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Graph Name Retrieved From View
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 282762f8bbaea57dd488115745ef798e128bade1
workflow graph harmonization_novoalign.cwl

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/harmonization/harmonization_novoalign.cwl

Branch/Commit ID: 7eb6b9f75db20866611a3bc55bee28746e84c1b6
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: 909f26beaf96c2cdfe208f87ecd1e9c3de20b81c
workflow graph Detect Docm variants

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: a3e26136043c03192c38c335316d2d36e3e67478
workflow graph Immunotherapy Workflow

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 789267ce0e3fed674ea5212a562315218fcf1bfc
workflow graph tt_fscr_calls_pass1

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: c28cfb9882dedd3c522160f933cff1050ae24100
workflow graph exome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_exome_mouse.cwl

Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d
workflow graph scatter-wf1_v1_1.cwl

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

Path: testdata/scatter-wf1_v1_1.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9
workflow graph cache_asnb_entries

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: a7fced3ed8c839272c8f3a8db9da7bc8cd50271f
workflow graph Genelists heatmap - RNA-seq expression data visualized

# Genelists heatmap - RNA-seq expression data visualized This visualization workflow takes as input 1 or more genelists derived from the DESeq and/or diffbind workflows along with user-selected samples and visualizes RNA-Seq expression data in a single morpheus heatmap. ### __References__ - Morpheus, https://software.broadinstitute.org/morpheus

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

Path: workflows/genelists-deseq-only.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869