Last updated: 2023-01-11
Checks: 5 2
Knit directory: Serreze-T1D_Workflow/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
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.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20220210) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.
| absolute | relative |
|---|---|
| /Users/corneb/Documents/MyJax/CS/Projects/Serreze/qc/workflowr/Serreze-T1D_Workflow | . |
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version c9fc66b. 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:
Ignored files:
Ignored: .DS_Store
Ignored: analysis/.DS_Store
Untracked files:
Untracked: analysis/0.1.1_preparing.data_bqc_4.batches_myo.Rmd
Untracked: analysis/0.1.1_preparing.data_bqc_4.batches_myo.Rmd.R
Untracked: analysis/0.1_samples_batch_20220729.Rmd
Untracked: analysis/0.1_samples_batch_20220729.Rmd.R
Untracked: analysis/0.1_samples_batch_20220826.Rmd
Untracked: analysis/0.1_samples_batch_20220826.Rmd.R
Untracked: analysis/0.1_samples_batch_20221006.Rmd
Untracked: analysis/0.1_samples_batch_20221006.Rmd.R
Untracked: analysis/0.1_samples_batch_20221116.Rmd
Untracked: analysis/0.1_samples_batch_20221116.Rmd.R
Untracked: analysis/0.2_haplotype_comparison_bqc_4.batches_myo_minprob.Rmd
Untracked: analysis/0.2_haplotype_comparison_bqc_4.batches_myo_minprob.Rmd.R
Untracked: analysis/2.1_sample_bqc_4.batches_myo.Rmd
Untracked: analysis/2.1_sample_bqc_4.batches_myo.Rmd.R
Untracked: analysis/2.2.1_snp_qc_4.batches_myo.Rmd
Untracked: analysis/2.2.1_snp_qc_4.batches_myo.Rmd.R
Untracked: analysis/2.2.1_snp_qc_4.batches_myo_mis.Rmd
Untracked: analysis/2.2.1_snp_qc_4.batches_myo_mis.Rmd.R
Untracked: analysis/2.4_preparing.data_aqc_4.batches_myo.Rmd
Untracked: analysis/2.4_preparing.data_aqc_4.batches_myo.Rmd.R
Untracked: analysis/2.4_preparing.data_aqc_4.batches_myo_mis.Rmd
Untracked: analysis/2.4_preparing.data_aqc_4.batches_myo_mis.Rmd.R
Untracked: analysis/3.1_phenotype.qc_corrected_4.batches_myo.Rmd
Untracked: analysis/3.1_phenotype.qc_corrected_4.batches_myo.Rmd.R
Untracked: analysis/3.1_phenotype.qc_corrected_4.batches_myo_mis.Rmd
Untracked: analysis/3.1_phenotype.qc_corrected_4.batches_myo_mis.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_het-ici-myo-yes.vs.het-ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_het-ici-myo-yes.vs.het-ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_het-ici.vs.het-pbs_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_het-ici.vs.het-pbs_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_ici-myo-yes.vs.ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_ici-myo-yes.vs.ici-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_ici-sick.vs.ici-eoi_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_ici-sick.vs.ici-eoi_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated_4.batches_myo_mis.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated_4.batches_myo_mis.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_myo-yes.vs.myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_myo-yes.vs.myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/4.1.1_qtl.analysis_binary_pbs-myo-yes.vs.pbs-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd
Untracked: analysis/4.1.1_qtl.analysis_binary_pbs-myo-yes.vs.pbs-myo-no_snpsqc_dis_no-x_updated_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.Rmd.R
Untracked: analysis/genotype.frequencies_het-ici.vs.het-pbs_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_het-ici.vs.het-pbs_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_het-ici.vs.het-pbs_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_het-ici.vs.het-pbs_4.batches_myo_mis.Rmd.R
