GEPIA generates dot plots profiling gene/isoform expression across multiple cancer types and paired normal samples, with each dot representing a distinct tumor or normal sample.

Parameters

• Gene: Input a gene/isoform of interest.
• Dataset/Tissue Order: Select cancer types of interest in the "Dataset" field and click "add" or "all" to build a dataset list in the "Tissue Order" field. Manual input of cancer types split by comma (e.g. ACC,BRCA,BLCA) is also acceptable. The x-axis of the plot will follow the order of datasets.
• Log Scale: Choose whether to use linear or log₂(TPM + 1) transformed expression data for plotting.
• Matched Normal data: Select "TCGA normal + GTEx normal", "Only TCGA normal" or "None" for matched normal data in plotting. [The matched normal samples for differential analysis are "TCGA normal + GTEx normal"]
• Plot Width: Set custom plot width.



Differential thresholds: [See detail of differential analysis methods here]

• Differential Methods: Statistical methods used for differential gene expression analysis.
• |log₂FC| Cutoff: Set custom fold-change threshold.
• q-value Cutoff: Set custom q-value threshold.
• Percentage Cutoff: Set custom percentage threshold.



For the ANOVA and LIMMA options, genes with higher |log₂FC| values and lower q values than pre-set thresholds are considered differentially expressed genes.

For the Top 10 option, genes with higher log₂FC values and higher percentage value than the thresholds are considered over-expressed genes.