Explore Workflows
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Graph | Name | Retrieved From | View |
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atac_encode_sample.cwl
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![]() Path: python/lib/MICGENT/data/cwl/atac_encode_sample.cwl Branch/Commit ID: f618ae02c567a8c6585680a0ce4e05f2afb978e7 |
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workflow_mock_ngtax.cwl
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![]() Path: cwl/workflows/workflow_mock_ngtax.cwl Branch/Commit ID: d944d61ddc34a5b24ebac6e1701efd6f8fdf54ae |
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revsort.cwl
Reverse the lines in a document, then sort those lines. |
![]() Path: tests/wf/revsort.cwl Branch/Commit ID: 12993a6eb60f5ccb4edbe77cb6de661cfc496090 |
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align_sort_sa
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![]() Path: task_types/tt_align_sort_sa.cwl Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8 |
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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. |
![]() Path: workflows/pca.cwl Branch/Commit ID: ed15394da780edc9bfea2cde9e9cee8b7f267bf4 |
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sec-wf.cwl
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![]() Path: tests/wf/sec-wf.cwl Branch/Commit ID: 12993a6eb60f5ccb4edbe77cb6de661cfc496090 |
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wffail.cwl
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![]() Path: tests/wf/wffail.cwl Branch/Commit ID: 12993a6eb60f5ccb4edbe77cb6de661cfc496090 |
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tt_hmmsearch_wnode.cwl
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![]() Path: task_types/tt_hmmsearch_wnode.cwl Branch/Commit ID: 369afa7090a7480e6a0b144eff967a4a52b6fde2 |
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samtools_sort
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![]() Path: structuralvariants/cwl/subworkflows/samtools_sort.cwl Branch/Commit ID: b62c7bfcf5eb7ac3c1ed06879200fdf5db947e4b |
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kmer_cache_store
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![]() Path: task_types/tt_kmer_cache_store.cwl Branch/Commit ID: 656113dcac0de7cef6cff6c688f61441ee05872a |