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# Differential gene expression analysis This differential gene expression (DGE) analysis takes as input samples from two experimental conditions that have been processed with a spike-in normalized RNA-Seq workflow (see list of \"Upstream workflows\" at top of file). The size factor estimation and application for normalization is disabled in this version of the DESeq workflow, otherwise all other aspects are the same. DESeq estimates variance-mean dependence in count data from high-throughput sequencing assays, then tests for DGE based on a model which assumes a negative binomial distribution of gene expression (aligned read count per gene). ### Experimental Setup and Results Interpretation The workflow design uses as its fold change (FC) calculation: condition 1 (c1, e.g. treatment) over condition 2 (c2, e.g. control). In other words: `FC == (c1/c2)` Therefore: - if FC<1 the log2(FC) is <0 (negative), meaning expression in condition1<condition2 (gene is downregulated in c1) - if FC>1 the log2(FC) is >0 (positive), meaning expression in condition1>condition2 (gene is upregulated in c1) In other words, if you have input TREATMENT samples as condition 1, and CONTROL samples as condition 2, a positive L2FC for a gene indicates that expression of the gene in TREATMENT is greater (or upregulated) compared to CONTROL. Next, threshold the p-adjusted values with your FDR (false discovery rate) cutoff to determine if the change may be considered significant or not. It is important to note when DESeq1 or DESeq2 is used in our DGE analysis workflow. If a user inputs only a single sample per condition DESeq1 is used for calculating DGE. In this experimental setup, there are no repeated measurements per gene per condition, therefore biological variability in each condition cannot be captured so the output p-values are assumed to be purely \"technical\". On the other hand, if >1 sample(s) are input per condition DESeq2 is used. In this case, biological variability per gene within each condition is available to be incorporated into the model, and resulting p-values are assumed to be \"biological\". Additionally, DESeq2 fold change is \"shrunk\" to account for sample variability, and as Michael Love (DESeq maintainer) puts it, \"it looks at the largest fold changes that are not due to low counts and uses these to inform a prior distribution. So the large fold changes from genes with lots of statistical information are not shrunk, while the imprecise fold changes are shrunk. This allows you to compare all estimated LFC across experiments, for example, which is not really feasible without the use of a prior\". In either case, the null hypothesis (H0) tested is that there are no significantly differentially expressed genes between conditions, therefore a smaller p-value indicates a lower probability of the H0 occurring by random chance and therefore, below a certain threshold (traditionally <0.05), H0 should be rejected. Additionally, due to the many thousands of independent hypotheses being tested (each gene representing an independent test), the p-values attained by the Wald test are adjusted using the Benjamini and Hochberg method by default. These \"padj\" values should be used for determination of significance (a reasonable value here would be <0.10, i.e. below a 10% FDR). Further Analysis: Output from the DESeq workflow may be used as input to the GSEA (Gene Set Enrichment Analysis) workflow for identifying enriched marker gene sets between conditions. ### 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://www.bioconductor.org/packages/3.8/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. ### __References__ - Anders S, Huber W (2010). “Differential expression analysis for sequence count data.” Genome Biology, 11, R106. doi: 10.1186/gb-2010-11-10-r106, http://genomebiology.com/2010/11/10/R106/. - Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.

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

Path: workflows/deseq-for-spikein.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 485f0be56cea77bff62b797ae7eff422a990a92c

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/schemadef-wf.cwl

Branch/Commit ID: 86c46cb397de029e4c91f02cca40fa2b54d22f37

Generates tag density heatmap and histogram for the centered list of features in a headerless regions file. - If provided regions file is a gene list with the following columns `chrom start end name score strand` set `Gene TSS` as a re-centering criteria. - If provided regions file is a peak list with the following columns `chrom start end name` set `Peak Center` as a re-centering criteria. `score` column is always ignored.

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

Path: workflows/heatmap.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: ce433f771ebf5677c9f40858e2ae91b1a7e75d30

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

Path: cwltool/schemas/v1.0/v1.0/search.cwl

Branch/Commit ID: 8010fd2bf1e7090ba6df6ca8c84bbb96e2272d32

Packed ID: main

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 7ebb8d2757914d16520b00571a281e2ad86a42cf

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: f5a467a21b8f69aef5666fb7bbf35efd98c0cbea

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: 9c0b1497c467393e1a54735575043dced73e95c4

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

Path: testdata/count-lines7-wf_v1_0.cwl

Branch/Commit ID: 0ad6983898f0d9001fe0f416f97c4d8b940e384a