Last updated: 2023-04-14
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Knit directory: Serreze-T1D_Workflow/
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Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNC26069905_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNCHS041223_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNC28542319_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCJPD006670_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNC28776739_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNCHS045594_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30414168_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30426276_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNC2031646_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNCHS003700_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-ICR5131_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-UNCHS006420_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC6759992_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC6765178_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9889957_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9900273_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC10800126_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC10832076_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS020903_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS021163_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC15471847_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC15548888_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18343990_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18343990_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNC20070077_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNC20070077_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21652584_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21652584_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS036579_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS036579_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS037782_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNC26069905_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNCHS041223_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-JAX00070117_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNC28542319_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCJPD006670_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC28776739_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNCHS045594_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30414168_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30426276_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNC2031646_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS003700_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-ICR5131_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNCHS006420_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC5667757_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC6759992_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC6765178_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9889957_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-5_peak.marker-UNC9900273_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC10800126_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-6_peak.marker-UNC10832076_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS020903_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-7_peak.marker-UNCHS021163_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC15471847_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-8_peak.marker-UNC15548888_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-9_peak.marker-UNC16231874_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_genes_chr-X_peak.marker-UNCHS049472_lod.drop-1.5_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
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Untracked: data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
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Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
Untracked: data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
Untracked: data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
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Untracked: data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
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Untracked: data/myo-yes.vs.myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/myo-yes.vs.myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
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Untracked: data/pbs-myo-yes.vs.pbs-myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
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Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
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Unstaged changes:
Modified: analysis/_site.yml
Modified: analysis/index.Rmd
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load("data/gm_allqc_7.batches_myo.RData")
#gm_allqc
gm=gm_allqc
gmObject 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 #pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")
##extracting animals with ici and pbs group status
##extracting animals
#Myocarditis Status
miceinfo <- gm$covar[gm$covar$"Myocarditis Status" == "YES",]
#table(miceinfo$group)
#MMurine MHC KO Status
miceinfo <- miceinfo[miceinfo$"Murine MHC KO Status" == "HOM",]
#Drug Treatment
miceinfo <- miceinfo[miceinfo$"Drug Treatment" == "ICI" | miceinfo$"Drug Treatment" == "PBS",]
#Clinical Phenotype
miceinfo <- miceinfo[miceinfo$"clinical pheno" == "SICK" | miceinfo$"clinical pheno" == "EOI",]
table(miceinfo$"Myocarditis Status")
YES
212 table(miceinfo$"Murine MHC KO Status")
HOM
212 table(miceinfo$"Drug Treatment")
ICI PBS
133 79 table(miceinfo$"clinical pheno")
EOI SICK
74 138 mice.ids <- rownames(miceinfo)
##removing X samples
x.samples = c("NM00179-ICI-EOI","NM00186-ICI-EOI","D22-ICI-EOI","D39-ICI-Myo","D67-ICI-Myo","D111-PBS-Myo","D120-ICI-Myo","6895-PBS-Myo","D704-ICI-Myo")
mice.ids = mice.ids[!as.character(do.call(rbind.data.frame, strsplit(mice.ids, "_"))[,7]) %in% x.samples]
#removing duplicates
dup.samples = c("The_Jackson_Lab_Serreze_MURGIGV01_20230225_D249-PBS-EOI_H3")
mice.ids = mice.ids[!mice.ids %in% dup.samples]
gm <- gm[mice.ids]
gmObject of class cross2 (crosstype "bc")
Total individuals 212
No. genotyped individuals 212
No. phenotyped individuals 212
No. with both geno & pheno 212
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 32660
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2482 2412 1742 1772 1648 1842 1541 1517 1769 1092 1754 1230 1441 1505 1128 842
17 18 19 X
655 820 945 4523 table(gm$covar$"Myocarditis Status")
YES
212 table(gm$covar$"Murine MHC KO Status")
HOM
212 table(gm$covar$"Drug Treatment")
ICI PBS
133 79 table(gm$covar$"clinical pheno")
EOI SICK
74 138 gm$covar$ici_vs_pbs <- ifelse(gm$covar$"Drug Treatment" == "PBS", 0, 1)
gm$covar$group = gm$covar$ici_vs_pbs
##loading genotypes
genos.1 <- read.csv("data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv")
dim(genos.1)[1] 26462 223genos.1 <- genos.1[genos.1$Include,]
dim(genos.1)[1] 12015 223#dis.genos.1 <- read.csv("data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv")
#dim(dis.genos.1)
rownames(genos.1) <- genos.1$marker
genos.1 <- t(genos.1[3:(ncol(genos.1)-9)])
rownames(genos.1) <- gsub("\\.","-",rownames(genos.1))
genos.2<- read.csv("data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv")
dim(genos.2)[1] 26462 223genos.2 <- genos.2[genos.2$Include,]
dim(genos.2)[1] 12015 223#dis.genos.2 <- read.csv("data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv")
rownames(genos.2) <- genos.2$marker
genos.2 <- t(genos.2[3:(ncol(genos.2)-9)])
rownames(genos.2) <- gsub("\\.","-",rownames(genos.2))
##merge with phenotypes (age.of.onset & binary)
names(gm$covar)[6] <- c("age.of.onset")
gm$covar$age.of.onset <- as.numeric(gm$covar$age.of.onset)
#if(0 %in% unique(gm$covar$age.of.onset)){
gm$covar[gm$covar$age.of.onset==0 | is.na(gm$covar$age.of.onset) | gm$covar$age.of.onset=="NA" ,]$age.of.onset = ""
gm$covar[gm$covar$age.of.onset=='' ,]$age.of.onset <- 40
gm$covar$age.of.onset <- as.numeric(gm$covar$age.of.onset)
geno.pheno.1 <- merge(data.frame(genos.1), gm$covar, by="row.names", all.x=T, sort=F)
