Explore Workflows

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda

Packed ID: main

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/somatic_exome_cle_gathered.cwl

Branch/Commit ID: 25eab0390f6866ce491b44c89d9e0435d228ab6f

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/chipseq_alignment_nonhuman.cwl

Branch/Commit ID: 25eab0390f6866ce491b44c89d9e0435d228ab6f

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: b7d9ace34664d3cedb16f2512c8a6dc6debfc8ca

https://github.com/ebi-wp/webservice-cwl.git

Path: workflows/workflow-fetch-phobius.cwl

Branch/Commit ID: 7c2c01c23d7a68a4f0c608881280576d65a01325

Workflow without inputs.

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/no-inputs-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/wf-loadContents.cwl

Branch/Commit ID: 5f27e234b4ca88ed1280dedf9e3391a01de12912

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html

https://github.com/datirium/workflows.git

Path: workflows/gseapy.cwl

Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/mut3.cwl

Branch/Commit ID: 03af16c9df2ee77485d4ab092cd64ae096d2e71c

https://github.com/Sage-Bionetworks/amp-workflows.git

Path: amp-rnaseq_reprocessing/amp-rnaseq_reprocess-workflow/main-paired.cwl

Branch/Commit ID: 50e640ce16be4287b2e87381850a211ad3e70dc1