Add RNA Seq normalization methods #136
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docs/rnaseq_normalization.ipynb
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| "\n", | ||
| "#### RPKM:\n", | ||
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| "RPKM (Reads per kilobase million) normalization at first determines a scaling factor, by calculating the sum of all reads in a sample and dividing that number by 1,000,000. That scaling factor is used to calculate RPM (Reads per million), by dividing the read counts for each sample with it, normalizing for sequencing depth. To get RPKM and normalize for gene length, RPM values are divided by genelength in kilobases. RPKM is applied by using the `RNASeq.rpkms` function.\n" |
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Here it would be beneficial to add the formula for RPKM.
| "\n", | ||
| "#### TPM:\n", | ||
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| "What differentiates TPM (Transcripts per kilobase million) from RPKM is the order of operations. To calculate TPM values, data gets normalized for gene length first. This is achieved by calculating RPK values (reads per kilobase), by dividing the read counts by genelength in kilobases. The sum of all RPK values is divided by 1,000,000, to get a scaling factor. Finally, TPM values are calculated by dividing the RPK values by the scaling factor, also normalizing for sequencing depth.\n", |
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Here it would be beneficial to add the formula for TPM.
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| "source": [ | ||
| "The effects of both normalizations becomes apparent when comparing the relation of the samples " |
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Not really, at least not at first glance. I would suggest setting the y axis range of the RPKM and TPM plots to the same range, which would show more clearly that tpm values are lower than rpkm values. As the plot stands now, the values look identical until one looks at the axes.
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Additionally, this plot would really benefit from a 4th chart showing the gene length of each gene.
docs/rnaseq_normalization.ipynb
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| "source": [ | ||
| "## RPKM & TPM\n", | ||
| "RNA-Seq is a transcriptomics technique, that quantifies RNA molecules in a biological sample. When dealing with RNA-sequencing data, normalization is needed to correct technical biases. RPKM and TPM are two metrics that normalize for gene length and sequencing depth. RNA-Sequencing data needs to be normalized for gene length, because longer genes show greater read counts when expressed at the same level and for sequencing depth, as deeper sequencing depth produces more read counts per gene.\n", |
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I think this introduction understates the complexity of these datasets a little. I would suggest adding a few more sentences about the method, e.g. that it is high-throughput and can quantify the full transcriptome.
| open System.Collections.Generic | ||
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| module RNASeq = |
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at least the public functions and types should have XML documentation to give context about what they do without the need of browsing the documentation page.
| testCase "RPKM" (fun _ -> | ||
| Expect.equal | ||
| (RNASeq.rpkms testInSeq | ||
| |> Array.ofSeq) |
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you can use Expect.sequenceEqual instead of casting to arrays here
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Nice!
small nitpick and we can merge this:
the y axis titles on the plot should be different from each other, as they are not all indicate 'Read Counts'. For that, you can set the axis title on the individual charts before creating the grid. Also, a little more space to improve readability and keep axes from overlapping would be nice (see Chart.withSize)
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This contribution adds RPKM and TPM normalization, as well as unit tests and documentation. RPKM and TPM are metrics for normalized RNA-sequencing data.
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