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 polyDFE outputs summary by Jun Chen.
We define a covariate \(\delta\) as the change in discretized SIFT scores
We filter away the cases where \(\epsilon_{anc}\) is too big

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 and there is a consistent change in the DFEs and this change is well captured by although there is not a neat/sharp divide between \(\delta \leq 0\) and \(\delta >0\)

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

Technical check: distribution of \(\epsilon_{anc}\)

A few species have a high estimated error epsilon_anc rate of SNP orientation and I think that all inference with eps> 0.2 should hardly be trusted , so these are filtered out.

qplot(log10(dfe_sift$eps), bins=20) + xlab("rate of SNP polarization error (eps_anc)")+theme_minimal(base_size = 16)

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

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 %>%
  filter(nsyn_counts>100) %>%
  ggplot(aes(x= log10(nsyn_counts), 
             y = log10(eps+0.000001),
             weight=sqrt(1+nsyn_counts))) + 
  geom_point(aes(color = delta<0), size=0.6) + 
  ylab("SNP polarization error, log10(eps)")+
  geom_smooth(method="loess", color= "cornflowerblue", span=0.75, se=T)+
  xlab("SFS sample size, log10( non-syn. counts in SFS)")+
  scale_color_colorblind()+
  theme_cowplot(font_size  = 17)+
  theme(legend.position = c(0.7,0.9))+
  theme(legend.background  = element_rect("white"))+
  ggsave2(filename = "eps_vs_lognsyn_counts.pdf", device = "pdf")+
  NULL

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

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

dfe_sift %>%
  filter(nsyn_counts>100) %>%
  ggplot(aes(x=delta, y = log10(eps + 0.000001), size = sqrt(nsyn_counts), weight=sqrt(1+nsyn_counts) ))+ 
  geom_jitter(height = 0,width = 0.2, color="grey80")+
  geom_smooth(method = "loess", aes(color = NULL), color = "black")+
  # geom_smooth(method = "lm", aes(color = NULL), color = "black", se =T)+
  geom_abline(intercept = log10(0.1), slope=0, color ="red")+
  theme_cowplot(font_size  = 17)+
  # facet_wrap(~group)+
  theme(legend.position = "none")+
  xlab(expression(delta))+
  ylab("SNP polarization error, log10(eps)")+
  scale_size("counts in n-syn SFS", range=c(1,4))+
  ggsave2(filename = "eps_vs_delta.pdf", device = "pdf")+
  NULL

Saving 7 x 5 in image

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

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

Filtering data before figure

We exclude:
* the \(\epsilon_{anc}\) > 0.1 * the number of chromosomes in the sfs < 6 species_low_nchr(manually curated list below)

unique(dfe_sift$species)

 [1] "Qa"     "Qv"     "Qd"     "Ayan"   "Cjapon" "Did"    "Dis"    "Laca"  
 [9] "Lfor"   "Peu"    "Pil"    "Pru"    "Pnig"   "Ptrich" "Bpend"  "Aly"   
[17] "Atha"   "Sbic"   "Cgr"    "Crub"   "Shab"   "Zmay"

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

The Flux of beneficial mutations \(p_b s_b\)

Detecting beneficial mutations is notoriously difficult as they are expected to be overall quite rare and therefore make a modest contribution to SFS counts. But \(\delta\) is a very relevant covariate Among the classes of mutations categorized as likely deleterious (negative \(\delta\)) we have virtually zero flux of beneficial mutations; but as \(\delta\) increases, so does the flux of beneficial mutations \(p_b S_b\):

the Flux \(p_b*S_b\)

Sylvain: The same but in log-scale

dfe_sift %>%
  ggplot(aes(x=delta, y=pb*Sb, weight = sqrt(1+nsyn_counts))) +
  # geom_point(aes(size = nsyn_counts))+
  geom_jitter(height = 0, width = 0.1, aes(size = sqrt(1+nsyn_counts)), color="grey40")+
  scale_y_log10() +
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T, aes(color = NULL), color = "black")+
  theme_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("Flux of beneficial mutation: pb*Sb")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  ggsave2(filename = "flux_benef_vsdelta.pdf", device = "pdf")+
  NULL

Saving 7 x 5 in image

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

the mere proportion \(p_b\)

Nb We dot not add a “trend fitting curve” as these get often into negative value which makes the figure confusing.

Sylvain: The same but in log-scale

dfe_sift %>%
  ggplot(aes(x=delta, y=pb,  weight = 1+nsyn_counts)) +
  # geom_point(aes(size = nsyn_counts))+
  geom_jitter(height = 0, width = 0.1, aes(size = sqrt(1+nsyn_counts)))+
  # facet_wrap(~ group) + 
  geom_smooth(method = "glm", formula = y ~ x, se =T, aes(color = NULL), color = "black", method.args=list(family="binomial") )+
  theme_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("proportion of beneficial mutations")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  ggsave2(filename = "pb_versus_delta.pdf",device = "pdf")+
  NULL

Saving 7 x 5 in image

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

Binning of DFE by \(N_e s\)

