> For the complete documentation index, see [llms.txt](https://documentation.panomics.bio/documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.panomics.bio/documentation/analyses/analysis-workbench/quality-control.md). # Quality Control The quality control module enables users to filter samples, cells, and genes, and to detect doublets. A [`Standard Compute`](/documentation/compute-runtimes/codeless-compute.md) runtime is required. | Workflow | Sample Filtering | Cell Filtering | Gene Filtering | Double Detection | Sample Listing | | --------------------------------- | -------------------- | -------------------- | -------------------- | -------------------- | -------------------- | | Microarray | :x: | :x: | :x: | :x: | :white\_check\_mark: | | Bulk RNA-seq w/ raw counts | :white\_check\_mark: | :x: | :white\_check\_mark: | :x: | :white\_check\_mark: | | Bulk RNA-seq w/ normalized counts | :x: | :x: | :x: | :x: | :white\_check\_mark: | | Single cell RNA-seq w/ raw counts | :white\_check\_mark: | :white\_check\_mark: | :white\_check\_mark: | :white\_check\_mark: | :white\_check\_mark: | ## Sample Filtering Sample filtering enables users to remove samples based on raw counts or mitochondrial percentage thresholds, so this submodule is not available for `Microarray` and `Bulk RNA-seq` with normalized counts datasets. {% embed url="" %} Sample filtering {% endembed %} ## Cell Filtering Remove cells with too few or too many counts, or cells with too high of a mitochondrial gene percentage count. {% embed url="" %} ## Gene Filtering Gene filtering differs between single cell RNA-seq and bulk RNA-seq. ### Single cell RNA-seq For single cell RNA-seq, you need to provide the minimum number of cells that must express the gene (usually 0.1% of cell count). {% embed url="" %} ### Bulk RNA-seq For bulk RNA-seq, you need to provide the minimum number of counts for the genes in a minimum percentage of samples (usually 3 counts in 10% of samples). {% embed url="" %} ## Double Detection Double detection should be used only if a droplet based library preparation kit was used. This command creates a new observation called `solo_doublet_prediction` with values of `singlet` or `doublet`. It is up to the user to eliminate the cell from the [Cell Explorer](/documentation/analyses/analysis-workbench/sample-cell-explorer.md). Doublet detection is performed using variational autoencoders, namely [scVI's SOLO](https://docs.scvi-tools.org/en/stable/user_guide/models/solo.html). ## Sample List The sample list submodule offers a quick way for users to see which samples have been added to the analysis and which ones are still in use.