Untracked: analysis/genotype.frequencies_ici-myo-yes.vs.ici-myo-no_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_ici-myo-yes.vs.ici-myo-no_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.Rmd.R
Untracked: analysis/genotype.frequencies_ici-sick.vs.ici-eoi_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_ici-sick.vs.ici-eoi_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_ici-sick.vs.ici-eoi_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_ici-sick.vs.ici-eoi_4.batches_myo_mis.Rmd.R
Untracked: analysis/genotype.frequencies_ici.vs.pbs_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_ici.vs.pbs_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_ici.vs.pbs_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_ici.vs.pbs_4.batches_myo_mis.Rmd.R
Untracked: analysis/genotype.frequencies_myo-yes.vs.myo-no_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_myo-yes.vs.myo-no_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_myo-yes.vs.myo-no_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_myo-yes.vs.myo-no_4.batches_myo_mis.Rmd.R
Untracked: analysis/genotype.frequencies_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.Rmd
Untracked: analysis/genotype.frequencies_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.Rmd.R
Untracked: analysis/genotype.frequencies_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.Rmd
Untracked: analysis/genotype.frequencies_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.Rmd.R
Untracked: analysis/gwas_csq_OTU_OTU_18_unclassified_Lachnospiraceae.log.txt
Untracked: analysis/index_4.batches_myo.Rmd
Untracked: analysis/index_4.batches_myo.Rmd.R
Untracked: data/GM_covar_4.batches_myo.csv
Untracked: data/bad_markers_all_4.batches_myo.RData
Untracked: data/covar_corrected.cleaned_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected.cleaned_het-ici.vs.het-pbs_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_het-ici.vs.het-pbs_4.batches_myo_mis.csv
Untracked: data/covar_corrected.cleaned_ici-myo-yes.vs.ici-myo-no_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected.cleaned_ici-sick.vs.ici-eoi_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_ici-sick.vs.ici-eoi_4.batches_myo_mis.csv
Untracked: data/covar_corrected.cleaned_ici.vs.pbs_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_ici.vs.pbs_4.batches_myo_mis.csv
Untracked: data/covar_corrected.cleaned_myo-yes.vs.myo-no_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_myo-yes.vs.myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected.cleaned_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.csv
Untracked: data/covar_corrected.cleaned_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo.csv
Untracked: data/covar_corrected_het-ici-myo-yes.vs.het-ici-myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected_het-ici.vs.het-pbs_4.batches_myo.csv
Untracked: data/covar_corrected_het-ici.vs.het-pbs_4.batches_myo_mis.csv
Untracked: data/covar_corrected_ici-myo-yes.vs.ici-myo-no_4.batches_myo.csv
Untracked: data/covar_corrected_ici-myo-yes.vs.ici-myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected_ici-sick.vs.ici-eoi_4.batches_myo.csv
Untracked: data/covar_corrected_ici-sick.vs.ici-eoi_4.batches_myo_mis.csv
Untracked: data/covar_corrected_ici.vs.pbs_4.batches_myo.csv
Untracked: data/covar_corrected_ici.vs.pbs_4.batches_myo_mis.csv
Untracked: data/covar_corrected_myo-yes.vs.myo-no_4.batches_myo.csv
Untracked: data/covar_corrected_myo-yes.vs.myo-no_4.batches_myo_mis.csv
Untracked: data/covar_corrected_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo.csv
Untracked: data/covar_corrected_pbs-myo-yes.vs.pbs-myo-no_4.batches_myo_mis.csv
