Merge pull request #109 from ARTbio/go

End of the GOseq part

docs/bulk_RNAseq-IOC/31_GO_enrichment_intro.md

@@ -1,17 +1,96 @@
-![](images/lamp.png)
+## Analyzing GO Enrichment from DEGs
 
-# Analysis of functional enrichment among the differentially expressed genes
+Gene Ontology (GO) enrichment analysis is used to
+identify `biological processes`, `cellular components`, and `molecular functions` that are
+significantly over-represented (or under-represented) in a set of genes compared to a
+background list. This is particularly valuable when analyzing differentially expressed
+genes (DEGs) identified from RNA-seq or microarray experiments.
 
-We have extracted genes that are differentially expressed in treated (Pasilla gene-depleted)
-samples compared to untreated samples. We would like to know if there are categories of
-genes that are enriched among the differentially expressed genes.
+### Individual Gene Analysis (IGA)
 
-Gene Ontology (GO) analysis is widely used to reduce complexity and highlight biological
-processes in genome-wide expression studies.
+- [x] **Concept**
+
+  This approach tests each GO term individually for enrichment within the DEG list.
 
-However, standard methods give biased results on RNA-seq data due to over-detection
-of differential expression for long and highly-expressed transcripts.
+- [x] **Methods:**
+    * **Hypergeometric test:** Calculates the probability of observing
+the number of DEGs in a specific GO term by chance.
+    * **Fisher's exact test:** Similar to
+the hypergeometric test but suitable for smaller datasets.
+
+- [x] **Limitations:**
+
+    * Ignores the hierarchical structure of GO, potentially missing related terms.
+    * Susceptible to multiple testing issues, requiring correction methods like Bonferroni
+    adjustment.
 
-The goseq tool provides methods for performing GO analysis of RNA-seq data,
-taking length bias into account. The methods and software used by goseq are equally
-applicable to other category based tests of RNA-seq data, such as KEGG pathway analysis.
+- [x] **Advanced Considerations:**
+
+    * **Multiple Testing Correction:** As mentioned, IGA is susceptible to multiple testing
+    issues. Here are some commonly used correction methods:
+        * *Bonferroni adjustment:* A conservative approach that controls the family-wise
+        error rate (FWER) but can be overly stringent.
+        * *Benjamini-Hochberg (BH) procedure:* Controls the false discovery rate (FDR) and
+        is less conservative than Bonferroni.
+        * *False discovery rate (q-value):* Provides a measure of significance adjusted
+        for multiple testing.
+    * **Gene Ontology Consortium (GOC) recommendations:** The GOC recommends using a
+    combination of statistical significance (p-value) and fold change thresholds to
+    identify relevant enriched terms, acknowledging the limitations of p-values alone.
+
+### Gene Set Analysis (GSA)
+
+- [x] **Concept:**
+
+  Considers the entire set of DEGs and their relationships within the GO hierarchy.
+
+- [x] **Methods:**
+
+    * **Pathway analysis tools:** Tools like Enrichr, clusterProfiler, and GSEA analyze
+    pre-defined gene sets like KEGG pathways and analyze enrichment within DEGs.
+    * **GO-based GSA methods:** 
+        * **Rank-based approaches:** Assign a rank to each gene based on its differential
+        expression and analyze enrichment within ranked gene sets. (e.g., GSEA)
+        * **Permutation-based approaches:** Randomly shuffle gene labels and recalculate
+        enrichment scores to assess statistical significance. (e.g., fgsea)
+        - Tools like GOseq, fgsea, and piano utilize various
+        statistical models to account for the hierarchical structure of GO and identify
+        enriched functional categories.
+
+- [x] **Advantages of using GSA:**
+    * Incorporates information about gene relationships within the GO hierarchy, leading
+    to more biologically relevant insights.
+    * Reduces the burden of multiple testing compared to individual GO term analysis.
+
+### Advanced Methods for Deeper Exploration
+
+- [x] **Cluster enrichment analysis:** Tools like CeaGO group related GO terms based on
+semantic similarity and analyze enrichment within these clusters. This approach can reveal
+broader functional themes beyond individual terms.
+- [x] **Network analysis:** Integrating protein-protein interaction data with GO
+annotations allows identifying functionally connected subnetworks enriched in DEGs. This
+provides a network-based understanding of the underlying biological processes.
+
+### Choosing the right method
+
+The choice of method depends on factors like:
+
+- [x] **Size of the DEG list:** For smaller lists, IGA might be sufficient, while larger lists
+benefit from GSA approaches.
+- [x] **Research question:** If interested in specific GO terms,
+IGA might be suitable. For broader functional insights, GSA is preferred.
+
+### Additional considerations
+
+- [x] **Over-detection bias** standard methods give biased results on RNA-seq data due to
+over-detection of differential expression for long and highly-expressed transcripts. The
+==**goseq**== tool provides methods for performing GO analysis of RNA-seq data, taking length
+bias into account. The methods and software used by goseq are equally applicable to other
+category based tests of RNA-seq data, such as KEGG pathway analysis.
+- [x] **Background gene list:** Choosing a relevant background list representing the genes not
+differentially expressed is crucial for accurate enrichment analysis.
+- [x] **Multiple testing correction:** Apply appropriate correction methods to account for
+testing multiple GO terms simultaneously.
+- [x] **Visualization:** Utilize graphical representations like bar
+charts or heatmaps to visualize enriched GO terms and their significance levels.
+---