# A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning
## 2 Materials and Methods
### Methods
#### 2.2.3 Bioinformatics Algorithms  
The original gene expression data consists of 20,429 genes. Most of them are likely, not relevant in the search for biomarkers because they are not differentially expressed between the two classes. To filter out those genes, we used DESeq2 available in R [23]. In the volcano plot in Figure 3 the significance of the genes is depicted. Figure 3a shows the differentially expressed genes between the patients with good and bad outcome and Figure 3b between the patients that are platinum sensitive or resistant. Unnormalized gene counts are used for that purpose. The counts are normalized using the geometrical mean across all samples for each gene. The gene count is divided by this mean to estimate the size factors using the median of the ratios in each sample. Those accommodate for library size and RNA composition bias. Subsequent steps estimate gene-wise dispersion and shrink estimates to accurate model counts. In the last step, the counts are used to fit a negative binomial model and use the Wald test for hypothesis testing [37]. Afterward, the log2 fold shrinkage method apeglm is used to determine which genes have significant changes between the two groups. The p-adjusted value is used to select the genes that are significantly expressed between the two groups. A p-adjusted value of 0.01 is used for the outcome studies and 0.05 for the platinum response studies.  
Figure 3: (**a**) The volcano plot shows the differentially expressed genes between the patients with good or bad outcomes. The dots in green represent genes that have an adjusted \(p\)-value of \(<\)0.01 and  
a log2 FoldChange between +2 and \(-2\). The red dots represent genes that have an adjusted \(p\)-value of <0.01 and a log2 FoldChange >2 or <\(-2\). The blue dots are all genes that have an adjusted \(p\)-value of >0.01. (**b**) The volcano plot depicted shows the differentially expressed genes between the patients that are sensitive or resistant to platinum-based chemotherapy. The dots in green represent genes that have an adjusted \(p\)-value of <0.05 and a log2 FoldChange between +2 and \(-2\). The red dots represent genes that have an adjusted \(p\)-value of <0.05 and a log2 FoldChang >2 or <\(-2\). The blue dots are all genes that have an adjusted \(p\)-value > 0.05.  
For the outcome studies, genes upregulated in patients with a good outcome have a log2 foldchange above zero, and the ones upregulated in patients with a bad outcome have a log2 foldchange lower than zero. For the platinum response studies, genes upregulated in platinum-sensitive patients have a log2 foldchange above zero, and the ones that are upregulated in platinum-resistant patients have a log2 foldchange lower than zero. The selected gene lists are further analyzed to determine their biological implications for ovarian cancer. Metascape has been used for gene set enrichment analysis, pathway analysis, and functional gene annotation. It is a web-based portal to provide those analyses in a combined tool leveraging 40 independent knowledge bases for the assessment [38].