Last updated: 2022-02-10
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Knit directory: Serreze-T1D_Workflow/
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##remember to run haplotype reconstruction (pre processing) to get out sample_inventory and hdf5 file
sample_inventory <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output/DODB_inventory_serreze_t1d_192_DO.csv", stringsAsFactors=FALSE, colClasses = c("character"))
hdf5_filename <- "/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output/hdf5_serreze_t1d_192_DO.h5"
##marker file
markers_v1 = read.csv("/Users/corneb/Documents/MyJax/CS/Projects/support.files/MUGAarrays/UWisc/gm_uwisc_v1.csv", as.is=T)
dim(markers_v1)[1] 143259 13markers_v2 = read.csv("/Users/corneb/Documents/MyJax/CS/Projects/support.files/MUGAarrays/UWisc/gm_uwisc_v2.csv", as.is=T)
markers_v1$index <- 1:nrow(markers_v1)
# Filter to retain markers with one unique position in GRCm38.
markers_v1 = subset(markers_v1, !is.na(chr) & !is.na(bp_mm10))
dim(markers_v1)[1] 137359 14##merging updated allele codes (from v2)
markers <- merge(markers_v1, markers_v2[c("marker","snp")], by=c("marker"), all.x=T)
names(markers)[c(7,15)] <- c("snps_v1","snps_v2")
markers <- markers[order(markers$index),]
##using only unique markers
markers_unique <- markers[markers$unique == TRUE, ]
##creating a code list for encoding markers for qtl2 (bc)
markers_1 <- markers_unique[,c("marker","chr","snps_v2")]
markers_1$A <- substr(markers_1$snps_v2, 1, 1)
markers_1$B <- substr(markers_1$snps_v2, 2, 2)
dim(markers_1)[1] 137359 5codes <- markers_1[,c("marker","chr","A","B")]
markers_2 <- markers_unique[markers_unique$chr %in% c(1:19, "X"), ]
markers_2$chr <- sub("^chr", "", markers_2$chr) ###remove prefix "chr"
colnames(markers_2)[colnames(markers_2)=="bp_mm10"] <- "pos"
colnames(markers_2)[colnames(markers_2)=="cM_cox"] <- "cM"
markers_2 <- markers_2 %>% drop_na(chr, marker)
markers_2$pos <- as.numeric(markers_2$pos) * 1e-6
rownames(markers_2) <- markers_2$marker
colnames(markers_2)[c(1:4)] <- c("marker", "chr", "pos", "pos")
#codes <- markers_1[markers_1$marker %in% markers_2$marker,]
#codes <- codes[,c("marker","chr","A","B")]
##keeping only markers in code list for chromosome 1:10,X
codes <- codes[codes$marker %in% markers_2$marker,]
dim(markers_2)[1] 137302 15dim(codes)[1] 137302 4h5_info <- h5ls(hdf5_filename)
h5_info <- h5_info[h5_info$group == "/G",]
h5_info <- h5_info[order(as.numeric(h5_info$name)),]
num_samples <- strsplit(h5_info$dim, " x ") ##num of samples per project
n=length(num_samples)
num_rows <- as.numeric(num_samples[[1]][1])
num_samples <- c(0, as.numeric(sapply(num_samples, "[", 2)))
rn <- h5read(hdf5_filename, "rownames/1")
geno <- matrix("", nrow = num_rows, ncol = sum(num_samples),dimnames = list(rn, rep("", sum(num_samples))))
for(i in 1:n) {
G <- h5read(hdf5_filename, paste0("G/", i))
cn <- h5read(hdf5_filename, paste0("colnames/", i))
colnames(G) <- cn
rng <- (sum(num_samples[1:i]) + 1):sum(num_samples[1:(i+1)])
geno[,rng] <- G
colnames(geno)[rng] <- colnames(G)
}
# Remove samples that should not be included.
idx2 <- intersect(colnames(geno), sample_inventory$Original.Mouse.ID)
geno <- geno[ ,colnames(geno) %in% idx2, drop=FALSE]
dim(geno)[1] 143259 192# Keep only the good SNPs.
