Last updated: 2023-04-13
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Knit directory: Serreze-T1D_Workflow/
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Untracked: output/Percent_missing_genotype_data_per_marker_4.batches_myo.pdf
Untracked: output/Percent_missing_genotype_data_per_marker_7.batches_myo.pdf
Untracked: output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.batches_myo.pdf
Untracked: output/Proportion_matching_genotypes_before_removal_of_bad_samples_7.batches_myo.pdf
Untracked: output/genotype_error_marker_4.batches_myo.pdf
Untracked: output/genotype_error_marker_7.batches_myo.pdf
Untracked: output/genotype_frequency_marker_4.batches_myo.pdf
Untracked: output/genotype_frequency_marker_7.batches_myo.pdf
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load("data/e_snpg_samqc_7.batches_myo.RData")
gm <- get(load("data/gm_samqc_7.batches_myo.RData"))
gm
Warning in check_cross2(object): 1249 invalid genotypes in cross
Object of class cross2 (crosstype "bc")
Total individuals 357
No. genotyped individuals 357
No. phenotyped individuals 357
No. with both geno & pheno 357
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 133716
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
10159 10172 7987 7736 7778 7911 7548 6561 6823 6471 7276 6226 6177
14 15 16 17 18 19 X
6082 5421 5075 5162 4682 3612 4857
It can also be useful to look at the proportion of missing genotypes by marker. Markers with a lot of missing data were likely difficult to call, and so the genotypes that were called may contain a lot of errors.
pmis_mar <- n_missing(gm, "marker", "proportion")*100
save(pmis_mar, file = "data/percent_missing_marker_7.batches_myo.RData")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
pdf(file = "output/Percent_missing_genotype_data_per_marker_7.batches_myo.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(pmis_mar, breaks=seq(0, 100, length=201),
main="", yaxt="n", ylab="", xlab="Percent missing genotypes")
rug(pmis_mar)
dev.off()
quartz_off_screen
2
count | |
---|---|
pmis_mar_5 | 5204 |
pmis_mar_10 | 2826 |
pmis_mar_15 | 1851 |
pmis_mar_25 | 1043 |
pmis_mar_50 | 257 |
pmis_mar_75 | 23 |
total_snps | 133716 |
g <- do.call("cbind", gm$geno[1:19])
#fg <- do.call("cbind", gm$founder_geno[1:19])
#g <- g[,colSums(g)!=0]
#fg <- fg[,colSums(fg==0)==0]
#fgn <- colSums(g==2)
gf_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar <- t(apply(g, 2, function(a) table(factor(a, 1:2))))
gf_mar <- gf_mar[gf_mar[,2] != "NaN",]
MAF <- apply(gf_mar, 1, function(x) min(x))
MAF <- as.data.frame(MAF)
MAF$index <- 1:nrow(gf_mar)
gf_mar_maf <- merge(gf_mar,as.data.frame(MAF), by="row.names")
gf_mar_maf <- gf_mar_maf[order(gf_mar_maf$index),]
pdf(file = "output/genotype_frequency_marker_7.batches_myo.pdf")
par(mar=c(5.1,0.6,0.6, 0.6))
hist(gf_mar_maf$MAF, breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="MAF")
rug(gf_mar_maf$MAF)
dev.off()
quartz_off_screen
2
par(mar=c(5.1,0.6,0.6, 0.6))
hist(gf_mar_maf$MAF, breaks=seq(0, 1, length=201),
main="", yaxt="n", ylab="", xlab="MAF")
