Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Transcriptomics is vital for understanding biological systems, with various tools available for analysis steps like normalization and differential expression.
• Existing research often focuses on method impacts on differential expression, neglecting downstream functional analysis effects.
• Functional analysis is key for interpreting transcriptomics data and guiding experiments.

Purpose of the Study:

• To introduce FLOP, a Nextflow-based workflow for end-to-end transcriptomics data analysis.
• To assess the impact of preprocessing method choices on downstream functional analysis.
• To evaluate the robustness of functional analyses derived from transcriptomics data.

Main Methods:

• Developed FLOP, a comprehensive Nextflow workflow integrating multiple transcriptomics analysis pipelines.
• Applied FLOP to diverse datasets, including heart failure patients and cancer cell lines.
• Conducted three benchmarks to evaluate 12 distinct analysis pipelines within FLOP.

Main Results:

• Identified biological effects not apparent at the gene level.
• Found that omitting data filtering had the most substantial impact on gene set analysis correlations.
• Confirmed filtering's essential role in analyses with low to moderate biological signals.

Conclusions:

• Preprocessing method selection critically influences downstream enrichment analysis outcomes.
• FLOP aids in measuring the robustness of functional analyses.
• The workflow promotes more reliable and conclusive biological discoveries from transcriptomics data.