Last updated: 2020-11-24
Checks: 7 0
Knit directory: 2020_HairPheno_manuscript/analysis/
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.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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(12345) 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.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
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 4b704ad. 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: .Rproj.user/
Untracked files:
Untracked: .DS_Store
Untracked: .gitignore
Untracked: 2020_HairPheno_manuscript.Rproj
Untracked: analysis/
Untracked: code/
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.
Welcome to the research website for the Hair Phenotyping Methods Project, run by Tina Lasisi.
This website contains background information on the project, as well as data pertaining to the development of these methods and protocols used for sample collection and preparation.
Scalp hair morphology is one of the most variable trait among human populations, yet there are no universal, or widely-used, methods for measuring variation in this morphology. As such, variation in hair morphology is generally described using subjective typologies. This project aims to develop high-throughput phenotyping methods for scalp hair morphology, specifically hair curvature and cross-sectional morphology.
This project has developed methods for hair sample preparation for imaging, and image analysis for both cross-sectional and curvature analysis of hair samples.
We developed sample preparation methods for the cross-sectional and for curvature analysis. These are available on protocols.io, a protocol repository for research - we hope this will facilitate feedback and improvements to the methods.
We created a Python package, fibermorph, which automates the image analysis process for images of both cross-sectional morphology and curvature in hair samples. The package is currently available on GitHub and PyPi.
The fibermorph package features an inbuilt validation script that generates curvature and section data which is then analyzed by the main curvature and section functions. The script provides the images 1) generated and their respective parameters, and 2) the spreadsheets with results of the relative error calculated from a comparison of the fibermorph results with the known parameters.
To validate the curvature estimation of the fibermorph package, we calculate percent relative error of our algorithm’s curvature estimation when applied to simulated arcs for a range of curvatures.
To validate the image analysis of cross-sectional geometry, we calculate the percent relative error of the fibermorph section algorithm on simulated ellipses for a range of sizes and shapes.
We measure curvature and cross-sectional morphology in a sample of African-European admixed individuals for whom we also have genotype information. This allows us to understand the biological significance of studying hair morphology quantitatively as opposed to qualitatively.
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
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_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
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.13 knitr_1.30 magrittr_1.5
[5] workflowr_1.6.2 R6_2.5.0 rlang_0.4.8 stringr_1.4.0
[9] tools_3.6.3 xfun_0.19 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 yaml_2.2.1 digest_0.6.27 rprojroot_2.0.2
[17] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.4 fs_1.5.0 promises_1.1.1 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.5 stringi_1.5.3 compiler_3.6.3
[29] pillar_1.4.6 httpuv_1.5.4 pkgconfig_2.0.3