Commit 45e067cd by Marie Bourdon

modify data and vignette with last modifs

parent bfce92d9
.RData 0 → 100644
 ... @@ -126,14 +126,11 @@ head(tab2) %>% print.data.frame() ... @@ -126,14 +126,11 @@ head(tab2) %>% print.data.frame() ### mark_prop ### mark_prop The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 and we use homo=0.1, hetero=0.1 so the function will exclude all markers with less than 10% of each genotype. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped. The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 and we use homo=0.1, hetero=0.1 so the function will exclude all markers with less than 10% of each genotype. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped. For chromosome X, we use the homo=0.1, hetero=0.1 {r mark_prop_ex_homo} {r mark_prop_ex_homo} tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1) tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1,homo1X=c(0.1,1),homo2X=c(0.1,1),heteroX=c(0.1,1)) head(tab2) %>% print.data.frame() head(tab2) %>% print.data.frame()   We could also use the pval argument which allows to exclude markers by performing a Chi2 test comparing observed distribution with Mendelian proportions. By using pval=0.5 we would exclude all markers with a Chi2 p-value inferior to 0.05. However, for some markers, Chi2 approximation may be incorrect. We could also use the pval argument which allows to exclude markers by performing a Chi2 test comparing observed distribution with Mendelian proportions. By using pval=0.5 we would exclude all markers with a Chi2 p-value inferior to 0.05. However, for some markers, Chi2 approximation may be incorrect. ... @@ -178,6 +175,10 @@ The cross object was saved in stuart. Here we can load it as well as the newmap ... @@ -178,6 +175,10 @@ The cross object was saved in stuart. Here we can load it as well as the newmap {r load_cross} {r load_cross} library(qtl) library(qtl) # stuart_cross <- read.cross(format="csv",file="../stuart_cross.csv", # genotypes=c("0","1","2"),na.strings=c("NA"), convertXdata=TRUE) # save(stuart_cross,file="../stuart_cross.rda") data(stuart_cross) data(stuart_cross) summary(stuart_cross) summary(stuart_cross) ... ...