Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph conpair-master

https://github.com/mskcc/roslin-variant.git

Path: setup/cwl/conpair/0.2/conpair-master.cwl

Branch/Commit ID: 3ec29bfdcbc444c6599e484640cf83a7c5d91aaa
workflow graph PCA - Principal Component Analysis

Principal Component Analysis -------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 12c29f88855329192bfff977f046990031f04931
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: 4ab9399a4777610a579ea2c259b9356f27641dcc
workflow graph Bacterial Annotation, pass 2, blastp-based functional annotation (first pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass2.cwl

Branch/Commit ID: da35c7b700912dd3643e3dd2c5c96b7be3a4edad
workflow graph Single-Cell Preprocessing Cell Ranger Pipeline

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

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

Path: workflows/single-cell-preprocess-cellranger.cwl

Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2
workflow graph conflict-wf.cwl#collision

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

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b

Packed ID: collision
workflow graph tt_blastn_wnode

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 7f857f7f2d7c080d27c775b67a6d6f7d94bce31f
workflow graph SPRM pipeline

https://github.com/hubmapconsortium/sprm.git

Path: pipeline.cwl

Branch/Commit ID: b465b8a40344de8d8f6211b1bec4f6d356e1442f
workflow graph scatter-valuefrom-wf1.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf1.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b
workflow graph extract_gencoll_ids

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760