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docs/further-examples/index.md

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# Further Examples
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Continuing on from the examples in [Getting Started with CountESS](../getting-started/)
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let's look at a more realistic experiment.
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let's look at some more realistic experiments:
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## Example 5: Deep Mutational Scan of BRCA1
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[![Screenshot 12](img/s5_12.jpg)](img/s5_12.png)
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[![Screenshot 13](img/s5_13.jpg)](img/s5_13.png)
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<!--[![Screenshot 13](img/s5_13.jpg)](img/s5_13.png)-->
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This will let us calculate scores and sigmas per
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DNA variant instead of per barcode:
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[![Screenshot 14](img/s5_14.jpg)](img/s5_14.png)
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[![Screenshot 16](img/s5_16.jpg)](img/s5_16.png)
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Scores can then be combined just like the "per barcode"
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example:
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<!--[![Screenshot 16](img/s5_16.jpg)](img/s5_16.png)-->
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[![Screenshot 17](img/s5_17.jpg)](img/s5_17.png)
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### Scoring by Protein
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... Or to collate by protein variant:
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... Or we can collate by protein variant:
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[![Screenshot 21](img/s5_21.jpg)](img/s5_21.png)
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[![Screenshot 22](img/s5_22.jpg)](img/s5_22.png)
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<!--[![Screenshot 22](img/s5_22.jpg)](img/s5_22.png)
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[![Screenshot 18](img/s5_18.jpg)](img/s5_18.png)
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[![Screenshot 18](img/s5_18.jpg)](img/s5_18.png)-->
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[![Screenshot 19](img/s5_19.jpg)](img/s5_19.png)
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### Other Options
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The above example uses two replicates and three time points.
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If only two timepoints are available, the scoring plugin falls
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back on the "log ratio" version of the scoring formula, also
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as per Enrich2.
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If there's no suitable "wild type indicator" column, the
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scoring plugin can use the total of all variants instead, also
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as per Enrich2.
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## Example 6: VAMPseq
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[VAMP-seq](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5980760/)
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sorts cells into bins and uses a weighted sum of the frequencies of
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variants in each bin to calculate a score for each variant.
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In this example we have three replicates each of which has counts in
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four bins.
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The first step is to load and collate these files just like in
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[Example 3](../getting-started/#example-3):
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[![Screenshot 1](img/s6_1.jpg)](img/s6_1.png)
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Then we can use the [VAMPseq Plugin](../included-plugins/#vampseq) to calculate scores
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for each variant:
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[![Screenshot 2](img/s6_2.jpg)](img/s6_2.png)
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Once that's done, pivot on replicate and use a simple formula to combine scores
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into an average score and an estimated standard deviation:
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[![Screenshot 3](img/s6_3.jpg)](img/s6_3.png)
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[![Screenshot 4](img/s6_4.jpg)](img/s6_4.png)

docs/getting-started/index.md

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git clone https://github.com/CountESS-Project/countess-demo/
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```
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This page includes some simple "toy" examples to illustrate the basic
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techniques of developing workflows with CountESS. For examples of
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more realistic experiments, see [Further Examples](../further-examples)
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## Example 1: Reading & Counting Sequences
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For this simplified example, we'll load up two CSV files with
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[![Example 2 Image 8](img/example_2_8.png)](img/example_2_8.png)
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[![Example 2 Image 11](img/example_2_11.png)](img/example_2_11.png)
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## Example 3: FASTQ and VAMP-seq
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## Example 3: FASTQ
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Load this example with `countess_gui example_3.ini`.
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[![Example 3 Image 6](img/example_3_6.png)](img/example_3_6.png)
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### VAMP-seq
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[VAMP-seq](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5980760/)
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uses a weighted sum of the bins to calculate a score for each variant,
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which is easily implemented in [Python Code](../included-pluyins/#python):
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[![Example 3 Image 7](img/example_3_7.png)](img/example_3_7.png)
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Files are then written out as before.
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<!--
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[![Example 3 Image 8](img/example_3_8.png)](img/example_3_8.png)
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[![Example 3 Image 9](img/example_3_9.png)](img/example_3_9.png)
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-->
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## Example 4: External Metadata
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Load this example with `countess_gui example_4.ini`.
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in each:
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[![Example 4 Image 3](img/example_4_3.png)](img/example_4_3.png)
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# Further Examples
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This page includes some simple "toy" examples to illustrate the basic
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techniques of developing workflows with CountESS. For examples of
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more realistic experiments, see [Further Examples](../further-examples)

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