preprocessing
Definition
Preprocessing refers to the systematic transformation and cleaning of raw biological data before analysis or visualization. This critical step involves removing noise, handling missing values, normalizing measurements, filtering low-quality data points, and standardizing formats across datasets. In life sciences, preprocessing ensures data quality and comparability, particularly when integrating multi-omics data (genomics, proteomics, metabolomics) or combining datasets from different experimental platforms. Effective preprocessing reduces technical artifacts, corrects batch effects, and transforms data into appropriate scales for downstream statistical analysis or machine learning. Without proper preprocessing, biological signals can be obscured by technical variation, leading to false discoveries or missed insights in pathway analysis, biomarker identification, and network inference.
Visualize preprocessing in Nodes Bio
Researchers can visualize the impact of different preprocessing strategies on network topology in Nodes Bio. Compare networks built from raw versus preprocessed data to identify spurious connections introduced by technical noise. Filter nodes based on preprocessing quality metrics, highlight edges that remain robust across different normalization methods, and visualize batch effect corrections through node clustering patterns to ensure biological signals drive network structure.
Visualization Ideas:
- Quality control networks showing sample clustering before and after preprocessing steps
- Differential co-expression networks comparing raw versus normalized data topology
- Multi-layer networks integrating preprocessed omics data with quality score annotations on nodes
Example Use Case
A cancer genomics team integrates RNA-seq data from three different sequencing centers to identify therapeutic targets. Before network construction, they apply preprocessing steps including read quality filtering, batch effect correction using ComBat, and log-transformation of expression values. By visualizing gene co-expression networks before and after preprocessing in Nodes Bio, they discover that uncorrected batch effects created false hub genes specific to each sequencing center. After proper preprocessing, biologically relevant cancer pathway modules emerge clearly, revealing EGFR signaling as a consistent driver across all samples.