Rank ordering plate data facilitates data visualization and normalization in high-throughput screening.

Area of Science:

• Chemical Biology
• Systems Biology
• Bioinformatics

Background:

• High-throughput screening (HTS) is crucial for chemical and systems biology.
• HTS data often contains systematic and random errors, leading to false results.
• Existing normalization methods have limitations, and no single method is universally adopted.

Purpose of the Study:

• To present a novel, intuitive, and spreadsheet-implementable method for HTS data visualization and normalization.
• To improve the accuracy and reliability of HTS data by addressing systematic errors and plate-to-plate variation.

Main Methods:

• Data visualization using ordered plots to create plate-specific curves.
• Normalization using the interquartile mean (IQM) to reduce plate-to-plate variation.
• Correction for positional effects using the interquartile mean of well positions across plates (IQMW).

Main Results:

• The proposed method provides intuitive visualization of plate data, enabling flagging of problematic plates.
• Normalization using IQM effectively reduces plate-to-plate variation.
• IQMW correction addresses positional biases, further enhancing data quality.

Conclusions:

• The presented method offers an effective, intuitive, and easily implementable approach for HTS data analysis.
• This technique improves the reliability of HTS data, supporting robust findings in chemical and systems biology.
• The method is demonstrated to be useful for both biochemical and phenotypic screening datasets.