Update Analysis: Add intro pathway analysis paragraph to microarray GSVA and review text in both GSVA examples
cansavvy opened this issue · comments
Background
Per this related comment on PR #347, the microarray pathway analysis GSVA example notebook will need to be updated to reflect changes made to the introductory paragraph for the pathway analyses examples.
Problem
Per issue #349 and this comment on #340, the pathway analyses examples do not currently contain background information on how to choose a pathway analysis based on one's question and dataset.
What potential "gotchas" do we know of?
Additional context changes may need to be made throughout the notebook based on what is added in the introductory paragraphs.
What are the recommended next steps?
The introductory paragraph (pasted below) will need to be brought over to 02-microarray/pathway-analysis_microarray_03_gsva.Rmd notebook.
TEMPLATE:
# Purpose of this analysis
This example is one of pathway analysis module set, we recommend looking at the [pathway analysis table below](#how-to-choose-a-pathway-analysis) to help you determine which pathway analysis method is best suited for your purposes.
{{EXPLAIN WHAT THIS ANALYSIS IS}}
⬇️ [**Jump to the analysis code**](#analysis) ⬇️
### What is pathway analysis?
Pathway analysis refers to any one of many techniques that uses predetermined sets of genes that are related or coordinated in their expression in some way (e.g., participate in the same molecular process, are regulated by the same transcription factor) to interpret a high-throughput experiment.
In the context of [refine.bio](https://www.refine.bio/), we use these techniques to analyze and interpret genome-wide gene expression experiments.
The rationale for performing pathway analysis is that looking at the pathway-level may be more biologically meaningful than considering individual genes, especially if a large number of genes are differentially expressed between conditions of interest.
In addition, many relatively small changes in the expression values of genes in the same pathway could lead to a phenotypic outcome and these small changes may go undetected in differential gene expression analysis.
We highly recommend taking a look at [Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002375) from @Khatri2012 for a more comprehensive overview. We have provided primary publications and documentation of the methods we will introduce below as well as some recommended reading in the [`Resources for further learning` section](#resources-for-further-learning).
### How to choose a pathway analysis?
This table summarizes the pathway analyses examples in this module.
|Analysis|What is required for input|What output looks like |✅ Pros| ⚠️ Cons|
|--------|--------------------------|-----------------------|-------|-------|
|[**ORA (Over-representation Analysis)**](https://alexslemonade.github.io/refinebio-examples/02-microarray/pathway-analysis_microarray_01_ora.html)|A list of gene IDs (no stats needed)|A per-pathway hypergeometric test result|- Simple<br><br> - Inexpensive computationally to calculate p-values| - Requires arbitrary thresholds and ignores any statistics associated with a gene<br><br> - Assumes independence of genes and pathways|
|[**GSEA (Gene Set Enrichment Analysis)**](https://alexslemonade.github.io/refinebio-examples/02-microarray/pathway-analysis_microarray_02_gsea.html)|A list of genes IDs with gene-level summary statistics|A per-pathway enrichment score|- Includes all genes (no arbitrary threshold!)<br><br> - Attempts to measure coordination of genes|- Permutations can be expensive<br><br> - Does not account for pathway overlap<br><br> - Two-group comparisons not always appropriate/feasible|
|[**GSVA (Gene Set Variation Analysis)**](https://alexslemonade.github.io/refinebio-examples/02-microarray/pathway-analysis_microarray_03_gsva.html)|A gene expression matrix (like what you get from refine.bio directly)|Pathway-level scores on a per-sample basis|- Does not require two groups to compare upfront<br><br> - Normally distributed scores|- Scores are not a good fit for gene sets that contain genes that go up AND down<br><br> - Method doesn’t assign statistical significance itself<br><br> - Recommended sample size n > 10|
The language in the RNA-seq GSVA example should get another look, too. See #404 (review).