QTL Analysis - binary [ICI Myocarditis Yes vs ICI Myocarditis No]

workflowr

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Last updated: 2023-04-16

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

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Seed: set.seed(20230413)

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Session information: recorded

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Cache: none

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File paths: absolute

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absolute	relative
/projects/serreze-lab/USERS/corneb/qtl2/workflowr/2023.04.13/Serreze-T1D_Workflow	.

Repository version: 03e83ae

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The results in this page were generated with repository version 03e83ae. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

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    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/het-ici.vs.het-pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo.csv
    Untracked:  data/ici-myo-yes.vs.ici-myo-no_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed_7.batches_myo_mis.csv
    Untracked:  data/ici-sick.vs.ici-eoi_blup_sub_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_blup_sub_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_blup_sub_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_blup_sub_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_blup_sub_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_blup_sub_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_blup_sub_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_blup_sub_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_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_gm_qtl_snpsqc_dis_no-x_updated_7.batches_myo.csv
    Untracked:  data/ici-sick.vs.ici-eoi_gm_qtl_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
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Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   analysis/index.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

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Data Information

• Myocarditis Status: Yes, No
• Murine MHC KO Status: Homo
• Drug Treatment(s): ICI
• Clinical Phenotype(s): Sick, EOI
• Covariates (Myocarditis Score, sex)

Loading Data

We will load the data and subset indivials out that are in the groups of interest.

load("data/gm_allqc_7.batches_myo.RData")

#gm_allqc
gm=gm_allqc
gm

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 

#pr <- readRDS("data/serreze_probs_allqc.rds")
#pr <- readRDS("data/serreze_probs.rds")

##extracting animals with ici and pbs group status
#miceinfo <- covars[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
#table(miceinfo$group)
#mice.ids <- rownames(miceinfo)

#gm <- gm[mice.ids]
#gm
#table(gm$covar$group)

#gm$covar$ici.myo.yes_vs_ici.myo.no <- ifelse(gm$covar$group == "PBS", 0, 1)
#gm.full <- gm

covars <- read_csv("data/covar_corrected_ici-myo-yes.vs.ici-myo-no_7.batches_myo.csv")
#removing any missing info
#covars <- subset(covars, covars$ici.myo.yes_vs_ici.myo.no!='')
nrow(covars)

[1] 145

table(covars$"Myocarditis Status")

 NO YES 
 12 133 

table(covars$"Murine MHC KO Status")

HOM 
145 

table(covars$"Drug Treatment")

ICI 
145 

table(covars$"clinical pheno")

 EOI SICK 
  31  114 

#keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm

Object of class cross2 (crosstype "bc")

Total individuals              145
No. genotyped individuals      145
No. phenotyped individuals     145
No. with both geno & pheno     145

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")

 NO YES 
 12 133 

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

HOM 
145 

table(gm$covar$"Drug Treatment")

ICI 
145 

table(gm$covar$"clinical pheno")

 EOI SICK 
  31  114 

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

##removing problmetic marker

#gm <- drop_markers(gm, "UNCHS013106")

##dropping monomorphic markers within the dataset

g <- do.call("cbind", gm$geno)

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",]

count <- rowSums(gf_mar <=0.05)
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 <- find_markerpos(gm, rownames(low_freq_df))
low_freq$id <- rownames(low_freq)

nrow(low_freq)

[1] 6192

low_freq_bad <- merge(low_freq,low_freq_df, by="row.names",all=T)
names(low_freq_bad)[1] <- c("marker")

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),]

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	4145
gfmar_mar_1	4623
gfmar_mar_5	6178
gfmar_mar_10	6514
gfmar_mar_15	6544
gfmar_mar_25	6645
gfmar_mar_50	32509
total_snps	32660

gm_qc <- drop_markers(gm, low_freq_bad$marker)
gm_qc <- drop_nullmarkers(gm_qc)

gm_qc

Object of class cross2 (crosstype "bc")

Total individuals              145
No. genotyped individuals      145
No. phenotyped individuals     145
No. with both geno & pheno     145

No. phenotypes                   1
No. covariates                  11
No. phenotype covariates         0

No. chromosomes                 20
Total markers                26468

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
2322 2278 1594 1658 1521 1685 1451 1442 1661  957 1660 1121 1364 1407 1035  744 
  17   18   19    X 
 560  739  898  371 

