scRNAseq: Count and Load with Cell Ranger
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Run on GalaxyTakes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| Fastqs for one sample | Fastqs for one sample | Typically the Forward reads are labelled _R1, barcodes. R2 is Reverse, mRNA sequences. List should contain seq for one biological sample only. |
|
| Reference genome | Reference genome | Built in reference dataset to use (human or mouse only) |
|
| Sample | Sample | Short, unique sample name |
|
Steps
| ID | Name | Description |
|---|---|---|
| 3 | Cell Ranger | cellranger |
| 4 | Scanpy Read10x | toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_read_10x/scanpy_read_10x/1.9.3+galaxy0 |
| 5 | Inspect AnnData | toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0 |
| 6 | Create text file | toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_text_file_with_recurring_lines/9.3+galaxy1 |
| 7 | Text transformation | This step replaces the first line with a header. Because the annotated data matrix also had a header, this works. toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sed_tool/9.3+galaxy1 |
| 8 | Loaded Anndata with Sample name | AnnData object with supplied sample id. toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.10.3+galaxy0 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| AnnData Loaded | AnnData Loaded | n/a |
|
Version History
main @ 7581788 (latest) Created 30th May 2024 at 06:49 by Sarah Williams
Updated workflow versions for anndata version conflict bugfix
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main7581788
main @ abc9384 (earliest) Created 9th Nov 2023 at 01:26 by Sarah Williams
now with cellranger pipelines
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main
abc9384
Creators and SubmitterViews: 3879 Downloads: 301 Runs: 0
Created: 9th Nov 2023 at 01:26
Last updated: 30th May 2024 at 06:56
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scRNAseq processing...
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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, National Computational Infrastructure (NCI) WorkflowHub team
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 ...