Poportion of mutations in Nes classes

We can see that conditioning on the \(\delta\) covariates is very informative: there is a strong covariation between the proportion of mutations in Ne*s classes inferred via polyDFE and the perceived functional categories as obtained via SIFT:

dfe_sift %>% 
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y= D1,  weight= 1+nsyn_counts)) + 
  geom_point(aes(size = nsyn_counts ))+ 
  # facet_wrap(~ group) + 
  # geom_smooth(method = "glm", formula ="y~ x", se =T,  color="black", method.args=list(family="binomial") )+
  theme_minimal(base_size = 17)+
  xlab(expression(delta))+
  ylab("prop in Nes[0-1]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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

dfe_sift %>% 
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y= D2,  weight= 1+nsyn_counts)) + 
  geom_point(aes(size = nsyn_counts))+ 
  # facet_wrap(~ group) + 
  geom_smooth(method = "glm", formula ="y~ x", se =T, color="black")+
  theme_minimal(base_size = 17)+
  xlab(expression(delta))+
  ylab("prop in Nes[-1, -10]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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

dfe_sift %>% 
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y= D3,  weight= 1+nsyn_counts)) + 
  geom_point(aes(size = nsyn_counts))+ 
  # facet_wrap(~ group) + 
  geom_smooth(method = "glm", formula ="y~ x", se =T)+
  theme_minimal(base_size = 17)+
  xlab(expression(delta))+
  ylab("prop in Nes[-10, -100]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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

dfe_sift %>% 
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y= D4,  weight= 1+nsyn_counts)) + 
  geom_point(aes(size = nsyn_counts))+ 
  # facet_wrap(~ group) + 
  # geom_smooth(method = "loess", formula ="y~ x", se =T)+
  theme_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("prop in Nes[- 100 -...]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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

Overall figure combining

Fig Legend, each line denotes the proportion of mutations in the DFE that are beneficial (orange) or that are (increasingly) deleterious mutations : D1 ( Nes in 0-1) in light grey, D2 (Nes in 1-10) dark grey, D3(10-100 blue), D4+D5(Nes > 100) red.

 fig_overview <- dfe_sift %>% 
  # filter(cat04==0) %>%
  ggplot(aes(weight= 1+nsyn_counts)) + 
  # geom_point(aes(size = nsyn_counts, color=species ))+ 
  # facet_wrap(~ group) + 
   geom_smooth(aes(x=delta, y= D1),  method = "loess", formula ="y~ x", se =F, color="grey90", span =0.5)+
  geom_smooth(aes(x=delta, y= D2),  method = "loess", formula ="y~ x", se =F, color = "grey70", span = 0.5)+
  # geom_smooth(aes(x=delta, y= D3),  method = "loess", formula ="y~ x", se =F, color = "cornflowerblue", span = 0.5)+
  geom_smooth(aes(x=delta, y= D3 +D4),  method = "loess", formula ="y~ x", se =F, color = "red", span=0.5)+
  geom_smooth(aes(x=delta, y= pb),  method = "loess", formula ="y~ x", se =F, color = "orange", span=0.5)+
  theme_cowplot(font_size = 17)+
  xlab(expression(delta))+
  ylab("proportion per in Nes class")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL
plot(fig_overview)

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

ggsave(plot = fig_overview, filename = "overview_dfe_bins_ByDelta.pdf", "pdf")

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PiN/PiS corrected

Another representation 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

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

dfe_sift %>%
  # filter(cat04==0) %>%
  ggplot(aes(x=delta, y=PiNPiScor, weight=(1+nsyn_counts)) )+ 
  # geom_point(aes(size = nsyn_counts)) + 
  geom_jitter(width = 0.1, aes(size = nsyn_counts)) + 
  scale_y_log10() +
  # facet_wrap(~ group) + 
    geom_smooth(method = "loess", formula = y ~ x, se =T, aes(color = NULL), color = "black", span=0.5 )+
  # geom_smooth(method = "loess", formula ="y~ x", se =T)+
  theme_cowplot(font_size =  15)+
  xlab("Delta SIFT score")+
  ylab("corrected piN/piS")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  # ggsave2("pin_pis_corrected_vs_delta.png", device = "png")+
  NULL

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

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] knitr_1.29      cowplot_1.1.0   magrittr_1.5    dplyr_1.0.2    
[5] ggthemes_4.2.0  ggplot2_3.3.2   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5       pillar_1.4.6     compiler_4.0.2   later_1.1.0.1   
 [5] git2r_0.27.1     tools_4.0.2      digest_0.6.25    lattice_0.20-41 
 [9] nlme_3.1-148     evaluate_0.14    lifecycle_0.2.0  tibble_3.0.3    
[13] gtable_0.3.0     mgcv_1.8-31      pkgconfig_2.0.3  rlang_0.4.7     
[17] Matrix_1.2-18    rstudioapi_0.11  yaml_2.2.1       xfun_0.16       
[21] withr_2.2.0      stringr_1.4.0    generics_0.0.2   fs_1.5.0        
[25] vctrs_0.3.2      rprojroot_2.0.2  grid_4.0.2       tidyselect_1.1.0
[29] glue_1.4.1       R6_2.4.1         rmarkdown_2.3    farver_2.0.3    
[33] purrr_0.3.4      whisker_0.4      splines_4.0.2    scales_1.1.1    
[37] promises_1.1.1   ellipsis_0.3.1   htmltools_0.5.0  colorspace_1.4-1
[41] httpuv_1.5.4     labeling_0.3     stringi_1.4.6    munsell_0.5.0   
[45] crayon_1.3.4

Source for Figures

Thomas Bataillon

12/9/2020 Last update:(last update 2021-06-25)