Last updated: 2023-01-24
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Knit directory: Serreze-T1D_Workflow/
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load("data/gm_allqc_4.batches_myo.RData")
#gm_allqc
gm=gm_allqc
gmObject of class cross2 (crosstype "bc")
Total individuals 208
No. genotyped individuals 208
No. phenotyped individuals 208
No. with both geno & pheno 208
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 32610
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109 835
17 18 19 X
674 813 940 4501 pr <- readRDS("data/serreze_probs_allqc_4.batches_myo.rds")
#pr <- readRDS("data/serreze_probs.rds")
geno <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_4.batches_myo_corrected/sample_geno_bc_4.batches_myo_BC217.csv", as.is=T)
names(geno) <- gsub("^X","",names(geno))
names(geno) <- gsub("\\.","-",names(geno))
rownames(geno) <- geno$marker## extracting animals with ici and pbs group status
#miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
#table(miceinfo$group)
#mice.ids <- rownames(miceinfo)
#gm <- gm[mice.ids]
#gm
#table(gm$covar$group)
covars <- read_csv("data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv")
# removing any missing info
#covars <- subset(covars, covars$age.of.onset!='')
nrow(covars)[1] 27table(covars$"Myocarditis Status")
NO YES
20 7 table(covars$"Murine MHC KO Status")
HET
27 table(covars$"Drug Treatment")
ICI
27 table(covars$"clinical pheno")
EOI SICK
13 14 # keeping only informative mice
gm <- gm[covars$Mouse.ID]
gmObject of class cross2 (crosstype "bc")
Total individuals 27
No. genotyped individuals 27
No. phenotyped individuals 27
No. with both geno & pheno 27
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 32610
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109 835
17 18 19 X
674 813 940 4501 table(gm$covar$"Myocarditis Status")
NO YES
20 7 table(gm$covar$"Murine MHC KO Status")
HET
27 table(gm$covar$"Drug Treatment")
ICI
27 table(gm$covar$"clinical pheno")
EOI SICK
13 14 pr.qc.ids <- pr
for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}
geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)[1] 32610 27## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$geno)
gf_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 0:2))))
#gf_mar_raw<- gf_mar_raw[gf_mar_raw[,2] != "NaN",]
colnames(gf_mar_geno) <- c("freq_AA_geno_table","freq_AB_geno_table")
colnames(gn_mar_geno) <- c("count_missing_geno_table","count_AA_geno_table","count_AB_geno_table")
gfn_mar_geno <- merge(as.data.frame(gn_mar_geno), as.data.frame(gf_mar_geno), by="row.names")
rownames(gfn_mar_geno) <- gfn_mar_geno[,1]
gfn_mar_geno <- gfn_mar_geno[-1]
### from raw using table function in R
#genosl <- list()
#for(i in 1:nrow(geno)){
##for(i in 1:3){
# genoi <- geno[i,]
# freqf <- table(factor(geno[i,], c("-","AA","AB")))
# genoi$count_AA_raw_rowSums <- rowSums(genoi == "AA")
# genoi$count_AB_raw_rowSums <- rowSums(genoi == "AB")
# genoi$count_missing_raw_rowSums <- rowSums(genoi == "-")
# freqf <- t(table(factor(geno[i,], c("-","AA","AB"))))
# freqf <- as.data.frame(t(freqf[1,]))
# rownames(freqf) <- rownames(genoi)
# colnames(freqf) <- c("count_missing_raw_table","count_AA_raw_table","count_AB_raw_table")
# genoif <- cbind(freqf,genoi[c("count_AA_raw_rowSums","count_AB_raw_rowSums","count_missing_raw_rowSums")])
# genosl[[i]] = genoif
#}
