Platform tutorial: nf-core/rnaseq full #131
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…into rnaseq-full-guide
…into rnaseq-full-guide
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In general I like the flow but it starts to drift into a descriptive document. This means it becomes quite verbose and isn't obvious what steps to do next.
I would tighten up the second half to be more focused. You could probably achieve this by hacking bits out without adding too much so I don't think it's a huge job.
platform_versioned_docs/version-23.4/getting-started/rnaseq.mdx
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| ```console | ||
| # Create MDS plot | ||
| # a. Display in RStudio | ||
| plotMDS(y, col=as.numeric(factor(targets$Group)), labels=targets$Group) | ||
| legend("topright", legend=levels(factor(targets$Group)), | ||
| col=1:nlevels(factor(targets$Group)), pch=20) | ||
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| # b. Save MDS plot to file (change `png` to `pdf` to create a PDF file) | ||
| png("MDS_plot.png", width = 800, height = 600) | ||
| plotMDS(y, col=as.numeric(factor(targets$Group)), labels=targets$Group) | ||
| legend("topright", legend=levels(factor(targets$Group)), | ||
| col=1:nlevels(factor(targets$Group)), pch=20) | ||
| dev.off() |
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Let's format the R code. You should be able to use cmd+shift+r in Rstudio or something.
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@adamrtalbot I need to run the R script again to used salmon.merged.gene_counts_length_scaled.tsv either way, so will reformat while I'm in there. Just to confirm, by reformat you mean more than just swapping out console with r in the Markdown code blocks, right?
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| | **Pipeline step** | **Tools** | **Resource needs** | **Description** | | ||
| |-------------------------------------|---------------------------|------------------------------|---------------------------------------------------------------------------------------------------| | ||
| | **Quality Control (QC)** | FastQC, MultiQC | Moderate CPU, low memory | Initial quality checks of raw reads to assess sequencing quality and identify potential issues. | | ||
| | **Read Trimming** | Trim Galore! | Moderate CPU, moderate memory| Removal of adapter sequences and low-quality bases to prepare reads for alignment. | | ||
| | **Read Alignment** | HISAT2, STAR | High CPU, high memory | Alignment of trimmed reads to a reference genome, typically the most resource-intensive step. | | ||
| | **Quantification** | featureCounts, Salmon | Moderate CPU, moderate memory| Counting the number of reads mapped to each gene or transcript to measure expression levels. | | ||
| | **Differential Expression Analysis**| DESeq2, edgeR | Low CPU, moderate memory | Statistical analysis to identify genes with significant changes in expression between conditions. | |
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Shall we just put real numbers here?
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
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| 1. Read and convert the count data and sample information: | ||
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| :::info | ||
| Replace `<PATH_TO_YOUR_COUNTS_FILE>` and `<PATH_TO_YOUR_SAMPLE_INFO_FILE>` with the paths to your `salmon.merged.gene_counts.tsv` and `sampleinfo.txt` files. |
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I'm not sure how pedantic we want to be here about correctness in analysis.
There can be important differences in effective gene length across conditions, which we normally account for in analysis. Using salmon.merged.gene_counts.tsv will just ignore that effect.
We can either model those length differences (preferable, but would probably add some unnecessary complexity here), or otherwise just use salmon.merged.gene_counts_length_scaled.tsv (which is probably the simplest thing to do here).
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Does RNA-Seq produce transcript-level estimates of gene quantification we can use (tximport)?
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The workflow does give you the raw outputs from Salmon / Kallisto, which are at the transcript level, and which is what tximport reads.
But it also uses tximport internally to produce those count matrices (salmon.merged.gene_counts_length_scaled.tsv, salmon.merged.gene_counts.tsv), and provides gene lengths (salmon.merged.gene_lengths.tsv) that can be used as offsets directly in downstream analysis.
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I'd err on the side of pedantic. So need to run the script again with salmon.merged.gene_counts_length_scaled.tsv and update the GIFs and steps. Thanks for the detailed feedback gents!
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Jonathan Manning <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
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Co-authored-by: Florian Wuennemann <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Florian Wuennemann <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Florian Wuennemann <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
Co-authored-by: Adam Talbot <[email protected]> Signed-off-by: Llewellyn vd Berg <[email protected]>
…into rnaseq-full-guide
| @@ -18,7 +18,7 @@ Platform offers two methods to import pipelines to your workspace Launchpad — | |||
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| To import the `nf-core/rnaseq` pipeline: | |||
| To import the `nf-core-rnaseq` pipeline: | |||
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| To import the `nf-core-rnaseq` pipeline: | |
| To import the `nf-core/rnaseq` pipeline: |
The gif above shows nf-core/rnaseq as the title, is there a reason you wanted to change it to nf-core-rnaseq?
Adds an nf-core/rnaseq 1-page tutorial, including compute environment recommendation and config, importing via Seqera Pipelines, adding data via DE and datasets, pipeline launch and monitoring, results analysis with Data Studios, pipeline optimization, and pipeline requirements based on input dataset size and benchmarking.