## dropping disproportionate markers
dismark <- read.csv("data/ici-myo-yes.vs.ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_7.batches_myo.csv")
nrow(dismark)

[1] 26468

names(dismark)[1] <- c("marker")
dismark <- dismark[!dismark$Include,]
nrow(dismark)

[1] 18326

gm_qc_dis <- drop_markers(gm_qc, dismark$marker)
gm_qc_dis <- drop_nullmarkers(gm_qc_dis)

gm = gm_qc_dis
gm

Object of class cross2 (crosstype "bc")

Total individuals             145
No. genotyped individuals     145
No. phenotyped individuals    145
No. with both geno & pheno    145

No. phenotypes                  1
No. covariates                 11
No. phenotype covariates        0

No. chromosomes                20
Total markers                8142

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
 509  806  213  807  250  705   89  450 1151  197  354  140  232  455  404  682 
  17   18   19    X 
  76   37  584    1 

markers <- marker_names(gm)
gmapdf <- read.csv("data/genetic_map_7.batches_myo.csv")
pmapdf <- read.csv("data/physical_map_7.batches_myo.csv")
#mapdf <- merge(gmapdf,pmapdf, by=c("marker","chr"), all=T)
#rownames(mapdf) <- mapdf$marker
#mapdf <- mapdf[markers,]
#names(mapdf) <- c('marker','chr','gmapdf','pmapdf')
#mapdfnd <- mapdf[!duplicated(mapdf[c(2:3)]),]

pr.qc <- calc_genoprob(gm)

colnames(covars) <- gsub(" ", ".", colnames(covars))

Genome-wide scan

For each of the phenotype analyzed, permutations were used for each model to obtain genome-wide LOD significance threshold for p < 0.01, p < 0.05, p < 0.10, respectively, separately for X and automsomes (A).

The table shows the estimated significance thresholds from permutation test.

We also looked at the kinship to see how correlated each sample is. Kinship values between pairs of samples range between 0 (no relationship) and 1.0 (completely identical). The darker the colour the more indentical the pairs are.

#Xcovar <- get_x_covar(gm)
addcovar = model.matrix(~Histology.Score, data = covars)[,-1]
covars$ici.myo.yes_vs_ici.myo.no= as.numeric(covars$ici.myo.yes_vs_ici.myo.no)

kinship <- calc_kinship(pr.qc)
heatmap(kinship)

operm <- scan1perm(pr.qc, covars["ici.myo.yes_vs_ici.myo.no"], model="binary", addcovar=addcovar, n_perm=1000)

summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01,  0.05, 0.1))))
names(summary_table) <- c("autosomes")
summary_table$X = summary_table$autosomes
summary_table$significance.level <- rownames(summary_table)

rownames(summary_table) <- NULL

summary_table[c(3,1:2)] %>%
  kable(escape = F,align = c("ccc")) %>%
  kable_styling("striped", full_width = T) %>%
  column_spec(1, bold=TRUE)

significance.level	autosomes	X
0.01	0	0
0.05	0	0
0.1	0	0

The figures below show QTL maps for each phenotype

#out <- scan1(pr.qc, covars["ici.myo.yes_vs_ici.myo.no"], Xcovar=Xcovar, model="binary")
out <- scan1(pr.qc, covars["ici.myo.yes_vs_ici.myo.no"], model="binary",addcovar=addcovar)

summary_table<-data.frame(unclass(summary(operm, alpha=c(0.01,  0.05, 0.1))))


plot_lod<-function(out,map){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

    ##par(mar=c(5.1, 6.1, 1.1, 1.1))
    #ymx <- 11 # overall maximum LOD score
    #plot(out, map, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " [positions in cM] \n(using same scale as eoi vs ici for easier comparison)"))
    #add_threshold(map,  summary(operm, alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    ##for (j in 1: dim(summary_table)[1]){
    ##  abline(h=summary_table[j, i],col="red")
    ##  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    ##}
    ##dev.off()
  }
}

plot_lod(out,gm$gmap)

LOD peaks

The table below shows QTL peaks associated with the phenotype. We use the 95% threshold from the permutations to find peaks.