aov.1 <- stats::aov(age.of.onset ~ group, geno.pheno.1)
mse.1 <- mean(aov.1$residuals^2)
geno.pheno.2 <- merge(data.frame(genos.2), gm$covar, by="row.names", all.x=T, sort=F)
aov.2 <- stats::aov(age.of.onset ~ group, geno.pheno.2)
mse.2 <- mean(aov.2$residuals^2)
## calculating effect (age.of.onset)
age.marker.sum.1 <- list()
geno.pheno.1a <- geno.pheno.1[-1]
for(i in 1:(ncol(geno.pheno.1a)-13)){
#geno.pheno.1$snp=geno.pheno.1[i]
age.marker.1 <- geno.pheno.1a %>%
group_by(geno.pheno.1a[i]) %>%
summarize(mean_age.of.onset = mean(as.numeric(age.of.onset), na.rm =T))
names(age.marker.1) <- c("genotypes", paste0(names(geno.pheno.1a[i]),"_age.of.onset"))
genotypes <- as.data.frame(c("-","AA","AB"))
names(genotypes) <- c("genotypes")
age.marker.1 <- merge(genotypes, age.marker.1, by=c("genotypes"), all=T, sort=T)
#print(age.marker.1)
age.marker.sum.1[[i]] <- as.data.frame(age.marker.1)
}
age.marker.sum.1.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.1)
rownames(age.marker.sum.1.all) <- age.marker.sum.1.all$genotypes
age.marker.sum.1.all.t <- t(age.marker.sum.1.all[-1])
rownames(age.marker.sum.1.all.t) <- gsub("_age.of.onset","",rownames(age.marker.sum.1.all.t))
age.marker.sum.1.all.t <- as.data.frame(age.marker.sum.1.all.t)
age.marker.sum.1.all.t$diffhethom <- age.marker.sum.1.all.t$AB - age.marker.sum.1.all.t$AA
max.1 <- max(age.marker.sum.1.all.t$diffhethom)
min.1 <- min(age.marker.sum.1.all.t$diffhethom)
write.csv(age.marker.sum.1.all.t,"data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv")
age.marker.sum.2 <- list()
geno.pheno.2a <- geno.pheno.2[-1]
for(i in 1:(ncol(geno.pheno.2a)-13)){
#geno.pheno.2$snp=geno.pheno.2[i]
age.marker.2 <- geno.pheno.2a %>%
group_by(geno.pheno.2a[i]) %>%
summarize(mean_age.of.onset = mean(as.numeric(age.of.onset), na.rm =T))
names(age.marker.2) <- c("genotypes", paste0(names(geno.pheno.2a[i]),"_age.of.onset"))
genotypes <- as.data.frame(c("-","AA","AB"))
names(genotypes) <- c("genotypes")
age.marker.2 <- merge(genotypes, age.marker.2, by=c("genotypes"), all=T, sort=T)
#print(age.marker.2)
age.marker.sum.2[[i]] <- as.data.frame(age.marker.2)
}
age.marker.sum.2.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.2)
rownames(age.marker.sum.2.all) <- age.marker.sum.2.all$genotypes
age.marker.sum.2.all.t <- t(age.marker.sum.2.all[-1])
rownames(age.marker.sum.2.all.t) <- gsub("_age.of.onset","",rownames(age.marker.sum.2.all.t))
age.marker.sum.2.all.t <- as.data.frame(age.marker.sum.2.all.t)
age.marker.sum.2.all.t$diffhethom <- age.marker.sum.2.all.t$AB - age.marker.sum.2.all.t$AA
max.2 <- max(age.marker.sum.2.all.t$diffhethom)
min.2 <- min(age.marker.sum.2.all.t$diffhethom)
write.csv(age.marker.sum.2.all.t,"data/mean.differences_age.of.onset_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv")
max.a <- max(max.1, max.2)
print(max.a)[1] 5.857844min.a <- min(min.1, min.2)
print(min.a)[1] -5.041677mse.a <- (mse.1 + mse.2)/2
print(mse.a)[1] 118.9566## calculating effect (group)
age.marker.sum.1 <- list()
geno.pheno.1a <- geno.pheno.1[-1]
for(i in 1:(ncol(geno.pheno.1a)-13)){
#geno.pheno.1$snp=geno.pheno.1[i]
age.marker.1 <- geno.pheno.1a %>%
group_by(geno.pheno.1a[i]) %>%
summarize(mean_group = mean(as.numeric(group), na.rm =T))
names(age.marker.1) <- c("genotypes", paste0(names(geno.pheno.1a[i]),"_group"))
genotypes <- as.data.frame(c("-","AA","AB"))
names(genotypes) <- c("genotypes")
age.marker.1 <- merge(genotypes, age.marker.1, by=c("genotypes"), all=T, sort=T)
#print(age.marker.1)
age.marker.sum.1[[i]] <- as.data.frame(age.marker.1)
}
age.marker.sum.1.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.1)
rownames(age.marker.sum.1.all) <- age.marker.sum.1.all$genotypes
age.marker.sum.1.all.t <- t(age.marker.sum.1.all[-1])
rownames(age.marker.sum.1.all.t) <- gsub("_group","",rownames(age.marker.sum.1.all.t))
age.marker.sum.1.all.t <- as.data.frame(age.marker.sum.1.all.t)
age.marker.sum.1.all.t$diffhethom <- age.marker.sum.1.all.t$AB - age.marker.sum.1.all.t$AA
write.csv(age.marker.sum.1.all.t,"data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv")
age.marker.sum.2 <- list()
geno.pheno.2a <- geno.pheno.2[-1]
for(i in 1:(ncol(geno.pheno.2a)-13)){