Untracked: data/e_4.batches_myo.RData
Untracked: data/e_snpg_samqc_4.batches_myo.RData
Untracked: data/errors_ind_4.batches_myo.RData
Untracked: data/genetic_map_4.batches_myo.csv
Untracked: data/genotype_errors_marker_4.batches_myo.RData
Untracked: data/genotype_freq_marker_4.batches_myo.RData
Untracked: data/gm_allqc_4.batches_myo.RData
Untracked: data/gm_allqc_4.batches_myo_mis.RData
Untracked: data/gm_samqc_4.batches_myo.RData
Untracked: data/gm_serreze.BC312.RData
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
Untracked: data/het-ici-myo-yes.vs.het-ici-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
Untracked: data/het-ici.vs.het-pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
Untracked: data/het-ici.vs.het-pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
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
Untracked: data/het-ici.vs.het-pbs_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_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.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-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
Untracked: data/ici-sick.vs.ici-eoi_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_genes_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_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
Untracked: data/ici-sick.vs.ici-eoi_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_4.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
Untracked: data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
Untracked: data/myo-yes.vs.myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_4.batches_myo_mis.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo.csv
Untracked: data/pbs-myo-yes.vs.pbs-myo-no_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_4.batches_myo_mis.csv
Untracked: data/percent_missing_id_4.batches_myo.RData
Untracked: data/percent_missing_marker_4.batches_myo.RData
Untracked: data/pheno_4.batches_myo.csv
Untracked: data/physical_map_4.batches_myo.csv
Untracked: data/qc_info_bad_sample_4.batches_myo.RData
Untracked: data/sample_geno_AHB_4.batches_myo.csv
Untracked: data/sample_geno_bc_4.batches_myo.csv
Untracked: data/serreze_probs_4.batches_myo.rds
Untracked: data/serreze_probs_allqc_4.batches_myo.rds
Untracked: data/serreze_probs_allqc_4.batches_myo_mis.rds
Untracked: data/summary.cg_4.batches_myo.RData
Untracked: output/Percent_missing_genotype_data_4.batches_myo.pdf
Untracked: output/Percent_missing_genotype_data_per_marker_4.batches_myo.pdf
Untracked: output/Proportion_matching_genotypes_before_removal_of_bad_samples_4.batches_myo.pdf
Untracked: output/genotype_error_marker_4.batches_myo.pdf
Untracked: output/genotype_frequency_marker_4.batches_myo.pdf
Unstaged changes:
Modified: analysis/index_5.batches.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.
There are no past versions. Publish this analysis with
wflow_publish() to start tracking its development.
##remember to run haplotype reconstruction (pre processing) to get out sample_inventory and hdf5 file
sample_inventory <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_4.batches_myo_corrected/DODB_inventory_serreze_myo_4.batches_DO.csv", stringsAsFactors=FALSE, colClasses = c("character"))
hdf5_filename <- "/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_4.batches_myo/hdf5_serreze_myo_4.batches_DO.h5"
##marker file
markers_v1 = read.csv("/Users/corneb/Documents/MyJax/CS/Projects/support.files/MUGAarrays/UWisc/gm_uwisc_v1.csv", as.is=T)
dim(markers_v1)[1] 143259 13markers_v2 = read.csv("/Users/corneb/Documents/MyJax/CS/Projects/support.files/MUGAarrays/UWisc/gm_uwisc_v2.csv", as.is=T)
markers_v1$index <- 1:nrow(markers_v1)
