Here is a typical workflow for analyzing single-cell RNA sequencing data. We can break this process down into three main sections:

• Data Preprocessing: This is the initial step, where we focus on quality control, alignment, and quantification of the data. It’s crucial to ensure that our data is clean and reliable.
• General Analyses: In this phase, we filter out low-quality cells, normalize the data, and select highly variable genes. We then perform dimensionality reduction, cluster the cells, and annotate the different cell types. This step helps us understand the data and identify distinct cell populations.
• Exploratory Analyses: Finally, we delve into exploratory analyses. This includes differential expression gene (DEG) analysis, functional enrichment studies, gene set variation analysis (GSVA), and transcription factor (TF) prediction. We also investigate cell trajectories, interactions between cells, cell cycles, and even spatial transcriptomics. This tutorial will focus on Gene Ontology (GO) Enrichment Analysis as part of the exploratory analysis process.

• Before we introduce GO enrichment analysis, let's first understand what Ontology and Gene Ontology (GO) mean.
• Ontology is a set of terms with precise definitions and defined relationships between them. For example, imagine you are organizing a library of books. You want to classify and organize these books so that others can easily find what they are looking for. In this context, ontology refers to a structured system for categorizing books.

Gene Ontology has 3 main classifications (Biological process, Molecular function, and Cellular component) this allows scientists to precisely describe what a gene does, how it does it, and where it happens in the cell.

• The structure of Gene Ontology is hierarchical, meaning that terms at lower levels are more specific, while those higher up are more general. We can visualize the structure of GO as a graph, where each term represents a node, and the relationships between these terms are represented as edges connecting the nodes. This organization helps in understanding the connections and relationships among various gene functions, these relationships are either is_a, part_of, has_part, or regulates.
• Note that it is possible for a term to have multiple parents. 

• "GO terms” are the unified vocabulary used within the Gene Ontology framework. Each GO term is accompanied by detailed information, and the GO list provides insights into these terms.
• Additionally, GO annotations illustrate the relationships between each GO term and the genes associated with them.

This simple example illustrates how Gene Ontology (GO) adds clarity and standardization when describing gene functions.

Now that we understand what GO mean, let’s explore what is GO Enrichment analysis in the context of scRNA-Seq data.

• Enrichment analysis is a statistical approach used to determine whether certain biological features are overrepresented (enriched) in a subset of genes(i.e., Marker genes) compared to a broader background set.

• Each GO term is associated with a set of genes.
• We try to find out which GO terms or gene sets are enriched in our list of marker genes.
• These GO terms are functionally assigned to various biological functions and locations, aiding our understanding of the roles of marker genes.
• Also note that each gene can be assigned to multiple GO terms allowing the capture of various functions, processes, and cellular components associated with a gene.

We start the analysis by selecting a list of differentially expressed genes (marker genes). Marker genes could be a list of differentially expressed genes between 2 different conditions or between different cell types.

Each gene can be "tagged" with one or more GO terms, similar to how books are categorized by genre or topic.

We then count the number of times each GO term shows up in your list of genes. For example, GO term A appears in 2 out of 3 genes

• Next, we compare how often each GO term appears in our gene list compared to how often it appears across the background gene set, this background may list all genes in the genome or all genes involved in the experiment.
• This comparison tells you whether certain GO terms are "enriched" or more common in the marker genes than you would expect by chance.

• Just because you see GO Term A a lot doesn’t necessarily mean it’s important—it could be by chance. So, we use statistical tests to see if the enrichment is statistically significant.
• While in this simple example, it is obvious that the proportion of genes associated with GO term A in the marker gene set (67%) is higher than the proportion in the background set (30%). This suggests that GO term A is potentially enriched in the marker gene set and we can add it to our purple list of enriched GO terms.

• In real-world scenarios where we have hundreds or thousands of genes we need to formally assess whether this difference is statistically significant (i.e., whether GO term A is truly enriched or if this difference is by chance). Fisher's Exact Test and the hypergeometric test are the most commonly used tests in this situation.
• Fisher’s Exact test substitutes the values of the contingency table in a formula to calculate the probability (P-value) that corresponds to how likely the observed distribution is by chance. A lower P-value suggests that the GO term is truly enriched in the list of marker genes.

After transforming the long list of marker genes into a shorter list of biological themes in the form of GO terms, we can proceed with the interpretation of the results. This can be done by visualizing the most common themes to identify patterns or relationships between the GO terms. Additionally, we can analyze the GO hierarchy, where higher-level categories (parent terms) provide broader biological contexts, while lower-level categories (child terms) offer more specific insights. We can also relate the enriched GO terms to existing biological knowledge.

• In a study by Zúñiga-León et al. (2018) they explored the differentially expressed platelet proteins in early-stage cancer in comparison to a control group.

• The study revealed that out of 4,384 unique proteins expressed in platelets, 85 proteins showed significant differences in abundance in early-stage cancer patients compared to controls. This highlights the potential of platelets as biomarkers for early cancer detection.

• The enrichment analysis identified 19 enriched biological processes, it also uncovered six enriched molecular functions.

• The 19 biological processes identified primarily relate to inflammatory response, immune response, and cancer annotations.
• Among the enriched GO terms, one notable member is protein (P06702), which has been found to be differentially expressed in several cancer types, including breast, colon, liver, gastric, and non-small cell lung cancer. This protein plays a crucial role in promoting cancer growth.
• Additionally, the analysis revealed five proteins that are directly associated with the GO term angiogenesis (GO:0001525). Angiogenesis is essential for tumor development, as it supplies the necessary blood flow for delivering nutrients and oxygen.

• Non-small cell lung cancer (NSCLC) is the most prevalent lung cancer. This study aims to identify gene biomarkers for NSCLC diagnosis and prognosis using single-cell RNA sequencing (scRNA-seq) data and bioinformatics techniques.

• 158 differentially expressed genes (DEGs) were identified, comprising 48 upregulated and 110 downregulated genes.

• Gene Ontology enrichment was then conducted on the differentially expressed genes (Marker genes), statistical testing was performed on each GO term and only GO terms with P-value < 0.01 were selected.

• To conclude, GO enrichment analysis helps scientists make sense of large gene lists by pointing out which biological processes, functions, or cellular locations are most relevant.
• Instead of looking at each gene individually, it allows researchers to focus on broader biological themes, making it easier to interpret the data.