rug(gf_mar_maf$MAF)
gfmar <- NULL
gfmar$gfmar_mar_0 <- sum(gf_mar_maf$MAF==0)
gfmar$gfmar_mar_1 <- sum(gf_mar_maf$MAF< 0.01)
gfmar$gfmar_mar_5 <- sum(gf_mar_maf$MAF< 0.05)
gfmar$gfmar_mar_10 <- sum(gf_mar_maf$MAF< 0.10)
gfmar$gfmar_mar_15 <- sum(gf_mar_maf$MAF< 0.15)
gfmar$gfmar_mar_25 <- sum(gf_mar_maf$MAF< 0.25)
gfmar$gfmar_mar_50 <- sum(gf_mar_maf$MAF<= 0.50)
gfmar$total_snps <- nrow(as.data.frame(gf_mar_maf))
gfmar <- t(as.data.frame(gfmar))
gfmar <- as.data.frame(gfmar)
gfmar$count <- gfmar$V1
gfmar[c(2)] %>%
kable(escape = F,align = c("ccccccccc"),linesep ="\\hline") %>%
kable_styling(full_width = F) %>%
kable_styling("striped", full_width = F) %>%
row_spec(8 ,bold=T,color= "white",background = "black")
count | |
---|---|
gfmar_mar_0 | 87114 |
gfmar_mar_1 | 92158 |
gfmar_mar_5 | 94870 |
gfmar_mar_10 | 95480 |
gfmar_mar_15 | 95744 |
gfmar_mar_25 | 96154 |
gfmar_mar_50 | 128857 |
total_snps | 128857 |
save(gf_mar, file = "data/genotype_freq_marker_7.batches_myo.RData")
errors_mar <- colSums(e>2)/n_typed(gm, "marker")*100
grayplot(pmis_mar, errors_mar,
xlab="Proportion missing", ylab="Proportion genotyping errors")
pdf(file = "output/genotype_error_marker_7.batches_myo.pdf")
grayplot(pmis_mar, errors_mar,
xlab="Proportion missing", ylab="Proportion genotyping errors")
dev.off()
quartz_off_screen
2
save(errors_mar, file = "data/genotype_errors_marker_7.batches_myo.RData")
Markers with higher rates of missing genotypes tend to show higher errors rates.
#Missingness
length(pmis_mar[pmis_mar >= 10])
[1] 2826
high_miss <- find_markerpos(gm, names(pmis_mar[pmis_mar >= 10]))
high_miss$id <- rownames(high_miss)
high_miss_df <- as.data.frame(pmis_mar[pmis_mar >= 10])
high_miss_df$index = 1: nrow(high_miss_df)
high_miss_df$id <- rownames(high_miss_df)
high_miss_bad <- merge(high_miss,high_miss_df, by=c("id"),all=T)
names(high_miss_bad)[5] <- c("high_miss")
names(high_miss_bad)[1] <- c("marker")
high_miss_bad <- high_miss_bad[order(high_miss_bad$index),]
#Monomorphic/Low Frequency markers
#low_freq_df <- as.data.frame(gf_mar)
count <- rowSums(gf_mar <= 0.01)
#count <- as.data.frame(count)
low_freq_df <- merge(as.data.frame(gf_mar),as.data.frame(count), by="row.names",all=T)
low_freq_df[is.na(low_freq_df)] <- ''
low_freq_df <- low_freq_df[low_freq_df$count == 1,]
rownames(low_freq_df) <- low_freq_df$Row.names
#low_freq_df$id <- rownames(low_freq_df)
#low_freq_df$index = 1: nrow(low_freq_df)
low_freq <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)
nrow(low_freq)
[1] 92159
low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
#names(low_freq_bad)[5] <- c("AA_freq")
#names(low_freq_bad)[6] <- c("AB_freq")
#names(low_freq_bad)[7] <- c("BB_freq")
names(low_freq_bad)[1] <- c("marker")
#low_freq_bad <- low_freq_bad[order(low_freq_bad$index),]
##Genotyping Error
length(errors_mar[errors_mar > 5])
[1] 11469
error_markers_names <- names(errors_mar[errors_mar > 5])
error_markers_names <- error_markers_names[complete.cases(error_markers_names)]
error_markers <- find_markerpos(gm, error_markers_names)
error_markers$id <- rownames(error_markers)