# Filter to retain markers with one unique position in GRCm38.
markers_v1 = subset(markers_v1, !is.na(chr) & !is.na(bp_mm10))
dim(markers_v1)[1] 137359 14##merging updated allele codes (from v2)
markers <- merge(markers_v1, markers_v2[c("marker","snp")], by=c("marker"), all.x=T)
names(markers)[c(7,15)] <- c("snps_v1","snps_v2")
markers <- markers[order(markers$index),]
##using only unique markers
markers_unique <- markers[markers$unique == TRUE, ]
##creating a code list for encoding markers for qtl2 (bc)
markers_1 <- markers_unique[,c("marker","chr","snps_v2")]
markers_1$A <- substr(markers_1$snps_v2, 1, 1)
markers_1$B <- substr(markers_1$snps_v2, 2, 2)
dim(markers_1)[1] 137359 5codes <- markers_1[,c("marker","chr","A","B")]
markers_2 <- markers_unique[markers_unique$chr %in% c(1:19, "X"), ]
markers_2$chr <- sub("^chr", "", markers_2$chr) ###remove prefix "chr"
colnames(markers_2)[colnames(markers_2)=="bp_mm10"] <- "pos"
colnames(markers_2)[colnames(markers_2)=="cM_cox"] <- "cM"
markers_2 <- markers_2 %>% drop_na(chr, marker)
markers_2$pos <- as.numeric(markers_2$pos) * 1e-6
rownames(markers_2) <- markers_2$marker
colnames(markers_2)[c(1:4)] <- c("marker", "chr", "pos", "pos")
#codes <- markers_1[markers_1$marker %in% markers_2$marker,]
#codes <- codes[,c("marker","chr","A","B")]
##keeping only markers in code list for chromosome 1:10,X
codes <- codes[codes$marker %in% markers_2$marker,]
dim(markers_2)[1] 137302 15dim(codes)[1] 137302 4h5_info <- h5ls(hdf5_filename)
h5_info <- h5_info[h5_info$group == "/G",]
h5_info <- h5_info[order(as.numeric(h5_info$name)),]
num_samples <- strsplit(h5_info$dim, " x ") ##num of samples per project
n=length(num_samples)
num_rows <- as.numeric(num_samples[[1]][1])
num_samples <- c(0, as.numeric(sapply(num_samples, "[", 2)))
rn <- h5read(hdf5_filename, "rownames/1")
geno <- matrix("", nrow = num_rows, ncol = sum(num_samples),dimnames = list(rn, rep("", sum(num_samples))))
for(i in 1:n) {
G <- h5read(hdf5_filename, paste0("G/", i))
cn <- h5read(hdf5_filename, paste0("colnames/", i))
colnames(G) <- cn
rng <- (sum(num_samples[1:i]) + 1):sum(num_samples[1:(i+1)])
geno[,rng] <- G
colnames(geno)[rng] <- colnames(G)
}
# Remove samples that should not be included.
idx2 <- intersect(colnames(geno), sample_inventory$Original.Mouse.ID)
geno <- geno[ ,colnames(geno) %in% idx2, drop=FALSE]
dim(geno)[1] 143259 217# Keep only the good SNPs.
geno <- geno[rownames(markers_2),]
dim(geno)[1] 137302 217#codes <- codes[codes$marker %in% rownames(geno),]
#codes <- codes[rownames(geno),]
##encdoing markers for qtl2
geno.1 <- qtl2convert::encode_geno(geno, as.matrix(codes[,c("A","B")]))
#encoding markers for backcross
geno.1[geno.1 == "A"] <- "AA"
geno.1[geno.1 == "H"] <- "AB"
geno.1[geno.1 == "B"] <- "AA"
geno.2 <- qtl2convert::encode_geno(geno, as.matrix(codes[,c("A","B")]))
##saving files--------------------
##reording markers file
#names(markers)[3:4] <- c("bp_mm10","cM_cox")
#markers_2 <- markers_2[order(markers_2$chr, markers_2$pos), ]
#markers_2 <- markers_2[mixedorder(markers_2$chr), ]
##physical map
write.csv(markers_2[,1:3], file = "data/physical_map_4.batches_myo.csv",row.names = FALSE, quote = FALSE)
##genetic map
write.csv(markers_2[,c(1,2,4)], file = "data/genetic_map_4.batches_myo.csv",row.names = FALSE, col.names =c("marker", "chr", "pos"), quote = FALSE)
