Serreze Workflow - 3 batches

workflowr

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Last updated: 2022-04-06

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

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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

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Cache: none

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File paths: relative

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Repository version: 2d6f369

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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_marker.freq_low.probs.freq.removed_geno.ratio_5.batches_mis.csv
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    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_5.batches.csv
    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
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    Untracked:  data/percent_missing_marker_5.batches.RData
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Unstaged changes:
    Modified:   analysis/0.1_samples_batch_20201209.Rmd
    Modified:   analysis/0.1_samples_batch_20210527.Rmd
    Modified:   analysis/0.1_samples_batch_20210909.Rmd
    Modified:   analysis/0.1_samples_batch_20211101.Rmd
    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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There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

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Neogen Files At A Glance [Total Samples: 168]

• 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

1. Phenotype QC

• Phenotypes

2. Genotype QC

• Step 1: Sample QC
  • 3 batches
• Step 2: SNP QC (Monomorphic, Genotype Error & Missing Data)
  • 3 batches
• Step 3: Effect of Data Cleaning
• Step 4: Preparing Files for QTL Analysis

3. QTL Analysis

• Step 1: Recombination Block Size & Founder Proportions
• Step 2: GCTA Heritability
• Step 3: QTL Mapping (R/qtl2)
• Step 4: QTL Permutation (R/qtl2)
• Step 5: QTL Best Linear Unbiased Prediction (BLUP) (R/qtl2)
• Step 6: Results from R/qtl2 analysis

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        gtable_0.3.0      lifecycle_1.0.1  
[37] DBI_1.1.1         git2r_0.26.1      magrittr_2.0.1    scales_1.1.1     
[41] cli_3.0.0         stringi_1.7.2     cachem_1.0.5      fs_1.4.1         
[45] promises_1.1.0    getopt_1.20.3     xml2_1.3.1        ellipsis_0.3.2   
[49] generics_0.0.2    vctrs_0.3.8       tools_3.6.2       bit64_4.0.5      
[53] glue_1.4.2        purrr_0.3.4       hms_0.5.3         parallel_3.6.2   
[57] fastmap_1.1.0     yaml_2.2.1        colorspace_2.0-2  rvest_0.3.5      
[61] memoise_2.0.0