Last updated: 2021-03-13

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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
We filter away the cases where eps 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. There is a sharp divide between \(\delta \leq 0\) and \(\delta >0\)

Reading the data

Note by TB Jan 14. Original analysis is made using the output summaries stored in Data/20201207/summary_PolyDFEOut.txt. Here I updated

dfe_sift <- read.table("Data/summary_polyDFE_siftCate_new.txt",header=T)

dim(dfe_sift)

[1] 507  26

names(dfe_sift)

 [1] "species"     "group"       "category"    "cat04"       "from"       
 [6] "to"          "mutation"    "gradient"    "eps"         "Sd"         
[11] "beta"        "pb"          "Sb"          "alpha"       "PiS"        
[16] "PiN"         "PiNPiS"      "syn_counts"  "nsyn_counts" "Lsyn"       
[21] "Lnsyn"       "D1"          "D2"          "D3"          "D4"         
[26] "D5"

dfe_sift$delta <- dfe_sift$to - dfe_sift$from

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

It looks like a few species have a really worrying estimated error epsilon_anc rate of SNP orientation and I think that all inference with eps> 0.2 should hardly be trusted ..

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

Warning: Removed 6 rows containing non-finite values (stat_bin).

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

names(dfe_sift)

 [1] "species"     "group"       "category"    "cat04"       "from"       
 [6] "to"          "mutation"    "gradient"    "eps"         "Sd"         
[11] "beta"        "pb"          "Sb"          "alpha"       "PiS"        
[16] "PiN"         "PiNPiS"      "syn_counts"  "nsyn_counts" "Lsyn"       
[21] "Lnsyn"       "D1"          "D2"          "D3"          "D4"         
[26] "D5"          "delta"

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=0.5) + 
  # geom_abline(intercept = log10(0.1), slope=0, color ="cornflowerblue")+
  geom_smooth(method="loess", color= "cornflowerblue", span=0.5, se=T)+
  ylab("rate of SNP polarization error, log10(eps)")+
  xlab("SFS sample size, log10( non-syn. counts in SFS)")+
  scale_color_colorblind()+
  theme_cowplot(font_size  = 16)+
  theme(legend.position = c(0.8,0.2))+
  theme(legend.background  = element_rect("grey"))+
  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), color=species, size = sqrt(nsyn_counts), weight=sqrt(1+nsyn_counts) ))+ 
  geom_jitter(height = 0,width = 0.1)+
  geom_smooth(method = "loess", aes(color = NULL), color = "black", span=0.75)+
  # geom_smooth(method = "lm", aes(color = NULL), color = "black", se =T)+
  theme_cowplot(font_size  = 13)+
  # facet_wrap(~group)+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  xlab(expression(delta))+
  ylab("rate of 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

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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

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] "Ayon"     "Alyr"     "Atha"     "Bnana"    "Bpend"    "Cgrand"  
 [7] "Cjap"     "Cmag"     "Crubel"   "Gprzew"   "Dsin"     "Lform"   
[13] "Zmays"    "Peuph"    "Pilcif"   "Pnigra"   "Pruni"    "Ptrich"  
[19] "Qacut"    "Qdent"    "Lacaly"   "Qmango"   "Qvaria"   "Sbicolor"
[25] "Shab"     "Shua"     "Ddyer"

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] 387  27

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 %>%
  filter(cat04==0) %>%
  ggplot(aes(x=delta, y=pb*Sb, color=species, weight = sqrt(1+nsyn_counts))) +
  # geom_point(aes(size = nsyn_counts))+
  geom_jitter(height = 0,width = 0.1, aes(size = sqrt(1+nsyn_counts)))+
  scale_y_log10() +
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T, aes(color = NULL), color = "black", span=0.65)+
  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

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

the mere proportion \(p_b\)

Sylvain: The same but in log-scale

dfe_sift %>%
  filter(cat04==0) %>%
  ggplot(aes(x=delta, y=pb, color=species, 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 = "loess", formula = y ~ x, se =T, aes(color = NULL), color = "black", span=0.7 )+
  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

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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, color=species ))+ 
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T, span = 0.5)+
  theme_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("prop in Nes[0-1]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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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, color=species))+ 
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T, span =.5)+
  theme_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("prop in Nes[-1, -10]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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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, color=species))+ 
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T)+
  theme_minimal(base_size = 15)+
  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+D5,  weight= 1+nsyn_counts)) + 
  geom_point(aes(size = nsyn_counts, color=species))+ 
  # facet_wrap(~ group) + 
  geom_smooth(method = "loess", formula ="y~ x", se =T, span =0.5)+
  theme_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("prop in Nes[- 100 -...]")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL

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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+D5),  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_minimal(base_size = 15)+
  xlab(expression(delta))+
  ylab("prop in Nes class")+
  scale_color_viridis_d()+
  theme(legend.position = "none")+
  NULL
plot(fig_overview)

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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, color=species, 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

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parametric, : There are other near singularities as well. 1

Warning in predLoess(object$y, object$x, newx = if
(is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at -1

Warning in predLoess(object$y, object$x, newx = if
(is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1

Warning in predLoess(object$y, object$x, newx = if
(is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
number 0

Warning in predLoess(object$y, object$x, newx = if
(is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near
singularities as well. 1

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_1.30      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     viridisLite_0.3.0
 [9] lattice_0.20-41   nlme_3.1-148      evaluate_0.14     lifecycle_0.2.0  
[13] tibble_3.0.3      gtable_0.3.0      mgcv_1.8-31       pkgconfig_2.0.3  
[17] rlang_0.4.7       Matrix_1.2-18     rstudioapi_0.11   yaml_2.2.1       
[21] xfun_0.19         withr_2.2.0       stringr_1.4.0     generics_0.0.2   
[25] fs_1.5.0          vctrs_0.3.2       tidyselect_1.1.0  rprojroot_1.3-2  
[29] grid_4.0.2        glue_1.4.1        R6_2.4.1          rmarkdown_2.3    
[33] farver_2.0.3      purrr_0.3.4       whisker_0.4       splines_4.0.2    
[37] backports_1.1.8   scales_1.1.1      promises_1.1.1    ellipsis_0.3.1   
[41] htmltools_0.5.0   colorspace_1.4-1  httpuv_1.5.4      labeling_0.3     
[45] stringi_1.4.6     munsell_0.5.0     crayon_1.3.4

Overview Fig: Properties of DFEs across Delta_SIFT classes

Thomas B

12/9/2020 Last update:(last update 2021-03-13)