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Add rOpenScPCA-related FAQs #991

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Closes #799

This PR adds three new FAQs to cover items related to rOpenScPCA. Note that rather than having a standalone question for "how do I install rOpenScPCA," I included information about this for each question that refers to the package.

I tried to be light on the details and refer readers out for more information rather than clogging up the FAQ. Let me know how you think I did for that balance and which additional details should be highlighted here. I'm also not totally enamored of the way I structure the "i want seurat?" answer, if you have different thoughts on framing. I also fixed a few poorly-spaced bullets in analyses/hello-clusters/README.md that I saw along the way.

@sjspielman sjspielman requested review from jashapiro and removed request for jaclyn-taroni January 16, 2025 17:25
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Looks good overall. I had some relatively small comments, and I don't think I really need to see it again, but would be happy to take another look if you prefer.

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Comment on lines 131 to 133
* They were _not_ processed with a `Seurat` pipeline.
They were processed using the same pipeline as all OpenScPCA objects were (e.g., with `Bioconductor`), and then converted to `Seurat` format
* Notably, they do contain the raw data counts, allowing you to perform normalization, dimension reduction, etc. with `Seurat` directly if you so choose
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Rather than focusing on what is not there, I would describe what is present:

Suggested change
* They were _not_ processed with a `Seurat` pipeline.
They were processed using the same pipeline as all OpenScPCA objects were (e.g., with `Bioconductor`), and then converted to `Seurat` format
* Notably, they do contain the raw data counts, allowing you to perform normalization, dimension reduction, etc. with `Seurat` directly if you so choose
* These `Seurat` objects include the same content as the `SingleCellExperiment` objects that they are derived from.
This includes raw and normalized counts, annotations of highly variable genes, PCA and UMAP transformations, as well as cell and feature metadata.
* Note that all calculations were performed using `Bioconductor` packages, so values will differ from the results obtained using `Seurat` functions from the same raw data.
* If `Seurat`-derived values are required, processing steps may need to be repeated.

(I'm not sure about the last sentence)

I'd also move the second main bullet point about feature names to be first.

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* `rOpenScPCA::sce_to_symbols()`
* This function converts row names in a `SingleCellExperiment` object from Ensembl ids to gene symbols

Please refer to these functions' help pages (e.g., `?rOpenScPCA::sce_to_symbols`) for additional information on their use, including options for handling duplicate and/or missing gene symbols.
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@jashapiro what do you think of the "and/or missing" I added here? Is this helpful, or confusing (in which case I'd make the "missing" part a separate thought)?

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I would do one of: choose and or or, because I don't think it matters. Or just leave off the missing symbols because there are other options too, but I just wanted to highlight the one that most people will think of.

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Add docs about rOpenScPCA and hello-clusters
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