Centimorgan (cM)

peaks <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05), thresholdX = summary(operm,alpha=0.05), peakdrop=3, drop=1.5)

if(nrow(peaks) >0){
peaks$marker <- find_marker(gm$gmap, chr=peaks$chr,pos=peaks$pos)
names(peaks)[2] <- c("phenotype")
peaks <- peaks[-1]

rownames(peaks) <- NULL
print(kable(peaks, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

#plot only peak chromosomes

plot_lod_chr<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    
    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in cM]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')

  }
}


for(i in unique(peaks$chr)){
#for (i in 1:nrow(peaks)){
  #plot_lod_chr(out,gm$gmap, peaks$chr[i])
  plot_lod_chr(out,gm$gmap, i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05), " [autosomes & x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes & x-chromosome]”

Megabase (MB)

print("peaks in MB positions")

[1] “peaks in MB positions”

peaks_mba <- find_peaks(out, gm$pmap, threshold=summary(operm,alpha=0.05), thresholdX = summary(operm,alpha=0.05), peakdrop=3, drop=1.5)

if(nrow(peaks) >0){
peaks_mba$marker <- find_marker(gm$pmap, chr=peaks_mba$chr,pos=peaks_mba$pos)
names(peaks_mba)[2] <- c("phenotype")
peaks_mba <- peaks_mba[-1]


rownames(peaks_mba) <- NULL
print(kable(peaks_mba, escape = F, align = c("cccccccc"), "html") 
  %>% kable_styling("striped", full_width = T)%>%
  column_spec(1, bold=TRUE)
  )

plot_lod_chr_mb<-function(out,map,chrom){
  for (i in 1:dim(out)[2]){
    #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/",colnames(out)[i],  "_lod.png"))
    
    #par(mar=c(5.1, 6.1, 1.1, 1.1))
    ymx <- maxlod(out) # overall maximum LOD score
    plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    #legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]"))
    add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')
    #for (j in 1: dim(summary_table)[1]){
    #  abline(h=summary_table[j, i],col="red")
    #  text(x=400, y =summary_table[j, i]+0.12, labels = paste("p=", row.names(summary_table)[j]))
    #}
    #dev.off()

    #ymx <- 11
    #plot(out, map, chr = chrom, lodcolumn=i, col="slateblue", ylim=c(0, ymx+0.5))
    ##legend("topright", lwd=2, colnames(out)[i], bg="gray90")
    #title(main = paste0(colnames(out)[i], " - chr", chrom, " [positions in MB]\n(using same scale as eoi vs. ici for easier comparison)"))
    #add_threshold(map,  summary(operm,alpha=0.1), col = 'purple')
    #add_threshold(map,  summary(operm, alpha=0.05), col = 'red')
    #add_threshold(map,  summary(operm, alpha=0.01), col = 'blue')


  }
}

for(i in unique(peaks_mba$chr)){
#for (i in 1:nrow(peaks_mba)){
  #plot_lod_chr_mb(out,gm$pmap, peaks_mba$chr[i])
  plot_lod_chr_mb(out,gm$pmap,i)
}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05), " [autosomes]/",summary(operm,alpha=0.05), " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

QTL effects

For each peak LOD location we give a list of gene

query_variants <- create_variant_query_func("code/cc_variants.sqlite")
query_genes <- create_gene_query_func("code/mouse_genes_mgi.sqlite")

if(nrow(peaks) >0){
for (i in 1:nrow(peaks)){


  g <- maxmarg(pr.qc, gm$gmap, chr=peaks$chr[i], pos=peaks$pos[i], return_char=TRUE)
  #png(filename=paste0("/Users/chenm/Documents/qtl/Jai/","qtl_effect_", i, ".png"))
  #par(mar=c(4.1, 4.1, 1.5, 0.6))
  plot_pxg(g, covars[,peaks$phenotype[i]], ylab=peaks$phenotype[i], sort=FALSE)
  title(main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," )"), line=0.2)
  ##dev.off()

  chr = peaks$chr[i]

