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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,20 +1,22 @@ | ||
| params { | ||
| // This closure facilitates the usage of sample specific parameters | ||
| // This closure facilitates the usage of sample specific parameters | ||
| parseConfig = { sample, paramsGlobal, paramsLocal -> | ||
| def lv = { a,b -> return MethodRankHelper.delDistance(a, b) } | ||
| def pL = paramsLocal.collectEntries { k,v -> | ||
| if (v instanceof Map) { | ||
| if (v.containsKey(sample)) | ||
| return [k, v[sample]] | ||
| if (v.containsKey('default')) | ||
| return [k, v['default']] | ||
| if (lv(k,sample) > 30) | ||
| return [k,v] | ||
| throw new Exception("Not a valid entry in " + k + ". The sample " + sample + " is not found in " + v +" ; Make sure your samples are correctly specified when using the multi-sample feature.") | ||
| } else { | ||
| def lv = { a,b -> return org.codehaus.groovy.runtime.MethodRankHelper.delDistance(a, b) } | ||
| def pL = paramsLocal.collectEntries { k,v -> | ||
| if (v instanceof Map) { | ||
| if (v.containsKey(sample)) | ||
| return [k, v[sample]] | ||
| if (v.containsKey('default')) | ||
| return [k, v['default']] | ||
| def closeMatches = v.collectEntries { vk, vv -> [lv(vk, sample), vk] }.keySet().findAll { it < 30} | ||
| if(closeMatches.size() > 0) | ||
| throw new Exception("The sample " + sample + " is not found in " + v +" ; Make sure your samples are correctly specified when using the multi-sample feature.") | ||
| else | ||
| return [k,v] | ||
| } else { | ||
| return [k,v] | ||
| } | ||
| } | ||
| return [global: paramsGlobal, local: pL] | ||
| } | ||
| } | ||
| return [global: paramsGlobal, local: pL] | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,49 +1,5 @@ | ||
| Case Studies | ||
| ============= | ||
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| Kurmangaliyev et al., 2019 | ||
| -------------------------- | ||
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| A single cell analysis of transcriptional control of neuronal connectivity in Drosophila, | ||
| based on `Kurmangaliyev et al., 2019 <https://elifesciences.org/articles/50822>`_. | ||
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| .. note:: `Full tutorial here <https://vsn-pipelines-examples.readthedocs.io/en/latest/Kurmangaliyev.html>`_. | ||
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| This case study illustrates the following steps: | ||
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| 1. **Input data** is loaded directly from the `Sequence Read Archive (SRA) <https://www.ncbi.nlm.nih.gov/sra>`_ by giving an SRA identifier to the ``sra`` input channel. | ||
| 2. Cell Ranger is run to generate expression counts | ||
| 3. Multiple samples are combined, and **batch effect correction** is performed with both BBKNN and Harmony (in separate pipeline runs). | ||
| 4. **Gene regulatory network inference** is performed using the SCENIC pipeline. The SCENIC append mode is used to include the SCENIC results with both independent batch effect correction methods, to avoid re-running SCENIC. | ||
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| Hung et al., 2019 | ||
| ----------------- | ||
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| A single cell analysis of the adult Drosophila midgut, based on | ||
| `Hung et al., 2019 <https://vsn-pipelines-examples.readthedocs.io/en/latest/PBMC10k.html>`_. | ||
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| .. note:: `Full tutorial here <https://vsn-pipelines-examples.readthedocs.io/en/latest/Hung.html>`_. | ||
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| This case study illustrates the following steps: | ||
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| 1. **Input data** is loaded directly from the `Sequence Read Archive (SRA) <https://www.ncbi.nlm.nih.gov/sra>`_ by giving an SRA identifier to the ``sra`` input channel. | ||
| 2. Cell Ranger is run to generate expression counts | ||
| 3. Multiple samples are combined, and **batch effect correction** is performed with BBKNN | ||
| 4. **Gene regulatory network inference** is performed using the SCENIC pipeline. | ||
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| PBMC10k | ||
| ------- | ||
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| An analysis of a sample dataset from 10x Genomics consisting of 10,000 PBMCs from a healthy human donor. | ||
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| .. note:: `Full tutorial here <https://vsn-pipelines-examples.readthedocs.io/en/latest/PBMC10k.html>`_. | ||
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| This case study illustrates the following steps: | ||
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| 1. **Input data** is filtered Cell Ranger counts downloaded from the 10x Genomics support website. | ||
| 2. The single sample is run through the standard ``single_sample`` pipeline. | ||
| 3. **Gene regulatory network inference** is performed using the SCENIC pipeline and integrated with the highly variable genes analysis. | ||
| See the full list of case studies and examples at `VSN-Pipelines-examples <https://vsn-pipelines-examples.readthedocs.io/en/latest/>`_. | ||
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