Description:
A core task in gene expression analysis is identifying differentially expressed genes (DEGs) between conditions. This feature will add a new module for performing differential expression analysis.
Plan
Choose a library: Decide on a suitable library for differential expression analysis (e.g., DESeq2, edgeR through an Rpy2 interface, or a Python-native library like pydeseq2).
Implementation: Create a new function in core/utils.py to run the analysis.
Integration: Add a new step in the run_pipeline method in core/main.py that triggers this analysis when metadata with condition information is provided.
Description:
A core task in gene expression analysis is identifying differentially expressed genes (DEGs) between conditions. This feature will add a new module for performing differential expression analysis.
Plan
Choose a library: Decide on a suitable library for differential expression analysis (e.g., DESeq2, edgeR through an Rpy2 interface, or a Python-native library like pydeseq2).
Implementation: Create a new function in core/utils.py to run the analysis.
Integration: Add a new step in the run_pipeline method in core/main.py that triggers this analysis when metadata with condition information is provided.