Explore Workflows

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: 23f48abfae31592d202cbc31394f6d5167d22014

https://github.com/ncbi/pgap.git

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: c7c674b873b9925b28ffbd602974eec4bfe78cf9

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

Path: tests/inpdir_update_wf.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

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

Path: testdata/record-output-wf_v1_1.cwl

Branch/Commit ID: 124a08ce3389eb49066c34a4163cbbed210a0355

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

Path: tests/echo-wf-default.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

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

Path: tests/count-lines6-wf.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

https://github.com/common-workflow-lab/wdl-cwl-translator.git

Path: wdl2cwl/tests/cwl_files/BuildCembaReferences.cwl

Branch/Commit ID: 81d4bdaecebaa843903b40834cb15e350aa047e8

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

Path: tests/conditionals/cond-wf-004_nojs.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

The entire test should take ~24 seconds. Test that the 20 second time limit applies to each step individually (so 1st step has 20 seconds and the 2nd step has 20 seconds). So this 20 second time limit should not cause the workflow to fail. The timing on this test was updated from shorter values to accommodate the startup time of certain container runners, the previous timelimit of 5 seconds was too short, which is why it is now 20 seconds.

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

Path: tests/timelimit2-wf.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

VDJ_GatherCalls collect the outputs from the multi-processed VDJ step into one file.

https://github.com/Chi-CRL/cwl_check_workflow.git

Path: rhapsody_pipeline_2.0.cwl

Branch/Commit ID: 50ed14112f9db254034dd5530cf1a768e04eb7ff

Packed ID: VDJ_GatherCalls.cwl