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Graph Name Retrieved From View
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

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.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3
workflow graph EMG pipeline v3.0 (paired end version)

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3-paired.cwl

Branch/Commit ID: a8abd0e66de7b5ffe24cfe7f39d7027103c6d3b4
workflow graph EMG pipeline v4.0 (single end version)

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v4-single.cwl

Branch/Commit ID: ecf044f3a5a7589cb2238487a19f22863c2bcdb1
workflow graph Detect Variants workflow for WGS pipeline

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470
workflow graph Detect DoCM variants

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d
workflow graph Find reads with predicted coding sequences above 60 AA in length

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/orf_prediction.cwl

Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d
workflow graph downsample unaligned BAM and align

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: 2979b565f88ceebca934611adbf3fb8cefd65a19
workflow graph downsample unaligned BAM and align

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: 3042812447d9e8889c6118986490e9c9b9b13223
workflow graph EMG pipeline v4.0 (paired end version)

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v4-paired.cwl

Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d
workflow graph Whole genome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_wgs.cwl

Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c