#gf_mar_raw <- do.call("rbind",genosl)
#gf_mar_raw <- gf_mar_raw[,c(1:3,6,4:5)]
#gf_mar_raw$index <- 1:nrow(gf_mar_raw)
### from probabilities
gf_mar_probs.1 <- calc_geno_freq(pr.qc.ids, by = "marker", omit_x = FALSE)
#gn_mar_probs <- calc_geno_freq(probs, by = "individual", omit_x = FALSE)
gf_mar_probs <- rbind(gf_mar_probs.1$A[,1:2], gf_mar_probs.1$X[,1:2])
colnames(gf_mar_probs) <- paste0("freq_",colnames(gf_mar_probs),"_probs")
gf_mar_probs <- as.data.frame(gf_mar_probs)
gf_mar_probs$index <- 1:nrow(gf_mar_probs)
### merging all genotype frequecies for all markers
#gf_mar.1 <- merge(as.data.frame(gf_mar_raw), as.data.frame(gfn_mar_geno), by="row.names")
#rownames(gf_mar.1) <- gf_mar.1[,1]
#gf_mar.1 <- gf_mar.1[-1]
#gf_mar <- merge(gf_mar.1,as.data.frame(gf_mar_probs), by="row.names")
gf_mar <- merge(as.data.frame(gfn_mar_geno),as.data.frame(gf_mar_probs), by="row.names")
rownames(gf_mar) <- gf_mar[,1]
gf_mar <- gf_mar[-1]
gf_mar <- gf_mar[order(gf_mar$index),]
dim(gf_mar)[1] 32610 8# Calculating ratio and flagging informative marker
gf_mar$ratio = as.numeric(gf_mar$freq_AA_geno_table)/as.numeric(gf_mar$freq_AB_geno_table)
gf_mar$Include = ifelse(gf_mar$ratio >= 0.90 & gf_mar$ratio <= 1.10, TRUE,FALSE)
table(gf_mar$Include)
FALSE TRUE
25503 7107 ## filtering out <= 0.05
gf_mar$count.geno <- rowSums(gf_mar[c("freq_AA_geno_table","freq_AB_geno_table")] <=0.05)
filtered_gf_mar_geno <- gf_mar[gf_mar$count.geno != 1,]
filtered_gf_mar_geno <- filtered_gf_mar_geno[,-which(names(filtered_gf_mar_geno) %in% c("count.geno","index"))]
dim(filtered_gf_mar_geno)[1] 25927 9table(filtered_gf_mar_geno$Include)
FALSE TRUE
18820 7107 gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)[1] 26981 9table(filtered_gf_mar_probs$Include)
FALSE TRUE
19880 7101 ## merging with sample_genos
filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
names(filtered_gf_mar_geno_sample)[1] <- c("marker")
dim(filtered_gf_mar_geno_sample)[1] 25927 37filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$index),]
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[,-which(names(filtered_gf_mar_probs_sample) %in% c("count.geno","count.probs","index"))]
names(filtered_gf_mar_probs_sample)[1] <- c("marker")
dim(filtered_gf_mar_probs_sample)[1] 26981 37## saving files
write.csv(filtered_gf_mar_geno_sample, "data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs_sample, "data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_geno, "data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv", quote=F)load("data/gm_allqc_4.batches_myo.RData")
#gm_allqc
gm=gm_allqc
gmObject of class cross2 (crosstype "bc")
Total individuals 208
No. genotyped individuals 208
No. phenotyped individuals 208
No. with both geno & pheno 208
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 32610
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109 835
17 18 19 X
674 813 940 4501 pr <- readRDS("data/serreze_probs_allqc_4.batches_myo.rds")
#pr <- readRDS("data/serreze_probs.rds")
geno <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_4.batches_myo_corrected/sample_geno_bc_4.batches_myo_BC217.csv", as.is=T)
names(geno) <- gsub("^X","",names(geno))
names(geno) <- gsub("\\.","-",names(geno))
