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Commit 1c7ffe49 authored by mariefbourdon's avatar mariefbourdon
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220622 freq rec

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article/article_figures.Rmd
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......@@ -22,10 +22,12 @@ library(cowplot)
library(grid)
library(gridExtra)
library(gridGraphics)
library(scales)
library(stuart)
source("files/QTL_plot.R")
source("files/find_linked_markers.R")
```
# Data load and use of stuart functions
......@@ -306,6 +308,7 @@ load("files/cluster/newmap_after.rda")
plotMap(cross_after,newmap_after,shift=TRUE)
plotmap_after <- ~plotMap(cross_after,newmap_after,shift=TRUE,main="After stuart")
```
### Remove last problematic markers with mark_estmap
```{r after_estmap}
......@@ -314,6 +317,17 @@ tab2 <- mark_estmap(tab2,newmap_after,annot_mini)
# create rqtl csv file
write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_",pos="cM_cox",path="files/cluster2/cross_after2.csv")
# are these markers misplaced ?
tab2 %>% filter(marker %in% c("S6J010381992","SS6071602326","S6J205609960"))
estrf_matrix_after <- pull.rf(cross_after, what=c("lod"))
find_linked_markers(estrf_matrix_after,mark="S6J010381992",annot=annot_mini)
find_linked_markers(estrf_matrix_after,mark="SS6071602326",annot=annot_mini)
find_linked_markers(estrf_matrix_after,mark="S6J205609960",annot=annot_mini)
```
### After: plot estimated map 2
......@@ -472,10 +486,9 @@ print(xtable::xtable(format_pheno, type = "latex"), file = "tables/tab_alleles.t
## est rf
```{r}
source("files/find_linked_markers.R")
estrf_matrix_after <- pull.rf(cross_after, what=c("lod"))
estrf_matrix_after2 <- pull.rf(cross_after2, what=c("lod"))
find_linked_markers(estrf_matrix_after,mark="S6J011498219",annot=annot_mini)
find_linked_markers(estrf_matrix_after2,mark="S6J011498219",annot=annot_mini)
```
......@@ -919,72 +932,6 @@ test_plot <- pgmap %>% filter(pos > 25 & pos < 35) %>%
test_plot
```
## Fold change distance between adjacent markers
```{r fold_change}
#before
names_mark <- c(names(newmap_before[["1"]]),names(newmap_before[["2"]]),names(newmap_before[["3"]]),names(newmap_before[["4"]]),
names(newmap_before[["5"]]),names(newmap_before[["6"]]),names(newmap_before[["7"]]),names(newmap_before[["8"]]),
names(newmap_before[["9"]]),names(newmap_before[["10"]]),names(newmap_before[["11"]]),names(newmap_before[["12"]]),
names(newmap_before[["13"]]),names(newmap_before[["14"]]),names(newmap_before[["15"]]),names(newmap_before[["16"]]),
names(newmap_before[["17"]]),names(newmap_before[["18"]]),names(newmap_before[["19"]]),names(newmap_before[["X"]]))
pos_mark <- c(newmap_before[["1"]],newmap_before[["2"]],newmap_before[["3"]],newmap_before[["4"]],
newmap_before[["5"]],newmap_before[["6"]],newmap_before[["7"]],newmap_before[["8"]],
newmap_before[["9"]],newmap_before[["10"]],newmap_before[["11"]],newmap_before[["12"]],
newmap_before[["13"]],newmap_before[["14"]],newmap_before[["15"]],newmap_before[["16"]],
newmap_before[["17"]],newmap_before[["18"]],newmap_before[["19"]],newmap_before[["X"]])
tibble_newmap_before <- tibble(marker=names_mark,
cM_calc=pos_mark)
compar_pos_before <- full_join(tibble_newmap_before,annot_mini) %>% select(marker,chr,cM_calc,cM_cox)
know <- compar_pos_before$cM_cox
calc <- compar_pos_before$cM_calc
compar_pos_before <- tibble(marker=compar_pos_before$marker,
chr=compar_pos_before$chr,
cM_cox=compar_pos_before$cM_cox,
cox_prev=c(NA,compar_pos_before$cM_cox[1:11124]),
cox_fol=c(compar_pos_before$cM_cox[2:11125],NA),
cM_calc=compar_pos_before$cM_calc,
calc_prev=c(NA,compar_pos_before$cM_calc[1:11124]),
calc_fol=c(compar_pos_before$cM_calc[2:11125],NA)) %>%
mutate(dif_prev=calc_prev/cox_prev,
dif_fol=calc_fol/cox_fol)
#after
names_mark <- c(names(newmap_after2[["1"]]),names(newmap_after2[["2"]]),names(newmap_after2[["3"]]),names(newmap_after2[["4"]]),
names(newmap_after2[["5"]]),names(newmap_after2[["6"]]),names(newmap_after2[["7"]]),names(newmap_after2[["8"]]),
