Last updated: 2023-01-24

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

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    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_4.batches_myo_mis.csv
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    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  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
    Untracked:  data/het-ici-myo-yes.vs.het-ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
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    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
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    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_4.batches_myo.csv
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    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.4.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.4.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_4.batches_myo.csv
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    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.4.batches_myo.csv
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    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.4.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.4.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_4.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-12_peak.marker-UNC21995304_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-JAX00370189_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-13_peak.marker-UNCHS035661_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNC24597582_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-14_peak.marker-UNCHS039096_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNC25489755_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-15_peak.marker-UNCHS040614_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-16_peak.marker-UNCHS042686_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCHS043777_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-17_peak.marker-UNCHS043880_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNC29296831_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-18_peak.marker-UNC29297751_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30069852_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-19_peak.marker-UNC30386742_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNCHS001121_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-1_peak.marker-UNCHS002308_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-UNC3990359_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-2_peak.marker-UNCHS006135_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-4_peak.marker-UNC8161950_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678100_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-5_peak.marker-UNC9678931_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-UNC12162881_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-6_peak.marker-backupUNC060363218_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNC12719038_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-7_peak.marker-UNCHS022024_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNC14948439_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-8_peak.marker-UNCHS023592_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC16009822_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-9_peak.marker-UNC17271730_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_chr-X_peak.marker-UNC31358512_lod.drop-1.5_snpsqc_dis_no-x_updated_4.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_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNC18805053_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-10_peak.marker-UNCHS029427_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031753_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-11_peak.marker-UNCHS031802_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-JAX00326005_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-12_peak.marker-UNC21995304_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-JAX00370189_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-13_peak.marker-UNCHS035661_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNC24597582_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-14_peak.marker-UNCHS039096_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNC25489755_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-15_peak.marker-UNCHS040614_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-16_peak.marker-UNCHS042686_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043777_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-17_peak.marker-UNCHS043880_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29296831_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-18_peak.marker-UNC29297751_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30069852_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-19_peak.marker-UNC30386742_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS001121_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-1_peak.marker-UNCHS002308_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNC3990359_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-2_peak.marker-UNCHS006135_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-JAX00105915_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-3_peak.marker-UNC6020011_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_genes_chr-4_peak.marker-UNC8099452_lod.drop-1.5_snpsqc_dis_no-x_updated_4.batches_myo.csv
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Unstaged changes:
    Modified:   analysis/index_5.batches.Rmd

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with sample outliers

load("data/gm_allqc_4.batches_myo.RData")

#gm_allqc
gm=gm_allqc
gm
Object 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_myo-yes.vs.myo-no_4.batches_myo.csv")
# removing any missing info
#covars <- subset(covars, covars$age.of.onset!='')
nrow(covars)
[1] 151
table(covars$"Myocarditis Status")

 NO YES 
 20 131 
table(covars$"Murine MHC KO Status")

HOM 
151 
table(covars$"Drug Treatment")

ICI PBS 
 93  58 
table(covars$"clinical pheno")

 EOI SICK 
  46  105 
# keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm
Object of class cross2 (crosstype "bc")

Total individuals              151
No. genotyped individuals      151
No. phenotyped individuals     151
No. with both geno & pheno     151

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 
 20 131 
table(gm$covar$"Murine MHC KO Status")

HOM 
151 
table(gm$covar$"Drug Treatment")

ICI PBS 
 93  58 
table(gm$covar$"clinical pheno")

 EOI SICK 
  46  105 
pr.qc.ids <- pr
for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)
[1] 32610   151
## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$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 
20873 11737 
## 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] 26149     9
table(filtered_gf_mar_geno$Include)

FALSE  TRUE 
14412 11737 
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] 27239     9
table(filtered_gf_mar_probs$Include)

FALSE  TRUE 
15503 11736 
## 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$index),]     
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
names(filtered_gf_mar_geno_sample)[1] <- c("marker")
dim(filtered_gf_mar_geno_sample)
[1] 26149   161
filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$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] 27239   161
## saving files
write.csv(filtered_gf_mar_geno_sample, "data/myo-yes.vs.myo-no_sample.genos_marker.freq_low.geno.freq.removed_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs_sample, "data/myo-yes.vs.myo-no_sample.genos_marker.freq_low.probs.freq.removed_4.batches_myo.csv", quote=F)

write.csv(filtered_gf_mar_geno, "data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv", quote=F)

sample outliers removed

load("data/gm_allqc_4.batches_myo.RData")

#gm_allqc
gm=gm_allqc
gm
Object 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_myo-yes.vs.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] 151
table(covars$"Myocarditis Status")

 NO YES 
 20 131 
table(covars$"Murine MHC KO Status")

HOM 
151 
table(covars$"Drug Treatment")

ICI PBS 
 93  58 
table(covars$"clinical pheno")

 EOI SICK 
  46  105 
# keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm
Object of class cross2 (crosstype "bc")

Total individuals              151
No. genotyped individuals      151
No. phenotyped individuals     151
No. with both geno & pheno     151

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 
 20 131 
table(gm$covar$"Murine MHC KO Status")

HOM 
151 
table(gm$covar$"Drug Treatment")

ICI PBS 
 93  58 
table(gm$covar$"clinical pheno")

 EOI SICK 
  46  105 
pr.qc.ids <- pr
for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)
[1] 32610   151
## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$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 
20873 11737 
## 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] 26149     9
table(filtered_gf_mar_geno$Include)

FALSE  TRUE 
14412 11737 
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] 27239     9
table(filtered_gf_mar_probs$Include)

FALSE  TRUE 
15503 11736 
## 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$index),]     
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
names(filtered_gf_mar_geno_sample)[1] <- c("marker")
dim(filtered_gf_mar_geno_sample)
[1] 26149   161
filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T, sort=F)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$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] 27239   161
## saving files
write.csv(filtered_gf_mar_geno_sample, "data/myo-yes.vs.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/myo-yes.vs.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/myo-yes.vs.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/myo-yes.vs.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