##sample genotypes
marker.names <- markers_2[,"marker"]
sample.geno.2 <- data.frame(marker = marker.names, geno.2[marker.names,], stringsAsFactors = F, check.names=F)
write.csv(sample.geno.2, file = "data/sample_geno_AHB_4.batches_myo.csv",row.names = F, quote = F)
sample.geno.1 <- data.frame(marker = marker.names, geno.1[marker.names,], stringsAsFactors = F, check.names=F)
write.csv(sample.geno.1, file = "data/sample_geno_bc_4.batches_myo.csv",row.names = F, quote = F)
# Write out temp covariates
covar <- data.frame(id = sample_inventory$Original.Mouse.ID, sex = sample_inventory$Sex)
rownames(covar) <- covar$id
write.csv(covar, file <- "data/GM_covar_4.batches_myo.csv", quote = FALSE)
# Write out temp phenotypes
pheno <- matrix(rnorm(ncol(geno)), nrow = ncol(geno), ncol = 1, dimnames =
list(colnames(geno), "pheno"))
rownames(pheno) <- make.unique(rownames(pheno))
write.csv(pheno, file <- "data/pheno_4.batches_myo.csv", row.names = TRUE, quote = FALSE)gm <- read_cross2("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_4.batches_myo_corrected/gm_bc_4.batches_myo_BC217.json")
gmObject of class cross2 (crosstype "bc")
Total individuals 217
No. genotyped individuals 217
No. phenotyped individuals 217
No. with both geno & pheno 217
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 137302
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
10423 10441 8206 7955 8030 8130 7760 6717 6984 6631 7433 6444 6327
14 15 16 17 18 19 X
6230 5534 5179 5323 4787 3676 5092 #Let’s omit markers without any genotype data
gm <- drop_nullmarkers(gm)Dropping 3586 markers with no datagmObject of class cross2 (crosstype "bc")
Total individuals 217
No. genotyped individuals 217
No. phenotyped individuals 217
No. with both geno & pheno 217
No. phenotypes 1
No. covariates 11
No. phenotype covariates 0
No. chromosomes 20
Total markers 133716
No. markers by chr:
1 2 3 4 5 6 7 8 9 10 11 12 13
10159 10172 7987 7736 7778 7911 7548 6561 6823 6471 7276 6226 6177
14 15 16 17 18 19 X
6082 5421 5075 5162 4682 3612 4857 save(gm, file = "data/gm_serreze.BC312.RData")
probsA <- calc_genoprob(gm, quiet = T)
saveRDS(probsA, file = "data/serreze_probs_4.batches_myo.rds")
e <- calc_errorlod(gm, probsA, cores=20)
e <- do.call("cbind", e)
save(e, file = "data/e_4.batches_myo.RData")
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gtools_3.9.4 abind_1.4-5 qtl2_0.30 reshape2_1.4.4
[5] ggplot2_3.4.0 tibble_3.1.8 psych_2.2.9 readxl_1.4.1
[9] cluster_2.1.4 dplyr_1.0.10 optparse_1.7.3 rhdf5_2.40.0
[13] mclust_6.0.0 tidyr_1.2.1 data.table_1.14.6 knitr_1.41
[17] kableExtra_1.3.4 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 qtl_1.54 whisker_0.4.1 getopt_1.20.3
[33] later_1.3.0 git2r_0.30.1 generics_0.1.3 ellipsis_0.3.2
[37] cachem_1.0.6 withr_2.5.0 cli_3.4.1 mnormt_2.1.1
[41] magrittr_2.0.3 memoise_2.0.1 evaluate_0.18 ps_1.7.2
[45] fs_1.5.2 fansi_1.0.3 nlme_3.1-160 xml2_1.3.3
[49] tools_4.2.2 qtl2convert_0.28 lifecycle_1.0.3 stringr_1.5.0
[53] Rhdf5lib_1.18.2 munsell_0.5.0 callr_3.7.3 compiler_4.2.2
[57] jquerylib_0.1.4 systemfonts_1.0.4 rlang_1.0.6 grid_4.2.2
[61] rhdf5filters_1.8.0 rstudioapi_0.14 rmarkdown_2.18 gtable_0.3.1
[65] DBI_1.1.3 R6_2.5.1 bit_4.0.5 fastmap_1.1.0
[69] utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.8 parallel_4.2.2
[73] Rcpp_1.0.9 vctrs_0.5.1 tidyselect_1.2.0 xfun_0.35