names(newmap_after2[["9"]]),names(newmap_after2[["10"]]),names(newmap_after2[["11"]]),names(newmap_after2[["12"]]),
names(newmap_after2[["13"]]),names(newmap_after2[["14"]]),names(newmap_after2[["15"]]),names(newmap_after2[["16"]]),
names(newmap_after2[["17"]]),names(newmap_after2[["18"]]),names(newmap_after2[["19"]]),names(newmap_after2[["X"]]))
pos_mark <- c(newmap_after2[["1"]],newmap_after2[["2"]],newmap_after2[["3"]],newmap_after2[["4"]],
newmap_after2[["5"]],newmap_after2[["6"]],newmap_after2[["7"]],newmap_after2[["8"]],
newmap_after2[["9"]],newmap_after2[["10"]],newmap_after2[["11"]],newmap_after2[["12"]],
newmap_after2[["13"]],newmap_after2[["14"]],newmap_after2[["15"]],newmap_after2[["16"]],
newmap_after2[["17"]],newmap_after2[["18"]],newmap_after2[["19"]],newmap_after2[["X"]])
tibble_newmap_after <- tibble(marker=names_mark,
cM_calc=pos_mark)
compar_pos_after <- full_join(tibble_newmap_after,annot_mini) %>% select(marker,chr,cM_calc,cM_cox)
know <- compar_pos_after$cM_cox
calc <- compar_pos_after$cM_calc
compar_pos_after <- tibble(marker=compar_pos_after$marker,
chr=compar_pos_after$chr,
cM_cox=compar_pos_after$cM_cox,
cox_prev=c(NA,compar_pos_after$cM_cox[1:11124]),
cox_fol=c(compar_pos_after$cM_cox[2:11125],NA),
cM_calc=compar_pos_after$cM_calc,
calc_prev=c(NA,compar_pos_after$cM_calc[1:11124]),
calc_fol=c(compar_pos_after$cM_calc[2:11125],NA)) %>%
mutate(dif_prev=calc_prev/cox_prev,
dif_fol=calc_fol/cox_fol)
mean(compar_pos_before$dif_prev,na.rm=TRUE)
sd(compar_pos_before$dif_prev,na.rm=TRUE)
mean(compar_pos_after$dif_prev,na.rm=TRUE)
sd(compar_pos_after$dif_prev,na.rm=TRUE)
```
```{r}
# #pgm: non
......@@ -1275,6 +1222,7 @@ write_csv(allele_rec,"sup/tableS2.csv")
rm(map_1,map_2,map_3,map_4,pos_1,pos_2,pos_3,pos_4,df)
```
## Grid with all 4 data sets
```{r}
load("data2/data2_peaks.rda")
......@@ -1311,10 +1259,10 @@ narrow_grid
ggsave(narrow_grid,file="sup/figureS1.pdf",width=10,height=17)
# Figure S1 : Analysis of the F2 cross data illustrating the identification of spurious narrow non-significant peaks in QTL mappig in 4 data sets.
# A: output of the scanone function of rqtl in a F2 between CC001 and Ifnar KO C57BL/6J showing the identification of 6 narrow non-significant peaks.
# B: output of the scanone function of rqtl in a F2 between Ifnar KO C57BL/6J and Ifnar KO 129S2/SvPas showing the identification of 1 narrow non-significant peak.
# C: output of the scanone function of rqtl in a (CC001xCC071)xCC071 backcross showing no narrow non-significant peak.
# D: output of the scanone function of rqtl in a F2 between Ifnar KO C57BL/6N and CC021 showing the identification of 5 narrow non-significant peaks.
# A: output of the scanone function of rqtl in a F2 between CC001 and Ifnar KO C57BL/6J showing the identification of 6 narrow non-significant peaks.
# B: output of the scanone function of rqtl in a F2 between Ifnar KO C57BL/6J and Ifnar KO 129S2/SvPas showing the identification of 1 narrow non-significant peak.
# C: output of the scanone function of rqtl in a (CC001xCC071)xCC071 backcross showing no narrow non-significant peak.
# D: output of the scanone function of rqtl in a F2 between Ifnar KO C57BL/6N and CC021 showing the identification of 5 narrow non-significant peaks.
# E: Zoom on Peak 1. Peak 1 is located on a pseudomarker next to a marker with non mendelian proportions (gUNC2731905). F: alleles and genotype proportions for the adjacent markers of Peak 1. Peaks 3, 7, 8 and 9 are also due to a pseudomarkers located next to a markers with non mendelian proportions
# G: Zoom on Peak 6. Peak 6 is located on a marker with non mendelian proportions (SAC132487883). H: alleles and genotype proportions for SAC132487883. Peaks 2, 4 and 12 are also due to a markers with non mendelian proportions.
# I: Zoom on Peak 11. Peak 11 is located on one marker with non mendelian proportions (SAC132487883) and one adjacent pseudomarker. J: alleles and genotype proportions for SAC132487883 and the other adjacent marker to the pseudomarker on the peak. Peak 10 is also due do one marker with non mendelian proportions and one adjacent pseudomarker.
