Explore Workflows

https://github.com/farahzkhan/bcbio_test_cwlprov.git

Path: somatic/somatic-workflow/main-somatic.cwl

Branch/Commit ID: master

https://github.com/common-workflow-library/legacy.git

Path: workflows/kallisto-demo.cwl

Branch/Commit ID: master

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: master

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

Path: testdata/workflow_input_sf_expr.cwl

Branch/Commit ID: main

https://github.com/fgomez02/analysis-workflows.git

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: No_filters_detect_variants

https://github.com/MgCoders/of-public.git

Path: 1st-wf-generated.cwl

Branch/Commit ID: master

https://github.com/DimitraPanou/scRNAseq-cwl.git

Path: subworkflows/steps.cwl

Branch/Commit ID: 4e9c15b

Option 1: DADA2 from https://docs.qiime2.org/2018.4/tutorials/moving-pictures/

https://github.com/Duke-GCB/bespin-cwl.git

Path: packed/qiime2-step2-dada2.cwl

Branch/Commit ID: qiime2-workflow-paired

Packed ID: qiime2-03-dada2.cwl

https://github.com/Gepiro/microbiomeAnalysisCWL.git

Path: workflow.cwl

Branch/Commit ID: main

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

Path: tests/conditionals/cond-wf-002_nojs.cwl

Branch/Commit ID: main