn_counts)) + 
  geom_jitter( width = 0.0025, height = 0.05, aes(size = nsyn_counts)) + 
  geom_smooth(method = "glm",
              method.args=list(family="binomial"), se=T, aes(color=NULL), color="black") + 
  theme_minimal(base_size = 15)+
  xlab(expression(paste("Change in SIFT score, " , delta)))+
  ylab("Probability of p_b >0.1 ")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

`geom_smooth()` using formula 'y ~ x'

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Version	Author	Date
706a03c	Thomas Bataillon	2021-03-13

 # updated pseudo R2
  1 -(deviance(mixed_2)/naive0$deviance)

[1] 0.6379991

Session information

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lmerTest_3.1-3  merTools_0.5.2  arm_1.11-2      MASS_7.3-51.6  
 [5] lme4_1.1-26     Matrix_1.2-18   knitr_1.29      magrittr_1.5   
 [9] dplyr_1.0.2     ggthemes_4.2.0  ggplot2_3.3.2   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] nlme_3.1-148        fs_1.5.0            RColorBrewer_1.1-2 
 [4] rprojroot_2.0.2     numDeriv_2016.8-1.1 tools_4.0.2        
 [7] TMB_1.7.20          backports_1.1.9     R6_2.4.1           
[10] rpart_4.1-15        Hmisc_4.5-0         mgcv_1.8-31        
[13] colorspace_1.4-1    nnet_7.3-14         withr_2.2.0        
[16] tidyselect_1.1.0    gridExtra_2.3       curl_4.3           
[19] compiler_4.0.2      git2r_0.27.1        htmlTable_2.1.0    
[22] labeling_0.3        scales_1.1.1        checkmate_2.0.0    
[25] mvtnorm_1.1-1       blme_1.0-5          stringr_1.4.0      
[28] digest_0.6.25       foreign_0.8-80      minqa_1.2.4        
[31] rmarkdown_2.3       rio_0.5.26          base64enc_0.1-3    
[34] jpeg_0.1-8.1        pkgconfig_2.0.3     htmltools_0.5.0    
[37] fastmap_1.0.1       highr_0.8           readxl_1.3.1       
[40] htmlwidgets_1.5.1   rlang_0.4.7         rstudioapi_0.11    
[43] shiny_1.5.0         generics_0.0.2      farver_2.0.3       
[46] broom.mixed_0.2.6   zip_2.1.1           car_3.0-10         
[49] Formula_1.2-4       Rcpp_1.0.5          munsell_0.5.0      
[52] abind_1.4-5         lifecycle_0.2.0     stringi_1.4.6      
[55] whisker_0.4         yaml_2.2.1          carData_3.0-4      
[58] plyr_1.8.6          grid_4.0.2          promises_1.1.1     
[61] forcats_0.5.0       crayon_1.3.4        lattice_0.20-41    
[64] haven_2.3.1         splines_4.0.2       hms_0.5.3          
[67] pillar_1.4.6        boot_1.3-25         reshape2_1.4.4     
[70] codetools_0.2-16    glue_1.4.1          evaluate_0.14      
[73] latticeExtra_0.6-29 data.table_1.13.0   png_0.1-7          
[76] vctrs_0.3.2         nloptr_1.2.2.2      httpuv_1.5.4       
[79] foreach_1.5.1       cellranger_1.1.0    gtable_0.3.0       
[82] purrr_0.3.4         tidyr_1.1.1         openxlsx_4.2.3     
[85] xfun_0.16           mime_0.9            xtable_1.8-4       
[88] broom_0.7.0         coda_0.19-4         later_1.1.0.1      
[91] viridisLite_0.4.0   survival_3.1-12     tibble_3.0.3       
[94] iterators_1.0.13    cluster_2.1.0       statmod_1.4.35     
[97] ellipsis_0.3.1