Serreze Workflow - 5 batches

workflowr

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Last updated: 2022-04-08

Checks: 6 1

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

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.

Seed: set.seed(20220210)

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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 2d6f369

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 2d6f369. 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/ici.vs.pbs_blup.qc_chr-X_snpsqc_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-X_lod.drop-1.5_5.batches.csv
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    Untracked:  data/ici.vs.pbs_blup_chr-X_lod.drop-1.5_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_blup_sub_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_genes_chr-18_peak.marker-UNCHS045343_lod.drop-1.5_snpsqc_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_gm_qtl_5.batches.csv
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    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_5.batches.csv
    Untracked:  data/ici.vs.pbs_gm_qtl_snpsqc_dis_no-x_updated_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio.csv
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    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_5.batches.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_5.batches.csv
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    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed.csv
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    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici.vs.pbs_scanone_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_5.batches_mis.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_5.batches.Rdata
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    Untracked:  data/ici.vs.pbs_scanone_snpsqc_dis_no-x_updated_5.batches.Rdata
    Untracked:  data/ici.vs.pbs_scanone_snpsqc_dis_no-x_updated_5.batches_mis.Rdata
    Untracked:  data/percent_missing_id_3.batches.RData
    Untracked:  data/percent_missing_id_4.batches.RData
    Untracked:  data/percent_missing_id_4.batches_bc.RData
    Untracked:  data/percent_missing_id_5.batches.RData
    Untracked:  data/percent_missing_marker_4.batches.RData
    Untracked:  data/percent_missing_marker_5.batches.RData
    Untracked:  data/pheno.csv
    Untracked:  data/pheno_BC312.csv
    Untracked:  data/physical_map.csv
    Untracked:  data/physical_map_BC312.csv
    Untracked:  data/qc_info_bad_sample_3.batches.RData
    Untracked:  data/qc_info_bad_sample_4.batches.RData
    Untracked:  data/qc_info_bad_sample_4.batches_bc.RData
    Untracked:  data/qc_info_bad_sample_5.batches.RData
    Untracked:  data/remaining.markers_geno.freq.xlsx
    Untracked:  data/sample_geno.csv
    Untracked:  data/sample_geno_AHB_BC312.csv
    Untracked:  data/sample_geno_bc.csv
    Untracked:  data/sample_geno_bc_BC312.csv
    Untracked:  data/serreze_probs.rds
    Untracked:  data/serreze_probs_BC312.rds
    Untracked:  data/serreze_probs_allqc.rds
    Untracked:  data/serreze_probs_allqc_5.batches.rds
    Untracked:  data/serreze_probs_allqc_5.batches_mis.rds
    Untracked:  data/summary.cg_3.batches.RData
    Untracked:  data/summary.cg_4.batches.RData
    Untracked:  data/summary.cg_4.batches_bc.RData
    Untracked:  data/summary.cg_5.batches.RData
    Untracked:  output/Percent_missing_genotype_data_5.batches.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_5.batches.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_5.batches.pdf
    Untracked:  output/genotype_error_marker_5.batches.pdf
    Untracked:  output/genotype_frequency_marker_5.batches.pdf

Unstaged changes:
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.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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Neogen Files At A Glance [Total Samples: 312]

• Samples: The_Jackson_Lab_Serreze_MURGIGV01_20201209
  • Total Samples: 24
• Samples: The_Jackson_Lab_Serreze_MURGIGV01_20210527
  • Total Samples: 72
• Samples: The_Jackson_Lab_Serreze_MURGIGV01_20210909
  • Total Samples: 72
• Samples: The_Jackson_Lab_Serreze_MURGIGV01_20211101
  • Total Samples: 24
• Samples: The_Jackson_Lab_Serreze_MURGIGV01_220220328
  • Total Samples: 120
• Preparing files for QC/Haplotype Phasing

Haplotype Phasing

• minimum probability set for estimation of genotypes

1. Experiment Design

abbreviations for strains:	AB: het; AA: hom
type of cross:	Backcross
number of mice phenotyped:	312
total number of mice phenotypes:	1 [binary]
phenotypes:	diabetic status/group [binary]
number of mice:	312
number of markers:	137302
covariates:	sex [F: 312; M: 0] though not used as all mice are females

2. Genotype QC

• Step 1: Sample QC
• Step 2: SNP QC(removing monomorphic, gnotype Error & missing markers)
• Step 3: Preparing Files for QTL Analysis
• Step 4: Identifying additional disproportionate markers within each sample set
  • ICI vs EOI
  • ICI vs PBS

3. Phenotype QC

• No QC as using binary phenotypes for best results

4. QTL Analysis

• binary phenotpe (removed additional low frequency markers as well as disproportionate markers (identified in Step 2.4) within each sample set)
  • ICI vs EOI
  • ICI vs PBS

Session information

R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggrepel_0.8.2     qtlcharts_0.11-6  qtl2_0.22         broman_0.70-4    
 [5] ggplot2_3.3.5     tibble_3.1.2      readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.8       optparse_1.6.6    mclust_5.4.6      tidyr_1.0.2      
[13] data.table_1.14.0 knitr_1.33        kableExtra_1.1.0  workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7        assertthat_0.2.1  rprojroot_1.3-2   digest_0.6.27    
 [5] utf8_1.2.1        R6_2.5.0          cellranger_1.1.0  backports_1.2.1  
 [9] RSQLite_2.2.7     evaluate_0.14     httr_1.4.1        pillar_1.6.1     
[13] rlang_1.0.2       rstudioapi_0.13   blob_1.2.1        rmarkdown_2.1    
[17] qtl_1.46-2        webshot_0.5.2     readr_1.3.1       stringr_1.4.0    
[21] bit_4.0.4         munsell_0.5.0     compiler_3.6.2    httpuv_1.5.2     
[25] xfun_0.24         pkgconfig_2.0.3   htmltools_0.5.1.1 tidyselect_1.1.2 
[29] fansi_0.5.0       viridisLite_0.4.0 crayon_1.4.1      withr_2.4.2      
[33] later_1.0.0       grid_3.6.2        jsonlite_1.7.2    gtable_0.3.0     
[37] lifecycle_1.0.1   DBI_1.1.1         git2r_0.26.1      magrittr_2.0.1   
[41] scales_1.1.1      cli_3.0.0         stringi_1.7.2     cachem_1.0.5     
[45] fs_1.4.1          promises_1.1.0    getopt_1.20.3     xml2_1.3.1       
[49] ellipsis_0.3.2    generics_0.0.2    vctrs_0.3.8       tools_3.6.2      
[53] bit64_4.0.5       glue_1.4.2        purrr_0.3.4       hms_0.5.3        
[57] parallel_3.6.2    fastmap_1.1.0     yaml_2.2.1        colorspace_2.0-2 
[61] rvest_0.3.5       memoise_2.0.0