Phenotype QC [Continious Phenotype - Age of Onset]
Belinda Cornes
2022-02-10
Last updated: 2022-02-10
Checks: 6 1
Knit directory: Serreze-T1D_Workflow/
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Loading Data
load("data/gm_allqc_4.batches.RData")
#gm_allqc
gm=gm_allqc
gmObject of class cross2 (crosstype "bc")
Total individuals 188
No. genotyped individuals 188
No. phenotyped individuals 188
No. with both geno & pheno 188
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131578
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
9977 10005 7858 7589 7621 7758 7413 6472 6725 6396 7154 6137 6085
14 15 16 17 18 19 X
5981 5346 5019 5093 4607 3564 4778 pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")ICI vs EOI
##extracting animals with ici and eoi group status
miceinfo <- gm$covar[gm$covar$group == "EOI" | gm$covar$group == "ICI",]
table(miceinfo$group)
EOI ICI
69 92 mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gmObject of class cross2 (crosstype "bc")
Total individuals 161
No. genotyped individuals 161
No. phenotyped individuals 161
No. with both geno & pheno 161
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131578
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
9977 10005 7858 7589 7621 7758 7413 6472 6725 6396 7154 6137 6085
14 15 16 17 18 19 X
5981 5346 5019 5093 4607 3564 4778 pr.qc <- pr
for (i in 1:20){pr.qc[[i]] = pr.qc[[i]][mice.ids,,]}
gm$covar$ICI.vs.EOI <- ifelse(gm$covar$group == "EOI", 0, 1)
names(gm$covar)[3] <- c("age.of.onset")
gm$covar$age.of.onset <- as.numeric(gm$covar$age.of.onset)
p <- ggplot(gm$covar, aes(x=as.numeric(age.of.onset))) + geom_histogram(color="black", fill="white")
p`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
phenos.covars.lg <- gm$covar %>% gather(variable, value, -c("id","group","sex","diabetic status","strain","ICI.vs.EOI"))
box1 <- ggplot(data=phenos.covars.lg, aes(x=variable, y=as.numeric(value), color=group, fill=group)) +
geom_boxplot(position = position_dodge(width=0.9)) +
#ggtitle(paste0("Values of ",v," [random dataframe: ",r,"]")) +
#labs(y = v) +
theme(strip.text.x = element_text(size=13),
axis.text.x = element_text(size = 13, angle = 0),
axis.text.y = element_text(size = 13, angle = 0),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
plot.title = element_text(size = 13, face = "bold",hjust = 0.5),
#legend.position = "none"
)
box1
QTL analysis requires variables follow normal distribution, from the above distributions, we need to ranknorm the data.
##ranknorm
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
gm$covar$rz.age <- rz.transform(gm$covar$age.of.onset)
p <- ggplot(gm$covar, aes(x=as.numeric(rz.age))) + geom_histogram(color="black", fill="white")
p`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
phenos.covars.lg <- gm$covar %>% gather(variable, value, -c("id","group","sex","diabetic status","strain","ICI.vs.EOI","age.of.onset"))
box1 <- ggplot(data=phenos.covars.lg, aes(x=variable, y=as.numeric(value), color=group, fill=group)) +
geom_boxplot(position = position_dodge(width=0.9)) +
#ggtitle(paste0("Values of ",v," [random dataframe: ",r,"]")) +
#labs(y = v) +
theme(strip.text.x = element_text(size=13),
axis.text.x = element_text(size = 13, angle = 0),
axis.text.y = element_text(size = 13, angle = 0),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
plot.title = element_text(size = 13, face = "bold",hjust = 0.5),
#legend.position = "none"
)
box1
And then remove any samples that are 3 standard deviations from the mean.
