Last updated: 2022-04-13
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Knit directory: Serreze-T1D_Workflow/
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Unstaged changes:
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
Modified: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
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Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
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Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici-early.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
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Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_pheno.corrected.cleaned_5.batches_mis.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.eoi_snpsqc_pheno.corrected.cleaned_5.batches_mis.Rmd
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Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches.Rmd
Modified: analysis/4.1.2_qtl.analysis_cont_rz.age_ici.vs.pbs_snpsqc_pheno.corrected.cleaned_dis_no-xk_5.batches_mis.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/genotype.frequencies_ici.vs.eoi_5.batches_mis.Rmd) and HTML (docs/genotype.frequencies_ici.vs.eoi_5.batches_mis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | d0c63e8 | Belinda Cornes | 2022-04-10 | analysis for 5 batches htmls |
| Rmd | c98df6c | Belinda Cornes | 2022-04-10 | analysis for 5 batches Rmds |
load("data/gm_allqc_5.batches_mis.RData")
#gm_allqc
gm=gm_allqc
gmObject of class cross2 (crosstype "bc")
Total individuals 308
No. genotyped individuals 308
No. phenotyped individuals 308
No. with both geno & pheno 308
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 pr <- readRDS("data/serreze_probs_allqc_5.batches_mis.rds")
#pr <- readRDS("data/serreze_probs.rds")
geno <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/sample_geno_bc.csv", as.is=T)
names(geno) <- gsub("\\.","-",names(geno))
rownames(geno) <- geno$marker## extracting animals with ici and eoi group status
miceinfo <- gm$covar[gm$covar$group == "EOI" | gm$covar$group == "ICI",]
table(miceinfo$group)
EOI ICI
164 104 mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gmObject of class cross2 (crosstype "bc")
Total individuals 268
No. genotyped individuals 268
No. phenotyped individuals 268
No. with both geno & pheno 268
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 table(gm$covar$group)
EOI ICI
164 104 covars <- read_csv("data/covar_corrected_ici.vs.eoi_5.batches_mis.csv")
# removing any missing info
#covars <- subset(covars, covars$age.of.onset!='')
nrow(covars)[1] 268table(covars$group)
EOI ICI
164 104 # keeping only informative mice
gm <- gm[covars$Mouse.ID]
gmObject of class cross2 (crosstype "bc")
Total individuals 268
No. genotyped individuals 268
No. phenotyped individuals 268
No. with both geno & pheno 268
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 table(gm$covar$group)
EOI ICI
164 104 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] 131356 268## 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$index),]
dim(gf_mar)[1] 131356 8# Calculating ratio and flagging informative marker
gf_mar$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
112334 19022 ## 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] 32284 9table(filtered_gf_mar_geno$Include)
FALSE TRUE
13262 19022 gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)[1] 33238 9table(filtered_gf_mar_probs$Include)
FALSE TRUE
18034 15204 ## merging with sample_genos
#filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
#names(filtered_gf_mar_geno_sample)[1] <- c("marker")
#dim(filtered_gf_mar_geno_sample)
#filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$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)
## saving files
#write.csv(filtered_gf_mar_geno_sample, "data/ici.vs.eoi_sample.genos_marker.freq_low.geno.freq.removed.csv", quote=F)
#write.csv(filtered_gf_mar_probs_sample, "data/ici.vs.eoi_sample.genos_marker.freq_low.probs.freq.removed.csv", quote=F)
write.csv(filtered_gf_mar_geno, "data/ici.vs.eoi_marker.freq_low.geno.freq.removed_geno.ratio_5.batches_mis.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/ici.vs.eoi_marker.freq_low.probs.freq.removed_geno.ratio_5.batches_mis.csv", quote=F)load("data/gm_allqc_5.batches_mis.RData")
#gm_allqc
gm=gm_allqc
gmObject of class cross2 (crosstype "bc")
Total individuals 308
No. genotyped individuals 308
No. phenotyped individuals 308
No. with both geno & pheno 308
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 pr <- readRDS("data/serreze_probs_allqc_5.batches_mis.rds")
#pr <- readRDS("data/serreze_probs.rds")
geno <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/sample_geno_bc.csv", as.is=T)
names(geno) <- gsub("\\.","-",names(geno))
rownames(geno) <- geno$marker## extracting animals with ici and eoi group status
miceinfo <- gm$covar[gm$covar$group == "EOI" | gm$covar$group == "ICI",]
table(miceinfo$group)
EOI ICI
164 104 mice.ids <- rownames(miceinfo)
gm <- gm[mice.ids]
gmObject of class cross2 (crosstype "bc")
Total individuals 268
No. genotyped individuals 268
No. phenotyped individuals 268
No. with both geno & pheno 268
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 table(gm$covar$group)
EOI ICI
164 104 covars <- read_csv("data/covar_corrected.cleaned_ici.vs.eoi_5.batches_mis.csv")
covars[is.na(covars$age.of.onset),]$age.of.onset <- 'eoi nmiss'
# removing any missing info
covars <- subset(covars, covars$age.of.onset!='')
covars[covars$age.of.onset=='eoi nmiss',]$age.of.onset <- NA
nrow(covars)[1] 265table(covars$group)
EOI ICI
164 101 # keeping only informative mice
gm <- gm[covars$Mouse.ID]
gmObject of class cross2 (crosstype "bc")
Total individuals 265
No. genotyped individuals 265
No. phenotyped individuals 265
No. with both geno & pheno 265
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 131356
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015
17 18 19 X
5080 4605 3562 4770 table(gm$covar$group)
EOI ICI
164 101 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] 131356 265## 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$index),]
dim(gf_mar)[1] 131356 8# Calculating ratio and flagging informative marker
gf_mar$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
112836 18520 ## 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] 32287 9table(filtered_gf_mar_geno$Include)
FALSE TRUE
13767 18520 gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)[1] 33245 9table(filtered_gf_mar_probs$Include)
FALSE TRUE
18411 14834 ## merging with sample_genos
#filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
#names(filtered_gf_mar_geno_sample)[1] <- c("marker")
#dim(filtered_gf_mar_geno_sample)
#filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$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)
## saving files
#write.csv(filtered_gf_mar_geno_sample, "data/ici.vs.eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv", quote=F)
#write.csv(filtered_gf_mar_probs_sample, "data/ici.vs.eoi_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv", quote=F)
write.csv(filtered_gf_mar_geno, "data/ici.vs.eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_5.batches_mis.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/ici.vs.eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_5.batches_mis.csv", quote=F)
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_1.0.8 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] generics_0.0.2 ellipsis_0.3.2 cachem_1.0.5 withr_2.4.2
[33] cli_3.0.0 mnormt_1.5-7 magrittr_2.0.1 crayon_1.4.1
[37] memoise_2.0.0 evaluate_0.14 fs_1.4.1 fansi_0.5.0
[41] nlme_3.1-142 xml2_1.3.1 tools_3.6.2 hms_0.5.3
[45] lifecycle_1.0.1 stringr_1.4.0 Rhdf5lib_1.6.3 munsell_0.5.0
[49] compiler_3.6.2 rlang_1.0.2 grid_3.6.2 rstudioapi_0.13
[53] rmarkdown_2.1 gtable_0.3.0 DBI_1.1.1 R6_2.5.0
[57] fastmap_1.1.0 bit_4.0.4 utf8_1.2.1 rprojroot_1.3-2
[61] readr_1.3.1 stringi_1.7.2 parallel_3.6.2 Rcpp_1.0.7
[65] vctrs_0.3.8 tidyselect_1.1.2 xfun_0.24