Explore Workflows

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

Path: testdata/step_valuefrom5_wf_v1_2.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee

https://github.com/kids-first/kf-jointgenotyping-workflow.git

Path: workflow/kfdrc-jointgenotyping-refinement-workflow.cwl

Branch/Commit ID: 04bb690ce736d90d76a0c05cf50d716b95d02bf3

Runs DESeq2 using LRT (Likelihood Ratio Test) ============================================= The LRT examines two models for the counts, a full model with a certain number of terms and a reduced model, in which some of the terms of the full model are removed. The test determines if the increased likelihood of the data using the extra terms in the full model is more than expected if those extra terms are truly zero. The LRT is therefore useful for testing multiple terms at once, for example testing 3 or more levels of a factor at once, or all interactions between two variables. The LRT for count data is conceptually similar to an analysis of variance (ANOVA) calculation in linear regression, except that in the case of the Negative Binomial GLM, we use an analysis of deviance (ANODEV), where the deviance captures the difference in likelihood between a full and a reduced model. When one performs a likelihood ratio test, the p values and the test statistic (the stat column) are values for the test that removes all of the variables which are present in the full design and not in the reduced design. This tests the null hypothesis that all the coefficients from these variables and levels of these factors are equal to zero. The likelihood ratio test p values therefore represent a test of all the variables and all the levels of factors which are among these variables. However, the results table only has space for one column of log fold change, so a single variable and a single comparison is shown (among the potentially multiple log fold changes which were tested in the likelihood ratio test). This indicates that the p value is for the likelihood ratio test of all the variables and all the levels, while the log fold change is a single comparison from among those variables and levels. **Technical notes** 1. At least two biological replicates are required for every compared category 2. Metadata file describes relations between compared experiments, for example ``` ,time,condition DH1,day5,WT DH2,day5,KO DH3,day7,WT DH4,day7,KO DH5,day7,KO ``` where `time, condition, day5, day7, WT, KO` should be a single words (without spaces) and `DH1, DH2, DH3, DH4, DH5` correspond to the experiment aliases set in **RNA-Seq experiments** input. 3. Design and reduced formulas should start with **~** and include categories or, optionally, their interactions from the metadata file header. See details in DESeq2 manual [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions) and [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#likelihood-ratio-test) 4. Contrast should be set based on your metadata file header and available categories in a form of `Factor Numerator Denominator`, where `Factor` - column name from metadata file, `Numerator` - category from metadata file to be used as numerator in fold change calculation, `Denominator` - category from metadata file to be used as denominator in fold change calculation. For example `condition WT KO`.

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

Path: workflows/deseq-lrt.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/transform_pack.cwl

Branch/Commit ID: 3cb464a3a5c39cc060cd23d9c60918bc9ffb169b

Packed ID: exome_metrics.cwl

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784

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

Path: tests/wf/js_output_workflow.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b

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

Path: cwltool/schemas/v1.0/v1.0/count-lines8-wf.cwl

Branch/Commit ID: 1eb6bfe3c77aebaf69453a669d21ae7a5a78056f

Processes Sounder SIPS L1A products into L1B Products

https://github.com/unity-sds/unity-sps-workflows.git

Path: sounder_sips/l1b_package.cwl

Branch/Commit ID: 84ecf33903c453db1228ed372ac676ac771136ef

Packed ID: main

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: adcae308fdccaa1190083616118dfadb4df65dca

Runs DESeq2 using LRT (Likelihood Ratio Test) ============================================= The LRT examines two models for the counts: a full model with a certain number of terms and a reduced model, in which some of the terms of the full model are removed. The test determines if the increased likelihood of the data using the extra terms in the full model is more than expected if those extra terms are truly zero. The LRT is useful for testing multiple terms at once, for example, testing 3 or more levels of a factor at once or all interactions between two variables. The LRT for count data is conceptually similar to an analysis of variance (ANOVA) calculation in linear regression, except that in the case of the Negative Binomial GLM, we use an analysis of deviance (ANODEV), where the deviance captures the difference in likelihood between a full and a reduced model. When performing a likelihood ratio test, the p-values and the test statistic (the 'stat' column) are values for the test that removes all of the variables which are present in the full design and not in the reduced design. This tests the null hypothesis that all the coefficients from these variables and levels of these factors are equal to zero. The likelihood ratio test p-values therefore represent a test of all the variables and all the levels of factors which are among these variables. However, the results table only has space for one column of log fold change, so a single variable and a single comparison is shown (among the potentially multiple log fold changes which were tested in the likelihood ratio test). This indicates that the p-value is for the likelihood ratio test of all the variables and all the levels, while the log fold change is a single comparison from among those variables and levels. **Technical notes** 1. **Biological Replicates:** At least two biological replicates are required for every compared category. 2. **Metadata File:** The metadata file describes relations between compared experiments. For example: ```csv ,time,condition DH1,day5,WT DH2,day5,KO DH3,day7,WT DH4,day7,KO DH5,day7,KO ``` where `time`, `condition`, `day5`, `day7`, `WT`, `KO` should be single words (without spaces), and `DH1`, `DH2`, `DH3`, `DH4`, `DH5` correspond to the experiment aliases set in **RNA-Seq experiments** input. 3. **Design and Reduced Formulas:** Design and reduced formulas should start with `~` and include categories or, optionally, their interactions from the metadata file header. See details in the DESeq2 manual [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions) and [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#likelihood-ratio-test). 4. **Batch Correction:** If batch correction is required, provide the `batch_file` input. This file should be a headerless TSV/CSV file where the first column contains sample names matching `expression_file_names`, and the second column contains the batch group name.

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

Path: workflows/deseq-lrt-step-1.cwl

Branch/Commit ID: 99066c338467af54064cc4eb1be7ea863a785202