# Plot 2
  pr_sub <- pull_genoprobint(pr.qc, gm$gmap, chr, c(peaks$ci_lo[i], peaks$ci_hi[i]))
  blup <- scan1blup(pr.qc[,chr], covars[peaks$phenotype[i]],addcovar = addcovar)
  blup_sub <- scan1blup(pr_sub[,chr], covars[peaks$phenotype[i]], addcovar = addcovar)

  write.csv(as.data.frame(blup_sub), paste0("data/ici-myo-yes.vs.ici-myo-no_blup_sub_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_no-sex_7.batches_myo.csv"), quote=F)

  plot_coef(blup, 
       gm$gmap, columns=1:2,
       bgcolor="gray95", legend="bottomleft", 
       main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i]," [scan1blup; positions in cM])")
       )

  plot_coef(blup_sub, 
       gm$gmap, columns=1:2,
       bgcolor="gray95", legend="bottomleft", 
       main = paste0("chr: ", chr=peaks$chr[i],"; pos: ", peaks$pos[i], "cM /",peaks_mba$pos[i],"MB\n(",peaks$phenotype[i],"; 1.5 LOD drop interval [scan1blup; positions in cM])")
       )

  #Table 1
  chr = peaks_mba$chr[i]
  start=as.numeric(peaks_mba$ci_lo[i])
  end=as.numeric(peaks_mba$ci_hi[i])

  genesgss = query_genes(chr, start, end)

  write.csv(genesgss, file=paste0("data/ici-myo-yes.vs.ici-myo-no_genes_chr-",chr,"_peak.marker-",peaks$marker[i],"_lod.drop-1.5_snpsqc_dis_no-x_updated_no-sex_7.batches_myo.csv"), quote=F)

  rownames(genesgss) <- NULL
  genesgss$strand_old = genesgss$strand
  genesgss$strand[genesgss$strand=="+"] <- "positive"
  genesgss$strand[genesgss$strand=="-"] <- "negative"

  print(kable(genesgss[,c("chr","type","start","stop","strand","ID","Name","Dbxref","gene_id","mgi_type","description")], "html") %>% kable_styling("striped", full_width = T))


}

} else {
  print(paste0("There are no peaks that have a LOD that reaches suggestive (p<0.05) level of ",summary(operm,alpha=0.05), " [autosomes]/",summary(operm,alpha=0.05), " [x-chromosome]"))
}

[1] “There are no peaks that have a LOD that reaches suggestive (p<0.05) level of 0 [autosomes]/0 [x-chromosome]”

R/qtl

scanone

Session information

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] abind_1.4-5       qtl2_0.24         reshape2_1.4.4    ggplot2_3.3.3    
 [5] tibble_3.0.6      psych_2.0.12      readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.4       optparse_1.6.6    rhdf5_2.34.0      mclust_5.4.7     
[13] tidyr_1.1.2       data.table_1.13.6 knitr_1.31        kableExtra_1.3.1 
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10        lattice_0.20-41    assertthat_0.2.1   rprojroot_2.0.2   
 [5] digest_0.6.27      R6_2.5.0           cellranger_1.1.0   plyr_1.8.6        
 [9] RSQLite_2.2.3      evaluate_0.14      httr_1.4.2         highr_0.8         
[13] pillar_1.4.7       rlang_0.4.10       rstudioapi_0.13    blob_1.2.1        
[17] rmarkdown_2.6      webshot_0.5.2      stringr_1.4.0      bit_4.0.4         
[21] munsell_0.5.0      compiler_4.0.3     httpuv_1.5.5       xfun_0.21         
[25] pkgconfig_2.0.3    mnormt_2.0.2       tmvnsim_1.0-2      htmltools_0.5.1.1 
[29] tidyselect_1.1.0   viridisLite_0.3.0  crayon_1.4.1       withr_2.4.1       
[33] later_1.1.0.1      rhdf5filters_1.2.1 grid_4.0.3         nlme_3.1-152      
[37] gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.1          git2r_0.28.0      
[41] magrittr_2.0.1     scales_1.1.1       cachem_1.0.3       stringi_1.5.3     
[45] fs_1.5.0           promises_1.1.1     getopt_1.20.3      xml2_1.3.2        
[49] ellipsis_0.3.1     generics_0.1.0     vctrs_0.3.6        Rhdf5lib_1.12.1   
[53] tools_4.0.3        bit64_4.0.5        glue_1.4.2         purrr_0.3.4       
[57] fastmap_1.1.0      parallel_4.0.3     yaml_2.2.1         colorspace_2.0-0  
[61] rvest_0.3.6        memoise_2.0.0