#geno.pheno.2$snp=geno.pheno.2[i]
age.marker.2 <- geno.pheno.2a %>%
group_by(geno.pheno.2a[i]) %>%
summarize(mean_group = mean(as.numeric(group), na.rm =T))
names(age.marker.2) <- c("genotypes", paste0(names(geno.pheno.2a[i]),"_group"))
genotypes <- as.data.frame(c("-","AA","AB"))
names(genotypes) <- c("genotypes")
age.marker.2 <- merge(genotypes, age.marker.2, by=c("genotypes"), all=T, sort=T)
#print(age.marker.2)
age.marker.sum.2[[i]] <- as.data.frame(age.marker.2)
}
age.marker.sum.2.all <- Reduce(function(x, y) merge(x, y, by=c("genotypes"),all=TRUE),age.marker.sum.2)
rownames(age.marker.sum.2.all) <- age.marker.sum.2.all$genotypes
age.marker.sum.2.all.t <- t(age.marker.sum.2.all[-1])
rownames(age.marker.sum.2.all.t) <- gsub("_group","",rownames(age.marker.sum.2.all.t))
age.marker.sum.2.all.t <- as.data.frame(age.marker.sum.2.all.t)
age.marker.sum.2.all.t$diffhethom <- age.marker.sum.2.all.t$AB - age.marker.sum.2.all.t$AA
write.csv(age.marker.sum.2.all.t,"data/mean.differences_group_ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv")
max.g <- max(max.1, max.2)
print(max.g)[1] 5.857844min.g <- min(min.1, min.2)
print(min.g)[1] -5.041677mse.g <- (mse.1 + mse.2)/2
print(mse.g)[1] 118.9566For powercalc the power is returned, along with the proportion of variance explained. LOD threshold set to 3 (which is roughly what is seen for suggestive significance)
## age.of onset
powercalc(cross = "bc",n=length(mice.ids),effect=min.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1) power percent.var.explained
[1,] 0.3625458 5.071074powercalc(cross = "bc",n=length(mice.ids),effect=max.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1) power percent.var.explained
[1,] 0.5765674 6.726444#powercalc("bc",100,31,sigma2=1,sel.frac=1,theta=0)
## group (binary)
powercalc(cross = "bc",n=length(mice.ids),effect=min.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1) power percent.var.explained
[1,] 0.3625458 5.071074powercalc(cross = "bc",n=length(mice.ids),effect=max.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1) power percent.var.explained
[1,] 0.5765674 6.726444## graphFor samplesize the sample size (rounded up to the nearest integer) is returned along with the proportion of variance explained. LOD threshold set to 3 (which is roughly what is seen for suggestive significance) and 80% power
## age.of onset
samplesize(cross = "bc",effect=min.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1) sample.size percent.var.explained
[1,] 390 5.071074samplesize(cross = "bc",effect=max.a,sigma2=mse.a,thresh=3,sel.frac=1,theta=0,bio.reps=1) sample.size percent.var.explained
[1,] 289 6.726444## group (binary)
samplesize(cross = "bc",effect=min.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1) sample.size percent.var.explained
[1,] 390 5.071074samplesize(cross = "bc",effect=max.g,sigma2=mse.g,thresh=3,sel.frac=1,theta=0,bio.reps=1) sample.size percent.var.explained
[1,] 289 6.726444## graph
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] qtlDesign_0.941 abind_1.4-5 qtl2_0.24 reshape2_1.4.4
[5] ggplot2_3.3.3 tibble_3.0.6 psych_2.0.12 readxl_1.3.1
[9] cluster_2.1.0 dplyr_1.0.4 optparse_1.6.6 rhdf5_2.34.0
[13] mclust_5.4.7 tidyr_1.1.2 data.table_1.13.6 knitr_1.31
[17] kableExtra_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 bit64_4.0.5 viridisLite_0.3.0 tmvnsim_1.0-2
[5] assertthat_0.2.1 highr_0.8 blob_1.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.7 RSQLite_2.2.3 lattice_0.20-41
[13] glue_1.4.2 digest_0.6.27 promises_1.1.1 rvest_0.3.6
[17] colorspace_2.0-0 htmltools_0.5.1.1 httpuv_1.5.5 plyr_1.8.6
[21] pkgconfig_2.0.3 purrr_0.3.4 scales_1.1.1 webshot_0.5.2
[25] getopt_1.20.3 later_1.1.0.1 git2r_0.28.0 generics_0.1.0
[29] ellipsis_0.3.1 cachem_1.0.3 withr_2.4.1 mnormt_2.0.2
[33] magrittr_2.0.1 crayon_1.4.1 memoise_2.0.0 evaluate_0.14
[37] fs_1.5.0 nlme_3.1-152 xml2_1.3.2 tools_4.0.3
[41] lifecycle_0.2.0 stringr_1.4.0 Rhdf5lib_1.12.1 munsell_0.5.0
[45] compiler_4.0.3 rlang_0.4.10 grid_4.0.3 rhdf5filters_1.2.1
[49] rstudioapi_0.13 rmarkdown_2.6 gtable_0.3.0 DBI_1.1.1
[53] R6_2.5.0 fastmap_1.1.0 bit_4.0.4 rprojroot_2.0.2
[57] stringi_1.5.3 parallel_4.0.3 Rcpp_1.0.10 vctrs_0.3.6
[61] tidyselect_1.1.0 xfun_0.21