Explore Workflows
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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: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5 |
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Subworkflow to allow calling different SV callers which require bam files as inputs
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![]() Path: definitions/subworkflows/single_sample_sv_callers.cwl Branch/Commit ID: 68eda647259849ca81ac0bb07a24bc8a4465a630 |
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kmer_top_n_extract
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![]() Path: task_types/tt_kmer_top_n_extract.cwl Branch/Commit ID: 33414c888997d558bdcb558ca33c3a728a3e6143 |
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extract_gencoll_ids
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![]() Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 041a234a935c7af7d3db95353ef80c61c88fc010 |
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format_rrnas_from_seq_entry
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![]() Path: task_types/tt_format_rrnas_from_seq_entry.cwl Branch/Commit ID: 33414c888997d558bdcb558ca33c3a728a3e6143 |
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Exome QC workflow
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![]() Path: definitions/subworkflows/qc_exome.cwl Branch/Commit ID: 233f026ffce240071edda526391be0c03186fce8 |
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bact_get_kmer_reference
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![]() Path: task_types/tt_bact_get_kmer_reference.cwl Branch/Commit ID: 1a6b9e5dea09caa0debbaff30ca39005dfa5e4d4 |
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Subworkflow that runs cnvkit in single sample mode and returns a vcf file
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![]() Path: definitions/subworkflows/cnvkit_single_sample.cwl Branch/Commit ID: 68eda647259849ca81ac0bb07a24bc8a4465a630 |
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dynresreq-workflow-inputdefault.cwl
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![]() Path: v1.0/v1.0/dynresreq-workflow-inputdefault.cwl Branch/Commit ID: e67f19d8a713759d761ecad050966d1eb043b85c |
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kfdrc_cram_reheader_wf.cwl
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![]() Path: workflows/kfdrc_cram_reheader_wf.cwl Branch/Commit ID: b8b1a96d015db74ea451bb7bf83a5e48e645ce1f |