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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: 7eef0294395d83ff0765fce61726a59d71126422

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

Path: tests/wf/default-wf5.cwl

Branch/Commit ID: f94719e862f86cc88600caf3628faba6c0d05042

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

Path: definitions/pipelines/aml_trio_cle.cwl

Branch/Commit ID: 5be54bf09092c53e6c7797a875f64a360d511d7f

Devel version of Single-Cell Cell Ranger Reanalyze ================================================== Workflow calls \"cellranger aggr\" command to rerun secondary analysis performed on the feature-barcode matrix (dimensionality reduction, clustering and visualization) using different parameter settings. As an input we use filtered feature-barcode matrices in HDF5 format from cellranger count or aggr experiments. Note, we don't pass aggregation_metadata from the upstream cellranger aggr step. Need to address this issue when needed.

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

Path: workflows/cellranger-reanalyze.cwl

Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c

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

Path: definitions/subworkflows/varscan_germline.cwl

Branch/Commit ID: 0a9a4ce83b49ed4e7eee5bcc09d83725136a36b0

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

Path: workflows/dev/ultra-opt/kfdrc_bwamem_subwf.cwl

Branch/Commit ID: 9fc3770230e1bd8495f5e6a18665bd21e7c6fafd

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

Path: testdata/md5sum.cwl

Branch/Commit ID: c1875d54dedc41b1d2fa08634dcf1caa8f1bc631

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

Path: bacterial_annot/wf_ab_initio_training.cwl

Branch/Commit ID: 2afb5ebafd1353ba063cc74ee9a7eaf347afce5c

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

Path: testdata/stdout-wf_v1_0.cwl

Branch/Commit ID: e78db9870cb744fe36674f43b3223c688e9989e1

https://github.com/apaul7/cancer-genomics-workflow.git

Path: definitions/subworkflows/cram_to_bam_and_index.cwl

Branch/Commit ID: bfcb5ffbea3d00a38cc03595d41e53ea976d599d