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: 12e5256de1b680c551c87fd5db6f3bc65428af67

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf2.cwl

Branch/Commit ID: f207d168f4e7eb4dd2279840d4062ba75d9c79c3

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: e71779665f42fcf34601b0f65e030bb0dd47fa79

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

Path: tests/count-lines1-wf-noET.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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

Path: definitions/pipelines/alignment_exome_nonhuman.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/ABRA/abra_workflow.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf

This workflow aligns the fastq files using STAR for no spliced genomes

https://github.com/ncbi/cwl-ngs-workflows-cbb.git

Path: workflows/Alignments/star-alignment-nosplice.cwl

Branch/Commit ID: e1c19e64f6fc210f65472ee227786d33c9b4909a

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

Path: tests/dynresreq-workflow-stepdefault.cwl

Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de

Devel version of Single-Cell Preprocessing Pipeline ===================================================

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

Path: workflows/single-cell-preprocess.cwl

Branch/Commit ID: 12e5256de1b680c551c87fd5db6f3bc65428af67

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: cec32f5b60c1d048257e3c3daed6912d5d2a054e