Last updated: 2020-12-23
Checks: 7 0
Knit directory: TARI_2020GS/
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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.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd
) and HTML (docs/index.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 9c8d010 | wolfemd | 2020-12-23 | Start workflowr project. |
Rmd | 3d46094 | wolfemd | 2020-12-15 | Start workflowr project. |
This repository and website documents all analyses, summary, tables and figures associated with TARI genomic prediction and related procedures (e.g. imputation).
GS C1.
Impute with E. Africa Imputation Reference Panel dataset, which can be found on the Cassavabase FTP server here with names e.g. chr*_ImputationReferencePanel_StageVI_91119.vcf.gz
with code/documentation here.
Steps:
Files:
chr*_ImputationReferencePanel_StageVI_91119.vcf.gz
chr*_DCas20_5629_EA_REFimputed.vcf.gz
chr*_DCas20_5629_EA_REFimputedAndFiltered.vcf.gz
DosageMatrix_ImputationReferencePanel_StageVI_91119.rds
DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds
HOW TO COMBINE DOSAGE MATRICES: Users will want to combine the genotypes in the imputation reference panel files, with the genotypes in the imputed DArT file. They can have slightly different sets of markers along the columns. Here is a basic example how to combine:
<-readRDS("DosageMatrix_ImputationReferencePanel_StageVI_91119.rds")
snps_refpanel<-readRDS("DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds")
snps_dcas20_5629
<-colnames(snps_refpanel)[,colnames(snps_refpanel) %in% colnames(snps_dcas20_5629)]
snps2keep<-bind_rows(snps_refpanel[,snps2keep],
snps snps_dcas20_5629[,snps2keep])
Get TARI TP data from Cassavabase. Use it with imputed data to predict GEBV/GETGV for all samples in the new reports (DCas20-5629).
Files: everything is in the output/
sub-directory.
GEBV_TARI_ModelA_2020Dec21.csv
GETGV_TARI_ModelADE_2020Dec21.csv
genomicPredictions_TARI_2020Dec21.csv
DOWNLOAD FROM CASSAVABASE FTP SERVER
or
The R package workflowr was used to document this study reproducibly.
Much of the supporting data and output from the analyses documented here are too large for GitHub.
The repository will be mirrored, here: ftp://ftp.cassavabase.org/marnin_datasets/TARI_2020GS/ with all data.
NOTICE: data/
and output/
are empty on GitHub. Please see ftp://ftp.cassavabase.org/marnin_datasets/TARI_2020GS/ for access.
data/
: raw data (e.g. unimputed SNP data)output/
: outputs (e.g. imputed SNP data)analysis/
: most code and workflow documented in .Rmd filesdocs/
: compiled .html, “knitted” from .RmdSupporting functions code/
The analyses in the html / Rmd files referenced above often source R scripts in the code/
sub-folder.
sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 whisker_0.4 knitr_1.30 magrittr_2.0.1
[5] R6_2.5.0 rlang_0.4.9 stringr_1.4.0 tools_4.0.2
[9] xfun_0.19 git2r_0.27.1 htmltools_0.5.0 ellipsis_0.3.1
[13] yaml_2.2.1 digest_0.6.27 rprojroot_2.0.2 tibble_3.0.4
[17] lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1 vctrs_0.3.5
[21] promises_1.1.1 fs_1.5.0 glue_1.4.2 evaluate_0.14
[25] rmarkdown_2.6 stringi_1.5.3 compiler_4.0.2 pillar_1.4.7
[29] httpuv_1.5.4 pkgconfig_2.0.3