#outliers
#des.3 <- Hmisc::describe(df_phenos[,c("R_AVG","L_AVG","Both_AVG")])
des.1 <- pastecs::stat.desc(gm$covar[,c("age.of.onset" ,"rz.age")])
des.2 <- psych::describe(gm$covar[,c("age.of.onset" ,"rz.age")])
#scale(df_phenos[,c("R_AVG")])
gm$covar$out.age.of.onset <- ifelse(gm$covar[,c("age.of.onset")] > (des.1[9,1] + 3*des.1[13,1]) | gm$covar[,c("age.of.onset")] < (des.1[9,1] - 3*des.1[13,1]), 'Outlier','Keep')
gm$covar$out.rz.age <- ifelse(gm$covar[,c("rz.age")] > (des.1[9,2] + 3*des.1[13,2]) | gm$covar[,c("rz.age")] < (des.1[9,2] - 3*des.1[13,2]), 'Outlier','Keep')
bad <- NULL
bad$Mouse.ID <- rownames(gm$covar)
bad$age.of.onset <- ifelse(gm$covar$out.age.of.onset =="Outlier", 'XX', '')
bad$rz.age <- ifelse(gm$covar$out.rz.age =="Outlier", 'XX', '')
bad[is.na(bad)] <- ""
bad[bad=='NA'] <- ""
df <- do.call(cbind, bad)
bad <- as.data.frame(df)
badind <- subset(bad,
bad$age.of.onset == 'XX'|
bad$rz.age == 'XX')
#badind <- bad[bad$no_pheno == 'XX',]
badind[] <- lapply(badind, as.character)
#badind$Thaiss_ID <- ifelse(badind$Thaiss == 994 | badind$Thaiss == 995 | badind$Thaiss == 996 |badind$Thaiss == 997 | badind$Thaiss == 998 | badind$Thaiss == 999, "--", bad$Thaiss_ID)
rownames(badind) <- NULL
badind[] %>%
dplyr::mutate(
age.of.onset = ifelse(age.of.onset == 'XX',
cell_spec(age.of.onset, color = 'gray',background = 'gray'),
''),
rz.age = ifelse(rz.age == 'XX',
cell_spec(rz.age, color = 'gray',background = 'gray'),
'')
) %>%
kable(escape = F,align = c("ccccccccc"),linesep ="\\hline") %>%
kable_styling("striped", full_width = F) %>%
column_spec(1:3, width = "3cm") | Mouse.ID | age.of.onset | rz.age |
|---|---|---|
| NG00453 | XX |
##removing outliers
gm$covar$Mouse.ID <- rownames(gm$covar)
gm$covar$age.of.onset[gm$covar$out.age.of.onset == "Outlier"] <- ''
gm$covar$rz.age[gm$covar$out.rz.age == "Outlier"] <- ''
#gm$covar <- gm$covar[c(1:15)]
#gm$covar$id <- rownames(gm$covar)
write.csv(gm$covar,"data/covar_cleaned_ici.vs.eoi.csv", row.names=F, quote=F)That is, those that have a grey square were removed for that particular phenotype in the QTL mapping.
ICI vs PBS
gm=gm_allqc
##extracting animals with ici and eoi group status
miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
table(miceinfo$group)
ICI PBS
92 21 mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gmObject of class cross2 (crosstype "bc")
Total individuals 113
No. genotyped individuals 113
No. phenotyped individuals 113
No. with both geno & pheno 113
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131578
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
9977 10005 7858 7589 7621 7758 7413 6472 6725 6396 7154 6137 6085
14 15 16 17 18 19 X
5981 5346 5019 5093 4607 3564 4778 pr.qc <- pr
for (i in 1:20){pr.qc[[i]] = pr.qc[[i]][mice.ids,,]}
gm$covar$ICI.vs.PBS <- ifelse(gm$covar$group == "PBS", 0, 1)
names(gm$covar)[3] <- c("age.of.onset")
gm$covar$age.of.onset <- as.numeric(gm$covar$age.of.onset)
p <- ggplot(gm$covar, aes(x=as.numeric(age.of.onset))) + geom_histogram(color="black", fill="white")
p`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
phenos.covars.lg <- gm$covar %>% gather(variable, value, -c("id","group","sex","diabetic status","strain","ICI.vs.PBS"))
box1 <- ggplot(data=phenos.covars.lg, aes(x=variable, y=as.numeric(value), color=group, fill=group)) +
geom_boxplot(position = position_dodge(width=0.9)) +
#ggtitle(paste0("Values of ",v," [random dataframe: ",r,"]")) +
#labs(y = v) +
theme(strip.text.x = element_text(size=13),
axis.text.x = element_text(size = 13, angle = 0),
axis.text.y = element_text(size = 13, angle = 0),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
plot.title = element_text(size = 13, face = "bold",hjust = 0.5),
#legend.position = "none"
)
box1
QTL analysis requires variables follow normal distribution, from the above distributions, we need to ranknorm the data.
##ranknorm
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
gm$covar$rz.age <- rz.transform(gm$covar$age.of.onset)
p <- ggplot(gm$covar, aes(x=as.numeric(rz.age))) + geom_histogram(color="black", fill="white")
p`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
phenos.covars.lg <- gm$covar %>% gather(variable, value, -c("id","group","sex","diabetic status","strain","ICI.vs.PBS","age.of.onset"))
box1 <- ggplot(data=phenos.covars.lg, aes(x=variable, y=as.numeric(value), color=group, fill=group)) +
geom_boxplot(position = position_dodge(width=0.9)) +
#ggtitle(paste0("Values of ",v," [random dataframe: ",r,"]")) +
#labs(y = v) +
theme(strip.text.x = element_text(size=13),
axis.text.x = element_text(size = 13, angle = 0),
axis.text.y = element_text(size = 13, angle = 0),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
plot.title = element_text(size = 13, face = "bold",hjust = 0.5),
#legend.position = "none"
)
box1
And then remove any samples that are 3 standard deviations from the mean.
