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workflow graph DESeq2 (LRT) - differential gene expression analysis using likelihood ratio test

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: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph step_valuefrom5_wf_v1_1.cwl

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

Path: testdata/step_valuefrom5_wf_v1_1.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9
workflow graph checkm

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

Path: checkm/wf_checkm.cwl

Branch/Commit ID: 1cfd46014be8d867044cb10d1ddde0cb3068ee84
workflow graph Workflow that executes the Sounder SIPS end-to-end L1a processing

Cognito credentials to access the U-DS services are retrieved from the AWS Parameter Store with the supplied keys.

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

Path: sounder_sips/ssips_L1a_workflow.cwl

Branch/Commit ID: ccf77398d4f98db13ef04908d6e8b7de23cc1f6e
workflow graph Interval overlapping alignments counts

Interval overlapping alignments counts ====================================== Reports the count of alignments from multiple samples that overlap specific intervals.

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

Path: workflows/bedtools-multicov.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph multiome pipeline using Salmon and Alevin (HuBMAP scRNA-seq pipeline) and HuBMAP scATAC-seq pipeline

https://github.com/hubmapconsortium/multiome-rna-atac-pipeline.git

Path: pipeline.cwl

Branch/Commit ID: c338cd390c994eb3601cc7749878add1056953ba
workflow graph Genomic regions intersection and visualization

Genomic regions intersection and visualization ============================================== 1. Merges intervals within each of the filtered peaks files from ChIP/ATAC experiments 2. Overlaps merged intervals and assigns the nearest genes to them

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

Path: workflows/intervene.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph count-lines7-wf_v1_2.cwl

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

Path: testdata/count-lines7-wf_v1_2.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9
workflow graph scatter-wf2_v1_0.cwl

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

Path: testdata/scatter-wf2_v1_0.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9
workflow graph map medium and long reads (> 100 bp) against reference genome

https://github.com/common-workflow-library/bio-cwl-tools.git

Path: bwa/BWA-Mem2-single.cwl

Branch/Commit ID: 1bfe9fc07978c8463399eec690798effb8431c35