Identifying and Handling Poorly Performing Technical Replicates

[axiomcura]

Currently, our pairwise comparison analysis between treatment replicates highlights instances where entire treatments appear to be performing poorly. While we could simply discard these entire treatments, this leads to a loss of valuable data and is not the ideal approach.

A more robust strategy would be to pinpoint the specific technical replicate within a treatment that are causing these low scores. By identifying and removing only the problematic replicate, we can improve the overall quality of our dataset for subsequent analysis without sacrificing entire treatment groups.

Proposed Solution:

1. Perform Pairwise Comparisons Against Control: Conduct pairwise comparisons for all replicates within each treatment against the control group replicates.
2. Calculate Average Performance per Replicate: For each individual well (representing a technical replicate), calculate its average performance score across all pairwise comparisons against the control.
3. Identify and Remove Poorly Performing Replicates: Establish a clear criterion (e.g., a threshold based on standard deviations or a specific performance metric) to identify wells that are consistently performing poorly based on their average scores. Remove only these identified problematic wells from the dataset.

Benefits of This Approach:

• Preserves Data: Avoids the unnecessary removal of entire treatment groups, retaining more valuable information for analysis.
• Improves Data Quality: By eliminating problematic technical replicates, the overall quality and reliability of the dataset for downstream analysis are enhanced.