rownames(geno) <- geno$marker## extracting animals with ici and pbs group status
#miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
#table(miceinfo$group)
#mice.ids <- rownames(miceinfo)
#gm <- gm[mice.ids]
#gm
#table(gm$covar$group)
covars <- read_csv("data/covar_corrected.cleaned_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv")
# removing any missing info
covars <- subset(covars, covars$out.age.of.onset=='Keep' & covars$out.rz.age=='Keep')
nrow(covars)[1] 27table(covars$"Myocarditis Status")
NO YES
20 7 table(covars$"Murine MHC KO Status")
HET
27 table(covars$"Drug Treatment")
ICI
27 table(covars$"clinical pheno")
EOI SICK
13 14 # keeping only informative mice
gm <- gm[covars$Mouse.ID]
gmObject of class cross2 (crosstype "bc")
Total individuals 27
No. genotyped individuals 27
No. phenotyped individuals 27
No. with both geno & pheno 27
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 32610
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2498 2407 1748 1770 1649 1835 1544 1515 1773 1102 1744 1214 1442 1497 1109 835
17 18 19 X
674 813 940 4501 table(gm$covar$"Myocarditis Status")
NO YES
20 7 table(gm$covar$"Murine MHC KO Status")
HET
27 table(gm$covar$"Drug Treatment")
ICI
27 table(gm$covar$"clinical pheno")
EOI SICK
13 14 pr.qc.ids <- pr
for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}
geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)[1] 32610 27## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$geno)
gf_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 0:2))))
#gf_mar_raw<- gf_mar_raw[gf_mar_raw[,2] != "NaN",]
colnames(gf_mar_geno) <- c("freq_AA_geno_table","freq_AB_geno_table")
colnames(gn_mar_geno) <- c("count_missing_geno_table","count_AA_geno_table","count_AB_geno_table")
gfn_mar_geno <- merge(as.data.frame(gn_mar_geno), as.data.frame(gf_mar_geno), by="row.names")
rownames(gfn_mar_geno) <- gfn_mar_geno[,1]
gfn_mar_geno <- gfn_mar_geno[-1]
### from raw using table function in R
#genosl <- list()
#for(i in 1:nrow(geno)){
##for(i in 1:3){
# genoi <- geno[i,]
# freqf <- table(factor(geno[i,], c("-","AA","AB")))
# genoi$count_AA_raw_rowSums <- rowSums(genoi == "AA")
# genoi$count_AB_raw_rowSums <- rowSums(genoi == "AB")
# genoi$count_missing_raw_rowSums <- rowSums(genoi == "-")
# freqf <- t(table(factor(geno[i,], c("-","AA","AB"))))
# freqf <- as.data.frame(t(freqf[1,]))
# rownames(freqf) <- rownames(genoi)
# colnames(freqf) <- c("count_missing_raw_table","count_AA_raw_table","count_AB_raw_table")
# genoif <- cbind(freqf,genoi[c("count_AA_raw_rowSums","count_AB_raw_rowSums","count_missing_raw_rowSums")])
# genosl[[i]] = genoif
#}
#gf_mar_raw <- do.call("rbind",genosl)
#gf_mar_raw <- gf_mar_raw[,c(1:3,6,4:5)]
#gf_mar_raw$index <- 1:nrow(gf_mar_raw)
### from probabilities
gf_mar_probs.1 <- calc_geno_freq(pr.qc.ids, by = "marker", omit_x = FALSE)
#gn_mar_probs <- calc_geno_freq(probs, by = "individual", omit_x = FALSE)
gf_mar_probs <- rbind(gf_mar_probs.1$A[,1:2], gf_mar_probs.1$X[,1:2])
colnames(gf_mar_probs) <- paste0("freq_",colnames(gf_mar_probs),"_probs")
gf_mar_probs <- as.data.frame(gf_mar_probs)
gf_mar_probs$index <- 1:nrow(gf_mar_probs)
### merging all genotype frequecies for all markers
#gf_mar.1 <- merge(as.data.frame(gf_mar_raw), as.data.frame(gfn_mar_geno), by="row.names")