geno <- geno[rownames(markers_2),]
dim(geno)[1] 137302 192##encdoing markers for qtl2
geno.1 <- qtl2convert::encode_geno(geno, as.matrix(codes[,c("A","B")]))
#encoding markers for backcross
geno.1[geno.1 == "A"] <- "AA"
geno.1[geno.1 == "H"] <- "AB"
geno.1[geno.1 == "B"] <- "AA"
geno.2 <- qtl2convert::encode_geno(geno, as.matrix(codes[,c("A","B")]))
##saving files--------------------
##physical map
write.csv(markers_2[,1:3], file = "data/physical_map.csv",row.names = FALSE, quote = FALSE)
##genetic map
write.csv(markers_2[,c(1,2,4)], file = "data/genetic_map.csv",row.names = FALSE, col.names =c("marker", "chr", "pos"), quote = FALSE)
##sample genotypes
marker.names <- markers_2[,"marker"]
sample.geno <- data.frame(marker = marker.names, geno.2[marker.names,], stringsAsFactors = F, check.names=F)
write.csv(sample.geno, file = "data/sample_geno.csv",row.names = F, quote = F)
sample.geno.1 <- data.frame(marker = marker.names, geno.1[marker.names,], stringsAsFactors = F, check.names=F)
write.csv(sample.geno.1, file = "data/sample_geno_bc.csv",row.names = F, quote = F)
# Write out temp covariates
covar <- data.frame(id = sample_inventory$Original.Mouse.ID, sex = sample_inventory$Sex)
rownames(covar) <- covar$id
write.csv(covar, file <- "data/GM_covar.csv", quote = FALSE)
# Write out temp phenotypes
pheno <- matrix(rnorm(ncol(geno)), nrow = ncol(geno), ncol = 1, dimnames =
list(colnames(geno), "pheno"))
rownames(pheno) <- make.unique(rownames(pheno))
write.csv(pheno, file <- "data/pheno.csv", row.names = TRUE, quote = FALSE)gm <- read_cross2("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_hh/gm.json")
gmObject of class cross2 (crosstype "bc")
Total individuals 192
No. genotyped individuals 192
No. phenotyped individuals 192
No. with both geno & pheno 192
No. phenotypes 1
No. covariates 6
No. phenotype covariates 0
No. chromosomes 20
Total markers 137302
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
10423 10441 8206 7955 8030 8130 7760 6717 6984 6631 7433 6444 6327
14 15 16 17 18 19 X
6230 5534 5179 5323 4787 3676 5092 #Let’s omit markers without any genotype data
gm <- drop_nullmarkers(gm)Dropping 3586 markers with no datagmObject of class cross2 (crosstype "bc")
Total individuals 192
No. genotyped individuals 192
No. phenotyped individuals 192
No. with both geno & pheno 192
No. phenotypes 1
No. covariates 6
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 6472 7276 6226 6177
14 15 16 17 18 19 X
6082 5421 5075 5161 4682 3612 4857 save(gm, file = "data/gm_serreze.192.RData")
probsA <- calc_genoprob(gm, quiet = T)
saveRDS(probsA, file = "data/serreze_probs.rds")
e <- calc_errorlod(gm, probsA, cores=20)
e <- do.call("cbind", e)
save(e, file = "data/e.RData")
sessionInfo()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 jsonlite_1.7.2 bit64_4.0.5 viridisLite_0.4.0
[5] assertthat_0.2.1 highr_0.9 blob_1.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.6.1 RSQLite_2.2.7 backports_1.2.1
[13] lattice_0.20-38 glue_1.4.2 digest_0.6.27 promises_1.1.0
[17] rvest_0.3.5 colorspace_2.0-2 htmltools_0.5.1.1 httpuv_1.5.2
[21] plyr_1.8.6 pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1
[25] webshot_0.5.2 qtl_1.46-2 whisker_0.4 getopt_1.20.3
[29] later_1.0.0 git2r_0.26.1 ellipsis_0.3.2 cachem_1.0.5
[33] withr_2.4.2 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 qtl2convert_0.22-7
[45] hms_0.5.3 lifecycle_1.0.0 stringr_1.4.0 Rhdf5lib_1.6.3
[49] munsell_0.5.0 compiler_3.6.2 rlang_0.4.11 grid_3.6.2
[53] rstudioapi_0.13 rmarkdown_2.1 gtable_0.3.0 DBI_1.1.1
[57] R6_2.5.0 fastmap_1.1.0 bit_4.0.4 utf8_1.2.1
[61] rprojroot_1.3-2 readr_1.3.1 stringi_1.7.2 parallel_3.6.2
[65] Rcpp_1.0.7 vctrs_0.3.8 tidyselect_1.0.0 xfun_0.24