#outliers
#des.3 <- Hmisc::describe(df_phenos[,c("R_AVG","L_AVG","Both_AVG")])
des.1 <- pastecs::stat.desc(gm$covar[,c("age.of.onset" ,"rz.age")])
des.2 <- psych::describe(gm$covar[,c("age.of.onset" ,"rz.age")])
#scale(df_phenos[,c("R_AVG")])
gm$covar$out.age.of.onset <- ifelse(gm$covar[,c("age.of.onset")] > (des.1[9,1] + 3*des.1[13,1]) | gm$covar[,c("age.of.onset")] < (des.1[9,1] - 3*des.1[13,1]), 'Outlier','Keep')
gm$covar$out.rz.age <- ifelse(gm$covar[,c("rz.age")] > (des.1[9,2] + 3*des.1[13,2]) | gm$covar[,c("rz.age")] < (des.1[9,2] - 3*des.1[13,2]), 'Outlier','Keep')
bad <- NULL
bad$Mouse.ID <- rownames(gm$covar)
bad$age.of.onset <- ifelse(gm$covar$out.age.of.onset =="Outlier", 'XX', '')
bad$rz.age <- ifelse(gm$covar$out.rz.age =="Outlier", 'XX', '')
bad[is.na(bad)] <- ""
bad[bad=='NA'] <- ""
df <- do.call(cbind, bad)
bad <- as.data.frame(df)
badind <- subset(bad,
bad$age.of.onset == 'XX'|
bad$rz.age == 'XX')
#badind <- bad[bad$no_pheno == 'XX',]
badind[] <- lapply(badind, as.character)
#badind$Thaiss_ID <- ifelse(badind$Thaiss == 994 | badind$Thaiss == 995 | badind$Thaiss == 996 |badind$Thaiss == 997 | badind$Thaiss == 998 | badind$Thaiss == 999, "--", bad$Thaiss_ID)
rownames(badind) <- NULL
badind[] %>%
dplyr::mutate(
age.of.onset = ifelse(age.of.onset == 'XX',
cell_spec(age.of.onset, color = 'gray',background = 'gray'),
''),
rz.age = ifelse(rz.age == 'XX',
cell_spec(rz.age, color = 'gray',background = 'gray'),
'')
) %>%
kable(escape = F,align = c("ccccccccc"),linesep ="\\hline") %>%
kable_styling("striped", full_width = F) %>%
column_spec(1:3, width = "3cm") | Mouse.ID | age.of.onset | rz.age |
|---|---|---|
| NG00453 | XX |
##removing outliers
gm$covar$Mouse.ID <- rownames(gm$covar)
gm$covar$age.of.onset[gm$covar$out.age.of.onset == "Outlier"] <- ''
gm$covar$rz.age[gm$covar$out.rz.age == "Outlier"] <- ''
#gm$covar <- gm$covar[c(1:15)]
#gm$covar$id <- rownames(gm$covar)
write.csv(gm$covar,"data/covar_cleaned_ici.vs.pbs.csv", row.names=F, quote=F)That is, those that have a grey square were removed for that particular phenotype in the QTL mapping.
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] abind_1.4-5 qtl2_0.22 reshape2_1.4.4 ggplot2_3.3.5
[5] tibble_3.1.2 psych_2.0.7 readxl_1.3.1 cluster_2.1.0
[9] dplyr_0.8.5 optparse_1.6.6 rhdf5_2.28.1 mclust_5.4.6
[13] tidyr_1.0.2 data.table_1.14.0 knitr_1.33 kableExtra_1.1.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.1 bit64_4.0.5 viridisLite_0.4.0 assertthat_0.2.1
[5] highr_0.9 blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[9] pillar_1.6.1 RSQLite_2.2.7 backports_1.2.1 lattice_0.20-38
[13] glue_1.4.2 digest_0.6.27 promises_1.1.0 rvest_0.3.5
[17] colorspace_2.0-2 htmltools_0.5.1.1 httpuv_1.5.2 plyr_1.8.6
[21] pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1 webshot_0.5.2
[25] whisker_0.4 getopt_1.20.3 later_1.0.0 git2r_0.26.1
[29] farver_2.1.0 ellipsis_0.3.2 cachem_1.0.5 withr_2.4.2
[33] mnormt_1.5-7 magrittr_2.0.1 crayon_1.4.1 memoise_2.0.0
[37] evaluate_0.14 fs_1.4.1 fansi_0.5.0 nlme_3.1-142
[41] xml2_1.3.1 tools_3.6.2 hms_0.5.3 lifecycle_1.0.0
[45] stringr_1.4.0 Rhdf5lib_1.6.3 munsell_0.5.0 pastecs_1.3.21
[49] compiler_3.6.2 rlang_0.4.11 grid_3.6.2 rstudioapi_0.13
[53] labeling_0.4.2 rmarkdown_2.1 boot_1.3-23 gtable_0.3.0
[57] DBI_1.1.1 R6_2.5.0 fastmap_1.1.0 bit_4.0.4
[61] utf8_1.2.1 rprojroot_1.3-2 readr_1.3.1 stringi_1.7.2
[65] parallel_3.6.2 Rcpp_1.0.7 vctrs_0.3.8 tidyselect_1.0.0
[69] xfun_0.24