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## Workflow overview
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This workflow is a best-practice workflow for systematically running `cellranger count` on one or more samples.
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See the [10X documentation choosing a pipeline](https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/running-pipelines/cr-choosing-a-pipeline) to see whether this is the preprocessing you need.
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If your assay setup suggests `cellranger multi`, have a look at the [standardised workflow for `cellranger multi` instead](https://snakemake.github.io/snakemake-workflow-catalog/docs/workflows/snakemake-workflows/cellranger-multi).
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The workflow is built using [snakemake](https://snakemake.readthedocs.io/en/stable/) and consists of the following steps:
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1. Link in files to a new file name that follows cellranger requirements.
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2. Create a per-sample cellranger library CSV sheet.
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3. Run cellranger count, parallelizing over samples.
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3. Run `cellranger count`, parallelizing over samples.
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4. Create a snakemake report with the Web Summaries.
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## Running the workflow
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The `lane_number` column is optional, and only necessary if a single sample is sequenced across multiple lanes.
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All other columns are required:
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The `lane_number` column is optional, and only necessary if a any sample is sequenced across multiple lanes.
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All other columns are required.
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`read1` and `read2` require relative paths to the main workflow directory (where you run the `snakemake` command).
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*`library_type` can be any of the [values listed in the `cellranger count` documentation on Library CSVs](https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/inputs/cr-libraries-csv).
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*`read1` and `read2` require file names with paths relative to the main workflow directory (where you run the `snakemake` command).
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