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: fa4f172486288a1a9d23864f1d6962d85a453e16

https://github.com/reanahub/reana-demo-worldpopulation.git

Path: workflow/cwl/worldpopulation.cwl

Branch/Commit ID: cf816c743b2df2acc6ce666470b640072376f0f6

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: 8cee1920920ed73384fb3ab74272da9c92a20cf2

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

Path: tests/wf/816_wf.cwl

Branch/Commit ID: 6d8c2a41e2c524e8d746020cc91711ecc3418a23

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

Path: tests/count-lines11-null-step-wf.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903

Run BQSR pre+post+plot flow with distribution

https://github.com/sentieon/sentieon-cwl.git

Path: stage/bqsr-flow-distr.cwl

Branch/Commit ID: d20382adfe7285cb517a25d95d2bcb7586546e23

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

Path: tests/conditionals/cond-wf-003.cwl

Branch/Commit ID: 7d7986a6e852ca6e3239c96d3a05dd536c76c903

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77

Validation by expert review (external instrument expert or member of the simulation team).

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

Path: workflows/ValidateParameterByExpert.cwl

Branch/Commit ID: 789752af87eb190387ff2acb4c95c7a5cdb961e7