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Last updated: 2024-10-10

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Knit directory: mito_age_fert/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). 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: up-to-date

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.

Repository version: c77ae9c

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 c77ae9c. 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:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store

Untracked files:
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Unstaged changes:
    Deleted:    analysis/sperm_comp.Rmd
    Modified:   data/README.md

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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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
html	6e91bf5	MartinGarlovsky	2024-10-09	Build site.
Rmd	0805159	MartinGarlovsky	2024-10-09	wflow_publish("analysis/index.Rmd")
html	f25e173	MartinGarlovsky	2023-02-13	Build site.
Rmd	de6bef6	MartinGarlovsky	2023-02-13	push test
html	6f8e46b	MartinGarlovsky	2023-02-13	Build site.
Rmd	8ffe9c4	MartinGarlovsky	2023-02-13	Start workflowr project.

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Click on the links below to see analysis steps for each experiment.

In general, we performed three sets of analysis on each dataset. For all models, we set options for contrasts to sum-to-zero and orthogonal polynomials.

Geography based analysis

• This uses the geographic origin for each mitochondrial and nuclear genotype.

Matched vs. mismatched analysis

• Next we fit a model testing whether matched (i.e., AA, BB, CC) differ from mismatched (e.g., AB, BC, CA, etc.) using the same random effects structure.

Mitotype analysis

• Finally, we fit a model breaking down the mitochondrial genotype in to the 9 different haplotypes segregating in the populations. This led to the fixed effect model matrix being rank deficient (not every combination of mito-type × nuclear genotype was possible) with corresponding R warning messages. We inspected model diagnostic plots and outputs when fitting these models to ensure reasonable model estimates despite the unbalanced design.

For all the analyses we plot reaction norms of the model estimated marginal means for each mitonuclear genotype combination. We also plot the raw data with LINE and mitonuclear genotype means with the raw data.

1. Body size analysis

2. Female fertility analysis

3. Male fertility analysis