Gene expression analysis helps researchers find which genes are more active in one condition versus another. This tool works with data from bulk RNA sequencing experiments. It uses a method called DESeq2 to compare groups and find meaningful changes.
The analysis starts with count data and information about the samples. You can create a formula to account for factors like batch effects or patient age. The tool runs statistical tests and corrects for multiple comparisons. It can also shrink effect sizes to get more reliable results.
Results can be viewed as tables and plots. This skill lets you run the whole workflow in Python without needing R. It is built specifically for bulk RNA-seq studies.
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