#rownames(gf_mar.1) <- gf_mar.1[,1]
#gf_mar.1 <- gf_mar.1[-1]
#gf_mar <- merge(gf_mar.1,as.data.frame(gf_mar_probs), by="row.names")
gf_mar <- merge(as.data.frame(gfn_mar_geno),as.data.frame(gf_mar_probs), by="row.names")
rownames(gf_mar) <- gf_mar[,1]
gf_mar <- gf_mar[-1]
gf_mar <- gf_mar[order(gf_mar$index),]
dim(gf_mar)[1] 32610 8# Calculating ratio and flagging informative marker
gf_mar$ratio = as.numeric(gf_mar$freq_AA_geno_table)/as.numeric(gf_mar$freq_AB_geno_table)
gf_mar$Include = ifelse(gf_mar$ratio >= 0.90 & gf_mar$ratio <= 1.10, TRUE,FALSE)
table(gf_mar$Include)
FALSE TRUE
25503 7107 ## filtering out <= 0.05
gf_mar$count.geno <- rowSums(gf_mar[c("freq_AA_geno_table","freq_AB_geno_table")] <=0.05)
filtered_gf_mar_geno <- gf_mar[gf_mar$count.geno != 1,]
filtered_gf_mar_geno <- filtered_gf_mar_geno[,-which(names(filtered_gf_mar_geno) %in% c("count.geno","index"))]
dim(filtered_gf_mar_geno)[1] 25927 9table(filtered_gf_mar_geno$Include)
FALSE TRUE
18820 7107 gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)[1] 26981 9table(filtered_gf_mar_probs$Include)
FALSE TRUE
19880 7101 ## merging with sample_genos
filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
names(filtered_gf_mar_geno_sample)[1] <- c("marker")
dim(filtered_gf_mar_geno_sample)[1] 25927 37filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$index),]
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[,-which(names(filtered_gf_mar_probs_sample) %in% c("count.geno","count.probs","index"))]
names(filtered_gf_mar_probs_sample)[1] <- c("marker")
dim(filtered_gf_mar_probs_sample)[1] 26981 37## saving files
write.csv(filtered_gf_mar_geno_sample, "data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs_sample, "data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_geno, "data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv", quote=F)
R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] abind_1.4-5 qtl2_0.30 reshape2_1.4.4 ggplot2_3.4.0
[5] tibble_3.1.8 psych_2.2.9 readxl_1.4.1 cluster_2.1.4
[9] dplyr_1.0.10 optparse_1.7.3 rhdf5_2.40.0 mclust_6.0.0
[13] tidyr_1.2.1 data.table_1.14.6 knitr_1.41 kableExtra_1.3.4
[17] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.4 bit64_4.0.5 jsonlite_1.8.4
[5] viridisLite_0.4.1 bslib_0.4.1 assertthat_0.2.1 getPass_0.2-2
[9] highr_0.9 blob_1.2.3 cellranger_1.1.0 yaml_2.3.6
[13] pillar_1.8.1 RSQLite_2.2.19 lattice_0.20-45 glue_1.6.2
[17] digest_0.6.30 promises_1.2.0.1 rvest_1.0.3 colorspace_2.0-3
[21] htmltools_0.5.3 httpuv_1.6.6 plyr_1.8.8 pkgconfig_2.0.3
[25] purrr_0.3.5 scales_1.2.1 webshot_0.5.4 processx_3.8.0
[29] svglite_2.1.0 whisker_0.4.1 getopt_1.20.3 later_1.3.0
[33] git2r_0.30.1 generics_0.1.3 cachem_1.0.6 withr_2.5.0
[37] cli_3.4.1 mnormt_2.1.1 magrittr_2.0.3 memoise_2.0.1
[41] evaluate_0.18 ps_1.7.2 fs_1.5.2 fansi_1.0.3
[45] nlme_3.1-160 xml2_1.3.3 tools_4.2.2 lifecycle_1.0.3
[49] stringr_1.5.0 Rhdf5lib_1.18.2 munsell_0.5.0 callr_3.7.3
[53] compiler_4.2.2 jquerylib_0.1.4 systemfonts_1.0.4 rlang_1.0.6
[57] grid_4.2.2 rhdf5filters_1.8.0 rstudioapi_0.14 rmarkdown_2.18
[61] gtable_0.3.1 DBI_1.1.3 R6_2.5.1 bit_4.0.5
[65] fastmap_1.1.0 utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.8
[69] parallel_4.2.2 Rcpp_1.0.9 vctrs_0.5.1 tidyselect_1.2.0
[73] xfun_0.35