......@@ -1347,16 +1295,35 @@ for(i in names(newmap_before)){
follow <- c(follow,fol)
}
annot <- annot_mini %>% filter(marker %in% mark)
kn_pos <- annot$cM_cox
kn_prev <- c(NA, annot[1:(nrow(annot) - 1), "cM_cox"])
kn_previous <- c(kn_previous, kn_prev)
kn_fol <- c(annot[2:nrow(annot), "cM_cox"], NA)
kn_follow <- c(kn_follow, kn_fol)
#create tab with positions
tab_map1 <- tibble(marker = mark,
chr = chr,
place = place,
pos = pos,
previous = pos-previous,
follow = follow-pos)
previous = previous,
prev_dif = pos-previous,
follow = follow,
fol_dif = follow-pos,
kn_pos = kn_pos,
kn_previous = kn_previous,
kn_prev_dif = kn_pos - kn_previous,
kn_follow = kn_follow,
kn_fol_dif = kn_follow - kn_pos)
tab_map1 <- tab_map1 %>% mutate(kn_prev_df = case_when(is.na(previous) == TRUE ~ NA_real_, T ~ kn_previous))
tab_map1 <- tab_map1 %>% mutate(kn_fol_dof = case_when(is.na(follow) == TRUE ~ NA_real_, T ~ kn_follow))
tab_map1 <- tab_map1 %>% mutate(rat_prev = prev_dif/kn_prev_dif)
tab_map1 %>% ggplot(aes(x=follow)) +
tab_map1 %>% filter(is.na(rat_prev)==FALSE & rat_prev != Inf) %>% ggplot(aes(x=rat_prev)) +
geom_density() +
scale_x_log10()
......@@ -1382,25 +1349,71 @@ for(i in names(newmap_after2)){
follow <- c(follow,fol)
}
annot <- annot_mini %>% filter(marker %in% mark)
kn_pos <- annot$cM_cox
kn_prev <- c(NA, annot[1:(nrow(annot) - 1), "cM_cox"])
kn_previous <- c(kn_previous, kn_prev)
kn_fol <- c(annot[2:nrow(annot), "cM_cox"], NA)
kn_follow <- c(kn_follow, kn_fol)
#create tab with positions
tab_map2 <- tibble(marker = mark,
chr = chr,
place = place,
pos = pos,
previous = pos-previous,
follow = follow-pos)
previous = previous,
prev_dif = pos-previous,
follow = follow,
fol_dif = follow-pos,
kn_pos = kn_pos,
kn_previous = kn_previous,
kn_prev_dif = kn_pos - kn_previous,
kn_follow = kn_follow,
kn_fol_dif = kn_follow - kn_pos)
tab_map2 <- tab_map2 %>% mutate(kn_prev_df = case_when(is.na(previous) == TRUE ~ NA_real_, T ~ kn_previous))
tab_map2 <- tab_map2 %>% mutate(kn_fol_dof = case_when(is.na(follow) == TRUE ~ NA_real_, T ~ kn_follow))
tab_map2 <- tab_map2 %>% mutate(rat_prev = prev_dif/kn_prev_dif)
# save in new df
rec_ratios <- bind_rows(tab_map1 %>% select(marker,chr,kn_pos,kn_previous,kn_prev_dif,pos,previous,prev_dif,rat_prev) %>% mutate(state="before"),
tab_map2 %>% select(marker,chr,kn_pos,kn_previous,kn_prev_dif,pos,previous,prev_dif,rat_prev) %>% mutate(state="after"))
fancy_scientific <- function(l) {
# turn in to character string in scientific notation
l <- format(l, scientific = TRUE)
# quote the part before the exponent to keep all the digits
l <- gsub("^(.*)e", "'\\1'e", l)
# turn the 'e+' into plotmath format
l <- gsub("e", "%*%10^", l)
# return this as an expression
parse(text=l)
}
ggplot() +
geom_density(data = tab_map1,aes(x=follow),color="red") +
geom_density(data = tab_map2,aes(x=follow),color="blue") +
scale_x_log10()
rec_ratios %>% ggplot(aes(x=rat_prev,color=state)) +
geom_density() +
scale_color_manual(values=c("blue","red"),labels=c("After filtering","Before filtering")) +
scale_x_log10(labels=fancy_scientific) +
labs(x="Ratio between the calculated and the known distance with the previous marker",
y="Density",
color="",
title="Fold change distance between adjacent markers") +
ggpubr::theme_classic2()
ggsave("distrib_freq_rec1.png")
ggplot() +
geom_density(data = tab_map1,aes(x=follow),color="red") +
geom_density(data = tab_map2,aes(x=follow),color="blue") +
scale_x_log10(limits=c(1e-03,1e+05))
rec_ratios %>% ggplot(aes(x=rat_prev,color=state)) +
geom_density() +
scale_color_manual(values=c("blue","red"),labels=c("After filtering","Before filtering")) +
scale_x_log10(labels=fancy_scientific,limits=c(1e-03,1e+05)) +
labs(x="Ratio between the calculated and the known distance with the previous marker",
y="Density",
color="",
title="Fold change distance between adjacent markers") +
ggpubr::theme_classic2()
ggsave("distrib_freq_rec2.png")
```
......
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