finish week 6

docs/bulk_RNAseq-IOC/32_GOseq.md

@@ -113,7 +113,7 @@ your background genes.
         --> `fields`
     - List of Fields
 
-        --> `boolean`
+        --> `column 1` and `column 3`
     - `Run Tool`
 
 :warning: As this is the last step of the construction of the gene set lists, you should

docs/bulk_RNAseq-IOC/36_GSEA_2.md

@@ -3,13 +3,108 @@
 EGSEA, an acronym for Ensemble of Gene Set Enrichment Analyses, is a Bioconductor package
 that utilizes the analysis results of eleven prominent GSE algorithms from the literature
 to calculate collective significance scores for gene sets. These methods are currently:
-ora, globaltest, plage, safe, zscore, gage, ssgsea, roast, fry, padog, camera, gsva. The
-ora, gage, camera and gsva methods depend on a competitive null hypothesis while the
-remaining seven methods are based on a self-contained hypothesis. EGSEA’s gene set
+**ora, globaltest, plage, safe, zscore, gage, ssgsea, roast, fry, padog, camera, gsva**.
+The **ora, gage, camera and gsva** methods depend on a competitive null hypothesis while
+the remaining seven methods are based on a self-contained hypothesis. EGSEA’s gene set
 database, the EGSEAdata Bioconductor package, contains around 25,000 gene sets from 16
 collections from MSigDB, KEGG and GeneSetDB. ==Supported organisms are human, mouse and rat,
 however MSigDB is only available for human and mouse==.
 
 An example
 [EGSEA workflow](https://www.bioconductor.org/help/workflows/EGSEA123/) is available at
-the Bioconductor workflows website.
+the Bioconductor workflows website.
+
+Currently, only the egsea.cnt function is implemented in this tool. ==This function takes a
+raw RNA-Seq count matrix and uses limma-voom with TMM normalization to convert the RNA-seq
+counts into expression values for EGSEA analysis==.
+
+EGSEA returns a HTML report of detailed analysis results for each contrast of interest and
+comparative analysis results. The heatmap view at both the gene set and summary level and
+the summary level bar plots can be useful summaries to include in publications to
+highlight the gene set testing results.
+
+## EGSEA inputs
+
+### 1. Counts Data
+This tool requires a counts matrix (counts table) containing the raw RNA-seq read counts.
+The counts data can either be input as separate counts files (one sample per file) or a
+single count matrix (one sample per column). The rows correspond to genes, and columns
+correspond to the counts for the samples. Values must be tab separated, with the first row
+containing the sample/column labels. The first column must contain Entrez Gene IDs that
+are unique (not repeated) within the counts file. Entrez IDs can be obtained from the
+annotateMyIDs Galaxy tool. Genes with low counts should be removed, such as in the
+filtered counts matrix that can be output from the limma tool.
+
+Example - **Separate Count Files**:
+
+| EntrezID | WT1 |
+|---|---|
+| 1 | 71 |
+| 1000 | 3 |
+| 10000 | 2310 |
+| 100009605 | 3 |
+| 100009613 | 9 |
+
+:warning: Note that this format is almost the format returned by Featurecounts, except that
+gene identifiers are EntrezID instead of Ensembl IDs. We will do this conversion
+programmatically in the workflow, using the appropriate conversion table.
+
+Example - **Single Count Matrix**:
+| EntrezID | WT1 | WT2 | WT3 | Mut1 | Mut2 | Mut3 |
+|---|---|---|---|---|---|---|
+| 1 | 71 | 73 | 69 | 36 | 22 | 28 |
+| 1000 | 3 | 4 | 2 | 4 | 0 | 1 |
+| 10000 | 2310 | 2142 | 2683 | 1683 | 2068 | 2172 |
+| 100009605 | 3 | 1 | 2 | 1 | 5 | 3 |
+| 100009613 | 9 | 11 | 4 | 13 | 6 | 10 |
+
+### 2. Factor Information
+
+We will enter factor names and groups in the tool form. Look at the tool help if you want
+to input your data using a count matrix.
+
+### 3. Symbols Mapping file
+
+A file containing the Gene Symbol for each Entrez Gene ID. The first column must be the
+Entrez Gene IDs and the second column must be the Gene Symbols. It is used for the heatmap
+visualization. The number of rows should match that of the Counts Matrix.
+
+:warning: Note that we already generated this file, taking care to start from raw count
+matrices produced by Featurecounts. Therefore, the requirement of "number of rows matching
+ that of the Counts Matrix" is fulfilled.
+
+## The PRJNA630433 EGSEA workflow
+
+--> Copy
+
+- [x] the collections `Dc FeatureCounts counts`, `Mo featureCounts Counts` and `Oc featureCounts Counts`
+- [x] the conversion tables `EnsemblID-ENTREZID table` and `ENTREZID-GeneSymbol table`
+
+in a new history named `PRJNA630433 EGSEA`
+
+From this history, we are going to run the [PRJNA630433 EGSA](Galaxy-Workflow-PRJNA630433_EGSEA.ga)
+Workflow:
+
+![](images/PRJNA630433_EGSEA.png)
+
+:warning: Take care to match you input data with the workflow instructions in the workflow
+form.
+
+## Additional considerations
+
+- [x] Note that the statistic metric used by the EGSA Galaxy wrapper is the fold change.
+  This fold change is estimated using the raw count matrices and the limma package.
+  As we previously saw, limma-voom is arguably less efficient to call DE genes than DESeq2
+  or edgeR, when it comes to RNAseq data. However, This is a marginal drawback because the
+  use of genesets gives EGSEA statistical power out of all proportion to that of these DE
+  callers.
+- [x] EGSEA implements numerous algorithms. Here, for the sake of simplicity, we only use
+  **camera**, **globaltest** and **ora**. Actually, the tool performs statistic aggregation
+  operations with all algorithms used, in order to increase the statistical power of the
+  analysis (ability to detected significant events of H0 rejection). Therefore, feel free
+  to experiment and use more (or all) algorithms in a EGSEA run.
+- One of the most powerful feature of EGSEA its ability to provide HTML reports. To take
+  all benefits, you should dig in these reports for at least a couple of hours ! If you
+  have any point to discuss about them, please do not hesitate to chat in the IOC slack
+  board.
+- [x]