scRNAseq_CellQC
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Run on usegalaxy.euTake an anndata file, and perform basic QC with scanpy. Produces a filtered AnnData object.
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| AnnData - Loaded | AnnData - Loaded | n/a |
|
| MaxMTpc | MaxMTpc | Cells with more than this percentage of counts coming from mitochondrial genes will be removed. |
|
| MinCountPerCell | MinCountPerCell | Cells with fewer than this number of total counts will be removed. |
|
| MinGenesPerCell | MinGenesPerCell | Cells with fewer than this many distinct genes observed will be removed. |
|
| Mitochondrial Prefix | Mitochondrial Prefix | Prefix on mitochondrial gene names. Default MT- typical for human, mt- for mouse. If mitochondrial genes are not prefixed, Mito flagging, plotting and filtering steps will need to be removed. |
|
Steps
| ID | Name | Description |
|---|---|---|
| 5 | Flag Mt- genes | toolshed.g2.bx.psu.edu/repos/ebi-gxa/anndata_ops/anndata_ops/1.8.1+1+galaxy1 |
| 6 | Plot | toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.7.1+galaxy1 |
| 7 | Plot | toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.7.1+galaxy1 |
| 8 | Cell Filtering | toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_filter_cells/scanpy_filter_cells/1.8.1+1+galaxy0 |
| 9 | Scanpy Scrublet | toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_multiplet_scrublet/scanpy_multiplet_scrublet/1.8.1+1+galaxy0 |
| 10 | Plot | toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.7.1+galaxy1 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| genecount_qc_plot | genecount_qc_plot | n/a |
|
| mito_qc_plot | mito_qc_plot | n/a |
|
| qc_anndata_object | qc_anndata_object | n/a |
|
| top_genes_plot | top_genes_plot | n/a |
|
Version History
main @ a95a4ee (latest) Created 22nd Jun 2023 at 07:28 by Sarah Williams
counts matrix workflow doc, affiliations
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main @ a95a4ee Created 21st Jun 2023 at 07:48 by Sarah Williams
counts matrix workflow doc, affiliations
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master @ 76f52ed (earliest) Created 5th May 2023 at 07:49 by Sarah Williams
count and load
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Created: 5th May 2023 at 07:49
Last updated: 9th Nov 2023 at 03:49
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Teams: QCIF Bioinformatics
Organizations: QCIF
The Australian BioCommons enhances digital life science research through world class collaborative distributed infrastructure. It aims to ensure that Australian life science research remains globally competitive, through sustained strategic leadership, research community engagement, digital service provision, training and support.
Teams: Australian BioCommons, QCIF Bioinformatics, Pawsey Supercomputing Research Centre, Sydney Informatics Hub, Janis, Melbourne Data Analytics Platform (MDAP), Galaxy Australia
Web page: https://www.biocommons.org.au/
Working closely with researchers, the QCIF Bioinformatics team apply data management, processing, integration, analysis and visualisation techniques to maximise the potential value of biological and clinical data sets. QCIF Bioinformatics is a partner in the Australian BioCommons.
Space: Australian BioCommons
Public web page: https://www.qcif.edu.au/
This collection houses some scanpy-based scRNAseq workflows on galaxy Australia.
The aim of these workflows is to handle the routine ‘boring’ part of single cell RNAseq data processing. It will produces an ‘AnnData’ object, which can then be used as a base for downstream analysis – either within galaxy or outside of it. AnnData is a standard format used by the ‘scanpy’ python package.
These workflows represent just one way of processing data for a ‘typical’ scRNAseq experiment – there are many ...