#rne <- rownames(as.data.frame(errors_mar))
error_mars_df <- as.data.frame(errors_mar[errors_mar > 5])
error_mars_df <- error_mars_df[complete.cases(error_mars_df$"errors_mar[errors_mar > 5]"),]
error_mars_df <- as.data.frame(error_mars_df)
#error_mars_df$id = rownames(error_mars_df)
error_mars_df$index = 1: nrow(error_mars_df)
#error_markers_bad <- merge(error_markers,error_mars_df, by=c("id"),all=T)
error_markers_bad <- cbind(error_markers,error_mars_df)
names(error_markers_bad)[5] <- c("error_mars")
names(error_markers_bad)[4] <- c("marker")
error_markers_bad <- error_markers_bad[order(error_markers_bad$index),]
### merge all
bad_markers <- rbind(high_miss_bad[c("marker","chr","gmap","pmap")], low_freq_bad[c("marker","chr","gmap","pmap")], error_markers_bad[c("marker","chr","gmap","pmap")])
#nrow(bad_markers)
duplicate <- bad_markers[duplicated(bad_markers),]
bad_markers <- bad_markers[!duplicated(bad_markers),]
nrow(bad_markers)
[1] 101056
save(bad_markers, file = "data/bad_markers_all_7.batches_myo.RData")
Only removing markers that are missing in at least 10% of the samples as well those that come as genotyping errors and have a allele frequency of less than 1%
#missing in at least 10% of the samples
gm_allqc2 <- drop_markers(gm_samqc, bad_markers$marker)
gm_allqc <- drop_nullmarkers(gm_allqc2)
gm_allqc
Warning in check_cross2(object): 461 invalid genotypes in cross
Object of class cross2 (crosstype "bc")
Total individuals 357
No. genotyped individuals 357
No. phenotyped individuals 357
No. with both geno & pheno 357
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 32660
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2482 2412 1742 1772 1648 1842 1541 1517 1769 1092 1754 1230 1441 1505 1128 842
17 18 19 X
655 820 945 4523
save(gm_allqc, file = "data/gm_allqc_7.batches_myo.RData")
sessionInfo()
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] fst_0.9.8 knitr_1.41 kableExtra_1.3.4 mclust_6.0.0
[5] ggrepel_0.9.2 ggplot2_3.4.0 qtlcharts_0.16 qtl2_0.30
[9] broman_0.80 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 svglite_2.1.0 getPass_0.2-2 ps_1.7.2
[5] assertthat_0.2.1 rprojroot_2.0.3 digest_0.6.30 utf8_1.2.2
[9] R6_2.5.1 RSQLite_2.2.19 evaluate_0.18 httr_1.4.4
[13] highr_0.9 pillar_1.8.1 rlang_1.0.6 rstudioapi_0.14
[17] data.table_1.14.6 whisker_0.4.1 callr_3.7.3 jquerylib_0.1.4
[21] blob_1.2.3 fstcore_0.9.12 rmarkdown_2.18 webshot_0.5.4
[25] qtl_1.54 stringr_1.5.0 bit_4.0.5 munsell_0.5.0
[29] compiler_4.2.2 httpuv_1.6.6 xfun_0.35 systemfonts_1.0.4
[33] pkgconfig_2.0.3 htmltools_0.5.3 tidyselect_1.2.0 tibble_3.1.8
[37] viridisLite_0.4.1 fansi_1.0.3 dplyr_1.0.10 withr_2.5.0
[41] later_1.3.0 grid_4.2.2 jsonlite_1.8.4 gtable_0.3.1
[45] lifecycle_1.0.3 DBI_1.1.3 git2r_0.30.1 magrittr_2.0.3
[49] scales_1.2.1 cli_3.4.1 stringi_1.7.8 cachem_1.0.6
[53] fs_1.5.2 promises_1.2.0.1 xml2_1.3.3 bslib_0.4.1
[57] vctrs_0.5.1 generics_0.1.3 tools_4.2.2 bit64_4.0.5
[61] glue_1.6.2 processx_3.8.0 parallel_4.2.2 fastmap_1.1.0
[65] yaml_2.3.6 colorspace_2.0-3 rvest_1.0.3 memoise_2.0.1
[69] sass_0.4.4